├── assets └── datacamp.svg ├── notebooks ├── python_live_session_template_spark.ipynb └── python_live_session_template.ipynb └── README.md /assets/datacamp.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /notebooks/python_live_session_template_spark.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Cleaning Data in Python live session.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "display_name": "Python 3", 11 | "language": "python", 12 | "name": "python3" 13 | }, 14 | "language_info": { 15 | "codemirror_mode": { 16 | "name": "ipython", 17 | "version": 3 18 | }, 19 | "file_extension": ".py", 20 | "mimetype": "text/x-python", 21 | "name": "python", 22 | "nbconvert_exporter": "python", 23 | "pygments_lexer": "ipython3", 24 | "version": "3.7.1" 25 | } 26 | }, 27 | "cells": [ 28 | { 29 | "cell_type": "markdown", 30 | "metadata": { 31 | "colab_type": "text", 32 | "id": "6Ijg5wUCTQYG" 33 | }, 34 | "source": [ 35 | "

\n", 36 | "\"DataCamp\n", 37 | "

\n", 38 | "

\n", 39 | "\n", 40 | "## **Python PySpark Live Training Template**\n", 41 | "\n", 42 | "_Enter a brief description of your session, here's an example below:_\n", 43 | "\n", 44 | "Welcome to this hands-on training where we will immerse yourself in data visualization in Python. Using both `matplotlib` and `seaborn`, we'll learn how to create visualizations that are presentation-ready.\n", 45 | "\n", 46 | "The ability to present and discuss\n", 47 | "\n", 48 | "* Create various types of plots, including bar-plots, distribution plots, box-plots and more using Seaborn and Matplotlib.\n", 49 | "* Format and stylize your visualizations to make them report-ready.\n", 50 | "* Create sub-plots to create clearer visualizations and supercharge your workflow.\n", 51 | "\n", 52 | "## **The Dataset**\n", 53 | "\n", 54 | "_Enter a brief description of your dataset and its columns, here's an example below:_\n", 55 | "\n", 56 | "\n", 57 | "The dataset to be used in this webinar is a CSV file named `airbnb.csv`, which contains data on airbnb listings in the state of New York. It contains the following columns:\n", 58 | "\n", 59 | "- `listing_id`: The unique identifier for a listing\n", 60 | "- `description`: The description used on the listing\n", 61 | "- `host_id`: Unique identifier for a host\n", 62 | "- `host_name`: Name of host\n", 63 | "- `neighbourhood_full`: Name of boroughs and neighbourhoods\n", 64 | "- `coordinates`: Coordinates of listing _(latitude, longitude)_\n", 65 | "- `Listing added`: Date of added listing\n", 66 | "- `room_type`: Type of room \n", 67 | "- `rating`: Rating from 0 to 5.\n", 68 | "- `price`: Price per night for listing\n", 69 | "- `number_of_reviews`: Amount of reviews received \n", 70 | "- `last_review`: Date of last review\n", 71 | "- `reviews_per_month`: Number of reviews per month\n", 72 | "- `availability_365`: Number of days available per year\n", 73 | "- `Number of stays`: Total number of stays thus far\n" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": { 79 | "id": "NgaAMtZKXppQ", 80 | "colab_type": "text" 81 | }, 82 | "source": [ 83 | "## **Setting up a PySpark session**\n", 84 | "\n", 85 | "This set of code lets you enable a PySpark session using google colabs, make sure to run the code snippets to enable PySpark." 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "metadata": { 91 | "id": "LfE-MdXOXppm", 92 | "colab_type": "code", 93 | "colab": {} 94 | }, 95 | "source": [ 96 | "# Just run this code\n", 97 | "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n", 98 | "!wget -q https://downloads.apache.org/spark/spark-2.4.5/spark-2.4.5-bin-hadoop2.7.tgz\n", 99 | "!tar xf spark-2.4.5-bin-hadoop2.7.tgz\n", 100 | "!pip install -q findspark" 101 | ], 102 | "execution_count": 0, 103 | "outputs": [] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "metadata": { 108 | "id": "tM2OWT4VXpp0", 109 | "colab_type": "code", 110 | "colab": {} 111 | }, 112 | "source": [ 113 | "# Just run this code too!\n", 114 | "import os\n", 115 | "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n", 116 | "os.environ[\"SPARK_HOME\"] = \"/content/spark-2.4.5-bin-hadoop2.7\"" 117 | ], 118 | "execution_count": 0, 119 | "outputs": [] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "metadata": { 124 | "id": "ORtvjWUFXpp7", 125 | "colab_type": "code", 126 | "colab": {} 127 | }, 128 | "source": [ 129 | "# Set up a Spark session\n", 130 | "import findspark\n", 131 | "findspark.init()\n", 132 | "from pyspark.sql import SparkSession\n", 133 | "spark = SparkSession.builder.master(\"local[*]\").getOrCreate()" 134 | ], 135 | "execution_count": 0, 136 | "outputs": [] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "metadata": { 141 | "colab_type": "text", 142 | "id": "BMYfcKeDY85K" 143 | }, 144 | "source": [ 145 | "## **Getting started**" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "metadata": { 151 | "colab_type": "code", 152 | "id": "EMQfyC7GUNhT", 153 | "colab": {} 154 | }, 155 | "source": [ 156 | "# Import other relevant libraries\n", 157 | "from pyspark.ml.feature import VectorAssembler\n", 158 | "from pyspark.ml.regression import LinearRegression" 159 | ], 160 | "execution_count": 0, 161 | "outputs": [] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "metadata": { 166 | "colab_type": "code", 167 | "id": "IAfz_jiu0NjN", 168 | "colab": {} 169 | }, 170 | "source": [ 171 | "# Get dataset into local environment\n", 172 | "!wget -O /tmp/airbnb.csv 'https://github.com/datacamp/python-live-training-template/blob/master/data/airbnb.csv?raw=True'\n", 173 | "airbnb = spark.read.csv('/tmp/airbnb.csv', inferSchema=True, header =True)" 174 | ], 175 | "execution_count": 0, 176 | "outputs": [] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "metadata": { 181 | "id": "6L1aMkQBXsdM", 182 | "colab_type": "text" 183 | }, 184 | "source": [ 185 | "### **Examples on use of markdown**\n", 186 | "\n", 187 | "#### **Images**\n", 188 | "\n", 189 | "To add images, gifs, or other assets of that kind, make sure to use the HTML `` function as in the following \n", 190 | "```\n", 191 | "

\n", 192 | "\"alt-text\"\n", 193 | "

\n", 194 | "

\n", 195 | "```\n", 196 | "\n", 197 | "- The `align` argument takes in `\"center\"`, `\"left\"`, `\"right\"`.\n", 198 | "- The `src` argument takes in the raw link of your image.\n", 199 | "- The `width` argument takes in a percentage, where `100%` is the original size of the image. \n", 200 | "\n", 201 | "\n", 202 | "#### **Formulas**\n", 203 | "\n", 204 | "To use formulas, feel free to use Latex Notation as such:\n", 205 | "\n", 206 | "$y = ax + b$\n", 207 | "\n", 208 | "You can even use color schemes like in this example, where coefficients are colored in red\n", 209 | "\n", 210 | "$y = \\color{red}a x + \\color{red}b$\n", 211 | "\n", 212 | "#### **Changing font color and size**\n", 213 | "\n", 214 | "To change or highlight specific texts in a color, you can use the following\n", 215 | "\n", 216 | "```\n", 217 | "**Example text**\n", 218 | "```\n", 219 | "\n", 220 | "Where the results will look like **Example text**.\n", 221 | "\n", 222 | "- The `color` argument takes in a HEX code for your color. " 223 | ] 224 | } 225 | ] 226 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # **Big Data for Pythonistas**
by **Hugo Bowne-Anderson** 2 | 3 | Live training sessions are designed to mimic the flow of how a real data scientist would address a problem or a task. As such, a session needs to have some “narrative” where learners are achieving stated learning objectives in the form of a real-life data science task or project. For example, a data visualization live session could be around analyzing a dataset and creating a report with a specific business objective in mind _(ex: analyzing and visualizing churn)_, a data cleaning live session could be about preparing a dataset for analysis etc ... 4 | 5 | As part of the 'Live training Spec' process, you will need to complete the following tasks: 6 | 7 | Edit this README by filling in the information for steps 1 - 4. 8 | 9 | ## Step 1: Foundations 10 | 11 | This part of the 'Live training Spec' process is designed to help guide you through session design by having you think through several key questions. Please make sure to delete the examples provided here for you. 12 | 13 | ### A. What problem(s) will students learn how to solve? (minimum of 5 problems) 14 | 15 | > - How to think about scaling your data science to larger datasets, when it's necessary, and when it's not; 16 | > - Appreciating how scaling data science can be required for both (i) big data sets (this is RAM-bound) and (ii) big models (CPU-bound); 17 | > - Understanding the landscape of packages for scalable data science in Python (dask, dask-ml, rapids, pyspark, koalas); 18 | > - Moving from numpy and pandas data workflows for out-of-core computing with dask when your data doesn't fit in RAM; 19 | > - Doing machine learning at scale with scikit-learn and dask-ml. 20 | 21 | 22 | ### B. What technologies, packages, or functions will students use? Please be exhaustive. 23 | 24 | > - pandas 25 | > - numpy 26 | > - matplotlib 27 | > - scikit-learn 28 | > - dask 29 | > - dask-ml 30 | 31 | ### C. What terms or jargon will you define? 32 | 33 | _Whether during your opening and closing talk or your live training, you might have to define some terms and jargon to walk students through a problem you’re solving. Intuitive explanations using analogies are encouraged._ 34 | 35 | > - Big data 36 | > - out-of-core computing 37 | > - scalable data science 38 | > - bursting to the cloud 39 | 40 | ### D. What mistakes or misconceptions do you expect? 41 | 42 | _To help minimize the amount of Q&As and make your live training re-usable, list out some mistakes and misconceptions you think students might encounter along the way._ 43 | 44 | TBD (this will be easier to answer once I've developed more of the material) 45 | 46 | ### E. What datasets will you use? 47 | 48 | Live training sessions are designed to walk students through something closer to a real-life data science workflow. Accordingly, the dataset needs to accommodate that user experience. 49 | As a rule of thumb, your dataset should always answer yes to the following question: 50 | > Is the dataset/problem I’m working on, something an industry data scientist/analyst could work on? 51 | 52 | TBD. 53 | 54 | Check our [datasets to avoid](https://instructor-support.datacamp.com/en/articles/2360699-datasets-to-avoid) list. 55 | 56 | ## Step 2: Who is this session for? 57 | 58 | Terms like "beginner" and "expert" mean different things to different people, so we use personas to help instructors clarify a live training's audience. When designing a specific live training, instructors should explain how it will or won't help these people, and what extra skills or prerequisite knowledge they are assuming their students have above and beyond what's included in the persona. 59 | 60 | - [ ] Please select the roles and industries that align with your live training. 61 | - [ ] Include an explanation describing your reasoning and any other relevant information. 62 | 63 | ### What roles would this live training be suitable for? 64 | 65 | *Check all that apply.* 66 | 67 | - [ ] Data Consumer 68 | - [ ] Leader 69 | - [x] Data Analyst 70 | - [x] Citizen Data Scientist 71 | - [x] Data Scientist 72 | - [x] Data Engineer 73 | - [ ] Database Administrator 74 | - [x] Statistician 75 | - [x] Machine Learning Scientist 76 | - [x] Programmer 77 | - [ ] Other (please describe) 78 | 79 | ### What industries would this apply to? 80 | 81 | *List one or more industries that the content would be appropriate for.* 82 | 83 | Healthcare, finance, astronomy, biomedical imaging, genomics, retail. 84 | 85 | 86 | ### What level of expertise should learners have before beginning the live training? 87 | 88 | *List three or more examples of skills that you expect learners to have before beginning the live training* 89 | 90 | > - It would help to know the basics of numpy, pandas, and scikit-learn. 91 | > - However, I have always found that the most important and beneficial prerequisite is a will to learn new things so if you have this quality, you'll definitely get something out of this live training! 92 | 93 | 94 | ## Step 3: Prerequisites 95 | 96 | List any prerequisite courses you think your live training could use from. This could be the live session’s companion course or a course you think students should take before the session. Prerequisites act as a guiding principle for your session and will set the topic framework, but you do not have to limit yourself in the live session to the syntax used in the prerequisite courses. 97 | 98 | > Introduction to Python 99 | > Intermediate Python 100 | > Supervised Learning with scikit-learn 101 | 102 | 103 | ## Step 4: Session Outline 104 | 105 | A live training session usually begins with an introductory presentation, followed by the live training itself, and an ending presentation. Your live session is expected to be around 2h30m-3h long (including Q&A) with a hard-limit at 3h30m. You can check out our live training content guidelines [here](_LINK_). 106 | 107 | 108 | > _Example from [Python for Spreadsheet Users](https://www.datacamp.com/resources/webinars/live-training-python-for-spreadsheet-users)_ 109 | > 110 | > ### Introduction Slides 111 | > - Introduction to the webinar and instructor (led by DataCamp TA) 112 | > - Introduction to the topics 113 | > - Discuss need to become data fluent 114 | > - Define data fluency 115 | > - Discuss how learning Python fits into that and go over session outline 116 | > - Set expectations about Q&A 117 | > 118 | > ### Live Training 119 | > #### Exploratory Data Analysis 120 | > - Import data and print header of DataFrame `pd.read_excel()`, `.head()` 121 | > - Glimpse at the data to 122 | > - Get column types using `.dtypes` 123 | > - Use `.describe()`, `.info()` 124 | > - **Q&A** 125 | > #### Data Cleaning and making it ready for analysis 126 | > - Convert date columns to datetime `pd.to_datetime()` 127 | > - Change column names 128 | > - Extract year, month from datetime `.strftime()` 129 | > - Drop an irrelevant column `.drop()` 130 | > - Fill missing values with `.fillna()` 131 | > #### Creating a report 132 | > - First report question: What is our overall sales performance this year? `.groupby()`, `.plt.plot()` 133 | > - Second report question: What is our overall sales performance this year? `.merge()`, `.groupby()`, `plt.plot()` 134 | > - Third report question: What is our overall sales performance this year? `.merge()`, `.groupby()`, `plt.plot()` 135 | > - **Q&A** 136 | > 137 | > ### Ending slides 138 | > - Recap of what we learned 139 | > - The data science mindset 140 | > - Call to action and course recommendations 141 | 142 | ## Authoring your session 143 | 144 | To get yourself started with setting up your live session, follow the steps below: 145 | 146 | 1. Download and install the "Open in Colabs" extension from [here](https://chrome.google.com/webstore/detail/open-in-colab/iogfkhleblhcpcekbiedikdehleodpjo?hl=en). This will let you take any jupyter notebook you see in a GitHub repository and open it as a **temporary** Colabs link. 147 | 2. Upload your dataset(s) to the `data` folder. 148 | 3. Upload your images, gifs, or any other assets you want to use in the notebook in the `assets` folder. 149 | 4. Check out the notebooks templates in the `notebooks` folder, and keep the template you want for your session while deleting all remaining ones. 150 | 151 | You can author and save your progress on your notebook using **either** of these methods. 152 | 153 | _**How to author your notebook: By directly saving into GitHub**_ 154 | 155 | 1. Preview your desired notebook, press on "Open in Colabs" extension - and start developing your content in colabs _(which will act as the solution code to the session)_. :warning: **Important** :warning: Your progress will **not** be saved on Google Colabs since it's a temporary link. To save your progress, make sure to press on `File`, `Save a copy in GitHub` and follow remaining prompts. 156 | 2. Once your notebooks is ready to go, give it the name `session_name_solution.ipynb` create an empty version of the Notebook to be filled out by you and learners during the session, end the file name with `session_name.ipynb`. 157 | 3. Create Colabs links for both sessions and save them in notebooks :tada: 158 | 159 | _**How to author your notebook: By uploading notebook into GitHub**_ 160 | 161 | 1. Preview your desired notebook, press on "Open in Colabs" extension - and start developing your content in colabs _(which will act as the solution code to the session)_. Once you're done, press on `file` - `download .ipynb` file - and overwrite the notebook by uploading it into GitHub. 162 | 2. Once your notebooks is ready to go, give it the name `session_name_solution.ipynb` create an empty version of the Notebook to be filled out by you and learners during the session, end the file name with `session_name.ipynb`. 163 | 3. Create Colabs links for both sessions and save them in notebooks :tada: 164 | 165 | 166 | You can check out either of those methods in action using this [recording](https://www.loom.com/share/1eeb148129244edd93fbc34bf5dc7f0d). 167 | -------------------------------------------------------------------------------- /notebooks/python_live_session_template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "python_live_session_template.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "display_name": "Python 3", 11 | "language": "python", 12 | "name": "python3" 13 | }, 14 | "language_info": { 15 | "codemirror_mode": { 16 | "name": "ipython", 17 | "version": 3 18 | }, 19 | "file_extension": ".py", 20 | "mimetype": "text/x-python", 21 | "name": "python", 22 | "nbconvert_exporter": "python", 23 | "pygments_lexer": "ipython3", 24 | "version": "3.7.1" 25 | } 26 | }, 27 | "cells": [ 28 | { 29 | "cell_type": "markdown", 30 | "metadata": { 31 | "colab_type": "text", 32 | "id": "6Ijg5wUCTQYG" 33 | }, 34 | "source": [ 35 | "

\n", 36 | "\"DataCamp\n", 37 | "

\n", 38 | "

\n", 39 | "\n", 40 | "## **Python Live Training Template**\n", 41 | "\n", 42 | "_Enter a brief description of your session, here's an example below:_\n", 43 | "\n", 44 | "Welcome to this hands-on training where we will immerse yourself in data visualization in Python. Using both `matplotlib` and `seaborn`, we'll learn how to create visualizations that are presentation-ready.\n", 45 | "\n", 46 | "The ability to present and discuss\n", 47 | "\n", 48 | "* Create various types of plots, including bar-plots, distribution plots, box-plots and more using Seaborn and Matplotlib.\n", 49 | "* Format and stylize your visualizations to make them report-ready.\n", 50 | "* Create sub-plots to create clearer visualizations and supercharge your workflow.\n", 51 | "\n", 52 | "## **The Dataset**\n", 53 | "\n", 54 | "_Enter a brief description of your dataset and its columns, here's an example below:_\n", 55 | "\n", 56 | "\n", 57 | "The dataset to be used in this webinar is a CSV file named `airbnb.csv`, which contains data on airbnb listings in the state of New York. It contains the following columns:\n", 58 | "\n", 59 | "- `listing_id`: The unique identifier for a listing\n", 60 | "- `description`: The description used on the listing\n", 61 | "- `host_id`: Unique identifier for a host\n", 62 | "- `host_name`: Name of host\n", 63 | "- `neighbourhood_full`: Name of boroughs and neighbourhoods\n", 64 | "- `coordinates`: Coordinates of listing _(latitude, longitude)_\n", 65 | "- `Listing added`: Date of added listing\n", 66 | "- `room_type`: Type of room \n", 67 | "- `rating`: Rating from 0 to 5.\n", 68 | "- `price`: Price per night for listing\n", 69 | "- `number_of_reviews`: Amount of reviews received \n", 70 | "- `last_review`: Date of last review\n", 71 | "- `reviews_per_month`: Number of reviews per month\n", 72 | "- `availability_365`: Number of days available per year\n", 73 | "- `Number of stays`: Total number of stays thus far\n" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": { 79 | "colab_type": "text", 80 | "id": "BMYfcKeDY85K" 81 | }, 82 | "source": [ 83 | "## **Getting started**" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "metadata": { 89 | "colab_type": "code", 90 | "id": "EMQfyC7GUNhT", 91 | "colab": {} 92 | }, 93 | "source": [ 94 | "# Import libraries\n", 95 | "import pandas as pd\n", 96 | "import matplotlib.pyplot as plt\n", 97 | "import numpy as np\n", 98 | "import seaborn as sns\n", 99 | "import missingno as msno\n", 100 | "import datetime as dt" 101 | ], 102 | "execution_count": 0, 103 | "outputs": [] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "metadata": { 108 | "colab_type": "code", 109 | "id": "l8t_EwRNZPLB", 110 | "outputId": "36a85c6f-f2ae-44e0-ac01-fc55462bc616", 111 | "colab": { 112 | "base_uri": "https://localhost:8080/", 113 | "height": 479 114 | } 115 | }, 116 | "source": [ 117 | "# Read in the dataset\n", 118 | "airbnb = pd.read_csv('https://github.com/adelnehme/python-for-spreadsheet-users-webinar/blob/master/datasets/airbnb.csv?raw=true', \n", 119 | " index_col = 'Unnamed: 0')\n", 120 | "\n", 121 | "# Print header\n", 122 | "airbnb.head()" 123 | ], 124 | "execution_count": 0, 125 | "outputs": [ 126 | { 127 | "output_type": "execute_result", 128 | "data": { 129 | "text/html": [ 130 | "
\n", 131 | "\n", 144 | "\n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | "
listing_idnamehost_idhost_nameneighbourhood_fullcoordinatesroom_typepricenumber_of_reviewslast_reviewreviews_per_monthavailability_365ratingnumber_of_stays5_starslisting_added
013740704Cozy,budget friendly, cable inc, private entra...20583125MichelBrooklyn, Flatlands(40.63222, -73.93398)Private room45$102018-12-120.70854.10095412.00.6094322018-06-08
122005115Two floor apartment near Central Park82746113CeciliaManhattan, Upper West Side(40.78761, -73.96862)Entire home/apt135$12019-06-301.001453.3676001.20.7461352018-12-25
221667615Beautiful 1BR in Brooklyn Heights78251LeslieBrooklyn, Brooklyn Heights(40.7007, -73.99517)Entire home/apt150$0NaNNaN65NaNNaNNaN2018-08-15
36425850Spacious, charming studio32715865YelenaManhattan, Upper West Side(40.79169, -73.97498)Entire home/apt86$52017-09-230.1304.7632036.00.7699472017-03-20
422986519Bedroom on the lively Lower East Side154262349BrookeManhattan, Lower East Side(40.71884, -73.98354)Private room160$232019-06-122.291023.82259127.60.6493832020-10-23
\n", 264 | "
" 265 | ], 266 | "text/plain": [ 267 | " listing_id ... listing_added\n", 268 | "0 13740704 ... 2018-06-08\n", 269 | "1 22005115 ... 2018-12-25\n", 270 | "2 21667615 ... 2018-08-15\n", 271 | "3 6425850 ... 2017-03-20\n", 272 | "4 22986519 ... 2020-10-23\n", 273 | "\n", 274 | "[5 rows x 16 columns]" 275 | ] 276 | }, 277 | "metadata": { 278 | "tags": [] 279 | }, 280 | "execution_count": 4 281 | } 282 | ] 283 | }, 284 | { 285 | "cell_type": "markdown", 286 | "metadata": { 287 | "id": "e_VZpFt7XfzO", 288 | "colab_type": "text" 289 | }, 290 | "source": [ 291 | "### **Examples on use of markdown**\n", 292 | "\n", 293 | "#### **Images**\n", 294 | "\n", 295 | "To add images, gifs, or other assets of that kind, make sure to use the HTML `` function as in the following \n", 296 | "```\n", 297 | "

\n", 298 | "\"alt-text\"\n", 299 | "

\n", 300 | "

\n", 301 | "```\n", 302 | "\n", 303 | "- The `align` argument takes in `\"center\"`, `\"left\"`, `\"right\"`.\n", 304 | "- The `src` argument takes in the raw link of your image.\n", 305 | "- The `width` argument takes in a percentage, where `100%` is the original size of the image. \n", 306 | "\n", 307 | "\n", 308 | "#### **Formulas**\n", 309 | "\n", 310 | "To use formulas, feel free to use Latex Notation as such:\n", 311 | "\n", 312 | "$y = ax + b$\n", 313 | "\n", 314 | "You can even use color schemes like in this example, where coefficients are colored in red\n", 315 | "\n", 316 | "$y = \\color{red}a x + \\color{red}b$\n", 317 | "\n", 318 | "#### **Changing font color and size**\n", 319 | "\n", 320 | "To change or highlight specific texts in a color, you can use the following\n", 321 | "\n", 322 | "```\n", 323 | "**Example text**\n", 324 | "```\n", 325 | "\n", 326 | "Where the results will look like **Example text**.\n", 327 | "\n", 328 | "- The `color` argument takes in a HEX code for your color. " 329 | ] 330 | } 331 | ] 332 | } --------------------------------------------------------------------------------