├── .gitignore ├── Aula#01 └── Aula 01 - Introdução.pdf ├── Aula#02 ├── AppleStore.csv ├── Aula 02 - Curso Introdutório de Python.pdf └── Lesson#02 Python crash course.ipynb ├── Aula#03 ├── AppleStore.csv ├── Aula 03 - Dicionários e Funções.pdf └── Lesson_03_Dictionaries_and_Functions.ipynb ├── Aula#04 ├── AppleStore.csv ├── Aula 04 - Projeto #01.pdf ├── Project_01.ipynb └── googleplaystore.csv ├── Aula#05 ├── Aula 05 - Introdução a Pandas.pdf ├── Lesson_05_Introduction_to_pandas.ipynb └── f500.csv ├── Aula#06 ├── Aula 06 - Exploração de Dados com Pandas.pdf ├── Lesson_06_Exploring_Data_with_pandas.ipynb └── f500.csv ├── Aula#07 ├── Aula 07 - Inputação, Higienização, Limpeza e Pivoteamento com Pandas.pdf ├── Lesson_07_Advanced_Pandas.ipynb └── titanic_survival.csv ├── Aula#09 ├── Aula 09 - Análise Exploratória de Dados_ Introdução.pdf ├── EDA - Multiple Charts.ipynb ├── EDA- Line Charts.ipynb └── unrate.csv ├── Aula#10 ├── Aula 10 - Análise Exploratória dos Dados II .pdf ├── Aula#10 - gráficos de barras e dispersão.ipynb └── fandango_scores.csv ├── Aula#11 ├── Aula 11 - Análise Exploratória de Dados III.pdf ├── Lesson #11 - Embedded Plotting with Pandas.ipynb ├── Lesson 11 - Histogram and Boxplot.ipynb └── fandango_scores.csv ├── Aula#12 ├── Aula 12 - Amostragem .pdf ├── Lesson_12_Sampling.ipynb └── wnba.csv ├── Aula#13 ├── Aula 13 - Variáveis.pdf ├── Lesson 13.ipynb └── wnba.csv ├── Aula#14 ├── Aula 14.pdf ├── Lesson 14 - Frequency Distributions.ipynb └── wnba.csv ├── Aula#15 ├── Aula 15 - Visualizando Tabelas de Frequência.pdf ├── Lesson 15.ipynb └── wnba.csv ├── Aula#16 ├── AmesHousing_1.txt ├── Aula16 - Média.pdf └── Lesson_16_The_mean.ipynb ├── Aula#17 ├── AmesHousing_1.txt ├── Aula17 - Média ponderada.pdf └── Lesson_17_The_Weighted_Mean_and_the_Median.ipynb ├── Aula#18 ├── AmesHousing_1.txt ├── Aula18 - Moda.pdf └── Lesson_18.ipynb ├── Aula#19 ├── AmesHousing_1.txt ├── Aula19 - Medidas de Variabilidade.pdf └── Lesson_19_Measures_of_Variability.ipynb ├── Aula#20 ├── AmesHousing_1.csv ├── Aula20 - Z-Score.pdf └── Aula20 - Zscores.ipynb ├── Aula#21 ├── Aula21 - Correlação e covariância.pdf ├── Lesson 21 - Covariation & Correlation.ipynb └── nba_2013.csv ├── Aula#22 ├── Aula22 - Propriedades básicas de Probabilidade.pdf └── Lesson_22_Estimating_Probabilities.ipynb ├── Aula#23 ├── Aula23.pdf └── Lesson_23.ipynb └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | **/.DS_Store 2 | -------------------------------------------------------------------------------- /Aula#01/Aula 01 - Introdução.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#01/Aula 01 - Introdução.pdf -------------------------------------------------------------------------------- /Aula#02/Aula 02 - Curso Introdutório de Python.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#02/Aula 02 - 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Introdução a Pandas.pdf -------------------------------------------------------------------------------- /Aula#06/Aula 06 - Exploração de Dados com Pandas.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#06/Aula 06 - Exploração de Dados com Pandas.pdf -------------------------------------------------------------------------------- /Aula#07/Aula 07 - Inputação, Higienização, Limpeza e Pivoteamento com Pandas.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#07/Aula 07 - Inputação, Higienização, Limpeza e Pivoteamento com Pandas.pdf -------------------------------------------------------------------------------- /Aula#09/Aula 09 - Análise Exploratória de Dados_ Introdução.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#09/Aula 09 - Análise Exploratória de Dados_ Introdução.pdf -------------------------------------------------------------------------------- /Aula#09/EDA- Line Charts.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"EDA- Line Charts.ipynb","version":"0.3.2","provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"id":"UpBzh08fOR2x","colab_type":"text"},"cell_type":"markdown","source":["## 1 - Representation of Data\n"]},{"metadata":{"id":"0H-hkd5vO8fD","colab_type":"text"},"cell_type":"markdown","source":["\n","So far, we've mostly been manipulating and working with data that are represented as tables. Microsoft Excel, the pandas library in Python, and the CSV file format for datasets were all developed around this representation. Because a table neatly organizes values into rows and columns, we can easily look up specific values at the intersection of a row value and a column value. Unfortunately, it's very difficult to explore a dataset to uncover patterns when it's represented as a table, especially when that dataset contains many values. We need a different representation of data that can help us identify patterns more easily.\n","\n","In this mission, we'll learn the basics of **data visualization**, a discipline that focuses on the visual representation of data. As humans, our brains have evolved to develop powerful visual processing capabilities. We can quickly find patterns in the visual information we encounter, which was incredibly important from a survivability standpoint. Unfortunately, when data is represented as tables of values, we can't really take advantage of our visual pattern matching capabilities. This is because our ability to quickly process symbolic values (like numbers and words) is very poor. Data visualization focuses on transforming data from table representations visual ones.\n","\n","In this lesson, named **Exploratory Data Analysis**, we'll focus on data visualization techniques to explore datasets and help us uncover patterns. In this mission, we'll use a specific type of data visualization to understand U.S. unemployment data."]},{"metadata":{"id":"BRBbuqK7OR2y","colab_type":"text"},"cell_type":"markdown","source":["## 2 - Introduction to the Data\n"]},{"metadata":{"id":"y4OgHZyjPDXR","colab_type":"text"},"cell_type":"markdown","source":["\n","The **United States Bureau of Labor Statistics (BLS)** surveys and calculates the monthly unemployment rate. The unemployment rate is the percentage of individuals in the labor force without a job. While unemployment rate isn't perfect, it's a commonly used proxy for the health of the economy. You may have heard politicians and reporters state the unemployment rate when commenting on the economy. You can read more about how the BLS calculates the unemployment rate [here](http://www.bls.gov/cps/cps_htgm.htm).\n","\n","The BLS releases monthly unemployment data available for download as an Excel file, with the **.xlsx** file extension. While the pandas library can read in XLSX files, it relies on an external library for actually parsing the format. Let's instead download the same dataset as a CSV file from the website of the [Federal Reserve Bank of St. Louis](https://www.stlouisfed.org/). We've downloaded the monthly unemployment rate as a CSV from January 1948 to August 2016, saved it as **unrate.csv**, and made it available in this mission.\n","\n","To download this dataset on your own, head to the Federal Reserve Bank of St. Louis's [website](https://fred.stlouisfed.org/series/UNRATE/downloaddata), select **Text, Comma Separated** as the **File Format**, make sure the **Date Range** field starts at **1948-01-01** and ends at **2016-08-01**.\n","\n","Before we get into visual representations of data, let's first read this CSV file into pandas to explore the table representation of this data. The dataset we'll be working with is a [time series](https://en.wikipedia.org/wiki/Time_series) dataset, which means the data points (monthly unemployment rates) are ordered by time. Here's a preview of the dataset:\n","\n","\"creating\n","\n","When we read the dataset into a DataFrame, pandas will set the data type of the **DATE** column as a text column. Because of how pandas reads in strings internally, this column is given a data type of **object**. We need to convert this column to the **datetime** type using the [pandas.to_datetime()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) function, which returns a Series object with the **datetime** data type that we can assign back to the DataFrame:\n","\n","```python\n","import pandas as pd\n","df['col'] = pd.to_datetime(df['col'])\n","```\n","\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","\n","**Description**:\n","\n","1. Read **unrate.csv** into a DataFrame and assign to **unrate**.\n","2. Use the [pandas.to_datetime()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) function to convert the **DATE** column into a series of **datetime** values.\n","3. Display the first 12 rows in unrate."]},{"metadata":{"id":"hNlhICEgRyDh","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"1trGi7SROR2z","colab_type":"text"},"cell_type":"markdown","source":["## 3 - Table representation\n"]},{"metadata":{"id":"JK-3zH6YQKlv","colab_type":"text"},"cell_type":"markdown","source":["\n","The dataset contains 2 columns:\n","\n","- DATE: date, always the first of the month. Here are some examples:\n"," - 1948-01-01: January 1, 1948.\n"," - 1948-02-01: February 1, 1948.\n"," - 1948-03-01: March 1, 1948.\n"," - 1948-12-01: December 1, 1948.\n","- VALUE: the corresponding unemployment rate, in percent.\n","\n","The first 12 rows reflect the unemployment rate from January 1948 to December 1948:\n","\n","\"creating\n","\n","Take a minute to visually scan the table and observe how the monthly unemployment rate has changed over time. When you're finished, head to the next cell in this notebook."]},{"metadata":{"id":"ZUaUkSWFOR2z","colab_type":"text"},"cell_type":"markdown","source":["## 4 - Observation from the table representation\n"]},{"metadata":{"id":"PWyqVs-VQkHW","colab_type":"text"},"cell_type":"markdown","source":["\n","We can make the following observations from the table:\n","\n","- In 1948:\n"," - monthly unemployment rate ranged between **3.4** and **4.0**.\n"," - highest unemployment rate was reached in both March and December.\n"," - lowest unemployment rate was reached in January.\n","- From January to March, unemployment rate trended up.\n","- From March to May, unemployment rate trended down.\n","- From May to August, unemployment rate trended up.\n","- From August to October, unemployment rate trended down.\n","- From October to December, unemployment rate trended up.\n","\n","Because the table only contained the data from 1948, it didn't take too much time to identify these observations. If we scale up the table to include all 824 rows, it would be very time-consuming and painful to understand. Tables shine at presenting information precisely at the intersection of rows and columns and allow us to perform quick lookups when we know the row and column we're interested in. In addition, problems that involve comparing values between adjacent rows or columns are well suited for tables. Unfortunately, many problems you'll encounter in data science require comparisons that aren't possible with just tables.\n","\n","For example, one thing we learned from looking at the monthly unemployment rates for 1948 is that every few months, the unemployment rate switches between trending up and trending down. It's not switching direction every month, however, and this could mean that there's a seasonal effect. **Seasonality** is when a pattern is observed on a regular, predictable basis for a specific reason. A simple example of seasonality would be a large increase textbook purchases every August every year. Many schools start their terms in August in north hemisphere and this spike in textbook sales is directly linked.\n","\n","We need to first understand if there's any seasonality by comparing the unemployment trends across many years so we can decide if we should investigate it further. The faster we're able to assess our data, the faster we can perform high-level analysis quickly. If we're reliant on just the table to help us figure this out, then we won't be able to perform a high level test quickly. Let's see how a visual representation of the same information can be more helpful than the table representation."]},{"metadata":{"id":"S7XYppFMOR21","colab_type":"text"},"cell_type":"markdown","source":["## 5 - Visual representation\n"]},{"metadata":{"id":"WNcdO1XYQwHU","colab_type":"text"},"cell_type":"markdown","source":["\n","Instead of representing data using text like tables do, visual representations use visual objects like dots, shapes, and lines on a grid. [Plots](https://en.wikipedia.org/wiki/Plot_%28graphics%29) are a category of visual representations that allow us to easily understand the relationships between variables. There are many types of plots and selecting the right one is an important skill that you'll hone as you create data visualizations. Because we want to compare the unemployment trends across time, we should use line charts. Here's an overview of **line charts** using 4 sample data points:\n","\n","\n","\"creating\n","\n","Line charts work best when there is a logical connection between adjacent points. In our case, that connection is the flow of time. Between 2 reported monthly unemployment values, the unemployment rate is fluctuating and time is passing. To emphasize how the visual representation of the line chart helps us observe trends easily, let's look at the same 12 data points from 1948 as a line chart.\n","\n","\"creating\n","\n","We can reach the same observations about the data from the line chart as we did from the table representation:\n","\n","\"creating\n","\n","In the rest of this mission, we'll explore how to recreate this line chart in Python. In the next mission, we'll explore how to create multiple line charts to help us compare unemployment trends."]},{"metadata":{"id":"rnj0T6ESOR21","colab_type":"text"},"cell_type":"markdown","source":["## 6 - Introduction to matplotlib\n"]},{"metadata":{"id":"y57tHw2sQ3Vq","colab_type":"text"},"cell_type":"markdown","source":["\n","To create the line chart, we'll use the [matplotlib](http://matplotlib.org/) library, which allows us to:\n","\n","- quickly create common plots using high-level functions\n","- extensively tweak plots\n","- create new kinds of plots from the ground up\n","\n","To help you become familiar with matplotlib, we'll focus on the first 2 use cases. When working with commonly used plots in matplotlib, the general workflow is:\n","\n","- create a plot using data\n","- customize the appearance of the plot\n","- display the plot\n","- edit and repeat until satisfied\n","\n","This interactive style aligns well with the exploratory workflow of data visualization because we're asking questions and creating data visualizations to help us get answers. The pyplot module provides a high-level interface for matplotlib that allows us to quickly create common data plots and perform common tweaks to them.\n","\n","The pyplot module is commonly imported as **plt** from **matplotlib**:\n","\n","```python\n","import matplotlib.pyplot as plt\n","```\n","\n","Using the different pyplot functions, we can create, customize, and display a plot. For example, we can use 2 functions to :\n","\n","```python\n","plt.plot()\n","plt.show()\n","```\n","\n","Because we didn't pass in any arguments, the [plot()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) function would generate an empty plot with just the axes and ticks and the [show()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show) function would display that plot. You'll notice that we didn't assign the plot to a variable and then call a method on the variable to display it. We instead called 2 functions on the pyplot module directly.\n","\n","This is because every time we call a pyplot function, the module maintains and updates the plot internally (also known as state). When we call **show()**, the plot is displayed and the internal state is destroyed. While this workflow isn't ideal when we're writing functions that create plots on a repeated basis as part of a larger application, it's useful when exploring data.\n","\n","Let's run this code to see the default properties matplotlib uses. If you'd like to follow along on your own computer, we recommend installing matplotlib using Anaconda: **conda install matplotlib**. We recommend working with matplotlib using Jupyter Notebook because it can render the plots in the notebook itself. You will need to run the following Jupyter magic in a code cell each time you open your notebook: **%matplotlib inline**. Whenever you call **show()**, the plots will be displayed in the output cell. You can read more [here](http://ipython.readthedocs.io/en/stable/interactive/plotting.html).\n","\n","\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","**Description**:\n","\n","1. Generate an empty plot using **plt.plot()** and display it using **plt.show()**.\n"]},{"metadata":{"id":"m_Gc7TWvR3Di","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"8BTyOPUSOR22","colab_type":"text"},"cell_type":"markdown","source":["## 7 - Adding data\n"]},{"metadata":{"id":"8DLAle04SFvX","colab_type":"text"},"cell_type":"markdown","source":["\n","\n","By default, Matplotlib displayed a coordinate grid with:\n","\n","- the x-axis and y-axis values ranging from **-0.06** to **0.06**\n","- no grid lines\n","- no data\n","\n","Even though no data was plotted, the x-axis and y-axis ticks corresponding to the **-0.06** to **0.06** value range. The axis ticks consist of tick marks and tick labels. Here's a focused view of the x-axis tick marks and x-axis tick labels:\n","\n","\n","\"creating\n","\n","\n","To create a line chart of the unemployment data from 1948, we need:\n","\n","- the x-axis to range from **01-01-1948** to **12-01-1948** (which corresponds to the first and last months in 1948)\n","- the y-axis to range from **3.4** to **4.0** (which correspond to the minimum and maximum unemployment values)\n","\n","Instead of manually updating the ticks, drawing each marker, and connecting the markers with lines, we can just specify the data we want plotted and let matplotlib handle the rest. To generate the line chart we're interested in, we pass in the list of x-values as the first parameter and the list of y-values as the second parameter to [plot()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot):\n","\n","```python\n","plt.plot(x_values, y_values)\n","```\n","\n","Matplotlib will accept any iterable object, like NumPy arrays and **pandas.Series** instances.\n","\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","**Description**:\n","\n","1. Generate a line chart that visualizes the unemployment rates from 1948:\n"," - x-values should be the first 12 values in the **DATE** column\n"," - y-values should be the first 12 values in the **VALUE** column\n","2. Display the plot."]},{"metadata":{"id":"832CrgoiShh3","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"RH03WZYMOR22","colab_type":"text"},"cell_type":"markdown","source":["## 8 - Fixing axis ticks\n"]},{"metadata":{"id":"vqIhhai8SuMw","colab_type":"text"},"cell_type":"markdown","source":["\n","\n","While the y-axis looks fine, the x-axis **tick labels** are too close together and can be unreadable. The line charts from earlier in the mission suggest a better way to display the x-axis tick labels.\n","\n","We can rotate the x-axis tick labels by 90 degrees so they don't overlap. The **xticks()** function within pyplot lets you customize the behavior of the x-axis ticks. If you head over to the [documentation for that function](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xticks), it's not immediately obvious the arguments it takes:\n","\n","```python\n","matplotlib.pyplot.xticks(*args, **kwargs)\n","```\n","\n","In the documentation for the function, you'll see a link to the matplotlib [Text](http://matplotlib.org/api/text_api.html#matplotlib.text.Text) class, which is what pyplot uses to represent the x-axis tick labels. You'll notice that there's a **rotation** parameter that accepts degrees of rotation as a parameter. We can specify degrees of rotation using a float or integer value.\n","\n","As a side note, if you read the documentation for [pyplot](http://matplotlib.org/api/pyplot_api.html), you'll notice that many functions for tweaking the x-axis have matching functions for the y-axis. For example, the y-axis counterpart to the [xticks()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xticks) function is the yticks() function.\n","\n","Use what we've discussed so far to rotate the x-axis tick labels by 90 degrees.\n","\n","\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","**Description**:\n","\n","1. Generate the same line chart from the last screen that visualizes the unemployment rates from 1948:\n"," - x-values should be the first 12 values in the **DATE** column\n"," - y-values should be the first 12 values in the **VALUE** column\n","2. Use **pyplot.xticks()** to rotate the x-axis tick labels by **90** degrees.\n","3. Display the plot.\n"]},{"metadata":{"id":"FneXsNyfTFsU","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_ZKwO8RVOR23","colab_type":"text"},"cell_type":"markdown","source":["## 9 - Adding axis label and a title\n"]},{"metadata":{"id":"vkarz7I0TJHr","colab_type":"text"},"cell_type":"markdown","source":["\n","Let's now finish tweaking this plot by adding axis labels and a title. Always adding axis labels and a title to your plot is a good habit to have, and is especially useful when we're trying to keep track of multiple plots down the road.\n","\n","Here's an overview of the pyplot functions we need to tweak the axis labels and the plot title:\n","\n","- [xlabel()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xlabel): accepts a string value, which gets set as the x-axis label.\n","- [ylabel()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylabel): accepts a string value, which is set as the y-axis label.\n","- [title()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.title): accepts a string value, which is set as the plot title.\n","\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","**Description**:\n","\n","1. Generate the same line chart from the last screen that visualizes the unemployment rates from 1948:\n"," - x-values should be the first 12 values in the **DATE** column\n"," - y-values should be the first 12 values in the **VALUE** column\n"," - Rotate the x-axis tick labels by **90** degrees.\n","2. Set the x-axis label to **\"Month\"**.\n","3. Set the y-axis label to **\"Unemployment Rate\"**.\n","4. Set the plot title to **\"Monthly Unemployment Trends, 1948\"**.\n","5. Display the plot."]},{"metadata":{"id":"6DkHdvGkTdA7","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]}]} -------------------------------------------------------------------------------- /Aula#09/unrate.csv: -------------------------------------------------------------------------------- 1 | DATE,VALUE 2 | 1948-01-01,3.4 3 | 1948-02-01,3.8 4 | 1948-03-01,4.0 5 | 1948-04-01,3.9 6 | 1948-05-01,3.5 7 | 1948-06-01,3.6 8 | 1948-07-01,3.6 9 | 1948-08-01,3.9 10 | 1948-09-01,3.8 11 | 1948-10-01,3.7 12 | 1948-11-01,3.8 13 | 1948-12-01,4.0 14 | 1949-01-01,4.3 15 | 1949-02-01,4.7 16 | 1949-03-01,5.0 17 | 1949-04-01,5.3 18 | 1949-05-01,6.1 19 | 1949-06-01,6.2 20 | 1949-07-01,6.7 21 | 1949-08-01,6.8 22 | 1949-09-01,6.6 23 | 1949-10-01,7.9 24 | 1949-11-01,6.4 25 | 1949-12-01,6.6 26 | 1950-01-01,6.5 27 | 1950-02-01,6.4 28 | 1950-03-01,6.3 29 | 1950-04-01,5.8 30 | 1950-05-01,5.5 31 | 1950-06-01,5.4 32 | 1950-07-01,5.0 33 | 1950-08-01,4.5 34 | 1950-09-01,4.4 35 | 1950-10-01,4.2 36 | 1950-11-01,4.2 37 | 1950-12-01,4.3 38 | 1951-01-01,3.7 39 | 1951-02-01,3.4 40 | 1951-03-01,3.4 41 | 1951-04-01,3.1 42 | 1951-05-01,3.0 43 | 1951-06-01,3.2 44 | 1951-07-01,3.1 45 | 1951-08-01,3.1 46 | 1951-09-01,3.3 47 | 1951-10-01,3.5 48 | 1951-11-01,3.5 49 | 1951-12-01,3.1 50 | 1952-01-01,3.2 51 | 1952-02-01,3.1 52 | 1952-03-01,2.9 53 | 1952-04-01,2.9 54 | 1952-05-01,3.0 55 | 1952-06-01,3.0 56 | 1952-07-01,3.2 57 | 1952-08-01,3.4 58 | 1952-09-01,3.1 59 | 1952-10-01,3.0 60 | 1952-11-01,2.8 61 | 1952-12-01,2.7 62 | 1953-01-01,2.9 63 | 1953-02-01,2.6 64 | 1953-03-01,2.6 65 | 1953-04-01,2.7 66 | 1953-05-01,2.5 67 | 1953-06-01,2.5 68 | 1953-07-01,2.6 69 | 1953-08-01,2.7 70 | 1953-09-01,2.9 71 | 1953-10-01,3.1 72 | 1953-11-01,3.5 73 | 1953-12-01,4.5 74 | 1954-01-01,4.9 75 | 1954-02-01,5.2 76 | 1954-03-01,5.7 77 | 1954-04-01,5.9 78 | 1954-05-01,5.9 79 | 1954-06-01,5.6 80 | 1954-07-01,5.8 81 | 1954-08-01,6.0 82 | 1954-09-01,6.1 83 | 1954-10-01,5.7 84 | 1954-11-01,5.3 85 | 1954-12-01,5.0 86 | 1955-01-01,4.9 87 | 1955-02-01,4.7 88 | 1955-03-01,4.6 89 | 1955-04-01,4.7 90 | 1955-05-01,4.3 91 | 1955-06-01,4.2 92 | 1955-07-01,4.0 93 | 1955-08-01,4.2 94 | 1955-09-01,4.1 95 | 1955-10-01,4.3 96 | 1955-11-01,4.2 97 | 1955-12-01,4.2 98 | 1956-01-01,4.0 99 | 1956-02-01,3.9 100 | 1956-03-01,4.2 101 | 1956-04-01,4.0 102 | 1956-05-01,4.3 103 | 1956-06-01,4.3 104 | 1956-07-01,4.4 105 | 1956-08-01,4.1 106 | 1956-09-01,3.9 107 | 1956-10-01,3.9 108 | 1956-11-01,4.3 109 | 1956-12-01,4.2 110 | 1957-01-01,4.2 111 | 1957-02-01,3.9 112 | 1957-03-01,3.7 113 | 1957-04-01,3.9 114 | 1957-05-01,4.1 115 | 1957-06-01,4.3 116 | 1957-07-01,4.2 117 | 1957-08-01,4.1 118 | 1957-09-01,4.4 119 | 1957-10-01,4.5 120 | 1957-11-01,5.1 121 | 1957-12-01,5.2 122 | 1958-01-01,5.8 123 | 1958-02-01,6.4 124 | 1958-03-01,6.7 125 | 1958-04-01,7.4 126 | 1958-05-01,7.4 127 | 1958-06-01,7.3 128 | 1958-07-01,7.5 129 | 1958-08-01,7.4 130 | 1958-09-01,7.1 131 | 1958-10-01,6.7 132 | 1958-11-01,6.2 133 | 1958-12-01,6.2 134 | 1959-01-01,6.0 135 | 1959-02-01,5.9 136 | 1959-03-01,5.6 137 | 1959-04-01,5.2 138 | 1959-05-01,5.1 139 | 1959-06-01,5.0 140 | 1959-07-01,5.1 141 | 1959-08-01,5.2 142 | 1959-09-01,5.5 143 | 1959-10-01,5.7 144 | 1959-11-01,5.8 145 | 1959-12-01,5.3 146 | 1960-01-01,5.2 147 | 1960-02-01,4.8 148 | 1960-03-01,5.4 149 | 1960-04-01,5.2 150 | 1960-05-01,5.1 151 | 1960-06-01,5.4 152 | 1960-07-01,5.5 153 | 1960-08-01,5.6 154 | 1960-09-01,5.5 155 | 1960-10-01,6.1 156 | 1960-11-01,6.1 157 | 1960-12-01,6.6 158 | 1961-01-01,6.6 159 | 1961-02-01,6.9 160 | 1961-03-01,6.9 161 | 1961-04-01,7.0 162 | 1961-05-01,7.1 163 | 1961-06-01,6.9 164 | 1961-07-01,7.0 165 | 1961-08-01,6.6 166 | 1961-09-01,6.7 167 | 1961-10-01,6.5 168 | 1961-11-01,6.1 169 | 1961-12-01,6.0 170 | 1962-01-01,5.8 171 | 1962-02-01,5.5 172 | 1962-03-01,5.6 173 | 1962-04-01,5.6 174 | 1962-05-01,5.5 175 | 1962-06-01,5.5 176 | 1962-07-01,5.4 177 | 1962-08-01,5.7 178 | 1962-09-01,5.6 179 | 1962-10-01,5.4 180 | 1962-11-01,5.7 181 | 1962-12-01,5.5 182 | 1963-01-01,5.7 183 | 1963-02-01,5.9 184 | 1963-03-01,5.7 185 | 1963-04-01,5.7 186 | 1963-05-01,5.9 187 | 1963-06-01,5.6 188 | 1963-07-01,5.6 189 | 1963-08-01,5.4 190 | 1963-09-01,5.5 191 | 1963-10-01,5.5 192 | 1963-11-01,5.7 193 | 1963-12-01,5.5 194 | 1964-01-01,5.6 195 | 1964-02-01,5.4 196 | 1964-03-01,5.4 197 | 1964-04-01,5.3 198 | 1964-05-01,5.1 199 | 1964-06-01,5.2 200 | 1964-07-01,4.9 201 | 1964-08-01,5.0 202 | 1964-09-01,5.1 203 | 1964-10-01,5.1 204 | 1964-11-01,4.8 205 | 1964-12-01,5.0 206 | 1965-01-01,4.9 207 | 1965-02-01,5.1 208 | 1965-03-01,4.7 209 | 1965-04-01,4.8 210 | 1965-05-01,4.6 211 | 1965-06-01,4.6 212 | 1965-07-01,4.4 213 | 1965-08-01,4.4 214 | 1965-09-01,4.3 215 | 1965-10-01,4.2 216 | 1965-11-01,4.1 217 | 1965-12-01,4.0 218 | 1966-01-01,4.0 219 | 1966-02-01,3.8 220 | 1966-03-01,3.8 221 | 1966-04-01,3.8 222 | 1966-05-01,3.9 223 | 1966-06-01,3.8 224 | 1966-07-01,3.8 225 | 1966-08-01,3.8 226 | 1966-09-01,3.7 227 | 1966-10-01,3.7 228 | 1966-11-01,3.6 229 | 1966-12-01,3.8 230 | 1967-01-01,3.9 231 | 1967-02-01,3.8 232 | 1967-03-01,3.8 233 | 1967-04-01,3.8 234 | 1967-05-01,3.8 235 | 1967-06-01,3.9 236 | 1967-07-01,3.8 237 | 1967-08-01,3.8 238 | 1967-09-01,3.8 239 | 1967-10-01,4.0 240 | 1967-11-01,3.9 241 | 1967-12-01,3.8 242 | 1968-01-01,3.7 243 | 1968-02-01,3.8 244 | 1968-03-01,3.7 245 | 1968-04-01,3.5 246 | 1968-05-01,3.5 247 | 1968-06-01,3.7 248 | 1968-07-01,3.7 249 | 1968-08-01,3.5 250 | 1968-09-01,3.4 251 | 1968-10-01,3.4 252 | 1968-11-01,3.4 253 | 1968-12-01,3.4 254 | 1969-01-01,3.4 255 | 1969-02-01,3.4 256 | 1969-03-01,3.4 257 | 1969-04-01,3.4 258 | 1969-05-01,3.4 259 | 1969-06-01,3.5 260 | 1969-07-01,3.5 261 | 1969-08-01,3.5 262 | 1969-09-01,3.7 263 | 1969-10-01,3.7 264 | 1969-11-01,3.5 265 | 1969-12-01,3.5 266 | 1970-01-01,3.9 267 | 1970-02-01,4.2 268 | 1970-03-01,4.4 269 | 1970-04-01,4.6 270 | 1970-05-01,4.8 271 | 1970-06-01,4.9 272 | 1970-07-01,5.0 273 | 1970-08-01,5.1 274 | 1970-09-01,5.4 275 | 1970-10-01,5.5 276 | 1970-11-01,5.9 277 | 1970-12-01,6.1 278 | 1971-01-01,5.9 279 | 1971-02-01,5.9 280 | 1971-03-01,6.0 281 | 1971-04-01,5.9 282 | 1971-05-01,5.9 283 | 1971-06-01,5.9 284 | 1971-07-01,6.0 285 | 1971-08-01,6.1 286 | 1971-09-01,6.0 287 | 1971-10-01,5.8 288 | 1971-11-01,6.0 289 | 1971-12-01,6.0 290 | 1972-01-01,5.8 291 | 1972-02-01,5.7 292 | 1972-03-01,5.8 293 | 1972-04-01,5.7 294 | 1972-05-01,5.7 295 | 1972-06-01,5.7 296 | 1972-07-01,5.6 297 | 1972-08-01,5.6 298 | 1972-09-01,5.5 299 | 1972-10-01,5.6 300 | 1972-11-01,5.3 301 | 1972-12-01,5.2 302 | 1973-01-01,4.9 303 | 1973-02-01,5.0 304 | 1973-03-01,4.9 305 | 1973-04-01,5.0 306 | 1973-05-01,4.9 307 | 1973-06-01,4.9 308 | 1973-07-01,4.8 309 | 1973-08-01,4.8 310 | 1973-09-01,4.8 311 | 1973-10-01,4.6 312 | 1973-11-01,4.8 313 | 1973-12-01,4.9 314 | 1974-01-01,5.1 315 | 1974-02-01,5.2 316 | 1974-03-01,5.1 317 | 1974-04-01,5.1 318 | 1974-05-01,5.1 319 | 1974-06-01,5.4 320 | 1974-07-01,5.5 321 | 1974-08-01,5.5 322 | 1974-09-01,5.9 323 | 1974-10-01,6.0 324 | 1974-11-01,6.6 325 | 1974-12-01,7.2 326 | 1975-01-01,8.1 327 | 1975-02-01,8.1 328 | 1975-03-01,8.6 329 | 1975-04-01,8.8 330 | 1975-05-01,9.0 331 | 1975-06-01,8.8 332 | 1975-07-01,8.6 333 | 1975-08-01,8.4 334 | 1975-09-01,8.4 335 | 1975-10-01,8.4 336 | 1975-11-01,8.3 337 | 1975-12-01,8.2 338 | 1976-01-01,7.9 339 | 1976-02-01,7.7 340 | 1976-03-01,7.6 341 | 1976-04-01,7.7 342 | 1976-05-01,7.4 343 | 1976-06-01,7.6 344 | 1976-07-01,7.8 345 | 1976-08-01,7.8 346 | 1976-09-01,7.6 347 | 1976-10-01,7.7 348 | 1976-11-01,7.8 349 | 1976-12-01,7.8 350 | 1977-01-01,7.5 351 | 1977-02-01,7.6 352 | 1977-03-01,7.4 353 | 1977-04-01,7.2 354 | 1977-05-01,7.0 355 | 1977-06-01,7.2 356 | 1977-07-01,6.9 357 | 1977-08-01,7.0 358 | 1977-09-01,6.8 359 | 1977-10-01,6.8 360 | 1977-11-01,6.8 361 | 1977-12-01,6.4 362 | 1978-01-01,6.4 363 | 1978-02-01,6.3 364 | 1978-03-01,6.3 365 | 1978-04-01,6.1 366 | 1978-05-01,6.0 367 | 1978-06-01,5.9 368 | 1978-07-01,6.2 369 | 1978-08-01,5.9 370 | 1978-09-01,6.0 371 | 1978-10-01,5.8 372 | 1978-11-01,5.9 373 | 1978-12-01,6.0 374 | 1979-01-01,5.9 375 | 1979-02-01,5.9 376 | 1979-03-01,5.8 377 | 1979-04-01,5.8 378 | 1979-05-01,5.6 379 | 1979-06-01,5.7 380 | 1979-07-01,5.7 381 | 1979-08-01,6.0 382 | 1979-09-01,5.9 383 | 1979-10-01,6.0 384 | 1979-11-01,5.9 385 | 1979-12-01,6.0 386 | 1980-01-01,6.3 387 | 1980-02-01,6.3 388 | 1980-03-01,6.3 389 | 1980-04-01,6.9 390 | 1980-05-01,7.5 391 | 1980-06-01,7.6 392 | 1980-07-01,7.8 393 | 1980-08-01,7.7 394 | 1980-09-01,7.5 395 | 1980-10-01,7.5 396 | 1980-11-01,7.5 397 | 1980-12-01,7.2 398 | 1981-01-01,7.5 399 | 1981-02-01,7.4 400 | 1981-03-01,7.4 401 | 1981-04-01,7.2 402 | 1981-05-01,7.5 403 | 1981-06-01,7.5 404 | 1981-07-01,7.2 405 | 1981-08-01,7.4 406 | 1981-09-01,7.6 407 | 1981-10-01,7.9 408 | 1981-11-01,8.3 409 | 1981-12-01,8.5 410 | 1982-01-01,8.6 411 | 1982-02-01,8.9 412 | 1982-03-01,9.0 413 | 1982-04-01,9.3 414 | 1982-05-01,9.4 415 | 1982-06-01,9.6 416 | 1982-07-01,9.8 417 | 1982-08-01,9.8 418 | 1982-09-01,10.1 419 | 1982-10-01,10.4 420 | 1982-11-01,10.8 421 | 1982-12-01,10.8 422 | 1983-01-01,10.4 423 | 1983-02-01,10.4 424 | 1983-03-01,10.3 425 | 1983-04-01,10.2 426 | 1983-05-01,10.1 427 | 1983-06-01,10.1 428 | 1983-07-01,9.4 429 | 1983-08-01,9.5 430 | 1983-09-01,9.2 431 | 1983-10-01,8.8 432 | 1983-11-01,8.5 433 | 1983-12-01,8.3 434 | 1984-01-01,8.0 435 | 1984-02-01,7.8 436 | 1984-03-01,7.8 437 | 1984-04-01,7.7 438 | 1984-05-01,7.4 439 | 1984-06-01,7.2 440 | 1984-07-01,7.5 441 | 1984-08-01,7.5 442 | 1984-09-01,7.3 443 | 1984-10-01,7.4 444 | 1984-11-01,7.2 445 | 1984-12-01,7.3 446 | 1985-01-01,7.3 447 | 1985-02-01,7.2 448 | 1985-03-01,7.2 449 | 1985-04-01,7.3 450 | 1985-05-01,7.2 451 | 1985-06-01,7.4 452 | 1985-07-01,7.4 453 | 1985-08-01,7.1 454 | 1985-09-01,7.1 455 | 1985-10-01,7.1 456 | 1985-11-01,7.0 457 | 1985-12-01,7.0 458 | 1986-01-01,6.7 459 | 1986-02-01,7.2 460 | 1986-03-01,7.2 461 | 1986-04-01,7.1 462 | 1986-05-01,7.2 463 | 1986-06-01,7.2 464 | 1986-07-01,7.0 465 | 1986-08-01,6.9 466 | 1986-09-01,7.0 467 | 1986-10-01,7.0 468 | 1986-11-01,6.9 469 | 1986-12-01,6.6 470 | 1987-01-01,6.6 471 | 1987-02-01,6.6 472 | 1987-03-01,6.6 473 | 1987-04-01,6.3 474 | 1987-05-01,6.3 475 | 1987-06-01,6.2 476 | 1987-07-01,6.1 477 | 1987-08-01,6.0 478 | 1987-09-01,5.9 479 | 1987-10-01,6.0 480 | 1987-11-01,5.8 481 | 1987-12-01,5.7 482 | 1988-01-01,5.7 483 | 1988-02-01,5.7 484 | 1988-03-01,5.7 485 | 1988-04-01,5.4 486 | 1988-05-01,5.6 487 | 1988-06-01,5.4 488 | 1988-07-01,5.4 489 | 1988-08-01,5.6 490 | 1988-09-01,5.4 491 | 1988-10-01,5.4 492 | 1988-11-01,5.3 493 | 1988-12-01,5.3 494 | 1989-01-01,5.4 495 | 1989-02-01,5.2 496 | 1989-03-01,5.0 497 | 1989-04-01,5.2 498 | 1989-05-01,5.2 499 | 1989-06-01,5.3 500 | 1989-07-01,5.2 501 | 1989-08-01,5.2 502 | 1989-09-01,5.3 503 | 1989-10-01,5.3 504 | 1989-11-01,5.4 505 | 1989-12-01,5.4 506 | 1990-01-01,5.4 507 | 1990-02-01,5.3 508 | 1990-03-01,5.2 509 | 1990-04-01,5.4 510 | 1990-05-01,5.4 511 | 1990-06-01,5.2 512 | 1990-07-01,5.5 513 | 1990-08-01,5.7 514 | 1990-09-01,5.9 515 | 1990-10-01,5.9 516 | 1990-11-01,6.2 517 | 1990-12-01,6.3 518 | 1991-01-01,6.4 519 | 1991-02-01,6.6 520 | 1991-03-01,6.8 521 | 1991-04-01,6.7 522 | 1991-05-01,6.9 523 | 1991-06-01,6.9 524 | 1991-07-01,6.8 525 | 1991-08-01,6.9 526 | 1991-09-01,6.9 527 | 1991-10-01,7.0 528 | 1991-11-01,7.0 529 | 1991-12-01,7.3 530 | 1992-01-01,7.3 531 | 1992-02-01,7.4 532 | 1992-03-01,7.4 533 | 1992-04-01,7.4 534 | 1992-05-01,7.6 535 | 1992-06-01,7.8 536 | 1992-07-01,7.7 537 | 1992-08-01,7.6 538 | 1992-09-01,7.6 539 | 1992-10-01,7.3 540 | 1992-11-01,7.4 541 | 1992-12-01,7.4 542 | 1993-01-01,7.3 543 | 1993-02-01,7.1 544 | 1993-03-01,7.0 545 | 1993-04-01,7.1 546 | 1993-05-01,7.1 547 | 1993-06-01,7.0 548 | 1993-07-01,6.9 549 | 1993-08-01,6.8 550 | 1993-09-01,6.7 551 | 1993-10-01,6.8 552 | 1993-11-01,6.6 553 | 1993-12-01,6.5 554 | 1994-01-01,6.6 555 | 1994-02-01,6.6 556 | 1994-03-01,6.5 557 | 1994-04-01,6.4 558 | 1994-05-01,6.1 559 | 1994-06-01,6.1 560 | 1994-07-01,6.1 561 | 1994-08-01,6.0 562 | 1994-09-01,5.9 563 | 1994-10-01,5.8 564 | 1994-11-01,5.6 565 | 1994-12-01,5.5 566 | 1995-01-01,5.6 567 | 1995-02-01,5.4 568 | 1995-03-01,5.4 569 | 1995-04-01,5.8 570 | 1995-05-01,5.6 571 | 1995-06-01,5.6 572 | 1995-07-01,5.7 573 | 1995-08-01,5.7 574 | 1995-09-01,5.6 575 | 1995-10-01,5.5 576 | 1995-11-01,5.6 577 | 1995-12-01,5.6 578 | 1996-01-01,5.6 579 | 1996-02-01,5.5 580 | 1996-03-01,5.5 581 | 1996-04-01,5.6 582 | 1996-05-01,5.6 583 | 1996-06-01,5.3 584 | 1996-07-01,5.5 585 | 1996-08-01,5.1 586 | 1996-09-01,5.2 587 | 1996-10-01,5.2 588 | 1996-11-01,5.4 589 | 1996-12-01,5.4 590 | 1997-01-01,5.3 591 | 1997-02-01,5.2 592 | 1997-03-01,5.2 593 | 1997-04-01,5.1 594 | 1997-05-01,4.9 595 | 1997-06-01,5.0 596 | 1997-07-01,4.9 597 | 1997-08-01,4.8 598 | 1997-09-01,4.9 599 | 1997-10-01,4.7 600 | 1997-11-01,4.6 601 | 1997-12-01,4.7 602 | 1998-01-01,4.6 603 | 1998-02-01,4.6 604 | 1998-03-01,4.7 605 | 1998-04-01,4.3 606 | 1998-05-01,4.4 607 | 1998-06-01,4.5 608 | 1998-07-01,4.5 609 | 1998-08-01,4.5 610 | 1998-09-01,4.6 611 | 1998-10-01,4.5 612 | 1998-11-01,4.4 613 | 1998-12-01,4.4 614 | 1999-01-01,4.3 615 | 1999-02-01,4.4 616 | 1999-03-01,4.2 617 | 1999-04-01,4.3 618 | 1999-05-01,4.2 619 | 1999-06-01,4.3 620 | 1999-07-01,4.3 621 | 1999-08-01,4.2 622 | 1999-09-01,4.2 623 | 1999-10-01,4.1 624 | 1999-11-01,4.1 625 | 1999-12-01,4.0 626 | 2000-01-01,4.0 627 | 2000-02-01,4.1 628 | 2000-03-01,4.0 629 | 2000-04-01,3.8 630 | 2000-05-01,4.0 631 | 2000-06-01,4.0 632 | 2000-07-01,4.0 633 | 2000-08-01,4.1 634 | 2000-09-01,3.9 635 | 2000-10-01,3.9 636 | 2000-11-01,3.9 637 | 2000-12-01,3.9 638 | 2001-01-01,4.2 639 | 2001-02-01,4.2 640 | 2001-03-01,4.3 641 | 2001-04-01,4.4 642 | 2001-05-01,4.3 643 | 2001-06-01,4.5 644 | 2001-07-01,4.6 645 | 2001-08-01,4.9 646 | 2001-09-01,5.0 647 | 2001-10-01,5.3 648 | 2001-11-01,5.5 649 | 2001-12-01,5.7 650 | 2002-01-01,5.7 651 | 2002-02-01,5.7 652 | 2002-03-01,5.7 653 | 2002-04-01,5.9 654 | 2002-05-01,5.8 655 | 2002-06-01,5.8 656 | 2002-07-01,5.8 657 | 2002-08-01,5.7 658 | 2002-09-01,5.7 659 | 2002-10-01,5.7 660 | 2002-11-01,5.9 661 | 2002-12-01,6.0 662 | 2003-01-01,5.8 663 | 2003-02-01,5.9 664 | 2003-03-01,5.9 665 | 2003-04-01,6.0 666 | 2003-05-01,6.1 667 | 2003-06-01,6.3 668 | 2003-07-01,6.2 669 | 2003-08-01,6.1 670 | 2003-09-01,6.1 671 | 2003-10-01,6.0 672 | 2003-11-01,5.8 673 | 2003-12-01,5.7 674 | 2004-01-01,5.7 675 | 2004-02-01,5.6 676 | 2004-03-01,5.8 677 | 2004-04-01,5.6 678 | 2004-05-01,5.6 679 | 2004-06-01,5.6 680 | 2004-07-01,5.5 681 | 2004-08-01,5.4 682 | 2004-09-01,5.4 683 | 2004-10-01,5.5 684 | 2004-11-01,5.4 685 | 2004-12-01,5.4 686 | 2005-01-01,5.3 687 | 2005-02-01,5.4 688 | 2005-03-01,5.2 689 | 2005-04-01,5.2 690 | 2005-05-01,5.1 691 | 2005-06-01,5.0 692 | 2005-07-01,5.0 693 | 2005-08-01,4.9 694 | 2005-09-01,5.0 695 | 2005-10-01,5.0 696 | 2005-11-01,5.0 697 | 2005-12-01,4.9 698 | 2006-01-01,4.7 699 | 2006-02-01,4.8 700 | 2006-03-01,4.7 701 | 2006-04-01,4.7 702 | 2006-05-01,4.6 703 | 2006-06-01,4.6 704 | 2006-07-01,4.7 705 | 2006-08-01,4.7 706 | 2006-09-01,4.5 707 | 2006-10-01,4.4 708 | 2006-11-01,4.5 709 | 2006-12-01,4.4 710 | 2007-01-01,4.6 711 | 2007-02-01,4.5 712 | 2007-03-01,4.4 713 | 2007-04-01,4.5 714 | 2007-05-01,4.4 715 | 2007-06-01,4.6 716 | 2007-07-01,4.7 717 | 2007-08-01,4.6 718 | 2007-09-01,4.7 719 | 2007-10-01,4.7 720 | 2007-11-01,4.7 721 | 2007-12-01,5.0 722 | 2008-01-01,5.0 723 | 2008-02-01,4.9 724 | 2008-03-01,5.1 725 | 2008-04-01,5.0 726 | 2008-05-01,5.4 727 | 2008-06-01,5.6 728 | 2008-07-01,5.8 729 | 2008-08-01,6.1 730 | 2008-09-01,6.1 731 | 2008-10-01,6.5 732 | 2008-11-01,6.8 733 | 2008-12-01,7.3 734 | 2009-01-01,7.8 735 | 2009-02-01,8.3 736 | 2009-03-01,8.7 737 | 2009-04-01,9.0 738 | 2009-05-01,9.4 739 | 2009-06-01,9.5 740 | 2009-07-01,9.5 741 | 2009-08-01,9.6 742 | 2009-09-01,9.8 743 | 2009-10-01,10.0 744 | 2009-11-01,9.9 745 | 2009-12-01,9.9 746 | 2010-01-01,9.8 747 | 2010-02-01,9.8 748 | 2010-03-01,9.9 749 | 2010-04-01,9.9 750 | 2010-05-01,9.6 751 | 2010-06-01,9.4 752 | 2010-07-01,9.4 753 | 2010-08-01,9.5 754 | 2010-09-01,9.5 755 | 2010-10-01,9.4 756 | 2010-11-01,9.8 757 | 2010-12-01,9.3 758 | 2011-01-01,9.1 759 | 2011-02-01,9.0 760 | 2011-03-01,9.0 761 | 2011-04-01,9.1 762 | 2011-05-01,9.0 763 | 2011-06-01,9.1 764 | 2011-07-01,9.0 765 | 2011-08-01,9.0 766 | 2011-09-01,9.0 767 | 2011-10-01,8.8 768 | 2011-11-01,8.6 769 | 2011-12-01,8.5 770 | 2012-01-01,8.3 771 | 2012-02-01,8.3 772 | 2012-03-01,8.2 773 | 2012-04-01,8.2 774 | 2012-05-01,8.2 775 | 2012-06-01,8.2 776 | 2012-07-01,8.2 777 | 2012-08-01,8.1 778 | 2012-09-01,7.8 779 | 2012-10-01,7.8 780 | 2012-11-01,7.7 781 | 2012-12-01,7.9 782 | 2013-01-01,8.0 783 | 2013-02-01,7.7 784 | 2013-03-01,7.5 785 | 2013-04-01,7.6 786 | 2013-05-01,7.5 787 | 2013-06-01,7.5 788 | 2013-07-01,7.3 789 | 2013-08-01,7.3 790 | 2013-09-01,7.3 791 | 2013-10-01,7.2 792 | 2013-11-01,6.9 793 | 2013-12-01,6.7 794 | 2014-01-01,6.6 795 | 2014-02-01,6.7 796 | 2014-03-01,6.7 797 | 2014-04-01,6.2 798 | 2014-05-01,6.2 799 | 2014-06-01,6.1 800 | 2014-07-01,6.2 801 | 2014-08-01,6.2 802 | 2014-09-01,6.0 803 | 2014-10-01,5.7 804 | 2014-11-01,5.8 805 | 2014-12-01,5.6 806 | 2015-01-01,5.7 807 | 2015-02-01,5.5 808 | 2015-03-01,5.5 809 | 2015-04-01,5.4 810 | 2015-05-01,5.5 811 | 2015-06-01,5.3 812 | 2015-07-01,5.3 813 | 2015-08-01,5.1 814 | 2015-09-01,5.1 815 | 2015-10-01,5.0 816 | 2015-11-01,5.0 817 | 2015-12-01,5.0 818 | 2016-01-01,4.9 819 | 2016-02-01,4.9 820 | 2016-03-01,5.0 821 | 2016-04-01,5.0 822 | 2016-05-01,4.7 823 | 2016-06-01,4.9 824 | 2016-07-01,4.9 825 | 2016-08-01,4.9 826 | -------------------------------------------------------------------------------- /Aula#10/Aula 10 - Análise Exploratória dos Dados II .pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#10/Aula 10 - Análise Exploratória dos Dados II .pdf -------------------------------------------------------------------------------- /Aula#10/Aula#10 - gráficos de barras e dispersão.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Exploratory Data Analysis - Bar Plots And Scatter Plots.ipynb","version":"0.3.2","provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"cells":[{"metadata":{"id":"mEOTsY3csCv4","colab_type":"text"},"cell_type":"markdown","source":["# 1 - Introduction to the data\n"]},{"metadata":{"id":"rjsEiohXsD5h","colab_type":"text"},"cell_type":"markdown","source":["\n","To investigate how different movie review sites the potential bias that movie reviews site has, **FiveThirtyEight** compiled data for 147 films from 2015 that have substantive reviews from both critics and consumers. Every time Hollywood releases a movie, critics from **Metacritic**, **Fandango**, **Rotten Tomatoes**, and **IMDB** review and rate the film. They also ask the users in their respective communities to review and rate the film. Then, they calculate the average rating from both critics and users and display them on their site. Here are screenshots from each site:\n","\n","\n","\"creating\n","\n","\n","FiveThirtyEight compiled this dataset to investigate if there was any bias to Fandango's ratings. In addition to aggregating ratings for films, Fandango is unique in that it also sells movie tickets, and so it has a direct commercial interest in showing higher ratings. After discovering that a few films that weren't good were still rated highly on Fandango, the team investigated and published an [article about bias in movie ratings](http://fivethirtyeight.com/features/fandango-movies-ratings/).\n","\n","\n","We'll be working with the **fandango_scores.csv** file, which can be downloaded from the [FiveThirtEight Github repo](https://github.com/fivethirtyeight/data/tree/master/fandango). Here are the columns we'll be working with in this mission:\n","\n","- **FILM** - film name\n","- **RT_user_norm** - average user rating from Rotten Tomatoes, normalized to a 1 to 5 point scale\n","- **Metacritic_user_nom** - average user rating from Metacritc, normalized to a 1 to 5 point scale\n","- **IMDB_norm** - average user rating from IMDB, normalized to a 1 to 5 point scale\n","- **Fandango_Ratingvalue** - average user rating from Fandango, normalized to a 1 to 5 point scale\n","- **Fandango_Stars** - the rating displayed on the Fandango website (rounded to nearest star, 1 to 5 point scale)\n","\n","\n","Instead of displaying the raw rating, the writer discovered that Fandango usually rounded the average rating to the next highest half star (next highest **0.5** value). The **Fandango_Ratingvalue** column reflects the true average rating while the **Fandango_Stars** column reflects the displayed, rounded rating.\n","\n","Let's read in this dataset, which allows us to compare how a movie fared across all 4 review sites."]},{"metadata":{"id":"H_GVImCnr1xt","colab_type":"text"},"cell_type":"markdown","source":["**Exercise**\n","\n","\n","\n","\n","\n","**Description**:\n","\n","\n","1. Read **fandango_scores.csv** into a Dataframe named **reviews**.\n","2. Select the following columns and assign the resulting Dataframe to **norm_reviews**:\n"," - **FILM**\n"," - **RT_user_norm**\n"," - **Metacritic_user_nom** (note the misspelling of norm)\n"," - **IMDB_norm**\n"," - **Fandango_Ratingvalue**\n"," - **Fandango_Stars**\n","3. Display the first row in **norm_reviews**"]},{"metadata":{"id":"Ks5LQH_guTih","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"DlIUgL7Qr1xt","colab_type":"text"},"cell_type":"markdown","source":["# 2 - Bar plot\n"]},{"metadata":{"id":"OzfkF4NktDxD","colab_type":"text"},"cell_type":"markdown","source":["\n","These sites use different scales for ratings. Some use a 5 star scale while others use a 100 point scale. In addition, Metacritic and Rotten Tomatoes aggregate scores from both users and film critics, while IMDB and Fandango aggregate only from their users. We'll focus on just the average scores from users, because not all of the sites have scores from critics.\n","\n","The **RT_user_norm**, **Metacritic_user_nom**, **IMDB_norm**, and **Fandango_Ratingvalue** columns contain the average user rating for each movie, normalized to a 0 to 5 point scale. This allows us to compare how the users on each site rated a movie. While using averages isn't perfect because films with a few reviews can skew the average rating, FiveThirtyEight only selected movies with a non-trivial number of ratings to ensure films with only a handful of reviews aren't included.\n","\n","If you look at the first row, which lists the average user ratings for **Avengers: Age of Ultron (2015)**, you'll notice that the Fandango ratings, both the actual and the displayed rating, are higher than those from the other sites for a given movie. While calculating and comparing summary statistics give us hard numbers for quantifying the bias, visualizing the data using plots can help us gain a more intuitive understanding. We need a visualization that scales graphical objects to the quantitative values we're interested in comparing. One of these visualizations is a **bar plot**.\n","\n","\"creating\n","\n","\n","In the bar plot above, the x-axis represented the different ratings and the y-axis represented the actual ratings. An effective bar plot uses categorical values on one axis and numerical values on the other axis. Because bar plots can help us find the category corresponding to the smallest or largest values, it's important that we restrict the number of bars in a single plot. Using a bar plot to visualize hundreds of values makes it difficult to trace the category with the smallest or largest value.\n","\n","If the x-axis contains the categorical values and the rectangular bars are scaled vertically, this is known as a vertical bar plot. A horizontal bar plot flips the axes, which is useful for quickly spotting the largest value.\n","\n","\"creating\n","\n","An effective bar plot uses a consistent width for each bar. This helps keep the visual focus on the heights of the bars when comparing. Let's now learn how to create a vertical bar plot in matplotlib that represents the different user scores for Avengers: Age of Ultron (2015)."]},{"metadata":{"id":"2LItQUj5r1xv","colab_type":"text"},"cell_type":"markdown","source":["# 3 - Creating bars\n"]},{"metadata":{"id":"nvVQsDVItgpq","colab_type":"text"},"cell_type":"markdown","source":["\n","When we generated line charts, we passed in the data to **pyplot.plot()** and matplotlib took care of the rest. Because the markers and lines in a line chart correspond directly with x-axis and y-axis coordinates, all matplotlib needed was the data we wanted plotted. To create a useful bar plot, however, we need to specify the positions of the bars, the widths of the bars, and the positions of the axis labels. Here's a diagram that shows the various values we need to specify:\n","\n","\"creating\n","\n","\n","We'll focus on positioning the bars on the x-axis in this step and on positioning the x-axis labels in the next step. We can generate a vertical bar plot using either [pyplot.bar()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar) or [Axes.bar()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.bar). We'll use **Axes.bar()** so we can extensively customize the bar plot more easily. We can use [pyplot.subplots()](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplots) to first generate a single subplot and return both the Figure and Axes object. This is a shortcut from the technique we used in the previous mission:\n","\n","```python\n","fig, ax = plt.subplots()\n","```\n","\n","The **Axes.bar()** method has 2 required parameters, left and height. We use the left parameter to specify the x coordinates of the left sides of the bar (marked in blue on the above image). We use the height parameter to specify the height of each bar. Both of these parameters accept a list-like object.\n","\n","The [np.arange()](http://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html) function returns evenly spaced values. We use **arange()** to generate the positions of the **left** side of our bars. This function requires a paramater that specifies the number of values we want to generate. We'll also want to add space between our bars for better readability:\n","\n","```python\n","# Positions of the left sides of the 5 bars. [0.75, 1.75, 2.75, 3.75, 4.75]\n","import numpy as np\n","bar_positions = np.arange(5) + 0.75\n","# Heights of the bars. In our case, the average rating for the first movie in the dataset.\n","num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']\n","bar_heights = norm_reviews[num_cols].iloc[0]\n","ax.bar(bar_positions, bar_heights)\n","```\n","\n","\n","We can also use the width parameter to specify the width of each bar. This is an optional parameter and the width of each bar is set to **0.8** by default. The following code sets the width parameter to **1.5**:\n","\n","```python\n","ax.bar(bar_positions, bar_heights, 1.5)\n","```\n","\n","**Exercise**\n","\n","\n","\n","\n","\n","1. Create a single subplot and assign the returned Figure object to **fig** and the returned Axes object to **ax**.\n","2. Generate a bar plot with:\n"," - **left** set to **bar_positions**\n"," - **height** set to **bar_heights**\n"," - **width** set to 0.5\n","3. Use **plt.show()** to display the bar plot."]},{"metadata":{"id":"r-HfAKqruWI5","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"geLLemCqr1xv","colab_type":"text"},"cell_type":"markdown","source":["# 4 - Aligning Axis Ticks And Labels\n","\n"]},{"metadata":{"id":"B1uv-yQ2uOyL","colab_type":"text"},"cell_type":"markdown","source":["\n","By default, matplotlib sets the x-axis tick labels to the integer values the bars spanned on the x-axis (from **0** to **6**). We only need tick labels on the x-axis where the bars are positioned. We can use [Axes.set_xticks()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_xticks) to change the positions of the ticks to **[1, 2, 3, 4, 5]**:\n","\n","```python\n","tick_positions = range(1,6)\n","ax.set_xticks(tick_positions)\n","```\n","\n","Then, we can use [Axes.set_xticklabels()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_xticklabels) to specify the tick labels:\n","\n","```python\n","num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']\n","ax.set_xticklabels(num_cols)\n","```\n","\n","If you look at the [documentation](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_xticklabels) for the method, you'll notice that we can specify the orientation for the labels using the **rotation** parameter:\n","\n","```python\n","ax.set_xticklabels(num_cols, rotation=90)\n","```\n","\n","Rotating the labels by 90 degrees keeps them readable. In addition to modifying the x-axis tick positions and labels, let's also set the x-axis label, y-axis label, and the plot title.\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","1. Create a single subplot and assign the returned Figure object to **fig** and the returned Axes object to **ax**.\n","2. Generate a bar plot with:\n"," - **left** set to **bar_positions**\n"," - **height** set to **bar_heights**\n"," - **width** set to **0.5**\n","3. Set the x-axis tick positions to **tick_positions**.\n","4. Set the x-axis tick labels to **num_cols** and rotate by **90** degrees.\n","5. Set the x-axis label to **\"Rating Source\"**.\n","6. Set the y-axis label to **\"Average Rating\"**.\n","7. Set the plot title to **\"Average User Rating For Avengers: Age of Ultron (2015)\"**.\n","8. Use **plt.show()** to display the bar plot."]},{"metadata":{"id":"6fZEdXW5uYe0","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"WW2-L9Dtr1xv","colab_type":"text"},"cell_type":"markdown","source":["# 5 - Horizontal bar plot\n"]},{"metadata":{"id":"9gu9f_mfubAy","colab_type":"text"},"cell_type":"markdown","source":["\n","We can create a horizontal bar plot in matplotlib in a similar fashion. Instead of using **Axes.bar()**, we use [Axes.barh()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.barh). This method has 2 required parameters, **bottom** and **width**. We use the **bottom** parameter to specify the y coordinate for the bottom sides for the bars and the **width** parameter to specify the lengths of the bars:\n","\n","```python\n","bar_widths = norm_reviews[num_cols].iloc[0]\n","bar_positions = arange(5) + 0.75\n","ax.barh(bar_positions, bar_widths, 0.5)\n","```\n","\n","To recreate the bar plot from the last step as horizontal bar plot, we essentially need to map the properties we set for the y-axis instead of the x-axis. We use **Axes.set_yticks()** to set the y-axis tick positions to **[1, 2, 3, 4, 5]** and **Axes.set_yticklabels()** to set the tick labels to the column names:\n","\n","```python\n","tick_positions = range(5) + 1\n","num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']\n","ax.set_yticks(tick_positions)\n","ax.set_yticklabels(num_cols)\n","```\n","\n","**Exercise**\n","\n","\n","\n","\n","1. Create a single subplot and assign the returned Figure object to fig and the returned Axes object to ax.\n","2. Generate a bar plot with:\n"," - bottom set to bar_positions\n"," - width set to bar_widths\n"," - height set to 0.5\n","3. Set the y-axis tick positions to tick_positions.\n","4. Set the y-axis tick labels to num_cols.\n","5. Set the y-axis label to \"Rating Source\".\n","6. Set the x-axis label to \"Average Rating\".\n","7. Set the plot title to \"Average User Rating For Avengers: Age of Ultron (2015)\".\n","8. Use plt.show() to display the bar plot."]},{"metadata":{"id":"59ArXcPTugCw","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"4PN0JKLlr1xw","colab_type":"text"},"cell_type":"markdown","source":["# 6 - Scatter plot\n"]},{"metadata":{"id":"lm6KW07kujds","colab_type":"text"},"cell_type":"markdown","source":["\n","From the horizontal bar plot, we can more easily determine that the 2 average scores from Fandango users are higher than those from the other sites. While bar plots help us visualize a few data points to quickly compare them, they aren't good at helping us visualize many data points. Let's look at a plot that can help us visualize many points.\n","\n","In the previous mission, the line charts we generated always connected points from left to right. This helped us show the trend, up or down, between each point as we scanned visually from left to right. Instead, we can avoid using lines to connect markers and just use the underlying markers. A plot containing just the markers is known as a **scatter plot**.\n","\n","\n","\"creating\n","\n","\n","A scatter plot helps us determine if 2 columns are weakly or strongly correlated. While calculating the [correlation coefficient](https://en.wikipedia.org/wiki/Correlation_coefficient) will give us a precise number, a scatter plot helps us find outliers, gain a more intuitive sense of how spread out the data is, and compare more easily.\n","\n","To generate a scatter plot, we use [Axes.scatter()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.scatter). The **scatter()** method has 2 required parameters, **x** and **y**, which matches the parameters of the **plot()** method. The values for these parameters need to be iterable objects of matching lengths (lists, NumPy arrays, or pandas series).\n","\n","Let's start by creating a scatter plot that visualizes the relationship between the **Fandango_RatingValue** and **RT_user_norm** columns. We're looking for at least a weak correlation between the columns.\n","\n","**Exercise**\n","\n","\n","\n","\n","1. Create a single subplot and assign the returned Figure object to **fig** and the returned Axes object to **ax**.\n","2. Generate a scatter plot with the **Fandango_Ratingvalue** column on the x-axis and the **RT_user_norm** column on the y-axis.\n","3. Set the x-axis label to **\"Fandango\"** and the y-axis label to **\"Rotten Tomatoes\"**.\n","4. Use **plt.show() ** to display the resulting plot."]},{"metadata":{"id":"DEQzE_cAum4c","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"pC590Kzcr1xx","colab_type":"text"},"cell_type":"markdown","source":["# 7 -Switching axes\n"]},{"metadata":{"id":"xDJpZvB-upWd","colab_type":"text"},"cell_type":"markdown","source":["\n","The scatter plot suggests that there's a weak, positive correlation between the user ratings on Fandango and the user ratings on Rotten Tomatoes. The correlation is weak because for many x values, there are multiple corresponding y values. The correlation is positive because, in general, as x increases, y also increases.\n","\n","When using scatter plots to understand how 2 variables are correlated, it's usually not important which one is on the x-axis and which one is on the y-axis. This is because the relationship is still captured either way, even if the plots look a little different. If you want to instead understand how an independent variable affects a dependent variables, you want to put the independent one on the x-axis and the dependent one on the y-axis. Doing so helps emphasize the potential cause and effect relation.\n","\n","In our case, we're not exploring if the ratings from Fandango influence those on Rotten Tomatoes and we're instead looking to understand how much they agree. Let's see what happens when we flip the columns."]},{"metadata":{"id":"Q-QmI3jxr1xz","colab_type":"text"},"cell_type":"markdown","source":["# 8 - Benchmarking correlation\n"]},{"metadata":{"id":"jT3PdQvmurZ3","colab_type":"text"},"cell_type":"markdown","source":["\n","The second scatter plot is a mirror reflection of the first second scatter plot. The nature of the correlation is still reflected, however, which is the important thing. Let's now generate scatter plots to see how Fandango ratings correlate with all 3 of the other review sites.\n","\n","When generating multiple scatter plots for the purpose of comparison, it's important that all plots share the same ranges in the x-axis and y-axis. In the 2 plots we generated in the last step, the ranges for both axes didn't match. We can use [Axes.set_xlim()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_xlim) and [Axes.set_ylim()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_ylim) to set the data limits for both axes:\n","\n","```python\n","ax.set_xlim(0, 5)\n","ax.set_ylim(0, 5)\n","```\n","\n","By default, matplotlib uses the minimal ranges for the data limits necessary to display all of the data we specify. By manually setting the data limits ranges to specific ranges for all plots, we're ensuring that we can accurately compare. We can even use the methods we just mentioned to zoom in on a part of the plots. For example, the following code will constrained the axes to the **4** to **5** range:\n","\n","```python\n","ax.set_xlim(4, 5)\n","ax.set_ylim(4, 5)\n","```\n","\n","This makes small changes in the actual values in the data appear larger in the plot. A difference of **0.1** in a plot that ranges from **0** to **5** is hard to visually observe. A difference of **0.1** in a plot that only ranges from **4** to **5** is easily visible since the difference is 1/10th of the range.\n","\n","**Exercise**\n","\n","\n","\n","\n","1. For the Subplot associated with **ax[0]**:\n"," - Generate a scatter plot with the **Fandango_Ratingvalue** column on the x-axis and the **RT_user_norm** column on the y-axis.\n"," - Set the x-axis label to **\"Fandango\"** and the y-axis label to **\"Rotten Tomatoes\"**.\n"," - Set the x-axis and y-axis data limits to range from **0** and **5**.\n","2. For the Subplot associated with **ax[1]**:\n"," - Generate a scatter plot with the **Fandango_Ratingvalue** column on the x-axis and the **Metacritic_user_nom** column on the y-axis.\n"," - Set the x-axis label to **\"Fandango\"** and the y-axis label to **\"Metacritic\"**.\n"," - Set the x-axis and y-axis data limits to range from **0** and **5**.\n","3. For the Subplot associated with **ax[2]**:\n"," - Generate a scatter plot with the **Fandango_Ratingvalue** column on the x-axis and the **IMDB_norm** column on the y-axis.\n"," - Set the x-axis label to **\"Fandango\"** and the y-axis label to **\"IMDB\"**.\n"," - Set the x-axis and y-axis data limits to range from **0** and **5**.\n","4. Use **plt.show()** to display the plots."]},{"metadata":{"id":"hJi8kgYsuuq6","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"0IizttQTr1xz","colab_type":"text"},"cell_type":"markdown","source":["## 9. Next steps\n","\n","From the scatter plots, we can conclude that user ratings from IMDB and Fandango are the most similar. In addition, user ratings from Metacritic and Rotten Tomatoes have positive but weak correlations with user ratings from Fandango. We can also notice that user ratings from Metacritic and Rotten Tomatoes span a larger range of values than those from IMDB or Fandango. User ratings from Metacritic and Rotten Tomatoes range from 1 to 5. User ratings from Fandango range approximately from 2.5 to 5 while those from IMDB range approximately from 2 to 4.5.\n","\n","The scatter plots unfortunately only give us a cursory understanding of the distributions of user ratings from each review site. For example, if a hundred movies had the same average user rating from IMDB and Fandango in the dataset, we would only see a single marker in the scatter plot. In the next mission, we'll learn about two types of plots that help us understand distributions of values."]}]} -------------------------------------------------------------------------------- /Aula#10/fandango_scores.csv: -------------------------------------------------------------------------------- 1 | FILM,RottenTomatoes,RottenTomatoes_User,Metacritic,Metacritic_User,IMDB,Fandango_Stars,Fandango_Ratingvalue,RT_norm,RT_user_norm,Metacritic_norm,Metacritic_user_nom,IMDB_norm,RT_norm_round,RT_user_norm_round,Metacritic_norm_round,Metacritic_user_norm_round,IMDB_norm_round,Metacritic_user_vote_count,IMDB_user_vote_count,Fandango_votes,Fandango_Difference 2 | Avengers: Age of Ultron (2015),74,86,66,7.1,7.8,5,4.5,3.7,4.3,3.3,3.55,3.9,3.5,4.5,3.5,3.5,4,1330,271107,14846,0.5 3 | Cinderella (2015),85,80,67,7.5,7.1,5,4.5,4.25,4,3.35,3.75,3.55,4.5,4,3.5,4,3.5,249,65709,12640,0.5 4 | Ant-Man (2015),80,90,64,8.1,7.8,5,4.5,4,4.5,3.2,4.05,3.9,4,4.5,3,4,4,627,103660,12055,0.5 5 | Do You Believe? (2015),18,84,22,4.7,5.4,5,4.5,0.9,4.2,1.1,2.35,2.7,1,4,1,2.5,2.5,31,3136,1793,0.5 6 | Hot Tub Time Machine 2 (2015),14,28,29,3.4,5.1,3.5,3,0.7,1.4,1.45,1.7,2.55,0.5,1.5,1.5,1.5,2.5,88,19560,1021,0.5 7 | The Water Diviner (2015),63,62,50,6.8,7.2,4.5,4,3.15,3.1,2.5,3.4,3.6,3,3,2.5,3.5,3.5,34,39373,397,0.5 8 | Irrational Man (2015),42,53,53,7.6,6.9,4,3.5,2.1,2.65,2.65,3.8,3.45,2,2.5,2.5,4,3.5,17,2680,252,0.5 9 | Top Five (2014),86,64,81,6.8,6.5,4,3.5,4.3,3.2,4.05,3.4,3.25,4.5,3,4,3.5,3.5,124,16876,3223,0.5 10 | Shaun the Sheep Movie (2015),99,82,81,8.8,7.4,4.5,4,4.95,4.1,4.05,4.4,3.7,5,4,4,4.5,3.5,62,12227,896,0.5 11 | Love & Mercy (2015),89,87,80,8.5,7.8,4.5,4,4.45,4.35,4,4.25,3.9,4.5,4.5,4,4.5,4,54,5367,864,0.5 12 | Far From The Madding Crowd (2015),84,77,71,7.5,7.2,4.5,4,4.2,3.85,3.55,3.75,3.6,4,4,3.5,4,3.5,35,12129,804,0.5 13 | Black Sea (2015),82,60,62,6.6,6.4,4,3.5,4.1,3,3.1,3.3,3.2,4,3,3,3.5,3,37,16547,218,0.5 14 | Leviathan (2014),99,79,92,7.2,7.7,4,3.5,4.95,3.95,4.6,3.6,3.85,5,4,4.5,3.5,4,145,22521,64,0.5 15 | Unbroken (2014),51,70,59,6.5,7.2,4.5,4.1,2.55,3.5,2.95,3.25,3.6,2.5,3.5,3,3.5,3.5,218,77518,9443,0.4 16 | The Imitation Game (2014),90,92,73,8.2,8.1,5,4.6,4.5,4.6,3.65,4.1,4.05,4.5,4.5,3.5,4,4,566,334164,8055,0.4 17 | Taken 3 (2015),9,46,26,4.6,6.1,4.5,4.1,0.45,2.3,1.3,2.3,3.05,0.5,2.5,1.5,2.5,3,240,104235,6757,0.4 18 | Ted 2 (2015),46,58,48,6.5,6.6,4.5,4.1,2.3,2.9,2.4,3.25,3.3,2.5,3,2.5,3.5,3.5,197,49102,6437,0.4 19 | Southpaw (2015),59,80,57,8.2,7.8,5,4.6,2.95,4,2.85,4.1,3.9,3,4,3,4,4,128,23561,5597,0.4 20 | Night at the Museum: Secret of the Tomb (2014),50,58,47,5.8,6.3,4.5,4.1,2.5,2.9,2.35,2.9,3.15,2.5,3,2.5,3,3,103,50291,5445,0.4 21 | Pixels (2015),17,54,27,5.3,5.6,4.5,4.1,0.85,2.7,1.35,2.65,2.8,1,2.5,1.5,2.5,3,246,19521,3886,0.4 22 | "McFarland, USA (2015)",79,89,60,7.2,7.5,5,4.6,3.95,4.45,3,3.6,3.75,4,4.5,3,3.5,4,59,13769,3364,0.4 23 | Insidious: Chapter 3 (2015),59,56,52,6.9,6.3,4.5,4.1,2.95,2.8,2.6,3.45,3.15,3,3,2.5,3.5,3,115,25134,3276,0.4 24 | The Man From U.N.C.L.E. (2015),68,80,55,7.9,7.6,4.5,4.1,3.4,4,2.75,3.95,3.8,3.5,4,3,4,4,144,22104,2686,0.4 25 | Run All Night (2015),60,59,59,7.3,6.6,4.5,4.1,3,2.95,2.95,3.65,3.3,3,3,3,3.5,3.5,141,50438,2066,0.4 26 | Trainwreck (2015),85,74,75,6,6.7,4.5,4.1,4.25,3.7,3.75,3,3.35,4.5,3.5,4,3,3.5,169,27380,8381,0.4 27 | Selma (2014),99,86,89,7.1,7.5,5,4.6,4.95,4.3,4.45,3.55,3.75,5,4.5,4.5,3.5,4,316,45344,7025,0.4 28 | Ex Machina (2015),92,86,78,7.9,7.7,4.5,4.1,4.6,4.3,3.9,3.95,3.85,4.5,4.5,4,4,4,672,154499,3458,0.4 29 | Still Alice (2015),88,85,72,7.8,7.5,4.5,4.1,4.4,4.25,3.6,3.9,3.75,4.5,4.5,3.5,4,4,153,57123,1258,0.4 30 | Wild Tales (2014),96,92,77,8.8,8.2,4.5,4.1,4.8,4.6,3.85,4.4,4.1,5,4.5,4,4.5,4,107,50285,235,0.4 31 | The End of the Tour (2015),92,89,84,7.5,7.9,4.5,4.1,4.6,4.45,4.2,3.75,3.95,4.5,4.5,4,4,4,19,1320,121,0.4 32 | Red Army (2015),96,86,82,7.4,7.7,4.5,4.1,4.8,4.3,4.1,3.7,3.85,5,4.5,4,3.5,4,11,2275,54,0.4 33 | When Marnie Was There (2015),89,89,71,6.4,7.8,4.5,4.1,4.45,4.45,3.55,3.2,3.9,4.5,4.5,3.5,3,4,29,4160,46,0.4 34 | The Hunting Ground (2015),92,72,77,7.8,7.5,4.5,4.1,4.6,3.6,3.85,3.9,3.75,4.5,3.5,4,4,4,6,1196,42,0.4 35 | The Boy Next Door (2015),10,35,30,5.5,4.6,4,3.6,0.5,1.75,1.5,2.75,2.3,0.5,2,1.5,3,2.5,75,19658,2800,0.4 36 | Aloha (2015),19,31,40,4,5.5,3.5,3.1,0.95,1.55,2,2,2.75,1,1.5,2,2,3,67,12255,2284,0.4 37 | The Loft (2015),11,40,24,2.4,6.3,4,3.6,0.55,2,1.2,1.2,3.15,0.5,2,1,1,3,80,21319,811,0.4 38 | 5 Flights Up (2015),52,47,55,6.8,6.1,4,3.6,2.6,2.35,2.75,3.4,3.05,2.5,2.5,3,3.5,3,6,2174,79,0.4 39 | Welcome to Me (2015),71,47,67,6.9,5.9,4,3.6,3.55,2.35,3.35,3.45,2.95,3.5,2.5,3.5,3.5,3,33,8301,56,0.4 40 | Saint Laurent (2015),51,45,52,6.8,6.3,3.5,3.1,2.55,2.25,2.6,3.4,3.15,2.5,2.5,2.5,3.5,3,8,2196,43,0.4 41 | Maps to the Stars (2015),60,46,67,5.8,6.3,3.5,3.1,3,2.3,3.35,2.9,3.15,3,2.5,3.5,3,3,46,22440,35,0.4 42 | I'll See You In My Dreams (2015),94,70,75,6.9,6.9,4,3.6,4.7,3.5,3.75,3.45,3.45,4.5,3.5,4,3.5,3.5,14,1151,281,0.4 43 | Timbuktu (2015),99,78,91,6.9,7.2,4,3.6,4.95,3.9,4.55,3.45,3.6,5,4,4.5,3.5,3.5,37,6246,74,0.4 44 | About Elly (2015),97,86,87,9.6,8.2,4,3.6,4.85,4.3,4.35,4.8,4.1,5,4.5,4.5,5,4,23,20659,43,0.4 45 | The Diary of a Teenage Girl (2015),95,81,87,6.3,7,4,3.6,4.75,4.05,4.35,3.15,3.5,5,4,4.5,3,3.5,18,1107,38,0.4 46 | Kingsman: The Secret Service (2015),75,84,58,7.9,7.8,4.5,4.2,3.75,4.2,2.9,3.95,3.9,4,4,3,4,4,1054,272204,15205,0.3 47 | Tomorrowland (2015),50,53,60,6.4,6.6,4,3.7,2.5,2.65,3,3.2,3.3,2.5,2.5,3,3,3.5,262,42937,8077,0.3 48 | The Divergent Series: Insurgent (2015),30,61,42,5.4,6.4,4.5,4.2,1.5,3.05,2.1,2.7,3.2,1.5,3,2,2.5,3,206,89618,7123,0.3 49 | Annie (2014),27,61,33,4.8,5.2,4.5,4.2,1.35,3.05,1.65,2.4,2.6,1.5,3,1.5,2.5,2.5,108,19222,6835,0.3 50 | Fantastic Four (2015),9,20,27,2.5,4,3,2.7,0.45,1,1.35,1.25,2,0.5,1,1.5,1.5,2,421,39838,6288,0.3 51 | Terminator Genisys (2015),26,60,38,6.4,6.9,4.5,4.2,1.3,3,1.9,3.2,3.45,1.5,3,2,3,3.5,779,85585,6272,0.3 52 | Pitch Perfect 2 (2015),67,68,63,5.7,6.7,4.5,4.2,3.35,3.4,3.15,2.85,3.35,3.5,3.5,3,3,3.5,192,56333,4577,0.3 53 | Entourage (2015),32,68,38,5.2,7.1,4.5,4.2,1.6,3.4,1.9,2.6,3.55,1.5,3.5,2,2.5,3.5,96,21914,4279,0.3 54 | The Age of Adaline (2015),54,68,51,7.4,7.3,4.5,4.2,2.7,3.4,2.55,3.7,3.65,2.5,3.5,2.5,3.5,3.5,100,45510,3325,0.3 55 | Hot Pursuit (2015),8,37,31,3.7,4.9,4,3.7,0.4,1.85,1.55,1.85,2.45,0.5,2,1.5,2,2.5,78,17061,2618,0.3 56 | The DUFF (2015),71,68,56,6.4,6.6,4.5,4.2,3.55,3.4,2.8,3.2,3.3,3.5,3.5,3,3,3.5,69,33594,2273,0.3 57 | Black or White (2015),39,68,45,7.9,6.6,4.5,4.2,1.95,3.4,2.25,3.95,3.3,2,3.5,2.5,4,3.5,24,4857,1862,0.3 58 | Project Almanac (2015),34,46,47,5.4,6.4,4,3.7,1.7,2.3,2.35,2.7,3.2,1.5,2.5,2.5,2.5,3,95,40057,1834,0.3 59 | Ricki and the Flash (2015),64,53,54,7,6.2,4,3.7,3.2,2.65,2.7,3.5,3.1,3,2.5,2.5,3.5,3,37,1769,1462,0.3 60 | Seventh Son (2015),12,35,30,3.9,5.5,3.5,3.2,0.6,1.75,1.5,1.95,2.75,0.5,2,1.5,2,3,126,41177,1213,0.3 61 | Mortdecai (2015),12,30,27,3.2,5.5,3.5,3.2,0.6,1.5,1.35,1.6,2.75,0.5,1.5,1.5,1.5,3,144,31878,1196,0.3 62 | Unfinished Business (2015),11,27,32,3.8,5.4,3.5,3.2,0.55,1.35,1.6,1.9,2.7,0.5,1.5,1.5,2,2.5,39,14346,821,0.3 63 | American Ultra (2015),46,52,50,6.8,6.5,4,3.7,2.3,2.6,2.5,3.4,3.25,2.5,2.5,2.5,3.5,3.5,42,3017,638,0.3 64 | True Story (2015),45,41,50,5.7,6.3,3.5,3.2,2.25,2.05,2.5,2.85,3.15,2.5,2,2.5,3,3,37,16069,540,0.3 65 | Child 44 (2015),26,44,41,5.3,6.4,4,3.7,1.3,2.2,2.05,2.65,3.2,1.5,2,2,2.5,3,73,19220,308,0.3 66 | Dark Places (2015),26,33,39,7.9,6.3,4,3.7,1.3,1.65,1.95,3.95,3.15,1.5,1.5,2,4,3,18,9856,55,0.3 67 | Birdman (2014),92,78,88,8,7.9,4,3.7,4.6,3.9,4.4,4,3.95,4.5,4,4.5,4,4,1171,303505,4194,0.3 68 | The Gift (2015),93,79,77,8.3,7.6,4,3.7,4.65,3.95,3.85,4.15,3.8,4.5,4,4,4,4,121,10891,2680,0.3 69 | Unfriended (2015),60,39,59,5.8,5.9,3,2.7,3,1.95,2.95,2.9,2.95,3,2,3,3,3,130,22348,2507,0.3 70 | Monkey Kingdom (2015),94,77,72,7.5,7.3,4.5,4.2,4.7,3.85,3.6,3.75,3.65,4.5,4,3.5,4,3.5,15,883,701,0.3 71 | Mr. Turner (2014),98,56,94,6.6,6.9,3.5,3.2,4.9,2.8,4.7,3.3,3.45,5,3,4.5,3.5,3.5,98,13296,290,0.3 72 | Seymour: An Introduction (2015),100,87,83,6,7.7,4.5,4.2,5,4.35,4.15,3,3.85,5,4.5,4,3,4,4,243,41,0.3 73 | The Wrecking Crew (2015),93,84,67,7,7.8,4.5,4.2,4.65,4.2,3.35,3.5,3.9,4.5,4,3.5,3.5,4,4,732,38,0.3 74 | American Sniper (2015),72,85,72,6.6,7.4,5,4.8,3.6,4.25,3.6,3.3,3.7,3.5,4.5,3.5,3.5,3.5,850,251856,34085,0.2 75 | Furious 7 (2015),81,84,67,6.8,7.4,5,4.8,4.05,4.2,3.35,3.4,3.7,4,4,3.5,3.5,3.5,764,207211,33538,0.2 76 | The Hobbit: The Battle of the Five Armies (2014),61,75,59,7,7.5,4.5,4.3,3.05,3.75,2.95,3.5,3.75,3,4,3,3.5,4,903,289464,15337,0.2 77 | San Andreas (2015),50,58,43,5.5,6.5,4.5,4.3,2.5,2.9,2.15,2.75,3.25,2.5,3,2,3,3.5,199,45723,9749,0.2 78 | Straight Outta Compton (2015),90,94,72,7.3,8.4,5,4.8,4.5,4.7,3.6,3.65,4.2,4.5,4.5,3.5,3.5,4,90,15982,8096,0.2 79 | Vacation (2015),27,55,34,6.2,6.3,4,3.8,1.35,2.75,1.7,3.1,3.15,1.5,3,1.5,3,3,72,8179,3815,0.2 80 | Chappie (2015),30,57,41,7.4,7,4,3.8,1.5,2.85,2.05,3.7,3.5,1.5,3,2,3.5,3.5,637,125088,3642,0.2 81 | Poltergeist (2015),31,24,47,3.7,5,3,2.8,1.55,1.2,2.35,1.85,2.5,1.5,1,2.5,2,2.5,142,21372,2704,0.2 82 | Paper Towns (2015),55,57,56,6.2,6.9,4,3.8,2.75,2.85,2.8,3.1,3.45,3,3,3,3,3.5,51,14156,1750,0.2 83 | Big Eyes (2014),72,69,62,7.5,7,4,3.8,3.6,3.45,3.1,3.75,3.5,3.5,3.5,3,4,3.5,127,39152,1501,0.2 84 | Blackhat (2015),34,25,51,5.4,5.4,3,2.8,1.7,1.25,2.55,2.7,2.7,1.5,1.5,2.5,2.5,2.5,80,27328,1430,0.2 85 | Self/less (2015),20,51,34,8.4,6.6,4,3.8,1,2.55,1.7,4.2,3.3,1,2.5,1.5,4,3.5,77,5626,1415,0.2 86 | Sinister 2 (2015),13,34,31,5,5.5,3.5,3.3,0.65,1.7,1.55,2.5,2.75,0.5,1.5,1.5,2.5,3,37,3200,973,0.2 87 | Little Boy (2015),20,81,30,5.9,7.4,4.5,4.3,1,4.05,1.5,2.95,3.7,1,4,1.5,3,3.5,38,5927,811,0.2 88 | Me and Earl and The Dying Girl (2015),81,89,74,8.4,8.2,4.5,4.3,4.05,4.45,3.7,4.2,4.1,4,4.5,3.5,4,4,41,5269,624,0.2 89 | Maggie (2015),54,32,52,6.5,5.6,3.5,3.3,2.7,1.6,2.6,3.25,2.8,2.5,1.5,2.5,3.5,3,90,18986,95,0.2 90 | Mad Max: Fury Road (2015),97,88,89,8.7,8.3,4.5,4.3,4.85,4.4,4.45,4.35,4.15,5,4.5,4.5,4.5,4,2375,292023,10509,0.2 91 | Spy (2015),93,82,75,6.3,7.3,4.5,4.3,4.65,4.1,3.75,3.15,3.65,4.5,4,4,3,3.5,318,66636,9418,0.2 92 | The SpongeBob Movie: Sponge Out of Water (2015),78,55,62,6.5,6.1,3.5,3.3,3.9,2.75,3.1,3.25,3.05,4,3,3,3.5,3,196,26046,4493,0.2 93 | Paddington (2015),98,81,77,8.2,7.2,4.5,4.3,4.9,4.05,3.85,4.1,3.6,5,4,4,4,3.5,149,38593,4045,0.2 94 | Dope (2015),87,86,72,7.2,7.5,4.5,4.3,4.35,4.3,3.6,3.6,3.75,4.5,4.5,3.5,3.5,4,43,4911,2195,0.2 95 | What We Do in the Shadows (2015),96,86,75,8.3,7.6,4.5,4.3,4.8,4.3,3.75,4.15,3.8,5,4.5,4,4,4,69,39561,259,0.2 96 | The Overnight (2015),82,65,65,8.6,6.9,3.5,3.3,4.1,3.25,3.25,4.3,3.45,4,3.5,3.5,4.5,3.5,13,867,110,0.2 97 | The Salt of the Earth (2015),96,90,83,7.8,8.4,4.5,4.3,4.8,4.5,4.15,3.9,4.2,5,4.5,4,4,4,13,6605,83,0.2 98 | Song of the Sea (2014),99,92,86,8.2,8.2,4.5,4.3,4.95,4.6,4.3,4.1,4.1,5,4.5,4.5,4,4,62,14067,66,0.2 99 | Fifty Shades of Grey (2015),25,42,46,3.2,4.2,4,3.9,1.25,2.1,2.3,1.6,2.1,1.5,2,2.5,1.5,2,778,179506,34846,0.1 100 | Get Hard (2015),29,48,34,3.8,6.1,4,3.9,1.45,2.4,1.7,1.9,3.05,1.5,2.5,1.5,2,3,145,50022,5933,0.1 101 | Focus (2015),57,54,56,6.2,6.6,4,3.9,2.85,2.7,2.8,3.1,3.3,3,2.5,3,3,3.5,167,101264,4933,0.1 102 | Jupiter Ascending (2015),26,40,40,4.5,5.5,3.5,3.4,1.3,2,2,2.25,2.75,1.5,2,2,2.5,3,503,105412,4122,0.1 103 | The Gallows (2015),16,27,30,7,4.4,3,2.9,0.8,1.35,1.5,3.5,2.2,1,1.5,1.5,3.5,2,80,5511,1896,0.1 104 | The Second Best Exotic Marigold Hotel (2015),62,63,51,6.1,6.6,4,3.9,3.1,3.15,2.55,3.05,3.3,3,3,2.5,3,3.5,41,12940,1870,0.1 105 | Strange Magic (2015),17,50,25,5.3,5.7,3.5,3.4,0.85,2.5,1.25,2.65,2.85,1,2.5,1.5,2.5,3,41,3658,1117,0.1 106 | The Gunman (2015),17,34,39,4.3,5.8,3.5,3.4,0.85,1.7,1.95,2.15,2.9,1,1.5,2,2,3,49,16663,996,0.1 107 | Hitman: Agent 47 (2015),7,49,28,3.3,5.9,4,3.9,0.35,2.45,1.4,1.65,2.95,0.5,2.5,1.5,1.5,3,67,4260,917,0.1 108 | Cake (2015),49,47,49,6.4,6.5,3.5,3.4,2.45,2.35,2.45,3.2,3.25,2.5,2.5,2.5,3,3.5,44,19627,482,0.1 109 | The Vatican Tapes (2015),13,21,37,5.4,4.6,3,2.9,0.65,1.05,1.85,2.7,2.3,0.5,1,2,2.5,2.5,5,952,210,0.1 110 | A Little Chaos (2015),40,47,51,7,6.4,4,3.9,2,2.35,2.55,3.5,3.2,2,2.5,2.5,3.5,3,7,4778,83,0.1 111 | The 100-Year-Old Man Who Climbed Out the Window and Disappeared (2015),67,69,58,4.6,7.1,4,3.9,3.35,3.45,2.9,2.3,3.55,3.5,3.5,3,2.5,3.5,5,17237,63,0.1 112 | Escobar: Paradise Lost (2015),52,52,56,6.9,6.6,4,3.9,2.6,2.6,2.8,3.45,3.3,2.5,2.5,3,3.5,3.5,7,7819,48,0.1 113 | Into the Woods (2014),71,50,69,6.1,6,3.5,3.4,3.55,2.5,3.45,3.05,3,3.5,2.5,3.5,3,3,307,81679,13055,0.1 114 | It Follows (2015),96,65,83,7.5,6.9,3,2.9,4.8,3.25,4.15,3.75,3.45,5,3.5,4,4,3.5,551,64656,2097,0.1 115 | Inherent Vice (2014),73,52,81,7.4,6.7,3,2.9,3.65,2.6,4.05,3.7,3.35,3.5,2.5,4,3.5,3.5,286,44711,1078,0.1 116 | A Most Violent Year (2014),90,69,79,7,7.1,3.5,3.4,4.5,3.45,3.95,3.5,3.55,4.5,3.5,4,3.5,3.5,133,32166,675,0.1 117 | While We're Young (2015),83,52,76,6.7,6.4,3,2.9,4.15,2.6,3.8,3.35,3.2,4,2.5,4,3.5,3,65,17647,449,0.1 118 | Clouds of Sils Maria (2015),89,67,78,7.1,6.8,3.5,3.4,4.45,3.35,3.9,3.55,3.4,4.5,3.5,4,3.5,3.5,36,11392,162,0.1 119 | Testament of Youth (2015),81,79,77,7.9,7.3,4,3.9,4.05,3.95,3.85,3.95,3.65,4,4,4,4,3.5,15,5495,127,0.1 120 | Infinitely Polar Bear (2015),80,76,64,7.9,7.2,4,3.9,4,3.8,3.2,3.95,3.6,4,4,3,4,3.5,8,1062,124,0.1 121 | Phoenix (2015),99,81,91,8,7.2,3.5,3.4,4.95,4.05,4.55,4,3.6,5,4,4.5,4,3.5,21,3687,70,0.1 122 | The Wolfpack (2015),84,73,75,7,7.1,3.5,3.4,4.2,3.65,3.75,3.5,3.55,4,3.5,4,3.5,3.5,8,1488,66,0.1 123 | The Stanford Prison Experiment (2015),84,87,68,8.5,7.1,4,3.9,4.2,4.35,3.4,4.25,3.55,4,4.5,3.5,4.5,3.5,6,950,51,0.1 124 | Tangerine (2015),95,86,86,7.3,7.4,4,3.9,4.75,4.3,4.3,3.65,3.7,5,4.5,4.5,3.5,3.5,14,696,36,0.1 125 | Magic Mike XXL (2015),62,64,60,5.4,6.3,4.5,4.4,3.1,3.2,3,2.7,3.15,3,3,3,2.5,3,52,11937,9363,0.1 126 | Home (2015),45,65,55,7.3,6.7,4.5,4.4,2.25,3.25,2.75,3.65,3.35,2.5,3.5,3,3.5,3.5,177,41158,7705,0.1 127 | The Wedding Ringer (2015),27,66,35,3.3,6.7,4.5,4.4,1.35,3.3,1.75,1.65,3.35,1.5,3.5,2,1.5,3.5,126,37292,6506,0.1 128 | Woman in Gold (2015),52,81,51,7.2,7.4,4.5,4.4,2.6,4.05,2.55,3.6,3.7,2.5,4,2.5,3.5,3.5,72,17957,2435,0.1 129 | The Last Five Years (2015),60,60,60,6.9,6,4.5,4.4,3,3,3,3.45,3,3,3,3,3.5,3,20,4110,99,0.1 130 | Mission: Impossible – Rogue Nation (2015),92,90,75,8,7.8,4.5,4.4,4.6,4.5,3.75,4,3.9,4.5,4.5,4,4,4,362,82579,8357,0.1 131 | Amy (2015),97,91,85,8.8,8,4.5,4.4,4.85,4.55,4.25,4.4,4,5,4.5,4.5,4.5,4,60,5630,729,0.1 132 | Jurassic World (2015),71,81,59,7,7.3,4.5,4.5,3.55,4.05,2.95,3.5,3.65,3.5,4,3,3.5,3.5,1281,241807,34390,0 133 | Minions (2015),54,52,56,5.7,6.7,4,4,2.7,2.6,2.8,2.85,3.35,2.5,2.5,3,3,3.5,204,55895,14998,0 134 | Max (2015),35,73,47,5.9,7,4.5,4.5,1.75,3.65,2.35,2.95,3.5,2,3.5,2.5,3,3.5,15,5444,3412,0 135 | Paul Blart: Mall Cop 2 (2015),5,36,13,2.4,4.3,3.5,3.5,0.25,1.8,0.65,1.2,2.15,0.5,2,0.5,1,2,211,15004,3054,0 136 | The Longest Ride (2015),31,73,33,4.8,7.2,4.5,4.5,1.55,3.65,1.65,2.4,3.6,1.5,3.5,1.5,2.5,3.5,49,25214,2603,0 137 | The Lazarus Effect (2015),14,23,31,4.9,5.2,3,3,0.7,1.15,1.55,2.45,2.6,0.5,1,1.5,2.5,2.5,62,17691,1651,0 138 | The Woman In Black 2 Angel of Death (2015),22,25,42,4.4,4.9,3,3,1.1,1.25,2.1,2.2,2.45,1,1.5,2,2,2.5,55,14873,1333,0 139 | Danny Collins (2015),77,75,58,7.1,7.1,4,4,3.85,3.75,2.9,3.55,3.55,4,4,3,3.5,3.5,33,11206,531,0 140 | Spare Parts (2015),52,83,50,7.1,7.2,4.5,4.5,2.6,4.15,2.5,3.55,3.6,2.5,4,2.5,3.5,3.5,7,47377,450,0 141 | Serena (2015),18,25,36,5.3,5.4,3,3,0.9,1.25,1.8,2.65,2.7,1,1.5,2,2.5,2.5,19,12165,50,0 142 | Inside Out (2015),98,90,94,8.9,8.6,4.5,4.5,4.9,4.5,4.7,4.45,4.3,5,4.5,4.5,4.5,4.5,807,96252,15749,0 143 | Mr. Holmes (2015),87,78,67,7.9,7.4,4,4,4.35,3.9,3.35,3.95,3.7,4.5,4,3.5,4,3.5,33,7367,1348,0 144 | '71 (2015),97,82,83,7.5,7.2,3.5,3.5,4.85,4.1,4.15,3.75,3.6,5,4,4,4,3.5,60,24116,192,0 145 | "Two Days, One Night (2014)",97,78,89,8.8,7.4,3.5,3.5,4.85,3.9,4.45,4.4,3.7,5,4,4.5,4.5,3.5,123,24345,118,0 146 | Gett: The Trial of Viviane Amsalem (2015),100,81,90,7.3,7.8,3.5,3.5,5,4.05,4.5,3.65,3.9,5,4,4.5,3.5,4,19,1955,59,0 147 | "Kumiko, The Treasure Hunter (2015)",87,63,68,6.4,6.7,3.5,3.5,4.35,3.15,3.4,3.2,3.35,4.5,3,3.5,3,3.5,19,5289,41,0 148 | -------------------------------------------------------------------------------- /Aula#11/Aula 11 - Análise Exploratória de Dados III.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#11/Aula 11 - Análise Exploratória de Dados III.pdf -------------------------------------------------------------------------------- /Aula#11/Lesson 11 - Histogram and Boxplot.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Lesson 11 - Histogram and Boxplot.ipynb","version":"0.3.2","provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"cells":[{"metadata":{"id":"-8zmP5ry1pzo","colab_type":"text"},"cell_type":"markdown","source":["# 1 - Introduction\n"]},{"metadata":{"id":"SU8iFVPt2e16","colab_type":"text"},"cell_type":"markdown","source":["\n","In the last lesson, we learned how to create bar plots to compare the average user rating a movie received from four movie review sites. We also learned how to create scatter plots to explore how ratings on one site compare with ratings on another site. We ended the mission with the observations that user ratings from Metacritic and Rotten Tomatoes spanned a larger range (1.0 to 5.0) while those from Fandango and IMDB spanned a smaller range (2.5 to 5 and 2 to 5 respectively).\n","\n","In this lesson, we'll learn how to visualize the distributions of user ratings using **histograms** and **box plots**. We'll continue working with the same dataset from the last lesson. Recall that you can download the dataset **fandango_scores.csv** from the [FiveThirtEight Github repo](https://github.com/fivethirtyeight/data/tree/master/fandango). We've read the dataset into pandas, selected the columns we're going to work with, and assigned the new Dataframe to **norm_reviews**."]},{"metadata":{"id":"l_vCVPY01pzo","colab_type":"code","outputId":"21fa215b-81c1-4b92-82f9-862470996e6d","executionInfo":{"status":"ok","timestamp":1538652860929,"user_tz":180,"elapsed":663,"user":{"displayName":"Ivanovitch Silva","photoUrl":"","userId":"06428777505436195303"}},"colab":{"base_uri":"https://localhost:8080/","height":198}},"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","reviews = pd.read_csv('fandango_scores.csv')\n","cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']\n","norm_reviews = reviews[cols]\n","norm_reviews.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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FILMRT_user_normMetacritic_user_nomIMDB_normFandango_Ratingvalue
0Avengers: Age of Ultron (2015)4.33.553.904.5
1Cinderella (2015)4.03.753.554.5
2Ant-Man (2015)4.54.053.904.5
3Do You Believe? (2015)4.22.352.704.5
4Hot Tub Time Machine 2 (2015)1.41.702.553.0
\n","
"],"text/plain":[" FILM RT_user_norm Metacritic_user_nom \\\n","0 Avengers: Age of Ultron (2015) 4.3 3.55 \n","1 Cinderella (2015) 4.0 3.75 \n","2 Ant-Man (2015) 4.5 4.05 \n","3 Do You Believe? (2015) 4.2 2.35 \n","4 Hot Tub Time Machine 2 (2015) 1.4 1.70 \n","\n"," IMDB_norm Fandango_Ratingvalue \n","0 3.90 4.5 \n","1 3.55 4.5 \n","2 3.90 4.5 \n","3 2.70 4.5 \n","4 2.55 3.0 "]},"metadata":{"tags":[]},"execution_count":2}]},{"metadata":{"id":"GRCM4dEk1pzv","colab_type":"text"},"cell_type":"markdown","source":["# 2 - Frequency distribution\n"]},{"metadata":{"id":"dP6RCKsZ29w9","colab_type":"text"},"cell_type":"markdown","source":["\n","Let's first compare the **frequency distributions** of user ratings from Fandango with those from IMDB using tables. A column's [frequency distribution](https://en.wikipedia.org/wiki/Frequency_distribution) consists of the unique values in that column along with the count for each of those values (or their frequency). We can use [Series.value_counts()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) to return the frequency distribution as Series object:\n","\n","```python\n","freq_counts = norm_reviews['Fandango_Ratingvalue'].value_counts()\n","```\n","\n","The resulting Series object will be sorted by frequency in descending order:\n","\n","\"creating\n","\n","\n","While this ordering is helpful when we're looking to quickly find the most common values in a given column, it's not helpful when trying to understand the range that the values in the column span. We can use [Series.sort_index()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) to sort the frequency distribution in ascending order by the values in the column (which make up the index for the Series object):\n","\n","```python\n","freq_counts = norm_reviews['Fandango_Ratingvalue'].value_counts()\n","sorted_freq_counts = freq_counts.sort_index()\n","```\n","\n","Here's what both frequency distributions look like side-by-side:\n","\n","\"creating\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","\n","**Description**:\n","\n","1. Use the **value_counts()** method to return the frequency counts for the **Fandango_Ratingvalue** column. Sort the resulting Series object by the index and assign to **fandango_distribution**.\n","2. Use the **value_counts()** method to return frequency counts the **IMDB_norm** column. Sort the resulting Series object by the index and assign to **imdb_distribution**.\n","3. Use the **print()** function to display fandango_distribution and **imdb_distribution**."]},{"metadata":{"id":"sqC0vw_14N4g","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"gJB1G7xU1pzv","colab_type":"text"},"cell_type":"markdown","source":["# 3 - Binning\n"]},{"metadata":{"id":"-BDn_pI94ej3","colab_type":"text"},"cell_type":"markdown","source":["\n","Because there are only a few unique values, we can quickly scan the frequency counts and confirm that the **Fandango_Ratingvalue** column ranges from 2.7 to 4.8 while the **IMDB_norm** column ranges from 2 to 4.3. While we can quickly determine the minimum and maximum values, we struggle to answer the following questions about a column:\n","\n","- What percent of the ratings are contained in the 2.0 to 4.0 range?\n"," - How does this compare with other sites?\n","- Which values represent the top 25% of the ratings? The bottom 25%?\n"," - How does this compare with other sites?\n"," \n","Comparing frequency distributions is also challenging because the **Fandango_Ratingvalue** column contains 21 unique values while **IMDB_norm** contains 41 unique values. We need a way to compare frequencies across a shared set of values. Because all ratings have been normalized to a range of 0 to 5, we can start by dividing the range of possible values into a series of fixed length intervals, called **bins**. We can then sum the frequencies for the values that fall into each bin. Here's a diagram that makes binning easier to understand:\n","\n","\"creating\n","\n","\n","The distributions for both of these columns are now easier to compare because of the shared x-axis (the bins). We can now plot the bins along with the frequency sums as a bar plot. This type of plot is called a [histogram](https://en.wikipedia.org/wiki/Histogram). Let's dive right into creating a histogram in matplotlib.\n"]},{"metadata":{"id":"qGT3mHwU1pzw","colab_type":"text"},"cell_type":"markdown","source":["# 4 - Histogram in matplotlib\n"]},{"metadata":{"id":"M0mbtHc4532N","colab_type":"text"},"cell_type":"markdown","source":["\n","We can generate a histogram using [Axes.hist()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.hist). This method has only 1 required parameter, an iterable object containing the values we want a histogram for. By default, matplotlib will:\n","\n","- calculate the minimum and maximum value from the sequence of values we passed in\n","- create 10 bins of equal length that span the range from the minimum to the maximum value\n","- group unique values into the bins\n","- sum up the associated unique values\n","- generate a bar for the frequency sum for each bin\n","\n","The default behavior of **Axes.hist()** is problematic for the use case of comparing distributions for multiple columns using the same binning strategy. This is because the binning strategy for each column would depend on the minimum and maximum values, instead of a shared binning strategy. We can use the range parameter to specify the **range** we want matplotlib to use as a tuple:\n","\n","```python\n","ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(0, 5))\n","```\n","\n","While histograms use bars whose lengths are scaled to the values they're representing, they differ from bar plots in a few ways. Histograms help us visualize continuous values using bins while bar plots help us visualize discrete values. The locations of the bars on the x-axis matter in a histogram but they don't in a simple bar plot. Lastly, bar plots also have gaps between the bars, to emphasize that the values are discrete.\n","\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","\n","**Description**:\n","\n","1. Create a single subplot and assign the returned Figure object to **fig** and the returned Axes object to **ax**.\n","2. Generate a histogram from the values in the **Fandango_Ratingvalue** column using a range of **0** to **5**.\n","3. Use **plt.show()** to display the plot."]},{"metadata":{"id":"6gLvYr185-AX","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"PshfMRol1pzw","colab_type":"text"},"cell_type":"markdown","source":["# 5 - Comparing histograms\n"]},{"metadata":{"id":"q0fLbvZP6BEd","colab_type":"text"},"cell_type":"markdown","source":["\n","If you recall, one of the questions we were looking to answer was:\n","\n","- What percent of the ratings are contained in the 2.0 to 4.0 range?\n","\n","We can visually examine the proportional area that the bars in the 2.0 to 4.0 range take up and determine that more than 50% of the movies on Fandango fall in this range. We can increase the number of bins from 10 to 20 for improved resolution of the distribution. The length of each bin will be 0.25 (5 / 20) instead of 0.5 (5 / 10). The **bins** parameter for **Axes.hist()** is the 2nd positional parameter, but can also be specified as a named parameter:\n","\n","```python\n","# Either of these will work.\n","ax.hist(norm_reviews['Fandango_Ratingvalue'], 20, range=(0, 5))\n","ax.hist(norm_reviews['Fandango_Ratingvalue'], bins=20, range=(0, 5))\n","```\n","\n","Let's now generate histograms using 20 bins for all four columns. To ensure that the scales for the y-axis are the same for all histograms, let's set them manually using **Axes.set_ylim()**.\n","\n","\n","**Exercise**\n","\n","\n","\n","\n","**Description**:\n","\n","1. For the subplot associated with **ax1**:\n"," - Generate a histogram of the values in the **Fandango_Ratingvalue** column using **20 bins** and a range of **0** to **5**.\n"," - Set the title to **Distribution of Fandango Ratings**.\n","2. For the subplot associated with **ax2**:\n"," - Generate a histogram of the values in the **RT_user_norm** column using **20 bins** and a range of **0** to **5**.\n"," - Set the title to **Distribution of Rotten Tomatoes Ratings**.\n","3. For the subplot associated with **ax3**:\n"," - Generate a histogram of the values in the **Metacritic_user_nom** column using **20 bins** and a range of **0** to **5**.\n"," - Set the title to **Distribution of Metacritic Ratings**.\n","4. For the subplot associated with **ax4**:\n"," - Generate a histogram of the values in the **IMDB_norm** column using **20 bins** and a range of **0** to **5**.\n"," - Set the title to **Distribution of IMDB Ratings**.\n","5. For all subplots:\n"," - Set the y-axis range to **0** to **50** using **Axes.set_ylim()**.\n"," - Set the y-axis label to **Frequency** using **Axes.set_ylabel()**.\n"," - Use **plt.show()** to display the plots."]},{"metadata":{"id":"Qs_7g8Zf6a4b","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"GnQdIocu1pzx","colab_type":"text"},"cell_type":"markdown","source":["# 6 - Quartiles\n"]},{"metadata":{"id":"EPN0S8JoC3aD","colab_type":"text"},"cell_type":"markdown","source":["\n","From the histograms, we can make the following observations:\n","\n","- Around 50% of user ratings from Fandango fall in the 2 to 4 score range\n","- Around 50% of user ratings from Rotten Tomatoes fall in the 2 to 4 score range\n","- Around 75% of the user ratings from Metacritic fall in the 2 to 4 score range\n","- Around 90% of the user ratings from IMDB fall in the 2 to 4 score range\n","\n","While histograms allow us to visually estimate the percentage of ratings that fall into a range of bins, they don't allow us to easily understand how the top 25% or the bottom 25% of the ratings differ across the sites. The bottom 25% of values and top 25% of values both represent [quartiles](https://en.wikipedia.org/wiki/Quartile). The four quartiles divide the range of values into four regions where each region contains 1/4th of the total values.\n","\n","While these regions may sound similar to bins, they differ in how values are grouped into each region. Each bin covers an equal proportion of the values in the range. On the other hand, each quantile covers an equal number of values (1/4th of the total values). To visualize quartiles, we need to use a box plot, also referred to as a [box-and-whisker plot](https://en.wikipedia.org/wiki/Box_plot)."]},{"metadata":{"id":"vP9ayO_91pzy","colab_type":"text"},"cell_type":"markdown","source":["# 7 - Box plot\n"]},{"metadata":{"id":"bqwngH_T7SVe","colab_type":"text"},"cell_type":"markdown","source":["\n","A box plot consists of **box-and-whisker** diagrams, which represents the different quartiles in a visual way. Here's a box plot of the values in the **RT_user_norm** column:\n","\n","\"creating\n","\n","The two regions contained within the box in the middle make up the **interquartile range**, or **IQR**. The [IQR](https://en.wikipedia.org/wiki/Interquartile_range) is used to measure dispersion of the values. The ratio of the length of the box to the whiskers around the box helps us understand how values in the distribution are spread out.\n","\n","We can generate a boxplot using [Axes.boxplot()](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.boxplot).\n","\n","```python\n","ax.boxplot(norm_reviews['RT_user_norm'])\n","```\n","\n","Matplotlib will sort the values, calculate the quartiles that divide the values into four equal regions, and generate the box and whisker diagram.\n","\n","\n","**Exercise**\n","\n","\n","\n","**Description**:\n","\n","1. Create a single subplot and assign the returned Figure object to **fig** and the returned Axes object to **ax**.\n","2. Generate a box plot from the values in the **RT_user_norm** column.\n"," - Set the y-axis limit to range from **0** to **5**.\n"," - Set the x-axis tick label to **Rotten Tomatoes**.\n","3. Use **plt.show()** to display the plot."]},{"metadata":{"id":"t5OuU7Co7ceX","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"2D5g7cwr1pzz","colab_type":"text"},"cell_type":"markdown","source":["# 8 - Multiple box plots\n"]},{"metadata":{"id":"Dqa7pC71C6gV","colab_type":"text"},"cell_type":"markdown","source":["\n","From the box plot we generated using Rotten Tomatoes ratings, we can conclude that:\n","- the bottom 25% of user ratings range from around 1 to 2.5\n","- the top 25% of of user ratings range from around 4 to 4.6\n","\n","To compare the lower and upper ranges with those for the other columns, we need to generate multiple box-and-whisker diagrams in the same box plot. When selecting multiple columns to pass in to **Axes.boxplot()**, we need to use the **values** accessor to return a multi-dimensional numpy array:\n","\n","```ptyhon\n","num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']\n","ax.boxplot(norm_reviews[num_cols].values)\n","```\n","\n","**Exercise**\n","\n","\n","\n","\n","**Description**:\n","\n","1. Create a single subplot and assign the returned Figure object to **fig** and the returned Axes object to **ax**.\n","2. Generate a box plot containing a box-and-whisker diagram for each column in **num_cols**.\n","3. Set the x-axis tick labels to the column names in **num_cols** and rotate the ticks by **90 degrees**.\n","4. Set the y-axis limit to range from **0** to **5**.\n","5. Use **plt.show()** to display the plot."]},{"metadata":{"id":"BiJookL_7ivR","colab_type":"code","colab":{}},"cell_type":"code","source":["# put your code here"],"execution_count":0,"outputs":[]},{"metadata":{"id":"fvPVGsQ_1pz0","colab_type":"text"},"cell_type":"markdown","source":["# 9 - Conclusion\n","\n","From the boxplot, we can reach the following conclusions:\n","\n","- user ratings from Rotten Tomatoes and Metacritic span a larger range of values\n","- user ratings from IMDB and Fandango are both skewed in the positive direction and span a more constrained range of values"]}]} -------------------------------------------------------------------------------- /Aula#11/fandango_scores.csv: -------------------------------------------------------------------------------- 1 | FILM,RottenTomatoes,RottenTomatoes_User,Metacritic,Metacritic_User,IMDB,Fandango_Stars,Fandango_Ratingvalue,RT_norm,RT_user_norm,Metacritic_norm,Metacritic_user_nom,IMDB_norm,RT_norm_round,RT_user_norm_round,Metacritic_norm_round,Metacritic_user_norm_round,IMDB_norm_round,Metacritic_user_vote_count,IMDB_user_vote_count,Fandango_votes,Fandango_Difference 2 | Avengers: Age of Ultron (2015),74,86,66,7.1,7.8,5,4.5,3.7,4.3,3.3,3.55,3.9,3.5,4.5,3.5,3.5,4,1330,271107,14846,0.5 3 | Cinderella (2015),85,80,67,7.5,7.1,5,4.5,4.25,4,3.35,3.75,3.55,4.5,4,3.5,4,3.5,249,65709,12640,0.5 4 | Ant-Man (2015),80,90,64,8.1,7.8,5,4.5,4,4.5,3.2,4.05,3.9,4,4.5,3,4,4,627,103660,12055,0.5 5 | Do You Believe? (2015),18,84,22,4.7,5.4,5,4.5,0.9,4.2,1.1,2.35,2.7,1,4,1,2.5,2.5,31,3136,1793,0.5 6 | Hot Tub Time Machine 2 (2015),14,28,29,3.4,5.1,3.5,3,0.7,1.4,1.45,1.7,2.55,0.5,1.5,1.5,1.5,2.5,88,19560,1021,0.5 7 | The Water Diviner (2015),63,62,50,6.8,7.2,4.5,4,3.15,3.1,2.5,3.4,3.6,3,3,2.5,3.5,3.5,34,39373,397,0.5 8 | Irrational Man (2015),42,53,53,7.6,6.9,4,3.5,2.1,2.65,2.65,3.8,3.45,2,2.5,2.5,4,3.5,17,2680,252,0.5 9 | Top Five (2014),86,64,81,6.8,6.5,4,3.5,4.3,3.2,4.05,3.4,3.25,4.5,3,4,3.5,3.5,124,16876,3223,0.5 10 | Shaun the Sheep Movie (2015),99,82,81,8.8,7.4,4.5,4,4.95,4.1,4.05,4.4,3.7,5,4,4,4.5,3.5,62,12227,896,0.5 11 | Love & Mercy (2015),89,87,80,8.5,7.8,4.5,4,4.45,4.35,4,4.25,3.9,4.5,4.5,4,4.5,4,54,5367,864,0.5 12 | Far From The Madding Crowd (2015),84,77,71,7.5,7.2,4.5,4,4.2,3.85,3.55,3.75,3.6,4,4,3.5,4,3.5,35,12129,804,0.5 13 | Black Sea (2015),82,60,62,6.6,6.4,4,3.5,4.1,3,3.1,3.3,3.2,4,3,3,3.5,3,37,16547,218,0.5 14 | Leviathan (2014),99,79,92,7.2,7.7,4,3.5,4.95,3.95,4.6,3.6,3.85,5,4,4.5,3.5,4,145,22521,64,0.5 15 | Unbroken (2014),51,70,59,6.5,7.2,4.5,4.1,2.55,3.5,2.95,3.25,3.6,2.5,3.5,3,3.5,3.5,218,77518,9443,0.4 16 | The Imitation Game (2014),90,92,73,8.2,8.1,5,4.6,4.5,4.6,3.65,4.1,4.05,4.5,4.5,3.5,4,4,566,334164,8055,0.4 17 | Taken 3 (2015),9,46,26,4.6,6.1,4.5,4.1,0.45,2.3,1.3,2.3,3.05,0.5,2.5,1.5,2.5,3,240,104235,6757,0.4 18 | Ted 2 (2015),46,58,48,6.5,6.6,4.5,4.1,2.3,2.9,2.4,3.25,3.3,2.5,3,2.5,3.5,3.5,197,49102,6437,0.4 19 | Southpaw (2015),59,80,57,8.2,7.8,5,4.6,2.95,4,2.85,4.1,3.9,3,4,3,4,4,128,23561,5597,0.4 20 | Night at the Museum: Secret of the Tomb (2014),50,58,47,5.8,6.3,4.5,4.1,2.5,2.9,2.35,2.9,3.15,2.5,3,2.5,3,3,103,50291,5445,0.4 21 | Pixels (2015),17,54,27,5.3,5.6,4.5,4.1,0.85,2.7,1.35,2.65,2.8,1,2.5,1.5,2.5,3,246,19521,3886,0.4 22 | "McFarland, USA (2015)",79,89,60,7.2,7.5,5,4.6,3.95,4.45,3,3.6,3.75,4,4.5,3,3.5,4,59,13769,3364,0.4 23 | Insidious: Chapter 3 (2015),59,56,52,6.9,6.3,4.5,4.1,2.95,2.8,2.6,3.45,3.15,3,3,2.5,3.5,3,115,25134,3276,0.4 24 | The Man From U.N.C.L.E. (2015),68,80,55,7.9,7.6,4.5,4.1,3.4,4,2.75,3.95,3.8,3.5,4,3,4,4,144,22104,2686,0.4 25 | Run All Night (2015),60,59,59,7.3,6.6,4.5,4.1,3,2.95,2.95,3.65,3.3,3,3,3,3.5,3.5,141,50438,2066,0.4 26 | Trainwreck (2015),85,74,75,6,6.7,4.5,4.1,4.25,3.7,3.75,3,3.35,4.5,3.5,4,3,3.5,169,27380,8381,0.4 27 | Selma (2014),99,86,89,7.1,7.5,5,4.6,4.95,4.3,4.45,3.55,3.75,5,4.5,4.5,3.5,4,316,45344,7025,0.4 28 | Ex Machina (2015),92,86,78,7.9,7.7,4.5,4.1,4.6,4.3,3.9,3.95,3.85,4.5,4.5,4,4,4,672,154499,3458,0.4 29 | Still Alice (2015),88,85,72,7.8,7.5,4.5,4.1,4.4,4.25,3.6,3.9,3.75,4.5,4.5,3.5,4,4,153,57123,1258,0.4 30 | Wild Tales (2014),96,92,77,8.8,8.2,4.5,4.1,4.8,4.6,3.85,4.4,4.1,5,4.5,4,4.5,4,107,50285,235,0.4 31 | The End of the Tour (2015),92,89,84,7.5,7.9,4.5,4.1,4.6,4.45,4.2,3.75,3.95,4.5,4.5,4,4,4,19,1320,121,0.4 32 | Red Army (2015),96,86,82,7.4,7.7,4.5,4.1,4.8,4.3,4.1,3.7,3.85,5,4.5,4,3.5,4,11,2275,54,0.4 33 | When Marnie Was There (2015),89,89,71,6.4,7.8,4.5,4.1,4.45,4.45,3.55,3.2,3.9,4.5,4.5,3.5,3,4,29,4160,46,0.4 34 | The Hunting Ground (2015),92,72,77,7.8,7.5,4.5,4.1,4.6,3.6,3.85,3.9,3.75,4.5,3.5,4,4,4,6,1196,42,0.4 35 | The Boy Next Door (2015),10,35,30,5.5,4.6,4,3.6,0.5,1.75,1.5,2.75,2.3,0.5,2,1.5,3,2.5,75,19658,2800,0.4 36 | Aloha (2015),19,31,40,4,5.5,3.5,3.1,0.95,1.55,2,2,2.75,1,1.5,2,2,3,67,12255,2284,0.4 37 | The Loft (2015),11,40,24,2.4,6.3,4,3.6,0.55,2,1.2,1.2,3.15,0.5,2,1,1,3,80,21319,811,0.4 38 | 5 Flights Up (2015),52,47,55,6.8,6.1,4,3.6,2.6,2.35,2.75,3.4,3.05,2.5,2.5,3,3.5,3,6,2174,79,0.4 39 | Welcome to Me (2015),71,47,67,6.9,5.9,4,3.6,3.55,2.35,3.35,3.45,2.95,3.5,2.5,3.5,3.5,3,33,8301,56,0.4 40 | Saint Laurent (2015),51,45,52,6.8,6.3,3.5,3.1,2.55,2.25,2.6,3.4,3.15,2.5,2.5,2.5,3.5,3,8,2196,43,0.4 41 | Maps to the Stars (2015),60,46,67,5.8,6.3,3.5,3.1,3,2.3,3.35,2.9,3.15,3,2.5,3.5,3,3,46,22440,35,0.4 42 | I'll See You In My Dreams (2015),94,70,75,6.9,6.9,4,3.6,4.7,3.5,3.75,3.45,3.45,4.5,3.5,4,3.5,3.5,14,1151,281,0.4 43 | Timbuktu (2015),99,78,91,6.9,7.2,4,3.6,4.95,3.9,4.55,3.45,3.6,5,4,4.5,3.5,3.5,37,6246,74,0.4 44 | About Elly (2015),97,86,87,9.6,8.2,4,3.6,4.85,4.3,4.35,4.8,4.1,5,4.5,4.5,5,4,23,20659,43,0.4 45 | The Diary of a Teenage Girl (2015),95,81,87,6.3,7,4,3.6,4.75,4.05,4.35,3.15,3.5,5,4,4.5,3,3.5,18,1107,38,0.4 46 | Kingsman: The Secret Service (2015),75,84,58,7.9,7.8,4.5,4.2,3.75,4.2,2.9,3.95,3.9,4,4,3,4,4,1054,272204,15205,0.3 47 | Tomorrowland (2015),50,53,60,6.4,6.6,4,3.7,2.5,2.65,3,3.2,3.3,2.5,2.5,3,3,3.5,262,42937,8077,0.3 48 | The Divergent Series: Insurgent (2015),30,61,42,5.4,6.4,4.5,4.2,1.5,3.05,2.1,2.7,3.2,1.5,3,2,2.5,3,206,89618,7123,0.3 49 | Annie (2014),27,61,33,4.8,5.2,4.5,4.2,1.35,3.05,1.65,2.4,2.6,1.5,3,1.5,2.5,2.5,108,19222,6835,0.3 50 | Fantastic Four (2015),9,20,27,2.5,4,3,2.7,0.45,1,1.35,1.25,2,0.5,1,1.5,1.5,2,421,39838,6288,0.3 51 | Terminator Genisys (2015),26,60,38,6.4,6.9,4.5,4.2,1.3,3,1.9,3.2,3.45,1.5,3,2,3,3.5,779,85585,6272,0.3 52 | Pitch Perfect 2 (2015),67,68,63,5.7,6.7,4.5,4.2,3.35,3.4,3.15,2.85,3.35,3.5,3.5,3,3,3.5,192,56333,4577,0.3 53 | Entourage (2015),32,68,38,5.2,7.1,4.5,4.2,1.6,3.4,1.9,2.6,3.55,1.5,3.5,2,2.5,3.5,96,21914,4279,0.3 54 | The Age of Adaline (2015),54,68,51,7.4,7.3,4.5,4.2,2.7,3.4,2.55,3.7,3.65,2.5,3.5,2.5,3.5,3.5,100,45510,3325,0.3 55 | Hot Pursuit (2015),8,37,31,3.7,4.9,4,3.7,0.4,1.85,1.55,1.85,2.45,0.5,2,1.5,2,2.5,78,17061,2618,0.3 56 | The DUFF (2015),71,68,56,6.4,6.6,4.5,4.2,3.55,3.4,2.8,3.2,3.3,3.5,3.5,3,3,3.5,69,33594,2273,0.3 57 | Black or White (2015),39,68,45,7.9,6.6,4.5,4.2,1.95,3.4,2.25,3.95,3.3,2,3.5,2.5,4,3.5,24,4857,1862,0.3 58 | Project Almanac (2015),34,46,47,5.4,6.4,4,3.7,1.7,2.3,2.35,2.7,3.2,1.5,2.5,2.5,2.5,3,95,40057,1834,0.3 59 | Ricki and the Flash (2015),64,53,54,7,6.2,4,3.7,3.2,2.65,2.7,3.5,3.1,3,2.5,2.5,3.5,3,37,1769,1462,0.3 60 | Seventh Son (2015),12,35,30,3.9,5.5,3.5,3.2,0.6,1.75,1.5,1.95,2.75,0.5,2,1.5,2,3,126,41177,1213,0.3 61 | Mortdecai (2015),12,30,27,3.2,5.5,3.5,3.2,0.6,1.5,1.35,1.6,2.75,0.5,1.5,1.5,1.5,3,144,31878,1196,0.3 62 | Unfinished Business (2015),11,27,32,3.8,5.4,3.5,3.2,0.55,1.35,1.6,1.9,2.7,0.5,1.5,1.5,2,2.5,39,14346,821,0.3 63 | American Ultra (2015),46,52,50,6.8,6.5,4,3.7,2.3,2.6,2.5,3.4,3.25,2.5,2.5,2.5,3.5,3.5,42,3017,638,0.3 64 | True Story (2015),45,41,50,5.7,6.3,3.5,3.2,2.25,2.05,2.5,2.85,3.15,2.5,2,2.5,3,3,37,16069,540,0.3 65 | Child 44 (2015),26,44,41,5.3,6.4,4,3.7,1.3,2.2,2.05,2.65,3.2,1.5,2,2,2.5,3,73,19220,308,0.3 66 | Dark Places (2015),26,33,39,7.9,6.3,4,3.7,1.3,1.65,1.95,3.95,3.15,1.5,1.5,2,4,3,18,9856,55,0.3 67 | Birdman (2014),92,78,88,8,7.9,4,3.7,4.6,3.9,4.4,4,3.95,4.5,4,4.5,4,4,1171,303505,4194,0.3 68 | The Gift (2015),93,79,77,8.3,7.6,4,3.7,4.65,3.95,3.85,4.15,3.8,4.5,4,4,4,4,121,10891,2680,0.3 69 | Unfriended (2015),60,39,59,5.8,5.9,3,2.7,3,1.95,2.95,2.9,2.95,3,2,3,3,3,130,22348,2507,0.3 70 | Monkey Kingdom (2015),94,77,72,7.5,7.3,4.5,4.2,4.7,3.85,3.6,3.75,3.65,4.5,4,3.5,4,3.5,15,883,701,0.3 71 | Mr. Turner (2014),98,56,94,6.6,6.9,3.5,3.2,4.9,2.8,4.7,3.3,3.45,5,3,4.5,3.5,3.5,98,13296,290,0.3 72 | Seymour: An Introduction (2015),100,87,83,6,7.7,4.5,4.2,5,4.35,4.15,3,3.85,5,4.5,4,3,4,4,243,41,0.3 73 | The Wrecking Crew (2015),93,84,67,7,7.8,4.5,4.2,4.65,4.2,3.35,3.5,3.9,4.5,4,3.5,3.5,4,4,732,38,0.3 74 | American Sniper (2015),72,85,72,6.6,7.4,5,4.8,3.6,4.25,3.6,3.3,3.7,3.5,4.5,3.5,3.5,3.5,850,251856,34085,0.2 75 | Furious 7 (2015),81,84,67,6.8,7.4,5,4.8,4.05,4.2,3.35,3.4,3.7,4,4,3.5,3.5,3.5,764,207211,33538,0.2 76 | The Hobbit: The Battle of the Five Armies (2014),61,75,59,7,7.5,4.5,4.3,3.05,3.75,2.95,3.5,3.75,3,4,3,3.5,4,903,289464,15337,0.2 77 | San Andreas (2015),50,58,43,5.5,6.5,4.5,4.3,2.5,2.9,2.15,2.75,3.25,2.5,3,2,3,3.5,199,45723,9749,0.2 78 | Straight Outta Compton (2015),90,94,72,7.3,8.4,5,4.8,4.5,4.7,3.6,3.65,4.2,4.5,4.5,3.5,3.5,4,90,15982,8096,0.2 79 | Vacation (2015),27,55,34,6.2,6.3,4,3.8,1.35,2.75,1.7,3.1,3.15,1.5,3,1.5,3,3,72,8179,3815,0.2 80 | Chappie (2015),30,57,41,7.4,7,4,3.8,1.5,2.85,2.05,3.7,3.5,1.5,3,2,3.5,3.5,637,125088,3642,0.2 81 | Poltergeist (2015),31,24,47,3.7,5,3,2.8,1.55,1.2,2.35,1.85,2.5,1.5,1,2.5,2,2.5,142,21372,2704,0.2 82 | Paper Towns (2015),55,57,56,6.2,6.9,4,3.8,2.75,2.85,2.8,3.1,3.45,3,3,3,3,3.5,51,14156,1750,0.2 83 | Big Eyes (2014),72,69,62,7.5,7,4,3.8,3.6,3.45,3.1,3.75,3.5,3.5,3.5,3,4,3.5,127,39152,1501,0.2 84 | Blackhat (2015),34,25,51,5.4,5.4,3,2.8,1.7,1.25,2.55,2.7,2.7,1.5,1.5,2.5,2.5,2.5,80,27328,1430,0.2 85 | Self/less (2015),20,51,34,8.4,6.6,4,3.8,1,2.55,1.7,4.2,3.3,1,2.5,1.5,4,3.5,77,5626,1415,0.2 86 | Sinister 2 (2015),13,34,31,5,5.5,3.5,3.3,0.65,1.7,1.55,2.5,2.75,0.5,1.5,1.5,2.5,3,37,3200,973,0.2 87 | Little Boy (2015),20,81,30,5.9,7.4,4.5,4.3,1,4.05,1.5,2.95,3.7,1,4,1.5,3,3.5,38,5927,811,0.2 88 | Me and Earl and The Dying Girl (2015),81,89,74,8.4,8.2,4.5,4.3,4.05,4.45,3.7,4.2,4.1,4,4.5,3.5,4,4,41,5269,624,0.2 89 | Maggie (2015),54,32,52,6.5,5.6,3.5,3.3,2.7,1.6,2.6,3.25,2.8,2.5,1.5,2.5,3.5,3,90,18986,95,0.2 90 | Mad Max: Fury Road (2015),97,88,89,8.7,8.3,4.5,4.3,4.85,4.4,4.45,4.35,4.15,5,4.5,4.5,4.5,4,2375,292023,10509,0.2 91 | Spy (2015),93,82,75,6.3,7.3,4.5,4.3,4.65,4.1,3.75,3.15,3.65,4.5,4,4,3,3.5,318,66636,9418,0.2 92 | The SpongeBob Movie: Sponge Out of Water (2015),78,55,62,6.5,6.1,3.5,3.3,3.9,2.75,3.1,3.25,3.05,4,3,3,3.5,3,196,26046,4493,0.2 93 | Paddington (2015),98,81,77,8.2,7.2,4.5,4.3,4.9,4.05,3.85,4.1,3.6,5,4,4,4,3.5,149,38593,4045,0.2 94 | Dope (2015),87,86,72,7.2,7.5,4.5,4.3,4.35,4.3,3.6,3.6,3.75,4.5,4.5,3.5,3.5,4,43,4911,2195,0.2 95 | What We Do in the Shadows (2015),96,86,75,8.3,7.6,4.5,4.3,4.8,4.3,3.75,4.15,3.8,5,4.5,4,4,4,69,39561,259,0.2 96 | The Overnight (2015),82,65,65,8.6,6.9,3.5,3.3,4.1,3.25,3.25,4.3,3.45,4,3.5,3.5,4.5,3.5,13,867,110,0.2 97 | The Salt of the Earth (2015),96,90,83,7.8,8.4,4.5,4.3,4.8,4.5,4.15,3.9,4.2,5,4.5,4,4,4,13,6605,83,0.2 98 | Song of the Sea (2014),99,92,86,8.2,8.2,4.5,4.3,4.95,4.6,4.3,4.1,4.1,5,4.5,4.5,4,4,62,14067,66,0.2 99 | Fifty Shades of Grey (2015),25,42,46,3.2,4.2,4,3.9,1.25,2.1,2.3,1.6,2.1,1.5,2,2.5,1.5,2,778,179506,34846,0.1 100 | Get Hard (2015),29,48,34,3.8,6.1,4,3.9,1.45,2.4,1.7,1.9,3.05,1.5,2.5,1.5,2,3,145,50022,5933,0.1 101 | Focus (2015),57,54,56,6.2,6.6,4,3.9,2.85,2.7,2.8,3.1,3.3,3,2.5,3,3,3.5,167,101264,4933,0.1 102 | Jupiter Ascending (2015),26,40,40,4.5,5.5,3.5,3.4,1.3,2,2,2.25,2.75,1.5,2,2,2.5,3,503,105412,4122,0.1 103 | The Gallows (2015),16,27,30,7,4.4,3,2.9,0.8,1.35,1.5,3.5,2.2,1,1.5,1.5,3.5,2,80,5511,1896,0.1 104 | The Second Best Exotic Marigold Hotel (2015),62,63,51,6.1,6.6,4,3.9,3.1,3.15,2.55,3.05,3.3,3,3,2.5,3,3.5,41,12940,1870,0.1 105 | Strange Magic (2015),17,50,25,5.3,5.7,3.5,3.4,0.85,2.5,1.25,2.65,2.85,1,2.5,1.5,2.5,3,41,3658,1117,0.1 106 | The Gunman (2015),17,34,39,4.3,5.8,3.5,3.4,0.85,1.7,1.95,2.15,2.9,1,1.5,2,2,3,49,16663,996,0.1 107 | Hitman: Agent 47 (2015),7,49,28,3.3,5.9,4,3.9,0.35,2.45,1.4,1.65,2.95,0.5,2.5,1.5,1.5,3,67,4260,917,0.1 108 | Cake (2015),49,47,49,6.4,6.5,3.5,3.4,2.45,2.35,2.45,3.2,3.25,2.5,2.5,2.5,3,3.5,44,19627,482,0.1 109 | The Vatican Tapes (2015),13,21,37,5.4,4.6,3,2.9,0.65,1.05,1.85,2.7,2.3,0.5,1,2,2.5,2.5,5,952,210,0.1 110 | A Little Chaos (2015),40,47,51,7,6.4,4,3.9,2,2.35,2.55,3.5,3.2,2,2.5,2.5,3.5,3,7,4778,83,0.1 111 | The 100-Year-Old Man Who Climbed Out the Window and Disappeared (2015),67,69,58,4.6,7.1,4,3.9,3.35,3.45,2.9,2.3,3.55,3.5,3.5,3,2.5,3.5,5,17237,63,0.1 112 | Escobar: Paradise Lost (2015),52,52,56,6.9,6.6,4,3.9,2.6,2.6,2.8,3.45,3.3,2.5,2.5,3,3.5,3.5,7,7819,48,0.1 113 | Into the Woods (2014),71,50,69,6.1,6,3.5,3.4,3.55,2.5,3.45,3.05,3,3.5,2.5,3.5,3,3,307,81679,13055,0.1 114 | It Follows (2015),96,65,83,7.5,6.9,3,2.9,4.8,3.25,4.15,3.75,3.45,5,3.5,4,4,3.5,551,64656,2097,0.1 115 | Inherent Vice (2014),73,52,81,7.4,6.7,3,2.9,3.65,2.6,4.05,3.7,3.35,3.5,2.5,4,3.5,3.5,286,44711,1078,0.1 116 | A Most Violent Year (2014),90,69,79,7,7.1,3.5,3.4,4.5,3.45,3.95,3.5,3.55,4.5,3.5,4,3.5,3.5,133,32166,675,0.1 117 | While We're Young (2015),83,52,76,6.7,6.4,3,2.9,4.15,2.6,3.8,3.35,3.2,4,2.5,4,3.5,3,65,17647,449,0.1 118 | Clouds of Sils Maria (2015),89,67,78,7.1,6.8,3.5,3.4,4.45,3.35,3.9,3.55,3.4,4.5,3.5,4,3.5,3.5,36,11392,162,0.1 119 | Testament of Youth (2015),81,79,77,7.9,7.3,4,3.9,4.05,3.95,3.85,3.95,3.65,4,4,4,4,3.5,15,5495,127,0.1 120 | Infinitely Polar Bear (2015),80,76,64,7.9,7.2,4,3.9,4,3.8,3.2,3.95,3.6,4,4,3,4,3.5,8,1062,124,0.1 121 | Phoenix (2015),99,81,91,8,7.2,3.5,3.4,4.95,4.05,4.55,4,3.6,5,4,4.5,4,3.5,21,3687,70,0.1 122 | The Wolfpack (2015),84,73,75,7,7.1,3.5,3.4,4.2,3.65,3.75,3.5,3.55,4,3.5,4,3.5,3.5,8,1488,66,0.1 123 | The Stanford Prison Experiment (2015),84,87,68,8.5,7.1,4,3.9,4.2,4.35,3.4,4.25,3.55,4,4.5,3.5,4.5,3.5,6,950,51,0.1 124 | Tangerine (2015),95,86,86,7.3,7.4,4,3.9,4.75,4.3,4.3,3.65,3.7,5,4.5,4.5,3.5,3.5,14,696,36,0.1 125 | Magic Mike XXL (2015),62,64,60,5.4,6.3,4.5,4.4,3.1,3.2,3,2.7,3.15,3,3,3,2.5,3,52,11937,9363,0.1 126 | Home (2015),45,65,55,7.3,6.7,4.5,4.4,2.25,3.25,2.75,3.65,3.35,2.5,3.5,3,3.5,3.5,177,41158,7705,0.1 127 | The Wedding Ringer (2015),27,66,35,3.3,6.7,4.5,4.4,1.35,3.3,1.75,1.65,3.35,1.5,3.5,2,1.5,3.5,126,37292,6506,0.1 128 | Woman in Gold (2015),52,81,51,7.2,7.4,4.5,4.4,2.6,4.05,2.55,3.6,3.7,2.5,4,2.5,3.5,3.5,72,17957,2435,0.1 129 | The Last Five Years (2015),60,60,60,6.9,6,4.5,4.4,3,3,3,3.45,3,3,3,3,3.5,3,20,4110,99,0.1 130 | Mission: Impossible – Rogue Nation (2015),92,90,75,8,7.8,4.5,4.4,4.6,4.5,3.75,4,3.9,4.5,4.5,4,4,4,362,82579,8357,0.1 131 | Amy (2015),97,91,85,8.8,8,4.5,4.4,4.85,4.55,4.25,4.4,4,5,4.5,4.5,4.5,4,60,5630,729,0.1 132 | Jurassic World (2015),71,81,59,7,7.3,4.5,4.5,3.55,4.05,2.95,3.5,3.65,3.5,4,3,3.5,3.5,1281,241807,34390,0 133 | Minions (2015),54,52,56,5.7,6.7,4,4,2.7,2.6,2.8,2.85,3.35,2.5,2.5,3,3,3.5,204,55895,14998,0 134 | Max (2015),35,73,47,5.9,7,4.5,4.5,1.75,3.65,2.35,2.95,3.5,2,3.5,2.5,3,3.5,15,5444,3412,0 135 | Paul Blart: Mall Cop 2 (2015),5,36,13,2.4,4.3,3.5,3.5,0.25,1.8,0.65,1.2,2.15,0.5,2,0.5,1,2,211,15004,3054,0 136 | The Longest Ride (2015),31,73,33,4.8,7.2,4.5,4.5,1.55,3.65,1.65,2.4,3.6,1.5,3.5,1.5,2.5,3.5,49,25214,2603,0 137 | The Lazarus Effect (2015),14,23,31,4.9,5.2,3,3,0.7,1.15,1.55,2.45,2.6,0.5,1,1.5,2.5,2.5,62,17691,1651,0 138 | The Woman In Black 2 Angel of Death (2015),22,25,42,4.4,4.9,3,3,1.1,1.25,2.1,2.2,2.45,1,1.5,2,2,2.5,55,14873,1333,0 139 | Danny Collins (2015),77,75,58,7.1,7.1,4,4,3.85,3.75,2.9,3.55,3.55,4,4,3,3.5,3.5,33,11206,531,0 140 | Spare Parts (2015),52,83,50,7.1,7.2,4.5,4.5,2.6,4.15,2.5,3.55,3.6,2.5,4,2.5,3.5,3.5,7,47377,450,0 141 | Serena (2015),18,25,36,5.3,5.4,3,3,0.9,1.25,1.8,2.65,2.7,1,1.5,2,2.5,2.5,19,12165,50,0 142 | Inside Out (2015),98,90,94,8.9,8.6,4.5,4.5,4.9,4.5,4.7,4.45,4.3,5,4.5,4.5,4.5,4.5,807,96252,15749,0 143 | Mr. Holmes (2015),87,78,67,7.9,7.4,4,4,4.35,3.9,3.35,3.95,3.7,4.5,4,3.5,4,3.5,33,7367,1348,0 144 | '71 (2015),97,82,83,7.5,7.2,3.5,3.5,4.85,4.1,4.15,3.75,3.6,5,4,4,4,3.5,60,24116,192,0 145 | "Two Days, One Night (2014)",97,78,89,8.8,7.4,3.5,3.5,4.85,3.9,4.45,4.4,3.7,5,4,4.5,4.5,3.5,123,24345,118,0 146 | Gett: The Trial of Viviane Amsalem (2015),100,81,90,7.3,7.8,3.5,3.5,5,4.05,4.5,3.65,3.9,5,4,4.5,3.5,4,19,1955,59,0 147 | "Kumiko, The Treasure Hunter (2015)",87,63,68,6.4,6.7,3.5,3.5,4.35,3.15,3.4,3.2,3.35,4.5,3,3.5,3,3.5,19,5289,41,0 148 | -------------------------------------------------------------------------------- /Aula#12/Aula 12 - Amostragem .pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#12/Aula 12 - Amostragem .pdf -------------------------------------------------------------------------------- /Aula#12/wnba.csv: -------------------------------------------------------------------------------- 1 | Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,15:00,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 2 | Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 3 | Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 4 | Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 5 | Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 6 | Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 7 | Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 8 | Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 9 | Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 10 | Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 11 | Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 12 | Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 13 | Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 14 | Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 15 | Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 16 | Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 17 | Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 18 | Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 19 | Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 20 | Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 21 | Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 22 | Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 23 | Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 24 | Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 25 | Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 26 | Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 27 | Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 28 | Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 29 | Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 30 | Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 31 | Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 32 | Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 33 | Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 34 | Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 35 | Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 36 | Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 37 | Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 38 | Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 39 | Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 40 | Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 41 | Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 42 | Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 43 | Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 44 | Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 45 | Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 46 | Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 47 | Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 48 | Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 49 | Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 50 | Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 51 | Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 52 | Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 53 | Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 54 | Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 55 | Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 56 | Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 57 | Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 58 | Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 59 | Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 60 | Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 61 | Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 62 | Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 63 | Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 64 | Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 65 | Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 66 | Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 67 | Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 68 | Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 69 | Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 70 | Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 71 | Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 72 | Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 73 | Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 74 | Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 75 | Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 76 | Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 77 | Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 78 | Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 79 | Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 80 | Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 81 | Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 82 | Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 83 | Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 84 | Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 85 | Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 86 | Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 87 | Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 88 | Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 89 | Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 90 | Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 91 | Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 92 | Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 93 | Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 94 | Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 95 | Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 96 | Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 97 | Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 98 | Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 99 | Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 100 | Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 101 | Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 102 | Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 103 | Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 104 | Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 105 | Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 106 | Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 107 | Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 108 | Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 109 | Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 110 | Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 111 | Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 112 | Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 113 | Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 114 | Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 115 | Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 116 | Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 117 | Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 118 | Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 119 | Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 120 | Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 121 | Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 122 | Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 123 | Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 124 | Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 125 | Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 126 | Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 127 | Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 128 | Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 129 | Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 130 | Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 131 | Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 132 | Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 133 | Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 134 | Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 135 | Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 136 | Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 137 | Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 138 | Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 139 | Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 140 | Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 141 | Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 142 | Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 143 | Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 144 | Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 -------------------------------------------------------------------------------- /Aula#13/Aula 13 - Variáveis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#13/Aula 13 - Variáveis.pdf -------------------------------------------------------------------------------- /Aula#13/wnba.csv: -------------------------------------------------------------------------------- 1 | Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,15:00,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 2 | Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 3 | Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 4 | Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 5 | Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 6 | Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 7 | Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 8 | Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 9 | Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 10 | Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 11 | Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 12 | Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 13 | Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 14 | Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 15 | Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 16 | Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 17 | Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 18 | Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 19 | Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 20 | Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 21 | Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 22 | Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 23 | Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 24 | Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 25 | Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 26 | Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 27 | Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 28 | Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 29 | Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 30 | Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 31 | Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 32 | Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 33 | Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 34 | Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 35 | Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 36 | Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 37 | Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 38 | Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 39 | Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 40 | Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 41 | Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 42 | Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 43 | Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 44 | Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 45 | Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 46 | Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 47 | Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 48 | Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 49 | Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 50 | Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 51 | Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 52 | Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 53 | Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 54 | Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 55 | Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 56 | Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 57 | Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 58 | Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 59 | Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 60 | Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 61 | Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 62 | Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 63 | Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 64 | Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 65 | Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 66 | Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 67 | Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 68 | Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 69 | Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 70 | Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 71 | Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 72 | Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 73 | Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 74 | Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 75 | Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 76 | Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 77 | Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 78 | Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 79 | Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 80 | Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 81 | Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 82 | Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 83 | Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 84 | Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 85 | Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 86 | Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 87 | Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 88 | Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 89 | Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 90 | Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 91 | Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 92 | Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 93 | Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 94 | Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 95 | Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 96 | Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 97 | Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 98 | Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 99 | Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 100 | Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 101 | Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 102 | Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 103 | Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 104 | Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 105 | Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 106 | Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 107 | Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 108 | Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 109 | Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 110 | Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 111 | Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 112 | Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 113 | Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 114 | Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 115 | Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 116 | Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 117 | Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 118 | Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 119 | Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 120 | Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 121 | Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 122 | Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 123 | Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 124 | Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 125 | Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 126 | Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 127 | Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 128 | Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 129 | Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 130 | Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 131 | Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 132 | Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 133 | Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 134 | Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 135 | Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 136 | Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 137 | Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 138 | Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 139 | Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 140 | Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 141 | Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 142 | Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 143 | Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 144 | Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 -------------------------------------------------------------------------------- /Aula#14/Aula 14.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#14/Aula 14.pdf -------------------------------------------------------------------------------- /Aula#14/wnba.csv: -------------------------------------------------------------------------------- 1 | Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,15:00,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 2 | Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 3 | Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 4 | Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 5 | Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 6 | Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 7 | Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 8 | Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 9 | Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 10 | Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 11 | Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 12 | Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 13 | Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 14 | Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 15 | Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 16 | Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 17 | Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 18 | Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 19 | Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 20 | Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 21 | Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 22 | Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 23 | Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 24 | Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 25 | Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 26 | Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 27 | Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 28 | Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 29 | Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 30 | Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 31 | Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 32 | Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 33 | Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 34 | Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 35 | Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 36 | Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 37 | Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 38 | Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 39 | Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 40 | Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 41 | Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 42 | Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 43 | Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 44 | Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 45 | Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 46 | Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 47 | Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 48 | Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 49 | Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 50 | Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 51 | Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 52 | Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 53 | Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 54 | Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 55 | Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 56 | Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 57 | Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 58 | Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 59 | Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 60 | Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 61 | Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 62 | Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 63 | Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 64 | Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 65 | Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 66 | Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 67 | Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 68 | Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 69 | Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 70 | Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 71 | Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 72 | Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 73 | Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 74 | Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 75 | Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 76 | Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 77 | Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 78 | Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 79 | Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 80 | Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 81 | Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 82 | Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 83 | Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 84 | Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 85 | Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 86 | Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 87 | Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 88 | Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 89 | Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 90 | Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 91 | Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 92 | Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 93 | Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 94 | Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 95 | Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 96 | Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 97 | Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 98 | Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 99 | Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 100 | Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 101 | Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 102 | Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 103 | Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 104 | Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 105 | Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 106 | Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 107 | Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 108 | Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 109 | Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 110 | Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 111 | Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 112 | Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 113 | Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 114 | Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 115 | Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 116 | Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 117 | Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 118 | Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 119 | Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 120 | Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 121 | Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 122 | Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 123 | Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 124 | Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 125 | Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 126 | Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 127 | Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 128 | Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 129 | Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 130 | Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 131 | Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 132 | Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 133 | Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 134 | Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 135 | Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 136 | Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 137 | Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 138 | Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 139 | Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 140 | Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 141 | Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 142 | Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 143 | Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 144 | Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 -------------------------------------------------------------------------------- /Aula#15/Aula 15 - Visualizando Tabelas de Frequência.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#15/Aula 15 - Visualizando Tabelas de Frequência.pdf -------------------------------------------------------------------------------- /Aula#15/wnba.csv: -------------------------------------------------------------------------------- 1 | Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,15:00,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3 2 | Aerial Powers,DAL,F,183,71,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0 3 | Alana Beard,LA,G/F,185,73,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0 4 | Alex Bentley,CON,G,170,69,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0 5 | Alex Montgomery,SAN,G/F,185,84,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0 6 | Alexis Jones,MIN,G,175,78,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0 7 | Alexis Peterson,SEA,G,170,63,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100,3,13,16,11,5,0,11,26,0,0 8 | Alexis Prince,PHO,G,188,81,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100,1,14,15,5,4,3,3,24,0,0 9 | Allie Quigley,CHI,G,178,64,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0 10 | Allisha Gray,DAL,G,185,76,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0 11 | Allison Hightower,WAS,G,178,77,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100,3,7,10,10,5,0,2,36,0,0 12 | Alysha Clark,SEA,F,180,76,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0 13 | Alyssa Thomas,CON,F,188,84,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0 14 | Amanda Zahui B.,NY,C,196,113,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0 15 | Amber Harris,CHI,F,196,88,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0 16 | Aneika Henry,ATL,F/C,193,87,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0 17 | Angel Robinson,PHO,F/C,198,88,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100,7,7,100,16,42,58,8,1,11,16,58,0,0 18 | Asia Taylor,WAS,F,185,76,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0 19 | Bashaara Graves,CHI,F,188,91,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0 20 | Breanna Lewis,DAL,C,196,93,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0 21 | Breanna Stewart,SEA,F/C,193,77,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0 22 | Bria Hartley,NY,G,173,66,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0 23 | Bria Holmes,ATL,G,185,77,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0 24 | Briann January,IND,G,173,65,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0 25 | Brionna Jones,CON,F,191,104,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0 26 | Brittany Boyd,NY,G,175,71,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0 27 | Brittney Griner,PHO,C,206,93,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0 28 | Brittney Sykes,ATL,G,175,66,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0 29 | Camille Little,PHO,F,188,82,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0 30 | Candace Parker,LA,F/C,193,79,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1 31 | Candice Dupree,IND,F,188,81,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0 32 | Cappie Pondexter,CHI,G,175,73,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0 33 | Carolyn Swords,SEA,C,198,95,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0 34 | Cayla George,PHO,C,193,87,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0 35 | Chelsea Gray,LA,G,180,77,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0 36 | Cheyenne Parker,CHI,F,193,86,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0 37 | Clarissa dos Santos,SAN,C,185,89,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100,0,0,0.0,3,7,10,7,1,1,5,17,0,0 38 | Courtney Paris,DAL,C,193,113,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0 39 | Courtney Vandersloot,CHI,G,173,66,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0 40 | Courtney Williams,CON,G,173,62,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0 41 | Crystal Langhorne,SEA,F/C,188,84,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0 42 | Damiris Dantas,ATL,C,191,89,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0 43 | Danielle Adams,CON,F/C,185,108,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100,6,4,10,4,4,4,7,49,0,0 44 | Danielle Robinson,PHO,G,175,57,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0 45 | Dearica Hamby,SAN,F,191,86,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0 46 | Devereaux Peters,IND,F,188,79,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0 47 | Diana Taurasi,PHO,G,183,74,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0 48 | Elena Delle Donne,WAS,G/F,196,85,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0 49 | Elizabeth Williams,ATL,F/C,191,87,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0 50 | Emma Cannon,PHO,F,188,86,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0 51 | Emma Meesseman,WAS,C,193,83,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0 52 | Epiphanny Prince,NY,G,175,81,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0 53 | Erica Wheeler,IND,G,170,65,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0 54 | Érika de Souza,SAN,C,196,86,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0 55 | Erlana Larkins,IND,F,185,93,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0 56 | Essence Carson,LA,G/F,183,74,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0 57 | Evelyn Akhator,DAL,F,191,82,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0 58 | Glory Johnson,DAL,F,191,77,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0 59 | Imani Boyette,ATL,C,201,88,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0 60 | Isabelle Harrison,SAN,C,191,83,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0 61 | Ivory Latta,WAS,G,168,63,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0 62 | Jantel Lavender,LA,C,193,84,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0 63 | Jasmine Thomas,CON,G,175,66,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0 64 | Jazmon Gwathmey,IND,G,188,65,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0 65 | Jeanette Pohlen,IND,G,183,78,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0 66 | Jennifer Hamson,IND,C,201,95,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0 67 | Jessica Breland,CHI,F,191,77,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0 68 | Jewell Loyd,SEA,G,178,67,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0 69 | Jia Perkins,MIN,G,173,75,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0 70 | Jonquel Jones,CON,F/C,198,86,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0 71 | Jordan Hooper,CHI,F,188,84,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0 72 | Kaela Davis,DAL,G,188,77,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0 73 | Kahleah Copper,CHI,G/F,185,70,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0 74 | Kaleena Mosqueda-Lewis,SEA,F,180,82,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0 75 | Karima Christmas-Kelly,DAL,G/F,183,82,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100,4,10,14,6,1,1,13,65,0,0 76 | Kayla Alexander,SAN,C,193,88,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0 77 | Kayla McBride,SAN,G/F,180,79,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0 78 | Kayla Pedersen,CON,F,193,86,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0 79 | Kayla Thornton,DAL,F,185,86,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0 80 | Keisha Hampton,CHI,F,185,78,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0 81 | Kelsey Plum,SAN,G,173,66,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0 82 | Kia Vaughn,NY,C,193,90,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0 83 | Kiah Stokes,NY,C,191,87,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0 84 | Kristi Toliver,WAS,G,170,59,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0 85 | Krystal Thomas,WAS,C,196,88,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0 86 | Lanay Montgomery,SEA,C,196,96,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0 87 | Layshia Clarendon,ATL,G,175,64,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0 88 | Leilani Mitchell,PHO,G,165,58,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0 89 | Lindsay Allen,NY,G,173,65,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0 90 | Lindsay Whalen,MIN,G,175,78,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0 91 | Lynetta Kizer,CON,C,193,104,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0 92 | Maimouna Diarra,LA,C,198,90,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0 93 | Makayla Epps,CHI,G,178,,,US,"June 6, 1995",22,Kentucky,R,14,52,2,14,14.3,0,5,0.0,2,5,40.0,2,0,2,4,1,0,4,6,0,0 94 | Marissa Coleman,IND,G/F,185,73,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0 95 | Matee Ajavon,ATL,G,173,73,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0 96 | Maya Moore,MIN,F,183,80,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0 97 | Monique Currie,PHO,G/F,183,80,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0 98 | Morgan Tuck,CON,F,188,91,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0 99 | Moriah Jefferson,SAN,G,168,55,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0 100 | Natalie Achonwa,IND,C,193,83,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0 101 | Natasha Cloud,WAS,G,183,73,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0 102 | Natasha Howard,MIN,F,188,75,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0 103 | Nayo Raincock-Ekunwe,NY,F/C,188,79,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0 104 | Nia Coffey,SAN,F,185,77,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0 105 | Nneka Ogwumike,LA,F,188,79,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0 106 | Noelle Quinn,SEA,G,183,81,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0 107 | Odyssey Sims,LA,G,173,73,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0 108 | Plenette Pierson,MIN,F/C,188,88,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0 109 | Rachel Banham,CON,G,175,76,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0 110 | Ramu Tokashiki,SEA,F,193,80,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0 111 | Rebecca Allen,NY,G/F,188,74,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0 112 | Rebekkah Brunson,MIN,F,188,84,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0 113 | Renee Montgomery,MIN,G,170,63,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0 114 | Riquna Williams,LA,G,170,75,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0 115 | Sami Whitcomb,SEA,G,178,66,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0 116 | Sancho Lyttle,ATL,F,193,79,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0 117 | Sandrine Gruda,LA,F/C,193,84,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0 118 | Saniya Chong,DAL,G,173,64,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0 119 | Seimone Augustus,MIN,G/F,183,77,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0 120 | Sequoia Holmes,SAN,G,185,70,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0 121 | Shatori Walker-Kimbrough,WAS,G,180,64,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0 122 | Shavonte Zellous,NY,G,178,85,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0 123 | Shay Murphy,SAN,G,180,74,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0 124 | Shekinna Stricklen,CON,G/F,188,81,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0 125 | Shenise Johnson,IND,G,180,78,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0 126 | Skylar Diggins-Smith,DAL,G,175,66,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0 127 | Stefanie Dolson,CHI,C,196,97,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0 128 | Stephanie Talbot,PHO,G,185,87,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0 129 | Sue Bird,SEA,G,175,68,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0 130 | Sugar Rodgers,NY,G,175,75,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0 131 | Sydney Colson,SAN,G,173,64,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0 132 | Sydney Wiese,LA,G,183,68,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0 133 | Sylvia Fowles,MIN,C,198,96,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0 134 | Tamera Young,ATL,G/F,188,77,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0 135 | Tayler Hill,WAS,G,175,66,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0 136 | Temi Fagbenle,MIN,C,193,89,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0 137 | Theresa Plaisance,DAL,F,196,91,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0 138 | Tianna Hawkins,WAS,F,191,87,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0 139 | Tierra Ruffin-Pratt,WAS,G,178,83,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0 140 | Tiffany Hayes,ATL,G,178,70,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0 141 | Tiffany Jackson,LA,F,191,84,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0 142 | Tiffany Mitchell,IND,G,175,69,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0 143 | Tina Charles,NY,F/C,193,84,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0 144 | Yvonne Turner,PHO,G,175,59,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0 -------------------------------------------------------------------------------- /Aula#16/Aula16 - Média.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#16/Aula16 - Média.pdf -------------------------------------------------------------------------------- /Aula#17/Aula17 - Média ponderada.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ivanovitchm/datascienceintroduction/49caa2f2e12501741a46900dd3fa6b7fd7071799/Aula#17/Aula17 - Média ponderada.pdf 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numerical indexes 24 | - Filtering data from boolean arrays 25 | - Data alignment 26 | - Use of data aggregation for more complex analysis 27 | - Class 07 28 | - Data imputation 29 | - Data sanitization 30 | - Table pivoting 31 | - Class 09 32 | - Case study: unemployment rate 33 | - Tabular vs visual representation 34 | - Matplotlib 35 | - Line charts 36 | - Multiplot 37 | - Personalization 38 | - Class 10 39 | - Bar and scatter charts 40 | - Case study: data bias 41 | - Class 11 42 | - Frequency graphs (histogram) and boxplot (box) 43 | - Class 12 44 | - Data sampling 45 | - random sampling 46 | - Stratified sampling 47 | - Sampling by cluster 48 | - Class 13 49 | - Quantitative and qualitative variables 50 | - Scale of measurements: nominal, ordinal, interval and ratio 51 | - Class 14 52 | - Frequency distribution tables 53 | - Sorting of frequency distribution tables (nominal, ordinal, interval, ratio) 54 | - Proportions and percentages 55 | - Percentile and percentile ranking 56 | - Grouping of frequency distribution tables 57 | - Loss of information 58 | - Class 15 59 | - Viewing distributions 60 | - Bar, pie and histogram charts 61 | - Asymmetry 62 | - Symmetric distributions 63 | - Bar chart groupings 64 | - Comparing histograms 65 | - Kernel density estimation 66 | - Stripe and box charts 67 | - Points outside the curve 68 | - Class 16 69 | - Average 70 | - The average as a break-even point 71 | - Defining the mean algebraically 72 | - Estimating the population mean 73 | - Estimating the population mean from small samples 74 | - Class 17 75 | - weighted average 76 | - The median of open distributions 77 | - Calculation of the median 78 | - The median as a strength statistic 79 | - The median for ordinal variables 80 | - Sensitivity to changes 81 | - Class 18 82 | - The fashion 83 | - Ordinal variables 84 | - Nominal variables 85 | - Discrete variables 86 | - Special cases 87 | - Unimodal 88 | - Bimodal 89 | - Multimodal 90 | - Asymmetric distributions 91 | - Symmetric distributions 92 | - Class 19 93 | - Range 94 | - Average Distance 95 | - Average absolute deviation 96 | - Variance and standard deviation 97 | - Sample standard deviation 98 | - Bessel correction 99 | - Class 20 100 | - Definition of Z-Score 101 | - standard distribution 102 | - Better understanding of off-curve points 103 | - Z-Score as a measure of comparison 104 | - Z-Table 105 | - Transformation of Z-Score into value 106 | - Class 21 107 | - Correlation and covariance 108 | - Correlation coefficient 109 | - Class 22 110 | - Estimating probabilities 111 | - Basic rules of probability 112 | - Class 23 113 | - Solving complex problems with probability 114 | - conditional probability 115 | - Bayes' theorem 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | --------------------------------------------------------------------------------