├── .DS_Store
├── .ipynb_checkpoints
└── Day57-58-checkpoint.ipynb
├── Article Published
├── .ipynb_checkpoints
│ └── test-checkpoint.ipynb
├── AutoJobSearch
│ ├── 20210411
│ │ ├── LinkedIn_Job_DataScience_2021_04_11_Chennai.csv
│ │ └── LinkedIn_Job_DataScience_2021_04_11_blr.csv
│ ├── 20210419
│ │ ├── LinkedIn_Job_DataScience_2021_04_19_blr.csv
│ │ ├── LinkedIn_Job_DataScience_2021_04_19_hyd.csv
│ │ └── LinkedIn_Job_DataScience_2021_04_19_mumbai.csv
│ ├── .DS_Store
│ ├── .ipynb_checkpoints
│ │ └── DataScienceJobSearchLinkedin-checkpoint.ipynb
│ ├── DataScienceJobSearchLinkedin.ipynb
│ └── Driver
│ │ ├── .DS_Store
│ │ └── chromedriver
├── Data Analysis using Pandas
│ ├── Data
│ │ ├── SearchTrendData_ModifiedData.xlsx
│ │ ├── SearchTrendData_ModifiedData_NewTopic.csv
│ │ └── SearchTrendData_ModifiedData_Original.csv
│ └── PandasArticle.ipynb
└── test.ipynb
├── Data
├── .DS_Store
├── DataAnalyst.csv
├── ForSampling.csv
├── For_Cor_Cov.csv
├── Image
│ ├── newImage_10colors.jpg
│ ├── stefano-d-andrea-UXIsw4vK1gM-unsplash-2.jpg
│ └── stefano-d-andrea-UXIsw4vK1gM-unsplash.jpg
├── MBA
│ ├── groceries - groceries.csv
│ └── groceries.csv
├── Mall_Customers.csv
├── Stats_Day2_Data.csv
├── clt_data.csv
├── clt_data2.csv
├── country_wise_covid.csv
├── cov_cor_simple_Example.xlsx
├── diabetes.csv
├── framingham.csv
├── heart_failure_clinical_records_dataset.csv
└── netflix_titles.csv
├── Day01 - Anaconda Installation
└── Day1.ipynb
├── Day02 - Variable and String
├── .ipynb_checkpoints
│ └── Day2-checkpoint.ipynb
└── Day2.ipynb
├── Day03 - Numeric, Boolean and Operator
├── .ipynb_checkpoints
│ └── Day3-checkpoint.ipynb
└── Day3.ipynb
├── Day04 - List and Tuple
├── .ipynb_checkpoints
│ └── Day4-checkpoint.ipynb
└── Day4.ipynb
├── Day05 - Sets and Dictionary
├── .ipynb_checkpoints
│ └── Day5-checkpoint.ipynb
└── Day5.ipynb
├── Day06 - If-Then-Else and Loop
├── .ipynb_checkpoints
│ └── Day6-checkpoint.ipynb
└── Day6.ipynb
├── Day07 - Functions and Lambda Functions
├── .ipynb_checkpoints
│ └── Day7-checkpoint.ipynb
└── Day7.ipynb
├── Day08 - Pandas Intro, Read and Write Dataframe
├── .ipynb_checkpoints
│ └── Day8-checkpoint.ipynb
├── Data
│ ├── Sample1.csv
│ ├── Sample1.txt
│ └── Write_to_file.csv
└── Day8.ipynb
├── Day09 - Index, Selection and Assignment
├── .ipynb_checkpoints
│ └── Day9-checkpoint.ipynb
├── Data
│ └── SearchTrendData.csv
└── Day9.ipynb
├── Day10 - Iteration and Sorting
├── .ipynb_checkpoints
│ └── Day10-checkpoint.ipynb
├── Data
│ └── SearchTrendData.csv
└── Day10.ipynb
├── Day11 - Aggregation and GroupBy
├── .ipynb_checkpoints
│ └── Day11-checkpoint.ipynb
├── Data
│ └── SearchTrendData.csv
└── Day11.ipynb
├── Day12 - Missing Values and Handling Them
├── .ipynb_checkpoints
│ └── Day12-checkpoint.ipynb
├── Data
│ └── SearchTrendData_WithMissing.csv
└── Day12.ipynb
├── Day13 - Rename and Replace
├── .ipynb_checkpoints
│ └── Day13-checkpoint.ipynb
├── Data
│ ├── SearchTrendData.csv
│ └── SearchTrendData_WithMissing.csv
└── Day13.ipynb
├── Day14 - Merging, Joining and Combine
├── .ipynb_checkpoints
│ └── Day14-checkpoint.ipynb
├── Data
│ ├── SearchTrendData1.csv
│ └── SearchTrendData2.csv
└── Day14.ipynb
├── Day15 - Summary, Crosstab and Pivot
├── .ipynb_checkpoints
│ └── Day15-checkpoint.ipynb
├── Data
│ └── SearchTrendData_WithMissing.csv
└── Day15.ipynb
├── Day16 - Date, Categorical and Sparse Data
├── .ipynb_checkpoints
│ └── Day16-checkpoint.ipynb
└── Day16.ipynb
├── Day17 - Pandas Visualizations
├── .ipynb_checkpoints
│ └── Day17-checkpoint.ipynb
├── Data
│ └── SearchTrendData.csv
└── Day17.ipynb
├── Day18 - Numpy Introduction
├── .ipynb_checkpoints
│ └── Day18-checkpoint.ipynb
└── Day18.ipynb
├── Day19 - Indexing, Slicing, Join and Split
├── .ipynb_checkpoints
│ └── Day19-checkpoint.ipynb
└── Day19.ipynb
├── Day20 - Iteration, manipulation, Radom and Distribution
├── .ipynb_checkpoints
│ └── Day20-checkpoint.ipynb
└── Day20.ipynb
├── Day21 - NumPy Operations
├── .ipynb_checkpoints
│ └── Day21-checkpoint.ipynb
└── Day21.ipynb
├── Day22 - Sort, Search and Filter
├── .ipynb_checkpoints
│ └── Day22-checkpoint.ipynb
└── Day22.ipynb
├── Day23 - Visualization Part 1 - Basics matplotlib
├── .ipynb_checkpoints
│ └── Day23-checkpoint.ipynb
└── Day23.ipynb
├── Day24 - Visualization Part 2 - Matplotlib
├── .ipynb_checkpoints
│ └── Day24-checkpoint.ipynb
└── Day24.ipynb
├── Day25 - Visualization Part 3 - Seaborn and Interactive Charts
├── .ipynb_checkpoints
│ └── Day25-checkpoint.ipynb
├── Day25.ipynb
└── test.html
├── Day26 - Intro to Stats and Sampling
├── .ipynb_checkpoints
│ └── Day26-checkpoint.ipynb
└── Day26.ipynb
├── Day27 - Descriptive Stats
├── .ipynb_checkpoints
│ └── Day27-checkpoint.ipynb
└── Day27.ipynb
├── Day28 - Relationship Stats and Distribution
├── .ipynb_checkpoints
│ └── Day28-checkpoint.ipynb
└── Day28.ipynb
├── Day29 - Central Limit Theorem
├── .ipynb_checkpoints
│ └── Day29-checkpoint.ipynb
└── Day29.ipynb
├── Day30 - Inferential Stats
├── .ipynb_checkpoints
│ └── Day30-checkpoint.ipynb
└── Day30.ipynb
├── Day36 - DB Concepts - Normalization and ER Design
└── Day36.sql
├── Day37 - Table Creation and Data Loading
├── Day37.sql
└── netflix_titles.csv
├── Day38 - Basic Queries and Filtering
└── Day38.sql
├── Day39 - Join and Union
├── Category.csv
├── Day39.sql
├── Film.csv
└── Sample.xlsx
├── Day40 - SubQueries - Sequencing and Other Functionalities
└── Day40.sql
├── Day41-43 - EDA and Feature Engineering using Titanic Dataset
├── .DS_Store
├── .ipynb_checkpoints
│ └── Day41-checkpoint.ipynb
├── Day41-43.ipynb
└── test.csv
├── Day44 - SweetViz
├── .DS_Store
├── .ipynb_checkpoints
│ └── Day44-checkpoint.ipynb
├── Day44.ipynb
├── Titanic_train_data_analysis.html
└── comparison.html
├── Day45 - D-Tale
├── .ipynb_checkpoints
│ └── Day45-checkpoint.ipynb
└── Day45.ipynb
├── Day46 - Log Transformation
├── .ipynb_checkpoints
│ └── Day46-checkpoint.ipynb
└── Day46.ipynb
├── Day47 - One Hot Encoding
├── .ipynb_checkpoints
│ └── Day47-checkpoint.ipynb
└── Day47.ipynb
├── Day48 - Scaling
├── .ipynb_checkpoints
│ └── Day48-checkpoint.ipynb
└── Day48.ipynb
├── Day49 - Binning
├── .ipynb_checkpoints
│ └── Day49-checkpoint.ipynb
└── Day49.ipynb
├── Day50 - EDA and Feature Engineering Wrap-up
└── .DS_Store
├── Day51-52 - Linear Regression Concept and Implementation
├── .DS_Store
├── .ipynb_checkpoints
│ ├── Multivariables-checkpoint.ipynb
│ ├── Simple Example1-checkpoint.ipynb
│ ├── Simple Example2-checkpoint.ipynb
│ └── SimpleImplementation-checkpoint.ipynb
├── Excel_Calc.numbers
├── Multivariables.ipynb
├── Not Used
│ ├── Lesson_Script.ipynb
│ └── Simple Example1.ipynb
├── Simple Example2.ipynb
└── SimpleImplementation.ipynb
├── Day53-54 - Logistic Regression Concepts and Implementation
├── .DS_Store
├── .ipynb_checkpoints
│ ├── Example1-checkpoint.ipynb
│ └── PredictingHeartDisease-checkpoint.ipynb
├── Example1.ipynb
├── PredictingHeartDisease.ipynb
└── framingham.csv
├── Day55-56 - Decision Tree Implementation
├── .ipynb_checkpoints
│ └── DecisionTreeImplementation_1-checkpoint.ipynb
├── DecisionTreeData.csv
└── DecisionTreeImplementation_1.ipynb
├── Day57-58 - SVM Concept and Implementation
├── .DS_Store
├── .ipynb_checkpoints
│ ├── Heart Disease Dataset - SVM-Copy1-checkpoint.ipynb
│ ├── Heart Disease Dataset - SVM-checkpoint.ipynb
│ └── SimpleExample-checkpoint.ipynb
├── Heart Disease Dataset - SVM.ipynb
├── SimpleExample.ipynb
└── framingham.csv
├── Day59-60 - Random Forest Concept and Implementation
├── .DS_Store
├── .ipynb_checkpoints
│ └── RF-Implementation - Classification-checkpoint.ipynb
└── RF-Implementation - Classification.ipynb
├── Day61-62 - Measuring Model Accuracy
├── .DS_Store
├── .ipynb_checkpoints
│ ├── Classification-checkpoint.ipynb
│ └── Regression-checkpoint.ipynb
├── Classification.ipynb
└── Regression.ipynb
├── Day63 - K Fold Cross Validation
├── .DS_Store
├── .ipynb_checkpoints
│ └── Regression-checkpoint.ipynb
└── Regression.ipynb
├── Day64-65 - Bagging and Boosting
├── .ipynb_checkpoints
│ ├── AdaBoost-checkpoint.ipynb
│ └── Bagging-checkpoint.ipynb
├── AdaBoost.ipynb
└── Bagging.ipynb
├── Day66-67 - K Means Clustering
├── .ipynb_checkpoints
│ ├── Implementing KMeans Algorithm-checkpoint.ipynb
│ └── K Means Example 2-checkpoint.ipynb
├── Implementing KMeans Algorithm.ipynb
└── K Means Example 2.ipynb
├── Day68-69 - Hierarchical Clustering
├── .ipynb_checkpoints
│ └── HierarchicalClustering-checkpoint.ipynb
└── HierarchicalClustering.ipynb
├── Day70-71 - Fuzzy C-Means
├── .ipynb_checkpoints
│ ├── Fuzzy C Means-checkpoint.ipynb
│ └── Image Quantization-checkpoint.ipynb
├── Fuzzy C Means.ipynb
└── Image Quantization.ipynb
├── Day72-73 - Market Basket Analysis
├── .ipynb_checkpoints
│ └── MarketBasketAnalysis-checkpoint.ipynb
└── MarketBasketAnalysis.ipynb
└── README.md
/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/.DS_Store
--------------------------------------------------------------------------------
/.ipynb_checkpoints/Day57-58-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### A simple SVM example - Linear "
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": null,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "import numpy as np\n",
17 | "from sklearn.svm import SVC"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": null,
23 | "metadata": {},
24 | "outputs": [],
25 | "source": [
26 | "X = np.array([[-3, -5], [-2, -2], [1, 3], [2, 4]])\n",
27 | "y = np.array([1, 1, 2, 2])"
28 | ]
29 | },
30 | {
31 | "cell_type": "code",
32 | "execution_count": 6,
33 | "metadata": {},
34 | "outputs": [
35 | {
36 | "data": {
37 | "text/plain": [
38 | "SVC(kernel='linear')"
39 | ]
40 | },
41 | "execution_count": 6,
42 | "metadata": {},
43 | "output_type": "execute_result"
44 | }
45 | ],
46 | "source": [
47 | "clf = SVC(kernel='linear')\n",
48 | "clf.fit(X, y)"
49 | ]
50 | },
51 | {
52 | "cell_type": "code",
53 | "execution_count": 11,
54 | "metadata": {},
55 | "outputs": [],
56 | "source": [
57 | "prediction = clf.predict([[4,6]])"
58 | ]
59 | },
60 | {
61 | "cell_type": "code",
62 | "execution_count": 10,
63 | "metadata": {},
64 | "outputs": [
65 | {
66 | "data": {
67 | "text/plain": [
68 | "array([1])"
69 | ]
70 | },
71 | "execution_count": 10,
72 | "metadata": {},
73 | "output_type": "execute_result"
74 | }
75 | ],
76 | "source": [
77 | "prediction"
78 | ]
79 | },
80 | {
81 | "cell_type": "code",
82 | "execution_count": null,
83 | "metadata": {},
84 | "outputs": [],
85 | "source": []
86 | }
87 | ],
88 | "metadata": {
89 | "kernelspec": {
90 | "display_name": "Python 3",
91 | "language": "python",
92 | "name": "python3"
93 | },
94 | "language_info": {
95 | "codemirror_mode": {
96 | "name": "ipython",
97 | "version": 3
98 | },
99 | "file_extension": ".py",
100 | "mimetype": "text/x-python",
101 | "name": "python",
102 | "nbconvert_exporter": "python",
103 | "pygments_lexer": "ipython3",
104 | "version": "3.8.5"
105 | }
106 | },
107 | "nbformat": 4,
108 | "nbformat_minor": 4
109 | }
110 |
--------------------------------------------------------------------------------
/Article Published/.ipynb_checkpoints/test-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [],
3 | "metadata": {},
4 | "nbformat": 4,
5 | "nbformat_minor": 4
6 | }
7 |
--------------------------------------------------------------------------------
/Article Published/AutoJobSearch/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Article Published/AutoJobSearch/.DS_Store
--------------------------------------------------------------------------------
/Article Published/AutoJobSearch/Driver/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Article Published/AutoJobSearch/Driver/.DS_Store
--------------------------------------------------------------------------------
/Article Published/AutoJobSearch/Driver/chromedriver:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Article Published/AutoJobSearch/Driver/chromedriver
--------------------------------------------------------------------------------
/Article Published/Data Analysis using Pandas/Data/SearchTrendData_ModifiedData.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Article Published/Data Analysis using Pandas/Data/SearchTrendData_ModifiedData.xlsx
--------------------------------------------------------------------------------
/Article Published/Data Analysis using Pandas/Data/SearchTrendData_ModifiedData_NewTopic.csv:
--------------------------------------------------------------------------------
1 | Week,Month,Year,Google_Search_Trend_Topic_DeepLearning
2 | 20/09/2015,9,2015,4
3 | 27/09/2015,9,2015,4
4 | 4/10/2015,10,2015,4
5 | 11/10/2015,10,2015,
6 | 18/10/2015,10,2015,4
7 | 25/10/2015,10,2015,4
8 | 1/11/2015,11,2015,3
9 | 8/11/2015,11,2015,5
10 | 15/11/2015,11,2015,4
11 | 22/11/2015,11,2015,4
12 | 29/11/2015,11,2015,4
13 | 6/12/2015,12,2015,5
14 | 13/12/2015,12,2015,
15 | 20/12/2015,12,2015,4
16 | 27/12/2015,12,2015,3
17 | 3/01/2016,1,2016,4
18 | 10/01/2016,1,2016,4
19 | 17/01/2016,1,2016,6
20 | 24/01/2016,1,2016,7
21 | 31/01/2016,1,2016,6
22 | 7/02/2016,2,2016,5
23 | 14/02/2016,2,2016,5
24 | 21/02/2016,2,2016,5
25 | 28/02/2016,2,2016,5
26 | 6/03/2016,3,2016,
27 | 13/03/2016,3,2016,8
28 | 20/03/2016,3,2016,6
29 | 27/03/2016,3,2016,6
30 | 3/04/2016,4,2016,7
31 | 10/04/2016,4,2016,6
32 | 17/04/2016,4,2016,6
33 | 24/04/2016,4,2016,6
34 | 1/05/2016,5,2016,6
35 | 8/05/2016,5,2016,6
36 | 15/05/2016,5,2016,7
37 | 22/05/2016,5,2016,6
38 | 29/05/2016,5,2016,6
39 | 5/06/2016,6,2016,7
40 | 12/06/2016,6,2016,6
41 | 19/06/2016,6,2016,7
42 | 26/06/2016,6,2016,7
43 | 3/07/2016,7,2016,6
44 | 10/07/2016,7,2016,6
45 | 17/07/2016,7,2016,6
46 | 24/07/2016,7,2016,6
47 | 31/07/2016,7,2016,
48 | 7/08/2016,8,2016,6
49 | 14/08/2016,8,2016,5
50 | 21/08/2016,8,2016,6
51 | 28/08/2016,8,2016,7
52 | 4/09/2016,9,2016,6
53 | 11/09/2016,9,2016,7
54 | 18/09/2016,9,2016,6
55 | 25/09/2016,9,2016,8
56 | 2/10/2016,10,2016,8
57 | 9/10/2016,10,2016,8
58 | 16/10/2016,10,2016,8
59 | 23/10/2016,10,2016,8
60 | 30/10/2016,10,2016,8
61 | 6/11/2016,11,2016,8
62 | 13/11/2016,11,2016,9
63 | 20/11/2016,11,2016,9
64 | 27/11/2016,11,2016,9
65 | 4/12/2016,12,2016,
66 | 11/12/2016,12,2016,9
67 | 18/12/2016,12,2016,9
68 | 25/12/2016,12,2016,7
69 | 1/01/2017,1,2017,9
70 | 8/01/2017,1,2017,10
71 | 15/01/2017,1,2017,10
72 | 22/01/2017,1,2017,10
73 | 29/01/2017,1,2017,10
74 | 5/02/2017,2,2017,10
75 | 12/02/2017,2,2017,11
76 | 19/02/2017,2,2017,11
77 | 26/02/2017,2,2017,11
78 | 5/03/2017,3,2017,11
79 | 12/03/2017,3,2017,12
80 | 19/03/2017,3,2017,11
81 | 26/03/2017,3,2017,11
82 | 2/04/2017,4,2017,12
83 | 9/04/2017,4,2017,
84 | 16/04/2017,4,2017,12
85 | 23/04/2017,4,2017,12
86 | 30/04/2017,4,2017,11
87 | 7/05/2017,5,2017,12
88 | 14/05/2017,5,2017,12
89 | 21/05/2017,5,2017,12
90 | 28/05/2017,5,2017,11
91 | 4/06/2017,6,2017,11
92 | 11/06/2017,6,2017,11
93 | 18/06/2017,6,2017,11
94 | 25/06/2017,6,2017,12
95 | 2/07/2017,7,2017,11
96 | 9/07/2017,7,2017,12
97 | 16/07/2017,7,2017,13
98 | 23/07/2017,7,2017,12
99 | 30/07/2017,7,2017,
100 | 6/08/2017,8,2017,13
101 | 13/08/2017,8,2017,13
102 | 20/08/2017,8,2017,12
103 | 27/08/2017,8,2017,12
104 | 3/09/2017,9,2017,13
105 | 10/09/2017,9,2017,13
106 | 17/09/2017,9,2017,14
107 | 24/09/2017,9,2017,13
108 | 1/10/2017,10,2017,13
109 | 8/10/2017,10,2017,13
110 | 15/10/2017,10,2017,14
111 | 22/10/2017,10,2017,13
112 | 29/10/2017,10,2017,14
113 | 5/11/2017,11,2017,14
114 | 12/11/2017,11,2017,15
115 | 19/11/2017,11,2017,14
116 | 26/11/2017,11,2017,15
117 | 3/12/2017,12,2017,15
118 | 10/12/2017,12,2017,15
119 | 17/12/2017,12,2017,14
120 | 24/12/2017,12,2017,12
121 | 31/12/2017,12,2017,
122 | 7/01/2018,1,2018,15
123 | 14/01/2018,1,2018,14
124 | 21/01/2018,1,2018,14
125 | 28/01/2018,1,2018,16
126 | 4/02/2018,2,2018,15
127 | 11/02/2018,2,2018,13
128 | 18/02/2018,2,2018,14
129 | 25/02/2018,2,2018,14
130 | 4/03/2018,3,2018,14
131 | 11/03/2018,3,2018,14
132 | 18/03/2018,3,2018,15
133 | 25/03/2018,3,2018,14
134 | 1/04/2018,4,2018,14
135 | 8/04/2018,4,2018,15
136 | 15/04/2018,4,2018,15
137 | 22/04/2018,4,2018,15
138 | 29/04/2018,4,2018,14
139 | 6/05/2018,5,2018,15
140 | 13/05/2018,5,2018,
141 | 20/05/2018,5,2018,14
142 | 27/05/2018,5,2018,14
143 | 3/06/2018,6,2018,14
144 | 10/06/2018,6,2018,14
145 | 17/06/2018,6,2018,14
146 | 24/06/2018,6,2018,14
147 | 1/07/2018,7,2018,13
148 | 8/07/2018,7,2018,14
149 | 15/07/2018,7,2018,14
150 | 22/07/2018,7,2018,13
151 | 29/07/2018,7,2018,14
152 | 5/08/2018,8,2018,13
153 | 12/08/2018,8,2018,12
154 | 19/08/2018,8,2018,14
155 | 26/08/2018,8,2018,14
156 | 2/09/2018,9,2018,14
157 | 9/09/2018,9,2018,14
158 | 16/09/2018,9,2018,15
159 | 23/09/2018,9,2018,15
160 | 30/09/2018,9,2018,15
161 | 7/10/2018,10,2018,15
162 | 14/10/2018,10,2018,15
163 | 21/10/2018,10,2018,
164 | 28/10/2018,10,2018,15
165 | 4/11/2018,11,2018,15
166 | 11/11/2018,11,2018,15
167 | 18/11/2018,11,2018,14
168 | 25/11/2018,11,2018,15
169 | 2/12/2018,12,2018,16
170 | 9/12/2018,12,2018,15
171 | 16/12/2018,12,2018,13
172 | 23/12/2018,12,2018,10
173 | 30/12/2018,12,2018,11
174 | 6/01/2019,1,2019,14
175 | 13/01/2019,1,2019,15
176 | 20/01/2019,1,2019,15
177 | 27/01/2019,1,2019,15
178 | 3/02/2019,2,2019,15
179 | 10/02/2019,2,2019,15
180 | 17/02/2019,2,2019,15
181 | 24/02/2019,2,2019,15
182 | 3/03/2019,3,2019,15
183 | 10/03/2019,3,2019,15
184 | 17/03/2019,3,2019,14
185 | 24/03/2019,3,2019,16
186 | 31/03/2019,3,2019,15
187 | 7/04/2019,4,2019,16
188 | 14/04/2019,4,2019,14
189 | 21/04/2019,4,2019,15
190 | 28/04/2019,4,2019,14
191 | 5/05/2019,5,2019,14
192 | 12/05/2019,5,2019,15
193 | 19/05/2019,5,2019,15
194 | 26/05/2019,5,2019,14
195 | 2/06/2019,6,2019,14
196 | 9/06/2019,6,2019,15
197 | 16/06/2019,6,2019,15
198 | 23/06/2019,6,2019,15
199 | 30/06/2019,6,2019,14
200 | 7/07/2019,7,2019,15
201 | 14/07/2019,7,2019,15
202 | 21/07/2019,7,2019,14
203 | 28/07/2019,7,2019,
204 | 4/08/2019,8,2019,14
205 | 11/08/2019,8,2019,14
206 | 18/08/2019,8,2019,14
207 | 25/08/2019,8,2019,14
208 | 1/09/2019,9,2019,15
209 | 8/09/2019,9,2019,15
210 | 15/09/2019,9,2019,15
211 | 22/09/2019,9,2019,15
212 | 29/09/2019,9,2019,15
213 | 6/10/2019,10,2019,15
214 | 13/10/2019,10,2019,15
215 | 20/10/2019,10,2019,14
216 | 27/10/2019,10,2019,14
217 | 3/11/2019,11,2019,14
218 | 10/11/2019,11,2019,15
219 | 17/11/2019,11,2019,15
220 | 24/11/2019,11,2019,15
221 | 1/12/2019,12,2019,14
222 | 8/12/2019,12,2019,15
223 | 15/12/2019,12,2019,15
224 | 22/12/2019,12,2019,11
225 | 29/12/2019,12,2019,12
226 | 5/01/2020,1,2020,15
227 | 12/01/2020,1,2020,15
228 | 19/01/2020,1,2020,14
229 | 26/01/2020,1,2020,14
230 | 2/02/2020,2,2020,
231 | 9/02/2020,2,2020,16
232 | 16/02/2020,2,2020,16
233 | 23/02/2020,2,2020,16
234 | 1/03/2020,3,2020,15
235 | 8/03/2020,3,2020,14
236 | 15/03/2020,3,2020,12
237 | 22/03/2020,3,2020,12
238 | 29/03/2020,3,2020,13
239 | 5/04/2020,4,2020,13
240 | 12/04/2020,4,2020,14
241 | 19/04/2020,4,2020,15
242 | 26/04/2020,4,2020,15
243 | 3/05/2020,5,2020,15
244 | 10/05/2020,5,2020,15
245 | 17/05/2020,5,2020,15
246 | 24/05/2020,5,2020,15
247 | 31/05/2020,5,2020,14
248 | 7/06/2020,6,2020,15
249 | 14/06/2020,6,2020,15
250 | 21/06/2020,6,2020,14
251 | 28/06/2020,6,2020,14
252 | 5/07/2020,7,2020,14
253 | 12/07/2020,7,2020,15
254 | 19/07/2020,7,2020,14
255 | 26/07/2020,7,2020,14
256 | 2/08/2020,8,2020,13
257 | 9/08/2020,8,2020,13
258 | 16/08/2020,8,2020,14
259 | 23/08/2020,8,2020,14
260 | 30/08/2020,8,2020,14
261 | 6/09/2020,9,2020,14
262 | 13/09/2020,9,2020,14
263 |
--------------------------------------------------------------------------------
/Article Published/test.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "dasa\n",
10 | "dsadsa\n",
11 | "dasdas"
12 | ]
13 | }
14 | ],
15 | "metadata": {
16 | "kernelspec": {
17 | "display_name": "Python 3",
18 | "language": "python",
19 | "name": "python3"
20 | },
21 | "language_info": {
22 | "codemirror_mode": {
23 | "name": "ipython",
24 | "version": 3
25 | },
26 | "file_extension": ".py",
27 | "mimetype": "text/x-python",
28 | "name": "python",
29 | "nbconvert_exporter": "python",
30 | "pygments_lexer": "ipython3",
31 | "version": "3.8.3"
32 | }
33 | },
34 | "nbformat": 4,
35 | "nbformat_minor": 4
36 | }
37 |
--------------------------------------------------------------------------------
/Data/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Data/.DS_Store
--------------------------------------------------------------------------------
/Data/For_Cor_Cov.csv:
--------------------------------------------------------------------------------
1 | Input,Output
2 | 1000,10
3 | 1074,16
4 | 1125,21
5 | 1197,26
6 | 1275,31
7 | 1343,37
8 | 1396,45
9 | 1466,54
10 | 1526,61
11 | 1585,70
12 | 1641,75
13 | 1714,84
14 | 1776,91
15 | 1851,99
16 | 1921,108
17 | 1997,118
18 | 2047,124
19 | 2121,134
20 | 2200,143
21 | 2258,148
22 | 2327,156
23 | 2404,166
24 | 2460,175
25 | 2523,180
26 | 2597,190
27 | 2664,200
28 | 2731,205
29 | 2793,210
30 | 2861,217
31 | 2935,224
32 | 2991,233
33 | 3047,242
34 | 3123,249
35 | 3180,256
36 | 3244,263
37 | 3297,269
38 | 3355,277
39 | 3433,286
40 | 3501,291
41 | 3579,300
42 | 3650,307
43 | 3729,317
44 | 3795,326
45 | 3847,333
46 | 3910,338
47 | 3972,346
48 | 4043,352
49 | 4097,357
50 | 4162,367
51 | 4219,372
52 | 4285,378
53 | 4358,388
54 | 4426,398
55 | 4485,408
56 | 4564,415
57 | 4636,425
58 | 4688,430
59 | 4766,435
60 | 4818,444
61 | 4894,452
62 | 4966,459
63 | 5020,465
64 | 5098,470
65 | 5171,479
66 | 5227,489
67 | 5283,495
68 | 5349,500
69 | 5402,506
70 | 5468,512
71 | 5547,518
72 | 5623,528
73 | 5698,534
74 | 5750,544
75 | 5816,551
76 | 5876,558
77 | 5930,566
78 | 6010,572
79 | 6068,578
80 | 6148,585
81 | 6209,593
82 | 6268,600
83 | 6330,605
84 | 6409,610
85 | 6487,619
86 | 6543,628
87 | 6600,637
88 | 6679,644
89 | 6759,651
90 | 6821,657
91 | 6895,665
92 | 6947,670
93 | 7008,679
94 | 7067,685
95 | 7125,690
96 | 7196,695
97 | 7272,701
98 | 7351,710
99 | 7429,720
100 | 7494,725
101 | 7552,734
102 |
--------------------------------------------------------------------------------
/Data/Image/newImage_10colors.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Data/Image/newImage_10colors.jpg
--------------------------------------------------------------------------------
/Data/Image/stefano-d-andrea-UXIsw4vK1gM-unsplash-2.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Data/Image/stefano-d-andrea-UXIsw4vK1gM-unsplash-2.jpg
--------------------------------------------------------------------------------
/Data/Image/stefano-d-andrea-UXIsw4vK1gM-unsplash.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Data/Image/stefano-d-andrea-UXIsw4vK1gM-unsplash.jpg
--------------------------------------------------------------------------------
/Data/Mall_Customers.csv:
--------------------------------------------------------------------------------
1 | CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100)
2 | 1,Male,19,15,39
3 | 2,Male,21,15,81
4 | 3,Female,20,16,6
5 | 4,Female,23,16,77
6 | 5,Female,31,17,40
7 | 6,Female,22,17,76
8 | 7,Female,35,18,6
9 | 8,Female,23,18,94
10 | 9,Male,64,19,3
11 | 10,Female,30,19,72
12 | 11,Male,67,19,14
13 | 12,Female,35,19,99
14 | 13,Female,58,20,15
15 | 14,Female,24,20,77
16 | 15,Male,37,20,13
17 | 16,Male,22,20,79
18 | 17,Female,35,21,35
19 | 18,Male,20,21,66
20 | 19,Male,52,23,29
21 | 20,Female,35,23,98
22 | 21,Male,35,24,35
23 | 22,Male,25,24,73
24 | 23,Female,46,25,5
25 | 24,Male,31,25,73
26 | 25,Female,54,28,14
27 | 26,Male,29,28,82
28 | 27,Female,45,28,32
29 | 28,Male,35,28,61
30 | 29,Female,40,29,31
31 | 30,Female,23,29,87
32 | 31,Male,60,30,4
33 | 32,Female,21,30,73
34 | 33,Male,53,33,4
35 | 34,Male,18,33,92
36 | 35,Female,49,33,14
37 | 36,Female,21,33,81
38 | 37,Female,42,34,17
39 | 38,Female,30,34,73
40 | 39,Female,36,37,26
41 | 40,Female,20,37,75
42 | 41,Female,65,38,35
43 | 42,Male,24,38,92
44 | 43,Male,48,39,36
45 | 44,Female,31,39,61
46 | 45,Female,49,39,28
47 | 46,Female,24,39,65
48 | 47,Female,50,40,55
49 | 48,Female,27,40,47
50 | 49,Female,29,40,42
51 | 50,Female,31,40,42
52 | 51,Female,49,42,52
53 | 52,Male,33,42,60
54 | 53,Female,31,43,54
55 | 54,Male,59,43,60
56 | 55,Female,50,43,45
57 | 56,Male,47,43,41
58 | 57,Female,51,44,50
59 | 58,Male,69,44,46
60 | 59,Female,27,46,51
61 | 60,Male,53,46,46
62 | 61,Male,70,46,56
63 | 62,Male,19,46,55
64 | 63,Female,67,47,52
65 | 64,Female,54,47,59
66 | 65,Male,63,48,51
67 | 66,Male,18,48,59
68 | 67,Female,43,48,50
69 | 68,Female,68,48,48
70 | 69,Male,19,48,59
71 | 70,Female,32,48,47
72 | 71,Male,70,49,55
73 | 72,Female,47,49,42
74 | 73,Female,60,50,49
75 | 74,Female,60,50,56
76 | 75,Male,59,54,47
77 | 76,Male,26,54,54
78 | 77,Female,45,54,53
79 | 78,Male,40,54,48
80 | 79,Female,23,54,52
81 | 80,Female,49,54,42
82 | 81,Male,57,54,51
83 | 82,Male,38,54,55
84 | 83,Male,67,54,41
85 | 84,Female,46,54,44
86 | 85,Female,21,54,57
87 | 86,Male,48,54,46
88 | 87,Female,55,57,58
89 | 88,Female,22,57,55
90 | 89,Female,34,58,60
91 | 90,Female,50,58,46
92 | 91,Female,68,59,55
93 | 92,Male,18,59,41
94 | 93,Male,48,60,49
95 | 94,Female,40,60,40
96 | 95,Female,32,60,42
97 | 96,Male,24,60,52
98 | 97,Female,47,60,47
99 | 98,Female,27,60,50
100 | 99,Male,48,61,42
101 | 100,Male,20,61,49
102 | 101,Female,23,62,41
103 | 102,Female,49,62,48
104 | 103,Male,67,62,59
105 | 104,Male,26,62,55
106 | 105,Male,49,62,56
107 | 106,Female,21,62,42
108 | 107,Female,66,63,50
109 | 108,Male,54,63,46
110 | 109,Male,68,63,43
111 | 110,Male,66,63,48
112 | 111,Male,65,63,52
113 | 112,Female,19,63,54
114 | 113,Female,38,64,42
115 | 114,Male,19,64,46
116 | 115,Female,18,65,48
117 | 116,Female,19,65,50
118 | 117,Female,63,65,43
119 | 118,Female,49,65,59
120 | 119,Female,51,67,43
121 | 120,Female,50,67,57
122 | 121,Male,27,67,56
123 | 122,Female,38,67,40
124 | 123,Female,40,69,58
125 | 124,Male,39,69,91
126 | 125,Female,23,70,29
127 | 126,Female,31,70,77
128 | 127,Male,43,71,35
129 | 128,Male,40,71,95
130 | 129,Male,59,71,11
131 | 130,Male,38,71,75
132 | 131,Male,47,71,9
133 | 132,Male,39,71,75
134 | 133,Female,25,72,34
135 | 134,Female,31,72,71
136 | 135,Male,20,73,5
137 | 136,Female,29,73,88
138 | 137,Female,44,73,7
139 | 138,Male,32,73,73
140 | 139,Male,19,74,10
141 | 140,Female,35,74,72
142 | 141,Female,57,75,5
143 | 142,Male,32,75,93
144 | 143,Female,28,76,40
145 | 144,Female,32,76,87
146 | 145,Male,25,77,12
147 | 146,Male,28,77,97
148 | 147,Male,48,77,36
149 | 148,Female,32,77,74
150 | 149,Female,34,78,22
151 | 150,Male,34,78,90
152 | 151,Male,43,78,17
153 | 152,Male,39,78,88
154 | 153,Female,44,78,20
155 | 154,Female,38,78,76
156 | 155,Female,47,78,16
157 | 156,Female,27,78,89
158 | 157,Male,37,78,1
159 | 158,Female,30,78,78
160 | 159,Male,34,78,1
161 | 160,Female,30,78,73
162 | 161,Female,56,79,35
163 | 162,Female,29,79,83
164 | 163,Male,19,81,5
165 | 164,Female,31,81,93
166 | 165,Male,50,85,26
167 | 166,Female,36,85,75
168 | 167,Male,42,86,20
169 | 168,Female,33,86,95
170 | 169,Female,36,87,27
171 | 170,Male,32,87,63
172 | 171,Male,40,87,13
173 | 172,Male,28,87,75
174 | 173,Male,36,87,10
175 | 174,Male,36,87,92
176 | 175,Female,52,88,13
177 | 176,Female,30,88,86
178 | 177,Male,58,88,15
179 | 178,Male,27,88,69
180 | 179,Male,59,93,14
181 | 180,Male,35,93,90
182 | 181,Female,37,97,32
183 | 182,Female,32,97,86
184 | 183,Male,46,98,15
185 | 184,Female,29,98,88
186 | 185,Female,41,99,39
187 | 186,Male,30,99,97
188 | 187,Female,54,101,24
189 | 188,Male,28,101,68
190 | 189,Female,41,103,17
191 | 190,Female,36,103,85
192 | 191,Female,34,103,23
193 | 192,Female,32,103,69
194 | 193,Male,33,113,8
195 | 194,Female,38,113,91
196 | 195,Female,47,120,16
197 | 196,Female,35,120,79
198 | 197,Female,45,126,28
199 | 198,Male,32,126,74
200 | 199,Male,32,137,18
201 | 200,Male,30,137,83
--------------------------------------------------------------------------------
/Data/cov_cor_simple_Example.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/rsharankumar/Learn_Data_Science_in_100Days/d1b8fc128c51a782f8cc1b91332cfdc550cf04c0/Data/cov_cor_simple_Example.xlsx
--------------------------------------------------------------------------------
/Day01 - Anaconda Installation/Day1.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Day 1\n",
8 | "1. Anaconda Installation\n",
9 | "2. Starting Jupyter Notebook\n",
10 | "3. Starting Spyder\n",
11 | "4. How Jupyter and Spyder Work?\n",
12 | "5. Installing Packages in Python\n",
13 | "6. Adding Comments"
14 | ]
15 | },
16 | {
17 | "cell_type": "code",
18 | "execution_count": 1,
19 | "metadata": {},
20 | "outputs": [
21 | {
22 | "name": "stdout",
23 | "output_type": "stream",
24 | "text": [
25 | "8\n"
26 | ]
27 | }
28 | ],
29 | "source": [
30 | "# To add comment to the code\n",
31 | "x =3\n",
32 | "y =5\n",
33 | "print(x+y)"
34 | ]
35 | },
36 | {
37 | "cell_type": "markdown",
38 | "metadata": {},
39 | "source": [
40 | "If any issues in launching Jupyter Notebook from the new environment then install below
\n",
41 | "conda install ipykernel
\n",
42 | "conda install jupyter"
43 | ]
44 | },
45 | {
46 | "cell_type": "code",
47 | "execution_count": null,
48 | "metadata": {},
49 | "outputs": [],
50 | "source": []
51 | }
52 | ],
53 | "metadata": {
54 | "kernelspec": {
55 | "display_name": "Python 3",
56 | "language": "python",
57 | "name": "python3"
58 | },
59 | "language_info": {
60 | "codemirror_mode": {
61 | "name": "ipython",
62 | "version": 3
63 | },
64 | "file_extension": ".py",
65 | "mimetype": "text/x-python",
66 | "name": "python",
67 | "nbconvert_exporter": "python",
68 | "pygments_lexer": "ipython3",
69 | "version": "3.6.9"
70 | }
71 | },
72 | "nbformat": 4,
73 | "nbformat_minor": 2
74 | }
75 |
--------------------------------------------------------------------------------
/Day02 - Variable and String/.ipynb_checkpoints/Day2-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics to be covered today\n",
8 | "- Creating Variables\n",
9 | "- Data Types - String\n",
10 | "- Use of Type()\n",
11 | "- Changing Data Types\n",
12 | "- String as Arrray\n",
13 | "- Indexing\n",
14 | "- Sting Length\n",
15 | "- Strip\n",
16 | "- Split\n",
17 | "- Concat\n",
18 | "- Replace"
19 | ]
20 | },
21 | {
22 | "cell_type": "markdown",
23 | "metadata": {},
24 | "source": [
25 | "Creating Variables and checking data type"
26 | ]
27 | },
28 | {
29 | "cell_type": "code",
30 | "execution_count": 3,
31 | "metadata": {},
32 | "outputs": [],
33 | "source": [
34 | "input1 = 10\n",
35 | "input2 = 23.5\n",
36 | "input3 = \"Learn Data Science in 100 Days\""
37 | ]
38 | },
39 | {
40 | "cell_type": "code",
41 | "execution_count": 4,
42 | "metadata": {},
43 | "outputs": [
44 | {
45 | "data": {
46 | "text/plain": [
47 | "str"
48 | ]
49 | },
50 | "execution_count": 4,
51 | "metadata": {},
52 | "output_type": "execute_result"
53 | }
54 | ],
55 | "source": [
56 | "type(input3)"
57 | ]
58 | },
59 | {
60 | "cell_type": "markdown",
61 | "metadata": {},
62 | "source": [
63 | "Changing string to numeric data type"
64 | ]
65 | },
66 | {
67 | "cell_type": "code",
68 | "execution_count": 6,
69 | "metadata": {},
70 | "outputs": [
71 | {
72 | "data": {
73 | "text/plain": [
74 | "float"
75 | ]
76 | },
77 | "execution_count": 6,
78 | "metadata": {},
79 | "output_type": "execute_result"
80 | }
81 | ],
82 | "source": [
83 | "input4 = \"12.25\"\n",
84 | "type(input4)\n",
85 | "type(float(input4))"
86 | ]
87 | },
88 | {
89 | "cell_type": "markdown",
90 | "metadata": {},
91 | "source": [
92 | "Strings are treated like array"
93 | ]
94 | },
95 | {
96 | "cell_type": "code",
97 | "execution_count": 10,
98 | "metadata": {},
99 | "outputs": [
100 | {
101 | "name": "stdout",
102 | "output_type": "stream",
103 | "text": [
104 | "Learn \n"
105 | ]
106 | }
107 | ],
108 | "source": [
109 | "print(input3[0:6])"
110 | ]
111 | },
112 | {
113 | "cell_type": "code",
114 | "execution_count": 11,
115 | "metadata": {},
116 | "outputs": [
117 | {
118 | "name": "stdout",
119 | "output_type": "stream",
120 | "text": [
121 | "ay\n"
122 | ]
123 | }
124 | ],
125 | "source": [
126 | "print(input3[-3:-1])"
127 | ]
128 | },
129 | {
130 | "cell_type": "code",
131 | "execution_count": 16,
132 | "metadata": {},
133 | "outputs": [
134 | {
135 | "name": "stdout",
136 | "output_type": "stream",
137 | "text": [
138 | "30\n"
139 | ]
140 | }
141 | ],
142 | "source": [
143 | "print(len(input3))"
144 | ]
145 | },
146 | {
147 | "cell_type": "markdown",
148 | "metadata": {},
149 | "source": [
150 | "Strip - to remove extra spaces in the beginning and end"
151 | ]
152 | },
153 | {
154 | "cell_type": "code",
155 | "execution_count": 1,
156 | "metadata": {},
157 | "outputs": [
158 | {
159 | "name": "stdout",
160 | "output_type": "stream",
161 | "text": [
162 | " Data Science \n",
163 | "Data Science\n"
164 | ]
165 | }
166 | ],
167 | "source": [
168 | "txt = \" Data Science \"\n",
169 | "print(txt)\n",
170 | "txt1 = txt.strip()\n",
171 | "print(txt1)"
172 | ]
173 | },
174 | {
175 | "cell_type": "markdown",
176 | "metadata": {},
177 | "source": [
178 | "Spliting"
179 | ]
180 | },
181 | {
182 | "cell_type": "code",
183 | "execution_count": 19,
184 | "metadata": {},
185 | "outputs": [
186 | {
187 | "name": "stdout",
188 | "output_type": "stream",
189 | "text": [
190 | "['12', '25']\n"
191 | ]
192 | }
193 | ],
194 | "source": [
195 | "print(input4.split(\".\"))"
196 | ]
197 | },
198 | {
199 | "cell_type": "markdown",
200 | "metadata": {},
201 | "source": [
202 | "Concatinating"
203 | ]
204 | },
205 | {
206 | "cell_type": "code",
207 | "execution_count": 3,
208 | "metadata": {},
209 | "outputs": [
210 | {
211 | "name": "stdout",
212 | "output_type": "stream",
213 | "text": [
214 | "Data Science\n"
215 | ]
216 | }
217 | ],
218 | "source": [
219 | "i1 = \"Data\"\n",
220 | "i2 = \"Science\"\n",
221 | "i3 = i1+\" \"+i2\n",
222 | "print(i3)"
223 | ]
224 | },
225 | {
226 | "cell_type": "markdown",
227 | "metadata": {},
228 | "source": [
229 | "Replacing text"
230 | ]
231 | },
232 | {
233 | "cell_type": "code",
234 | "execution_count": 4,
235 | "metadata": {},
236 | "outputs": [
237 | {
238 | "name": "stdout",
239 | "output_type": "stream",
240 | "text": [
241 | "As data scientists, our job is to extract information from noise.\n"
242 | ]
243 | }
244 | ],
245 | "source": [
246 | "txt1 = \"As data scientists, our job is to extract signal from noise.\"\n",
247 | "new_txt = txt1.replace(\"signal\", \"information\")\n",
248 | "print(new_txt)"
249 | ]
250 | },
251 | {
252 | "cell_type": "markdown",
253 | "metadata": {},
254 | "source": [
255 | "To Try\n",
256 | "1. Define a number as a string\n",
257 | "2. Convert the above to numeric\n",
258 | "3. Create a variable with a decimal number\n",
259 | "4. Split the above decimal number into 2 (before decimal and after decimal point)\n",
260 | "5. Copy a random sentence and pass it to a variable\n",
261 | "6. Identify a specific words thats gets repeated and replace it with your name"
262 | ]
263 | },
264 | {
265 | "cell_type": "code",
266 | "execution_count": null,
267 | "metadata": {},
268 | "outputs": [],
269 | "source": []
270 | }
271 | ],
272 | "metadata": {
273 | "kernelspec": {
274 | "display_name": "Python 3",
275 | "language": "python",
276 | "name": "python3"
277 | },
278 | "language_info": {
279 | "codemirror_mode": {
280 | "name": "ipython",
281 | "version": 3
282 | },
283 | "file_extension": ".py",
284 | "mimetype": "text/x-python",
285 | "name": "python",
286 | "nbconvert_exporter": "python",
287 | "pygments_lexer": "ipython3",
288 | "version": "3.6.9"
289 | }
290 | },
291 | "nbformat": 4,
292 | "nbformat_minor": 2
293 | }
294 |
--------------------------------------------------------------------------------
/Day02 - Variable and String/Day2.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics to be covered today\n",
8 | "- Creating Variables\n",
9 | "- Data Types - String\n",
10 | "- Use of Type()\n",
11 | "- Changing Data Types\n",
12 | "- String as Arrray\n",
13 | "- Indexing\n",
14 | "- Sting Length\n",
15 | "- Strip\n",
16 | "- Split\n",
17 | "- Concat\n",
18 | "- Replace"
19 | ]
20 | },
21 | {
22 | "cell_type": "markdown",
23 | "metadata": {},
24 | "source": [
25 | "Creating Variables and checking data type"
26 | ]
27 | },
28 | {
29 | "cell_type": "code",
30 | "execution_count": 5,
31 | "metadata": {},
32 | "outputs": [],
33 | "source": [
34 | "input1 = 10\n",
35 | "input2 = 23.5\n",
36 | "input3 = \"Learn Data Science in 100 Days\""
37 | ]
38 | },
39 | {
40 | "cell_type": "code",
41 | "execution_count": 8,
42 | "metadata": {},
43 | "outputs": [
44 | {
45 | "data": {
46 | "text/plain": [
47 | "str"
48 | ]
49 | },
50 | "execution_count": 8,
51 | "metadata": {},
52 | "output_type": "execute_result"
53 | }
54 | ],
55 | "source": [
56 | "type(input3)"
57 | ]
58 | },
59 | {
60 | "cell_type": "markdown",
61 | "metadata": {},
62 | "source": [
63 | "Changing string to numeric data type"
64 | ]
65 | },
66 | {
67 | "cell_type": "code",
68 | "execution_count": 14,
69 | "metadata": {},
70 | "outputs": [
71 | {
72 | "name": "stdout",
73 | "output_type": "stream",
74 | "text": [
75 | "\n"
76 | ]
77 | }
78 | ],
79 | "source": [
80 | "input4 = \"12\"\n",
81 | "type(input4)\n",
82 | "print(type(int(input4)))"
83 | ]
84 | },
85 | {
86 | "cell_type": "markdown",
87 | "metadata": {},
88 | "source": [
89 | "Strings are treated like array"
90 | ]
91 | },
92 | {
93 | "cell_type": "code",
94 | "execution_count": 23,
95 | "metadata": {},
96 | "outputs": [
97 | {
98 | "name": "stdout",
99 | "output_type": "stream",
100 | "text": [
101 | " Data Science in 100 Days\n"
102 | ]
103 | }
104 | ],
105 | "source": [
106 | "print(input3[5:])"
107 | ]
108 | },
109 | {
110 | "cell_type": "code",
111 | "execution_count": 21,
112 | "metadata": {},
113 | "outputs": [
114 | {
115 | "name": "stdout",
116 | "output_type": "stream",
117 | "text": [
118 | "n 100 Days\n"
119 | ]
120 | }
121 | ],
122 | "source": [
123 | "print(input3[-10:])"
124 | ]
125 | },
126 | {
127 | "cell_type": "code",
128 | "execution_count": 24,
129 | "metadata": {},
130 | "outputs": [
131 | {
132 | "name": "stdout",
133 | "output_type": "stream",
134 | "text": [
135 | "30\n"
136 | ]
137 | }
138 | ],
139 | "source": [
140 | "print(len(input3))"
141 | ]
142 | },
143 | {
144 | "cell_type": "markdown",
145 | "metadata": {},
146 | "source": [
147 | "Strip - to remove extra spaces in the beginning and end"
148 | ]
149 | },
150 | {
151 | "cell_type": "code",
152 | "execution_count": 26,
153 | "metadata": {},
154 | "outputs": [
155 | {
156 | "name": "stdout",
157 | "output_type": "stream",
158 | "text": [
159 | " Data Science \n",
160 | "Data Science\n"
161 | ]
162 | }
163 | ],
164 | "source": [
165 | "txt = \" Data Science \"\n",
166 | "print(txt)\n",
167 | "txt1 = txt.strip()\n",
168 | "print(txt1)"
169 | ]
170 | },
171 | {
172 | "cell_type": "markdown",
173 | "metadata": {},
174 | "source": [
175 | "Spliting"
176 | ]
177 | },
178 | {
179 | "cell_type": "code",
180 | "execution_count": 29,
181 | "metadata": {},
182 | "outputs": [
183 | {
184 | "name": "stdout",
185 | "output_type": "stream",
186 | "text": [
187 | "['2020', '09', '01']\n"
188 | ]
189 | }
190 | ],
191 | "source": [
192 | "input4 = \"2020-09-01\"\n",
193 | "print(input4.split(\"-\"))"
194 | ]
195 | },
196 | {
197 | "cell_type": "markdown",
198 | "metadata": {},
199 | "source": [
200 | "Concatinating"
201 | ]
202 | },
203 | {
204 | "cell_type": "code",
205 | "execution_count": 33,
206 | "metadata": {},
207 | "outputs": [
208 | {
209 | "name": "stdout",
210 | "output_type": "stream",
211 | "text": [
212 | "Data 12\n"
213 | ]
214 | }
215 | ],
216 | "source": [
217 | "i1 = \"Data\"\n",
218 | "i2 = \"12\"\n",
219 | "i3 = i1+\" \"+i2\n",
220 | "print(i3)"
221 | ]
222 | },
223 | {
224 | "cell_type": "markdown",
225 | "metadata": {},
226 | "source": [
227 | "Replacing text"
228 | ]
229 | },
230 | {
231 | "cell_type": "code",
232 | "execution_count": 36,
233 | "metadata": {},
234 | "outputs": [
235 | {
236 | "name": "stdout",
237 | "output_type": "stream",
238 | "text": [
239 | "As data scientists, our job is to extract information from noise. information signal signal\n"
240 | ]
241 | }
242 | ],
243 | "source": [
244 | "txt1 = \"As data scientists, our job is to extract signal from noise. signal signal signal\"\n",
245 | "new_txt = txt1.replace(\"signal\", \"information\", 2)\n",
246 | "print(new_txt)"
247 | ]
248 | },
249 | {
250 | "cell_type": "markdown",
251 | "metadata": {},
252 | "source": [
253 | "To Try\n",
254 | "1. Define a number as a string\n",
255 | "2. Convert the above to numeric\n",
256 | "3. Create a variable with a decimal number\n",
257 | "4. Split the above decimal number into 2 (before decimal and after decimal point)\n",
258 | "5. Copy a random sentence and pass it to a variable\n",
259 | "6. Identify a specific words thats gets repeated and replace it with your name"
260 | ]
261 | }
262 | ],
263 | "metadata": {
264 | "kernelspec": {
265 | "display_name": "Python 3",
266 | "language": "python",
267 | "name": "python3"
268 | },
269 | "language_info": {
270 | "codemirror_mode": {
271 | "name": "ipython",
272 | "version": 3
273 | },
274 | "file_extension": ".py",
275 | "mimetype": "text/x-python",
276 | "name": "python",
277 | "nbconvert_exporter": "python",
278 | "pygments_lexer": "ipython3",
279 | "version": "3.8.5"
280 | }
281 | },
282 | "nbformat": 4,
283 | "nbformat_minor": 2
284 | }
285 |
--------------------------------------------------------------------------------
/Day03 - Numeric, Boolean and Operator/.ipynb_checkpoints/Day3-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics to be covered today\n",
8 | "1. Numeric data type (Integer and Float)\n",
9 | "2. Boolean\n",
10 | "3. Operators\n"
11 | ]
12 | },
13 | {
14 | "cell_type": "markdown",
15 | "metadata": {},
16 | "source": [
17 | "1a. Define variable and find data type"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 46,
23 | "metadata": {},
24 | "outputs": [
25 | {
26 | "name": "stdout",
27 | "output_type": "stream",
28 | "text": [
29 | "\n",
30 | "\n"
31 | ]
32 | }
33 | ],
34 | "source": [
35 | "input1 = 20\n",
36 | "print(type(input1))\n",
37 | "# Pass a decimal value\n",
38 | "input2 = 3.14\n",
39 | "print(type(input2))"
40 | ]
41 | },
42 | {
43 | "cell_type": "markdown",
44 | "metadata": {},
45 | "source": [
46 | "1b. Casting (Changing) to a different datatype"
47 | ]
48 | },
49 | {
50 | "cell_type": "code",
51 | "execution_count": 47,
52 | "metadata": {},
53 | "outputs": [
54 | {
55 | "name": "stdout",
56 | "output_type": "stream",
57 | "text": [
58 | "20.0\n"
59 | ]
60 | }
61 | ],
62 | "source": [
63 | "new_i1 = float(input1)\n",
64 | "print(new_i1)"
65 | ]
66 | },
67 | {
68 | "cell_type": "code",
69 | "execution_count": null,
70 | "metadata": {},
71 | "outputs": [],
72 | "source": [
73 | "type(input2)"
74 | ]
75 | },
76 | {
77 | "cell_type": "code",
78 | "execution_count": 48,
79 | "metadata": {},
80 | "outputs": [
81 | {
82 | "name": "stdout",
83 | "output_type": "stream",
84 | "text": [
85 | "3\n"
86 | ]
87 | }
88 | ],
89 | "source": [
90 | "new_i2 = int(input2)\n",
91 | "print(new_i2)"
92 | ]
93 | },
94 | {
95 | "cell_type": "code",
96 | "execution_count": 49,
97 | "metadata": {},
98 | "outputs": [
99 | {
100 | "name": "stdout",
101 | "output_type": "stream",
102 | "text": [
103 | "3\n"
104 | ]
105 | }
106 | ],
107 | "source": [
108 | "#What if it is 3.94\n",
109 | "input3 = 3.94\n",
110 | "new_i3 = int(input3)\n",
111 | "print(new_i3)"
112 | ]
113 | },
114 | {
115 | "cell_type": "markdown",
116 | "metadata": {},
117 | "source": [
118 | "2. Boolean Operator"
119 | ]
120 | },
121 | {
122 | "cell_type": "code",
123 | "execution_count": 51,
124 | "metadata": {},
125 | "outputs": [
126 | {
127 | "name": "stdout",
128 | "output_type": "stream",
129 | "text": [
130 | "False\n"
131 | ]
132 | }
133 | ],
134 | "source": [
135 | "#boolean\n",
136 | "print(input1 < input2)"
137 | ]
138 | },
139 | {
140 | "cell_type": "code",
141 | "execution_count": 52,
142 | "metadata": {},
143 | "outputs": [
144 | {
145 | "name": "stdout",
146 | "output_type": "stream",
147 | "text": [
148 | "True\n"
149 | ]
150 | }
151 | ],
152 | "source": [
153 | "print(bool(input1))"
154 | ]
155 | },
156 | {
157 | "cell_type": "code",
158 | "execution_count": 54,
159 | "metadata": {},
160 | "outputs": [
161 | {
162 | "name": "stdout",
163 | "output_type": "stream",
164 | "text": [
165 | "True\n"
166 | ]
167 | }
168 | ],
169 | "source": [
170 | "input4 = None\n",
171 | "print(bool(input4))"
172 | ]
173 | },
174 | {
175 | "cell_type": "code",
176 | "execution_count": 56,
177 | "metadata": {},
178 | "outputs": [
179 | {
180 | "name": "stdout",
181 | "output_type": "stream",
182 | "text": [
183 | "True\n",
184 | "False\n"
185 | ]
186 | }
187 | ],
188 | "source": [
189 | "input5 = [1,2,3,4,5]\n",
190 | "print(bool(input5))\n",
191 | "#now create an emply array\n",
192 | "input6 = []\n",
193 | "print(bool(input6))"
194 | ]
195 | },
196 | {
197 | "cell_type": "code",
198 | "execution_count": null,
199 | "metadata": {},
200 | "outputs": [],
201 | "source": [
202 | "a = None\n",
203 | "print(bool(a))\n",
204 | "# pass with 0, None and None1 as string"
205 | ]
206 | },
207 | {
208 | "cell_type": "markdown",
209 | "metadata": {},
210 | "source": [
211 | "3a. Arthimetic operator
\n",
212 | " To Try - Division, addition, subtraction, mod and exp"
213 | ]
214 | },
215 | {
216 | "cell_type": "code",
217 | "execution_count": 65,
218 | "metadata": {},
219 | "outputs": [
220 | {
221 | "name": "stdout",
222 | "output_type": "stream",
223 | "text": [
224 | "1\n"
225 | ]
226 | }
227 | ],
228 | "source": [
229 | "#Operators\n",
230 | "#Arithmetic Operators\n",
231 | "x = 11\n",
232 | "y = 2\n",
233 | "print(x%y)\n"
234 | ]
235 | },
236 | {
237 | "cell_type": "markdown",
238 | "metadata": {},
239 | "source": [
240 | "3b. Assignment Operator"
241 | ]
242 | },
243 | {
244 | "cell_type": "code",
245 | "execution_count": 69,
246 | "metadata": {},
247 | "outputs": [
248 | {
249 | "name": "stdout",
250 | "output_type": "stream",
251 | "text": [
252 | "1000\n"
253 | ]
254 | }
255 | ],
256 | "source": [
257 | "#Assignment Operators\n",
258 | "x = 10\n",
259 | "x **= 3\n",
260 | "#x=x+3\n",
261 | "print(x)"
262 | ]
263 | },
264 | {
265 | "cell_type": "markdown",
266 | "metadata": {},
267 | "source": [
268 | "3c. Comparison Operator"
269 | ]
270 | },
271 | {
272 | "cell_type": "code",
273 | "execution_count": 73,
274 | "metadata": {},
275 | "outputs": [
276 | {
277 | "name": "stdout",
278 | "output_type": "stream",
279 | "text": [
280 | "False\n"
281 | ]
282 | }
283 | ],
284 | "source": [
285 | "#Comparison Operators\n",
286 | "x = 11\n",
287 | "y = 2\n",
288 | "print(x<=y)\n",
289 | "# Equal, Not Equal, Greater than, less than, Greater or equal and less than equal"
290 | ]
291 | },
292 | {
293 | "cell_type": "markdown",
294 | "metadata": {},
295 | "source": [
296 | "3d. Logical Operator"
297 | ]
298 | },
299 | {
300 | "cell_type": "code",
301 | "execution_count": 76,
302 | "metadata": {},
303 | "outputs": [
304 | {
305 | "name": "stdout",
306 | "output_type": "stream",
307 | "text": [
308 | "True\n"
309 | ]
310 | }
311 | ],
312 | "source": [
313 | "#Logical Operators\n",
314 | "x = 5\n",
315 | "print(x > 10 or x < 15)\n",
316 | "# or and not"
317 | ]
318 | },
319 | {
320 | "cell_type": "markdown",
321 | "metadata": {},
322 | "source": [
323 | "3e. Is and In Operators"
324 | ]
325 | },
326 | {
327 | "cell_type": "code",
328 | "execution_count": 81,
329 | "metadata": {},
330 | "outputs": [
331 | {
332 | "name": "stdout",
333 | "output_type": "stream",
334 | "text": [
335 | "False\n",
336 | "False\n"
337 | ]
338 | }
339 | ],
340 | "source": [
341 | "# is and in operators\n",
342 | "x = ['addition', 'subtraction', 'multiplication', 'division']\n",
343 | "y = ['addition', 'subtraction', 'multiplication', 'division']\n",
344 | "z = y\n",
345 | "print(x is y)\n",
346 | "print(\"addition\" in x)\n",
347 | "#print(z)"
348 | ]
349 | },
350 | {
351 | "cell_type": "markdown",
352 | "metadata": {},
353 | "source": [
354 | "To Try\n",
355 | "1. Create a float variable and convert it to integer\n",
356 | "2. Multiple a float value with integer and check the data type of the resultant value\n",
357 | "3. Create 2 variable with a integer and float value and use Boolean to check if the first one is greater than the second variable\n"
358 | ]
359 | }
360 | ],
361 | "metadata": {
362 | "kernelspec": {
363 | "display_name": "Python 3",
364 | "language": "python",
365 | "name": "python3"
366 | },
367 | "language_info": {
368 | "codemirror_mode": {
369 | "name": "ipython",
370 | "version": 3
371 | },
372 | "file_extension": ".py",
373 | "mimetype": "text/x-python",
374 | "name": "python",
375 | "nbconvert_exporter": "python",
376 | "pygments_lexer": "ipython3",
377 | "version": "3.7.4"
378 | }
379 | },
380 | "nbformat": 4,
381 | "nbformat_minor": 2
382 | }
383 |
--------------------------------------------------------------------------------
/Day03 - Numeric, Boolean and Operator/Day3.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics to be covered today\n",
8 | "1. Numeric data type (Integer and Float)\n",
9 | "2. Boolean\n",
10 | "3. Operators\n"
11 | ]
12 | },
13 | {
14 | "cell_type": "markdown",
15 | "metadata": {},
16 | "source": [
17 | "1a. Define variable and find data type"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 46,
23 | "metadata": {},
24 | "outputs": [
25 | {
26 | "name": "stdout",
27 | "output_type": "stream",
28 | "text": [
29 | "\n",
30 | "\n"
31 | ]
32 | }
33 | ],
34 | "source": [
35 | "input1 = 20\n",
36 | "print(type(input1))\n",
37 | "# Pass a decimal value\n",
38 | "input2 = 3.14\n",
39 | "print(type(input2))"
40 | ]
41 | },
42 | {
43 | "cell_type": "markdown",
44 | "metadata": {},
45 | "source": [
46 | "1b. Casting (Changing) to a different datatype"
47 | ]
48 | },
49 | {
50 | "cell_type": "code",
51 | "execution_count": 47,
52 | "metadata": {},
53 | "outputs": [
54 | {
55 | "name": "stdout",
56 | "output_type": "stream",
57 | "text": [
58 | "20.0\n"
59 | ]
60 | }
61 | ],
62 | "source": [
63 | "new_i1 = float(input1)\n",
64 | "print(new_i1)"
65 | ]
66 | },
67 | {
68 | "cell_type": "code",
69 | "execution_count": null,
70 | "metadata": {},
71 | "outputs": [],
72 | "source": [
73 | "type(input2)"
74 | ]
75 | },
76 | {
77 | "cell_type": "code",
78 | "execution_count": 48,
79 | "metadata": {},
80 | "outputs": [
81 | {
82 | "name": "stdout",
83 | "output_type": "stream",
84 | "text": [
85 | "3\n"
86 | ]
87 | }
88 | ],
89 | "source": [
90 | "new_i2 = int(input2)\n",
91 | "print(new_i2)"
92 | ]
93 | },
94 | {
95 | "cell_type": "code",
96 | "execution_count": 49,
97 | "metadata": {},
98 | "outputs": [
99 | {
100 | "name": "stdout",
101 | "output_type": "stream",
102 | "text": [
103 | "3\n"
104 | ]
105 | }
106 | ],
107 | "source": [
108 | "#What if it is 3.94\n",
109 | "input3 = 3.94\n",
110 | "new_i3 = int(input3)\n",
111 | "print(new_i3)"
112 | ]
113 | },
114 | {
115 | "cell_type": "markdown",
116 | "metadata": {},
117 | "source": [
118 | "2. Boolean Operator"
119 | ]
120 | },
121 | {
122 | "cell_type": "code",
123 | "execution_count": 51,
124 | "metadata": {},
125 | "outputs": [
126 | {
127 | "name": "stdout",
128 | "output_type": "stream",
129 | "text": [
130 | "False\n"
131 | ]
132 | }
133 | ],
134 | "source": [
135 | "#boolean\n",
136 | "print(input1 < input2)"
137 | ]
138 | },
139 | {
140 | "cell_type": "code",
141 | "execution_count": 52,
142 | "metadata": {},
143 | "outputs": [
144 | {
145 | "name": "stdout",
146 | "output_type": "stream",
147 | "text": [
148 | "True\n"
149 | ]
150 | }
151 | ],
152 | "source": [
153 | "print(bool(input1))"
154 | ]
155 | },
156 | {
157 | "cell_type": "code",
158 | "execution_count": 54,
159 | "metadata": {},
160 | "outputs": [
161 | {
162 | "name": "stdout",
163 | "output_type": "stream",
164 | "text": [
165 | "True\n"
166 | ]
167 | }
168 | ],
169 | "source": [
170 | "input4 = None\n",
171 | "print(bool(input4))"
172 | ]
173 | },
174 | {
175 | "cell_type": "code",
176 | "execution_count": 56,
177 | "metadata": {},
178 | "outputs": [
179 | {
180 | "name": "stdout",
181 | "output_type": "stream",
182 | "text": [
183 | "True\n",
184 | "False\n"
185 | ]
186 | }
187 | ],
188 | "source": [
189 | "input5 = [1,2,3,4,5]\n",
190 | "print(bool(input5))\n",
191 | "#now create an emply array\n",
192 | "input6 = []\n",
193 | "print(bool(input6))"
194 | ]
195 | },
196 | {
197 | "cell_type": "code",
198 | "execution_count": null,
199 | "metadata": {},
200 | "outputs": [],
201 | "source": [
202 | "a = None\n",
203 | "print(bool(a))\n",
204 | "# pass with 0, None and None1 as string"
205 | ]
206 | },
207 | {
208 | "cell_type": "markdown",
209 | "metadata": {},
210 | "source": [
211 | "3a. Arthimetic operator
\n",
212 | " To Try - Division, addition, subtraction, mod and exp"
213 | ]
214 | },
215 | {
216 | "cell_type": "code",
217 | "execution_count": 65,
218 | "metadata": {},
219 | "outputs": [
220 | {
221 | "name": "stdout",
222 | "output_type": "stream",
223 | "text": [
224 | "1\n"
225 | ]
226 | }
227 | ],
228 | "source": [
229 | "#Operators\n",
230 | "#Arithmetic Operators\n",
231 | "x = 11\n",
232 | "y = 2\n",
233 | "print(x%y)\n"
234 | ]
235 | },
236 | {
237 | "cell_type": "markdown",
238 | "metadata": {},
239 | "source": [
240 | "3b. Assignment Operator"
241 | ]
242 | },
243 | {
244 | "cell_type": "code",
245 | "execution_count": 69,
246 | "metadata": {},
247 | "outputs": [
248 | {
249 | "name": "stdout",
250 | "output_type": "stream",
251 | "text": [
252 | "1000\n"
253 | ]
254 | }
255 | ],
256 | "source": [
257 | "#Assignment Operators\n",
258 | "x = 10\n",
259 | "x **= 3\n",
260 | "#x=x+3\n",
261 | "print(x)"
262 | ]
263 | },
264 | {
265 | "cell_type": "markdown",
266 | "metadata": {},
267 | "source": [
268 | "3c. Comparison Operator"
269 | ]
270 | },
271 | {
272 | "cell_type": "code",
273 | "execution_count": 73,
274 | "metadata": {},
275 | "outputs": [
276 | {
277 | "name": "stdout",
278 | "output_type": "stream",
279 | "text": [
280 | "False\n"
281 | ]
282 | }
283 | ],
284 | "source": [
285 | "#Comparison Operators\n",
286 | "x = 11\n",
287 | "y = 2\n",
288 | "print(x<=y)\n",
289 | "# Equal, Not Equal, Greater than, less than, Greater or equal and less than equal"
290 | ]
291 | },
292 | {
293 | "cell_type": "markdown",
294 | "metadata": {},
295 | "source": [
296 | "3d. Logical Operator"
297 | ]
298 | },
299 | {
300 | "cell_type": "code",
301 | "execution_count": 76,
302 | "metadata": {},
303 | "outputs": [
304 | {
305 | "name": "stdout",
306 | "output_type": "stream",
307 | "text": [
308 | "True\n"
309 | ]
310 | }
311 | ],
312 | "source": [
313 | "#Logical Operators\n",
314 | "x = 5\n",
315 | "print(x > 10 or x < 15)\n",
316 | "# or and not"
317 | ]
318 | },
319 | {
320 | "cell_type": "markdown",
321 | "metadata": {},
322 | "source": [
323 | "3e. Is and In Operators"
324 | ]
325 | },
326 | {
327 | "cell_type": "code",
328 | "execution_count": 81,
329 | "metadata": {},
330 | "outputs": [
331 | {
332 | "name": "stdout",
333 | "output_type": "stream",
334 | "text": [
335 | "False\n",
336 | "False\n"
337 | ]
338 | }
339 | ],
340 | "source": [
341 | "# is and in operators\n",
342 | "x = ['addition', 'subtraction', 'multiplication', 'division']\n",
343 | "y = ['addition', 'subtraction', 'multiplication', 'division']\n",
344 | "z = y\n",
345 | "print(x is y)\n",
346 | "print(\"addition\" in x)\n",
347 | "#print(z)"
348 | ]
349 | },
350 | {
351 | "cell_type": "markdown",
352 | "metadata": {},
353 | "source": [
354 | "To Try\n",
355 | "1. Create a float variable and convert it to integer\n",
356 | "2. Multiple a float value with integer and check the data type of the resultant value\n",
357 | "3. Create 2 variable with a integer and float value and use Boolean to check if the first one is greater than the second variable\n"
358 | ]
359 | }
360 | ],
361 | "metadata": {
362 | "kernelspec": {
363 | "display_name": "Python 3",
364 | "language": "python",
365 | "name": "python3"
366 | },
367 | "language_info": {
368 | "codemirror_mode": {
369 | "name": "ipython",
370 | "version": 3
371 | },
372 | "file_extension": ".py",
373 | "mimetype": "text/x-python",
374 | "name": "python",
375 | "nbconvert_exporter": "python",
376 | "pygments_lexer": "ipython3",
377 | "version": "3.6.9"
378 | }
379 | },
380 | "nbformat": 4,
381 | "nbformat_minor": 2
382 | }
383 |
--------------------------------------------------------------------------------
/Day06 - If-Then-Else and Loop/.ipynb_checkpoints/Day6-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics covered today are\n",
8 | "1. If Condition\n",
9 | "2. For Loop\n",
10 | "3. While Loop"
11 | ]
12 | },
13 | {
14 | "cell_type": "markdown",
15 | "metadata": {},
16 | "source": [
17 | "1. If Condition"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 14,
23 | "metadata": {},
24 | "outputs": [
25 | {
26 | "name": "stdout",
27 | "output_type": "stream",
28 | "text": [
29 | "a is greater than b\n"
30 | ]
31 | }
32 | ],
33 | "source": [
34 | "a = 101\n",
35 | "b = 100\n",
36 | "\n",
37 | "if a>b: \n",
38 | " print(\"a is greater than b\")\n",
39 | "elif a==b:\n",
40 | " print(\"a is equal to b\")\n",
41 | "else:\n",
42 | " print(\"a is less than b\")\n",
43 | " "
44 | ]
45 | },
46 | {
47 | "cell_type": "markdown",
48 | "metadata": {},
49 | "source": [
50 | "2. For Loop"
51 | ]
52 | },
53 | {
54 | "cell_type": "code",
55 | "execution_count": 18,
56 | "metadata": {},
57 | "outputs": [
58 | {
59 | "name": "stdout",
60 | "output_type": "stream",
61 | "text": [
62 | "[0, 1, 2, 3, 4]\n",
63 | "Apple\n",
64 | "Google\n",
65 | "Microsoft\n",
66 | "Amazon\n",
67 | "Tesla\n"
68 | ]
69 | }
70 | ],
71 | "source": [
72 | "a = ['Apple', 'Google', 'Microsoft', 'Amazon', 'Tesla']\n",
73 | "\n",
74 | "print(list(range(len(a))))\n",
75 | "for i in range(len(a)):\n",
76 | " print(a[i])"
77 | ]
78 | },
79 | {
80 | "cell_type": "code",
81 | "execution_count": 19,
82 | "metadata": {},
83 | "outputs": [
84 | {
85 | "name": "stdout",
86 | "output_type": "stream",
87 | "text": [
88 | "11\n",
89 | "12\n",
90 | "13\n",
91 | "14\n",
92 | "15\n",
93 | "16\n",
94 | "17\n",
95 | "18\n",
96 | "19\n",
97 | "20\n",
98 | "21\n",
99 | "22\n",
100 | "23\n",
101 | "24\n"
102 | ]
103 | }
104 | ],
105 | "source": [
106 | "a = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
107 | "for i in a:\n",
108 | " print(i)"
109 | ]
110 | },
111 | {
112 | "cell_type": "code",
113 | "execution_count": 21,
114 | "metadata": {},
115 | "outputs": [
116 | {
117 | "name": "stdout",
118 | "output_type": "stream",
119 | "text": [
120 | "11\n",
121 | "13\n",
122 | "15\n",
123 | "17\n",
124 | "19\n",
125 | "21\n",
126 | "23\n"
127 | ]
128 | }
129 | ],
130 | "source": [
131 | "a = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
132 | "#print(len(a))\n",
133 | "for i in range(0, len(a), 2): # starts from index 0 and till len(a) minus 1 and skips every alternate number\n",
134 | " print(a[i])"
135 | ]
136 | },
137 | {
138 | "cell_type": "code",
139 | "execution_count": 22,
140 | "metadata": {},
141 | "outputs": [
142 | {
143 | "name": "stdout",
144 | "output_type": "stream",
145 | "text": [
146 | "21\n",
147 | "22\n",
148 | "23\n",
149 | "24\n",
150 | "25\n"
151 | ]
152 | }
153 | ],
154 | "source": [
155 | "a = [11, 12, 13, 14, 15]\n",
156 | "for i in range(0, len(a)):\n",
157 | " x = a[i]\n",
158 | " x += 10\n",
159 | " print(x)"
160 | ]
161 | },
162 | {
163 | "cell_type": "markdown",
164 | "metadata": {},
165 | "source": [
166 | "3. While Loop"
167 | ]
168 | },
169 | {
170 | "cell_type": "code",
171 | "execution_count": 25,
172 | "metadata": {},
173 | "outputs": [
174 | {
175 | "name": "stdout",
176 | "output_type": "stream",
177 | "text": [
178 | "245\n"
179 | ]
180 | }
181 | ],
182 | "source": [
183 | "a = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
184 | "i= 0\n",
185 | "total = 0\n",
186 | "while i < len(a):\n",
187 | " total = total + a[i]\n",
188 | " i+=1\n",
189 | "print(total)"
190 | ]
191 | },
192 | {
193 | "cell_type": "markdown",
194 | "metadata": {},
195 | "source": [
196 | "#### Examples to try out\n",
197 | "1. Create a list with few random values and use a for loop to check if all the items of the list are integer\n",
198 | "2. Create a list with integer values and use loop (try both for and while loop) to go through the items in the list and print if they are odd or even number\n",
199 | "3. Create a numberic list and find the multiple of the all the elements in the list"
200 | ]
201 | },
202 | {
203 | "cell_type": "code",
204 | "execution_count": null,
205 | "metadata": {},
206 | "outputs": [],
207 | "source": []
208 | }
209 | ],
210 | "metadata": {
211 | "kernelspec": {
212 | "display_name": "Python 3",
213 | "language": "python",
214 | "name": "python3"
215 | },
216 | "language_info": {
217 | "codemirror_mode": {
218 | "name": "ipython",
219 | "version": 3
220 | },
221 | "file_extension": ".py",
222 | "mimetype": "text/x-python",
223 | "name": "python",
224 | "nbconvert_exporter": "python",
225 | "pygments_lexer": "ipython3",
226 | "version": "3.8.5"
227 | }
228 | },
229 | "nbformat": 4,
230 | "nbformat_minor": 2
231 | }
232 |
--------------------------------------------------------------------------------
/Day06 - If-Then-Else and Loop/Day6.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics covered today are\n",
8 | "1. If Condition\n",
9 | "2. For Loop\n",
10 | "3. While Loop"
11 | ]
12 | },
13 | {
14 | "cell_type": "markdown",
15 | "metadata": {},
16 | "source": [
17 | "1. If Condition"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 14,
23 | "metadata": {},
24 | "outputs": [
25 | {
26 | "name": "stdout",
27 | "output_type": "stream",
28 | "text": [
29 | "a is greater than b\n"
30 | ]
31 | }
32 | ],
33 | "source": [
34 | "a = 101\n",
35 | "b = 100\n",
36 | "\n",
37 | "if a>b: \n",
38 | " print(\"a is greater than b\")\n",
39 | "elif a==b:\n",
40 | " print(\"a is equal to b\")\n",
41 | "else:\n",
42 | " print(\"a is less than b\")\n",
43 | " "
44 | ]
45 | },
46 | {
47 | "cell_type": "markdown",
48 | "metadata": {},
49 | "source": [
50 | "2. For Loop"
51 | ]
52 | },
53 | {
54 | "cell_type": "code",
55 | "execution_count": 18,
56 | "metadata": {},
57 | "outputs": [
58 | {
59 | "name": "stdout",
60 | "output_type": "stream",
61 | "text": [
62 | "[0, 1, 2, 3, 4]\n",
63 | "Apple\n",
64 | "Google\n",
65 | "Microsoft\n",
66 | "Amazon\n",
67 | "Tesla\n"
68 | ]
69 | }
70 | ],
71 | "source": [
72 | "a = ['Apple', 'Google', 'Microsoft', 'Amazon', 'Tesla']\n",
73 | "\n",
74 | "print(list(range(len(a))))\n",
75 | "for i in range(len(a)):\n",
76 | " print(a[i])"
77 | ]
78 | },
79 | {
80 | "cell_type": "code",
81 | "execution_count": 19,
82 | "metadata": {},
83 | "outputs": [
84 | {
85 | "name": "stdout",
86 | "output_type": "stream",
87 | "text": [
88 | "11\n",
89 | "12\n",
90 | "13\n",
91 | "14\n",
92 | "15\n",
93 | "16\n",
94 | "17\n",
95 | "18\n",
96 | "19\n",
97 | "20\n",
98 | "21\n",
99 | "22\n",
100 | "23\n",
101 | "24\n"
102 | ]
103 | }
104 | ],
105 | "source": [
106 | "a = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
107 | "for i in a:\n",
108 | " print(i)"
109 | ]
110 | },
111 | {
112 | "cell_type": "code",
113 | "execution_count": 21,
114 | "metadata": {},
115 | "outputs": [
116 | {
117 | "name": "stdout",
118 | "output_type": "stream",
119 | "text": [
120 | "11\n",
121 | "13\n",
122 | "15\n",
123 | "17\n",
124 | "19\n",
125 | "21\n",
126 | "23\n"
127 | ]
128 | }
129 | ],
130 | "source": [
131 | "a = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
132 | "#print(len(a))\n",
133 | "for i in range(0, len(a), 2): # starts from index 0 and till len(a) minus 1 and skips every alternate number\n",
134 | " print(a[i])"
135 | ]
136 | },
137 | {
138 | "cell_type": "code",
139 | "execution_count": 22,
140 | "metadata": {},
141 | "outputs": [
142 | {
143 | "name": "stdout",
144 | "output_type": "stream",
145 | "text": [
146 | "21\n",
147 | "22\n",
148 | "23\n",
149 | "24\n",
150 | "25\n"
151 | ]
152 | }
153 | ],
154 | "source": [
155 | "a = [11, 12, 13, 14, 15]\n",
156 | "for i in range(0, len(a)):\n",
157 | " x = a[i]\n",
158 | " x += 10\n",
159 | " print(x)"
160 | ]
161 | },
162 | {
163 | "cell_type": "markdown",
164 | "metadata": {},
165 | "source": [
166 | "3. While Loop"
167 | ]
168 | },
169 | {
170 | "cell_type": "code",
171 | "execution_count": 25,
172 | "metadata": {},
173 | "outputs": [
174 | {
175 | "name": "stdout",
176 | "output_type": "stream",
177 | "text": [
178 | "245\n"
179 | ]
180 | }
181 | ],
182 | "source": [
183 | "a = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
184 | "i= 0\n",
185 | "total = 0\n",
186 | "while i < len(a):\n",
187 | " total = total + a[i]\n",
188 | " i+=1\n",
189 | "print(total)"
190 | ]
191 | },
192 | {
193 | "cell_type": "markdown",
194 | "metadata": {},
195 | "source": [
196 | "#### Examples to try out\n",
197 | "1. Create a list with few random values and use a for loop to check if all the items of the list are integer\n",
198 | "2. Create a list with integer values and use loop (try both for and while loop) to go through the items in the list and print if they are odd or even number\n",
199 | "3. Create a numberic list and find the multiple of the all the elements in the list"
200 | ]
201 | },
202 | {
203 | "cell_type": "code",
204 | "execution_count": null,
205 | "metadata": {},
206 | "outputs": [],
207 | "source": []
208 | }
209 | ],
210 | "metadata": {
211 | "kernelspec": {
212 | "display_name": "Python 3",
213 | "language": "python",
214 | "name": "python3"
215 | },
216 | "language_info": {
217 | "codemirror_mode": {
218 | "name": "ipython",
219 | "version": 3
220 | },
221 | "file_extension": ".py",
222 | "mimetype": "text/x-python",
223 | "name": "python",
224 | "nbconvert_exporter": "python",
225 | "pygments_lexer": "ipython3",
226 | "version": "3.8.5"
227 | }
228 | },
229 | "nbformat": 4,
230 | "nbformat_minor": 2
231 | }
232 |
--------------------------------------------------------------------------------
/Day07 - Functions and Lambda Functions/.ipynb_checkpoints/Day7-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics to be covered today\n",
8 | "1. Functions\n",
9 | "2. Lambda Function"
10 | ]
11 | },
12 | {
13 | "cell_type": "markdown",
14 | "metadata": {},
15 | "source": [
16 | "1a. Function Introduction\n",
17 | "- Can be called multiple times"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 15,
23 | "metadata": {},
24 | "outputs": [
25 | {
26 | "name": "stdout",
27 | "output_type": "stream",
28 | "text": [
29 | "This is outside function\n"
30 | ]
31 | }
32 | ],
33 | "source": [
34 | "def func():\n",
35 | " print(\"Hi\")\n",
36 | " print(\"Bye\") # Both should be aligned\n",
37 | "print(\"This is outside function\")"
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "execution_count": 16,
43 | "metadata": {},
44 | "outputs": [
45 | {
46 | "name": "stdout",
47 | "output_type": "stream",
48 | "text": [
49 | "Hi\n",
50 | "Bye\n"
51 | ]
52 | }
53 | ],
54 | "source": [
55 | "func()"
56 | ]
57 | },
58 | {
59 | "cell_type": "markdown",
60 | "metadata": {},
61 | "source": [
62 | "1b. Function to perform arithmetic function"
63 | ]
64 | },
65 | {
66 | "cell_type": "code",
67 | "execution_count": 20,
68 | "metadata": {},
69 | "outputs": [],
70 | "source": [
71 | "def add_func(x):\n",
72 | " return x+20\n",
73 | "\n",
74 | "# Try square and multiplication"
75 | ]
76 | },
77 | {
78 | "cell_type": "code",
79 | "execution_count": 21,
80 | "metadata": {},
81 | "outputs": [
82 | {
83 | "name": "stdout",
84 | "output_type": "stream",
85 | "text": [
86 | "25\n",
87 | "30\n"
88 | ]
89 | }
90 | ],
91 | "source": [
92 | "print(add_func(5))\n",
93 | "print(add_func(10))"
94 | ]
95 | },
96 | {
97 | "cell_type": "code",
98 | "execution_count": 23,
99 | "metadata": {},
100 | "outputs": [
101 | {
102 | "ename": "TypeError",
103 | "evalue": "add_func() missing 1 required positional argument: 'x'",
104 | "output_type": "error",
105 | "traceback": [
106 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
107 | "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
108 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#print(add_func(\"10\"))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madd_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
109 | "\u001b[1;31mTypeError\u001b[0m: add_func() missing 1 required positional argument: 'x'"
110 | ]
111 | }
112 | ],
113 | "source": [
114 | "#print(add_func(\"10\"))\n",
115 | "#print(add_func())"
116 | ]
117 | },
118 | {
119 | "cell_type": "markdown",
120 | "metadata": {},
121 | "source": [
122 | "1c. Multiply elements in a list"
123 | ]
124 | },
125 | {
126 | "cell_type": "code",
127 | "execution_count": null,
128 | "metadata": {},
129 | "outputs": [],
130 | "source": [
131 | "def multi_func(*values):\n",
132 | " n = 1\n",
133 | " for item in values:\n",
134 | " n *= item\n",
135 | " return n"
136 | ]
137 | },
138 | {
139 | "cell_type": "code",
140 | "execution_count": 24,
141 | "metadata": {},
142 | "outputs": [
143 | {
144 | "data": {
145 | "text/plain": [
146 | "120"
147 | ]
148 | },
149 | "execution_count": 24,
150 | "metadata": {},
151 | "output_type": "execute_result"
152 | }
153 | ],
154 | "source": [
155 | "multi_func(2,3,4,5)"
156 | ]
157 | },
158 | {
159 | "cell_type": "markdown",
160 | "metadata": {},
161 | "source": [
162 | "2a. Lambda Function Introduction\n",
163 | "\n",
164 | "Lambda Functions:\n",
165 | "- Single use functions\n",
166 | "- One line function\n",
167 | "- Syntax: lambda argument:expression (where expression is the value that gets returned)"
168 | ]
169 | },
170 | {
171 | "cell_type": "code",
172 | "execution_count": 26,
173 | "metadata": {},
174 | "outputs": [
175 | {
176 | "name": "stdout",
177 | "output_type": "stream",
178 | "text": [
179 | "55\n"
180 | ]
181 | }
182 | ],
183 | "source": [
184 | "lb_fn = lambda x: x + 5\n",
185 | "print(lb_fn(50))"
186 | ]
187 | },
188 | {
189 | "cell_type": "code",
190 | "execution_count": 27,
191 | "metadata": {},
192 | "outputs": [
193 | {
194 | "name": "stdout",
195 | "output_type": "stream",
196 | "text": [
197 | "25\n"
198 | ]
199 | }
200 | ],
201 | "source": [
202 | "lb_fn = lambda x: x*5\n",
203 | "print(lb_fn(5))"
204 | ]
205 | },
206 | {
207 | "cell_type": "markdown",
208 | "metadata": {},
209 | "source": [
210 | "2b. Passing Lambda as an argument"
211 | ]
212 | },
213 | {
214 | "cell_type": "code",
215 | "execution_count": 29,
216 | "metadata": {},
217 | "outputs": [
218 | {
219 | "name": "stdout",
220 | "output_type": "stream",
221 | "text": [
222 | "[('Apple', 1111), ('Google', 2222), ('Microsoft', 3333), ('Amazon', 4444), ('Tesla', 5555)]\n"
223 | ]
224 | }
225 | ],
226 | "source": [
227 | "comp_list = [('Apple', 1111), ('Google', 2222), ('Microsoft', 3333), ('Amazon', 4444), ('Tesla', 5555)]\n",
228 | "comp_list.sort(key = lambda x: x[1])\n",
229 | "print(comp_list)"
230 | ]
231 | },
232 | {
233 | "cell_type": "markdown",
234 | "metadata": {},
235 | "source": [
236 | "2c. Using Lamda for filtering"
237 | ]
238 | },
239 | {
240 | "cell_type": "code",
241 | "execution_count": 31,
242 | "metadata": {},
243 | "outputs": [
244 | {
245 | "name": "stdout",
246 | "output_type": "stream",
247 | "text": [
248 | "[3, 6, 9]\n"
249 | ]
250 | }
251 | ],
252 | "source": [
253 | "numbers = [1,2,3,4,5,6,7,8,9,10]\n",
254 | "even_num = list(filter(lambda x: x%3 == 0, numbers))\n",
255 | "print(even_num)"
256 | ]
257 | },
258 | {
259 | "cell_type": "markdown",
260 | "metadata": {},
261 | "source": [
262 | "#### Example to try\n",
263 | "1. Create a function to add two number (Hint - Pass 2 values to the function)\n",
264 | "2. Create an list with number from 1 to 100 and use lambda function to filter all the elements that are divible by 3\n",
265 | "3. Create a List of Dictionary and use lambda function to sort them"
266 | ]
267 | },
268 | {
269 | "cell_type": "code",
270 | "execution_count": null,
271 | "metadata": {},
272 | "outputs": [],
273 | "source": []
274 | }
275 | ],
276 | "metadata": {
277 | "kernelspec": {
278 | "display_name": "Python 3",
279 | "language": "python",
280 | "name": "python3"
281 | },
282 | "language_info": {
283 | "codemirror_mode": {
284 | "name": "ipython",
285 | "version": 3
286 | },
287 | "file_extension": ".py",
288 | "mimetype": "text/x-python",
289 | "name": "python",
290 | "nbconvert_exporter": "python",
291 | "pygments_lexer": "ipython3",
292 | "version": "3.8.5"
293 | }
294 | },
295 | "nbformat": 4,
296 | "nbformat_minor": 2
297 | }
298 |
--------------------------------------------------------------------------------
/Day07 - Functions and Lambda Functions/Day7.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics to be covered today\n",
8 | "1. Functions\n",
9 | "2. Lambda Function"
10 | ]
11 | },
12 | {
13 | "cell_type": "markdown",
14 | "metadata": {},
15 | "source": [
16 | "1a. Function Introduction\n",
17 | "- Can be called multiple times"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 15,
23 | "metadata": {},
24 | "outputs": [
25 | {
26 | "name": "stdout",
27 | "output_type": "stream",
28 | "text": [
29 | "This is outside function\n"
30 | ]
31 | }
32 | ],
33 | "source": [
34 | "def func():\n",
35 | " print(\"Hi\")\n",
36 | " print(\"Bye\") # Both should be aligned\n",
37 | "print(\"This is outside function\")"
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "execution_count": 16,
43 | "metadata": {},
44 | "outputs": [
45 | {
46 | "name": "stdout",
47 | "output_type": "stream",
48 | "text": [
49 | "Hi\n",
50 | "Bye\n"
51 | ]
52 | }
53 | ],
54 | "source": [
55 | "func()"
56 | ]
57 | },
58 | {
59 | "cell_type": "markdown",
60 | "metadata": {},
61 | "source": [
62 | "1b. Function to perform arithmetic function"
63 | ]
64 | },
65 | {
66 | "cell_type": "code",
67 | "execution_count": 20,
68 | "metadata": {},
69 | "outputs": [],
70 | "source": [
71 | "def add_func(x):\n",
72 | " return x+20\n",
73 | "\n",
74 | "# Try square and multiplication"
75 | ]
76 | },
77 | {
78 | "cell_type": "code",
79 | "execution_count": 21,
80 | "metadata": {},
81 | "outputs": [
82 | {
83 | "name": "stdout",
84 | "output_type": "stream",
85 | "text": [
86 | "25\n",
87 | "30\n"
88 | ]
89 | }
90 | ],
91 | "source": [
92 | "print(add_func(5))\n",
93 | "print(add_func(10))"
94 | ]
95 | },
96 | {
97 | "cell_type": "code",
98 | "execution_count": 23,
99 | "metadata": {},
100 | "outputs": [
101 | {
102 | "ename": "TypeError",
103 | "evalue": "add_func() missing 1 required positional argument: 'x'",
104 | "output_type": "error",
105 | "traceback": [
106 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
107 | "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
108 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#print(add_func(\"10\"))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madd_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
109 | "\u001b[1;31mTypeError\u001b[0m: add_func() missing 1 required positional argument: 'x'"
110 | ]
111 | }
112 | ],
113 | "source": [
114 | "#print(add_func(\"10\"))\n",
115 | "#print(add_func())"
116 | ]
117 | },
118 | {
119 | "cell_type": "markdown",
120 | "metadata": {},
121 | "source": [
122 | "1c. Multiply elements in a list"
123 | ]
124 | },
125 | {
126 | "cell_type": "code",
127 | "execution_count": null,
128 | "metadata": {},
129 | "outputs": [],
130 | "source": [
131 | "def multi_func(*values):\n",
132 | " n = 1\n",
133 | " for item in values:\n",
134 | " n *= item\n",
135 | " return n"
136 | ]
137 | },
138 | {
139 | "cell_type": "code",
140 | "execution_count": 24,
141 | "metadata": {},
142 | "outputs": [
143 | {
144 | "data": {
145 | "text/plain": [
146 | "120"
147 | ]
148 | },
149 | "execution_count": 24,
150 | "metadata": {},
151 | "output_type": "execute_result"
152 | }
153 | ],
154 | "source": [
155 | "multi_func(2,3,4,5)"
156 | ]
157 | },
158 | {
159 | "cell_type": "markdown",
160 | "metadata": {},
161 | "source": [
162 | "2a. Lambda Function Introduction\n",
163 | "\n",
164 | "Lambda Functions:\n",
165 | "- Single use functions\n",
166 | "- One line function\n",
167 | "- Syntax: lambda argument:expression (where expression is the value that gets returned)"
168 | ]
169 | },
170 | {
171 | "cell_type": "code",
172 | "execution_count": 26,
173 | "metadata": {},
174 | "outputs": [
175 | {
176 | "name": "stdout",
177 | "output_type": "stream",
178 | "text": [
179 | "55\n"
180 | ]
181 | }
182 | ],
183 | "source": [
184 | "lb_fn = lambda x: x + 5\n",
185 | "print(lb_fn(50))"
186 | ]
187 | },
188 | {
189 | "cell_type": "code",
190 | "execution_count": 27,
191 | "metadata": {},
192 | "outputs": [
193 | {
194 | "name": "stdout",
195 | "output_type": "stream",
196 | "text": [
197 | "25\n"
198 | ]
199 | }
200 | ],
201 | "source": [
202 | "lb_fn = lambda x: x*5\n",
203 | "print(lb_fn(5))"
204 | ]
205 | },
206 | {
207 | "cell_type": "markdown",
208 | "metadata": {},
209 | "source": [
210 | "2b. Passing Lambda as an argument"
211 | ]
212 | },
213 | {
214 | "cell_type": "code",
215 | "execution_count": 29,
216 | "metadata": {},
217 | "outputs": [
218 | {
219 | "name": "stdout",
220 | "output_type": "stream",
221 | "text": [
222 | "[('Apple', 1111), ('Google', 2222), ('Microsoft', 3333), ('Amazon', 4444), ('Tesla', 5555)]\n"
223 | ]
224 | }
225 | ],
226 | "source": [
227 | "comp_list = [('Apple', 1111), ('Google', 2222), ('Microsoft', 3333), ('Amazon', 4444), ('Tesla', 5555)]\n",
228 | "comp_list.sort(key = lambda x: x[1])\n",
229 | "print(comp_list)"
230 | ]
231 | },
232 | {
233 | "cell_type": "markdown",
234 | "metadata": {},
235 | "source": [
236 | "2c. Using Lamda for filtering"
237 | ]
238 | },
239 | {
240 | "cell_type": "code",
241 | "execution_count": 31,
242 | "metadata": {},
243 | "outputs": [
244 | {
245 | "name": "stdout",
246 | "output_type": "stream",
247 | "text": [
248 | "[3, 6, 9]\n"
249 | ]
250 | }
251 | ],
252 | "source": [
253 | "numbers = [1,2,3,4,5,6,7,8,9,10]\n",
254 | "even_num = list(filter(lambda x: x%3 == 0, numbers))\n",
255 | "print(even_num)"
256 | ]
257 | },
258 | {
259 | "cell_type": "markdown",
260 | "metadata": {},
261 | "source": [
262 | "#### Example to try\n",
263 | "1. Create a function to add two number (Hint - Pass 2 values to the function)\n",
264 | "2. Create an list with number from 1 to 100 and use lambda function to filter all the elements that are divible by 3\n",
265 | "3. Create a List of Dictionary and use lambda function to sort them"
266 | ]
267 | },
268 | {
269 | "cell_type": "code",
270 | "execution_count": null,
271 | "metadata": {},
272 | "outputs": [],
273 | "source": []
274 | }
275 | ],
276 | "metadata": {
277 | "kernelspec": {
278 | "display_name": "Python 3",
279 | "language": "python",
280 | "name": "python3"
281 | },
282 | "language_info": {
283 | "codemirror_mode": {
284 | "name": "ipython",
285 | "version": 3
286 | },
287 | "file_extension": ".py",
288 | "mimetype": "text/x-python",
289 | "name": "python",
290 | "nbconvert_exporter": "python",
291 | "pygments_lexer": "ipython3",
292 | "version": "3.8.5"
293 | }
294 | },
295 | "nbformat": 4,
296 | "nbformat_minor": 2
297 | }
298 |
--------------------------------------------------------------------------------
/Day08 - Pandas Intro, Read and Write Dataframe/Data/Sample1.csv:
--------------------------------------------------------------------------------
1 | Companies,Seq
2 | Apple,1111
3 | Google,2222
4 | Facebook,3333
5 | Amazon,4444
6 |
--------------------------------------------------------------------------------
/Day08 - Pandas Intro, Read and Write Dataframe/Data/Sample1.txt:
--------------------------------------------------------------------------------
1 | Companies Seq
2 | Apple 1111
3 | Google 2222
4 | Facebook 3333
5 | Amazon 4444
6 |
--------------------------------------------------------------------------------
/Day08 - Pandas Intro, Read and Write Dataframe/Data/Write_to_file.csv:
--------------------------------------------------------------------------------
1 | Company,Seq
2 | Apple,111
3 | Facebook,222
4 | Google,333
5 | Amazon,444
6 |
--------------------------------------------------------------------------------
/Day09 - Index, Selection and Assignment/Data/SearchTrendData.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,12,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,35,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,5
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,13,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,30,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,7
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,12,7
31 | 10/04/2016,29,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,10,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,6
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,28,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,10
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,10,15,12,7
69 | 1/01/2017,21,13,20,14,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,27,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,11
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,29,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,20,16,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,12
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,18,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,21,37,25,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,18,19,30,20,13
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,21,36,24,15
137 | 22/04/2018,33,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,15
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,36,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,19,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,23,15
163 | 21/10/2018,42,26,40,24,15
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,38,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,23,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,35,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,41,24,16
186 | 31/03/2019,41,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,27,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,13
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,27,37,43,22,14
208 | 1/09/2019,33,34,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,23,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,29,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,31,36,44,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,15
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,32,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,20,15
247 | 31/05/2020,65,33,41,21,14
248 | 7/06/2020,69,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,35,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,38,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day10 - Iteration and Sorting/.ipynb_checkpoints/Day10-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics Covered\n",
8 | "- Iteration\n",
9 | "- Sorting"
10 | ]
11 | },
12 | {
13 | "cell_type": "code",
14 | "execution_count": null,
15 | "metadata": {},
16 | "outputs": [],
17 | "source": [
18 | "#import pandas library and provide alias name as pd\n",
19 | "import pandas as pd\n",
20 | "\n",
21 | "trend_data = pd.read_csv('Data/SearchTrendData.csv')\n",
22 | "trend_data.head(5)"
23 | ]
24 | },
25 | {
26 | "cell_type": "markdown",
27 | "metadata": {},
28 | "source": [
29 | "1. Iteration"
30 | ]
31 | },
32 | {
33 | "cell_type": "markdown",
34 | "metadata": {},
35 | "source": [
36 | "1a. Iterates through each row in the data frame"
37 | ]
38 | },
39 | {
40 | "cell_type": "code",
41 | "execution_count": null,
42 | "metadata": {},
43 | "outputs": [],
44 | "source": [
45 | "for row_index,row in trend_data.iterrows():\n",
46 | " print(row_index,row)"
47 | ]
48 | },
49 | {
50 | "cell_type": "markdown",
51 | "metadata": {},
52 | "source": [
53 | "1b. Iterates through each column as key value pair"
54 | ]
55 | },
56 | {
57 | "cell_type": "code",
58 | "execution_count": null,
59 | "metadata": {},
60 | "outputs": [],
61 | "source": [
62 | "for key,value in trend_data.iteritems():\n",
63 | " print(key,value)"
64 | ]
65 | },
66 | {
67 | "cell_type": "markdown",
68 | "metadata": {},
69 | "source": [
70 | "1c. Considers each row as a tuple and iterates through them"
71 | ]
72 | },
73 | {
74 | "cell_type": "code",
75 | "execution_count": null,
76 | "metadata": {},
77 | "outputs": [],
78 | "source": [
79 | "for row_tuple in trend_data.itertuples():\n",
80 | " print(row_tuple)"
81 | ]
82 | },
83 | {
84 | "cell_type": "markdown",
85 | "metadata": {},
86 | "source": [
87 | "2. Sorting"
88 | ]
89 | },
90 | {
91 | "cell_type": "code",
92 | "execution_count": null,
93 | "metadata": {},
94 | "outputs": [],
95 | "source": [
96 | "sorted_data = trend_data.sort_index(ascending=False)\n",
97 | "print(sorted_data)"
98 | ]
99 | },
100 | {
101 | "cell_type": "markdown",
102 | "metadata": {},
103 | "source": [
104 | "2b. Sorting by column"
105 | ]
106 | },
107 | {
108 | "cell_type": "code",
109 | "execution_count": null,
110 | "metadata": {},
111 | "outputs": [],
112 | "source": [
113 | "sorted_data=trend_data.sort_index(axis=1)\n",
114 | "print(sorted_data)"
115 | ]
116 | },
117 | {
118 | "cell_type": "markdown",
119 | "metadata": {},
120 | "source": [
121 | "2c. Sort by values of a specific column"
122 | ]
123 | },
124 | {
125 | "cell_type": "code",
126 | "execution_count": null,
127 | "metadata": {},
128 | "outputs": [],
129 | "source": [
130 | "sorted_data = trend_data.sort_values(by='DataScience')\n",
131 | "print(sorted_data)"
132 | ]
133 | },
134 | {
135 | "cell_type": "code",
136 | "execution_count": null,
137 | "metadata": {},
138 | "outputs": [],
139 | "source": [
140 | "sorted_data = trend_data.sort_values(by='DataScience', ascending=False)\n",
141 | "print(sorted_data)"
142 | ]
143 | },
144 | {
145 | "cell_type": "markdown",
146 | "metadata": {},
147 | "source": [
148 | "2d. Sorting by a combination of columns"
149 | ]
150 | },
151 | {
152 | "cell_type": "code",
153 | "execution_count": null,
154 | "metadata": {},
155 | "outputs": [],
156 | "source": [
157 | "sorted_data = trend_data.sort_values(by=['DataScience','MachineLearning'])\n",
158 | "print(sorted_data)"
159 | ]
160 | },
161 | {
162 | "cell_type": "markdown",
163 | "metadata": {},
164 | "source": [
165 | "2e. What kind of sorting algorithm to be used
\n",
166 | "The options avaiable are \n",
167 | "- mergesort\n",
168 | "- heapsort\n",
169 | "- quicksort\n",
170 | "It's better to go with the default one 'mergesort' as it is more stable"
171 | ]
172 | },
173 | {
174 | "cell_type": "code",
175 | "execution_count": null,
176 | "metadata": {},
177 | "outputs": [],
178 | "source": [
179 | "sorted_data = trend_data.sort_values(by='DataScience' ,kind='mergesort')\n",
180 | "print(sorted_data)"
181 | ]
182 | },
183 | {
184 | "cell_type": "markdown",
185 | "metadata": {},
186 | "source": [
187 | "#### To Try\n",
188 | "- Sort based on eLearning and Deep Learning\n",
189 | "- Filter the last 50 weeks and sort based on the Data Scice trend score\n",
190 | "- Print on the rows(Weeks) where the trand score for atleast one item is over 40\n",
191 | "- Try the above worked example on a different dataset"
192 | ]
193 | },
194 | {
195 | "cell_type": "code",
196 | "execution_count": null,
197 | "metadata": {},
198 | "outputs": [],
199 | "source": []
200 | }
201 | ],
202 | "metadata": {
203 | "kernelspec": {
204 | "display_name": "Python 3",
205 | "language": "python",
206 | "name": "python3"
207 | },
208 | "language_info": {
209 | "codemirror_mode": {
210 | "name": "ipython",
211 | "version": 3
212 | },
213 | "file_extension": ".py",
214 | "mimetype": "text/x-python",
215 | "name": "python",
216 | "nbconvert_exporter": "python",
217 | "pygments_lexer": "ipython3",
218 | "version": "3.8.5"
219 | }
220 | },
221 | "nbformat": 4,
222 | "nbformat_minor": 4
223 | }
224 |
--------------------------------------------------------------------------------
/Day10 - Iteration and Sorting/Data/SearchTrendData.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,12,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,35,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,5
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,13,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,30,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,7
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,12,7
31 | 10/04/2016,29,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,10,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,6
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,28,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,10
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,10,15,12,7
69 | 1/01/2017,21,13,20,14,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,27,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,11
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,29,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,20,16,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,12
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,18,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,21,37,25,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,18,19,30,20,13
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,21,36,24,15
137 | 22/04/2018,33,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,15
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,36,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,19,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,23,15
163 | 21/10/2018,42,26,40,24,15
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,38,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,23,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,35,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,41,24,16
186 | 31/03/2019,41,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,27,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,13
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,27,37,43,22,14
208 | 1/09/2019,33,34,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,23,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,29,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,31,36,44,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,15
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,32,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,20,15
247 | 31/05/2020,65,33,41,21,14
248 | 7/06/2020,69,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,35,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,38,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day11 - Aggregation and GroupBy/Data/SearchTrendData.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,12,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,35,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,5
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,13,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,30,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,7
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,12,7
31 | 10/04/2016,29,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,10,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,6
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,28,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,10
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,10,15,12,7
69 | 1/01/2017,21,13,20,14,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,27,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,11
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,29,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,20,16,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,12
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,18,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,21,37,25,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,18,19,30,20,13
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,21,36,24,15
137 | 22/04/2018,33,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,15
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,36,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,19,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,23,15
163 | 21/10/2018,42,26,40,24,15
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,38,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,23,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,35,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,41,24,16
186 | 31/03/2019,41,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,27,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,13
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,27,37,43,22,14
208 | 1/09/2019,33,34,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,23,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,29,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,31,36,44,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,15
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,32,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,20,15
247 | 31/05/2020,65,33,41,21,14
248 | 7/06/2020,69,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,35,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,38,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day12 - Missing Values and Handling Them/Data/SearchTrendData_WithMissing.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,,7
31 | 10/04/2016,,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,,15,12,7
69 | 1/01/2017,21,13,20,,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,,,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,,37,,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,,19,30,20,
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,,36,24,15
137 | 22/04/2018,,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,,15
163 | 21/10/2018,42,26,40,24,
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,,24,16
186 | 31/03/2019,,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,,37,43,22,14
208 | 1/09/2019,33,,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,,36,,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,,15
247 | 31/05/2020,65,33,,21,14
248 | 7/06/2020,,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day13 - Rename and Replace/Data/SearchTrendData.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,12,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,35,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,5
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,13,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,30,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,7
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,12,7
31 | 10/04/2016,29,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,10,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,6
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,28,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,10
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,10,15,12,7
69 | 1/01/2017,21,13,20,14,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,27,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,11
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,29,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,20,16,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,12
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,18,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,21,37,25,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,18,19,30,20,13
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,21,36,24,15
137 | 22/04/2018,33,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,15
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,36,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,19,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,23,15
163 | 21/10/2018,42,26,40,24,15
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,38,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,23,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,35,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,41,24,16
186 | 31/03/2019,41,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,27,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,13
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,27,37,43,22,14
208 | 1/09/2019,33,34,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,23,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,29,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,31,36,44,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,15
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,32,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,20,15
247 | 31/05/2020,65,33,41,21,14
248 | 7/06/2020,69,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,35,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,38,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day13 - Rename and Replace/Data/SearchTrendData_WithMissing.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,,7
31 | 10/04/2016,,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,,15,12,7
69 | 1/01/2017,21,13,20,,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,,,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,,37,,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,,19,30,20,
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,,36,24,15
137 | 22/04/2018,,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,,15
163 | 21/10/2018,42,26,40,24,
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,,24,16
186 | 31/03/2019,,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,,37,43,22,14
208 | 1/09/2019,33,,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,,36,,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,,15
247 | 31/05/2020,65,33,,21,14
248 | 7/06/2020,,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day14 - Merging, Joining and Combine/Data/SearchTrendData1.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning
2 | 20/09/2015,32,9,11
3 | 27/09/2015,35,10,11
4 | 4/10/2015,38,10,12
5 | 11/10/2015,37,9,12
6 | 18/10/2015,38,9,12
7 | 25/10/2015,38,10,12
8 | 1/11/2015,37,9,13
9 | 8/11/2015,35,8,14
10 | 15/11/2015,35,9,13
11 | 22/11/2015,31,8,12
12 | 29/11/2015,35,9,13
13 | 6/12/2015,33,9,13
14 | 13/12/2015,30,9,14
15 | 20/12/2015,17,7,11
16 | 27/12/2015,17,7,10
17 | 3/01/2016,24,10,12
18 | 10/01/2016,26,9,12
19 | 17/01/2016,26,11,13
20 | 24/01/2016,28,11,15
21 | 31/01/2016,28,11,14
22 | 7/02/2016,28,10,13
23 | 14/02/2016,30,10,13
24 | 21/02/2016,30,10,14
25 | 28/02/2016,30,10,13
26 | 6/03/2016,30,10,15
27 | 13/03/2016,27,10,15
28 | 20/03/2016,27,9,14
29 | 27/03/2016,29,9,14
30 | 3/04/2016,29,10,15
31 | 10/04/2016,29,10,14
32 | 17/04/2016,29,10,15
33 | 24/04/2016,28,10,14
34 | 1/05/2016,26,9,14
35 | 8/05/2016,28,11,15
36 | 15/05/2016,27,10,15
37 | 22/05/2016,27,10,16
38 | 29/05/2016,25,10,14
39 | 5/06/2016,25,11,15
40 | 12/06/2016,25,10,16
41 | 19/06/2016,21,10,16
42 | 26/06/2016,20,10,15
43 | 3/07/2016,17,9,14
44 | 10/07/2016,19,10,14
45 | 17/07/2016,18,10,15
46 | 24/07/2016,17,9,14
47 | 31/07/2016,18,11,15
48 | 7/08/2016,16,10,15
49 | 14/08/2016,15,10,12
50 | 21/08/2016,19,12,16
51 | 28/08/2016,21,13,15
52 | 4/09/2016,26,13,16
53 | 11/09/2016,29,14,17
54 | 18/09/2016,32,14,19
55 | 25/09/2016,33,13,18
56 | 2/10/2016,36,12,17
57 | 9/10/2016,38,12,18
58 | 16/10/2016,36,13,20
59 | 23/10/2016,35,13,19
60 | 30/10/2016,34,13,20
61 | 6/11/2016,32,13,18
62 | 13/11/2016,30,13,21
63 | 20/11/2016,28,12,21
64 | 27/11/2016,30,14,22
65 | 4/12/2016,30,13,22
66 | 11/12/2016,28,14,21
67 | 18/12/2016,22,11,20
68 | 25/12/2016,17,10,15
69 | 1/01/2017,21,13,20
70 | 8/01/2017,29,15,23
71 | 15/01/2017,26,15,23
72 | 22/01/2017,25,15,23
73 | 29/01/2017,25,16,23
74 | 5/02/2017,28,16,23
75 | 12/02/2017,27,15,25
76 | 19/02/2017,29,16,24
77 | 26/02/2017,31,17,27
78 | 5/03/2017,30,16,26
79 | 12/03/2017,29,16,26
80 | 19/03/2017,31,16,25
81 | 26/03/2017,32,16,28
82 | 2/04/2017,30,17,27
83 | 9/04/2017,28,15,26
84 | 16/04/2017,26,16,26
85 | 23/04/2017,30,16,27
86 | 30/04/2017,28,15,27
87 | 7/05/2017,26,17,27
88 | 14/05/2017,26,16,29
89 | 21/05/2017,24,17,29
90 | 28/05/2017,25,16,27
91 | 4/06/2017,25,15,29
92 | 11/06/2017,24,16,28
93 | 18/06/2017,20,16,28
94 | 25/06/2017,19,16,28
95 | 2/07/2017,21,15,27
96 | 9/07/2017,19,16,29
97 | 16/07/2017,18,16,28
98 | 23/07/2017,17,17,28
99 | 30/07/2017,16,17,29
100 | 6/08/2017,15,17,27
101 | 13/08/2017,15,16,28
102 | 20/08/2017,18,18,27
103 | 27/08/2017,19,18,28
104 | 3/09/2017,24,19,30
105 | 10/09/2017,27,20,31
106 | 17/09/2017,32,20,32
107 | 24/09/2017,34,20,31
108 | 1/10/2017,35,19,33
109 | 8/10/2017,35,20,34
110 | 15/10/2017,36,20,32
111 | 22/10/2017,34,20,35
112 | 29/10/2017,33,21,35
113 | 5/11/2017,33,21,37
114 | 12/11/2017,31,20,35
115 | 19/11/2017,29,17,33
116 | 26/11/2017,29,19,35
117 | 3/12/2017,30,20,36
118 | 10/12/2017,30,20,37
119 | 17/12/2017,24,17,32
120 | 24/12/2017,16,14,26
121 | 31/12/2017,18,19,30
122 | 7/01/2018,29,22,32
123 | 14/01/2018,27,21,34
124 | 21/01/2018,27,21,34
125 | 28/01/2018,26,22,36
126 | 4/02/2018,27,20,35
127 | 11/02/2018,27,21,34
128 | 18/02/2018,30,21,35
129 | 25/02/2018,33,21,36
130 | 4/03/2018,34,22,37
131 | 11/03/2018,34,22,36
132 | 18/03/2018,37,23,37
133 | 25/03/2018,36,22,36
134 | 1/04/2018,30,22,36
135 | 8/04/2018,33,23,36
136 | 15/04/2018,32,21,36
137 | 22/04/2018,33,22,37
138 | 29/04/2018,31,22,35
139 | 6/05/2018,31,21,37
140 | 13/05/2018,32,22,37
141 | 20/05/2018,30,23,37
142 | 27/05/2018,29,22,34
143 | 3/06/2018,30,23,37
144 | 10/06/2018,25,21,36
145 | 17/06/2018,24,21,36
146 | 24/06/2018,24,21,35
147 | 1/07/2018,24,20,34
148 | 8/07/2018,21,23,36
149 | 15/07/2018,21,23,36
150 | 22/07/2018,18,22,36
151 | 29/07/2018,17,23,36
152 | 5/08/2018,18,23,37
153 | 12/08/2018,17,22,36
154 | 19/08/2018,19,24,37
155 | 26/08/2018,23,26,38
156 | 2/09/2018,28,27,37
157 | 9/09/2018,34,25,38
158 | 16/09/2018,39,25,40
159 | 23/09/2018,38,25,39
160 | 30/09/2018,44,26,39
161 | 7/10/2018,43,26,39
162 | 14/10/2018,45,26,39
163 | 21/10/2018,42,26,40
164 | 28/10/2018,39,24,39
165 | 4/11/2018,37,24,36
166 | 11/11/2018,41,26,39
167 | 18/11/2018,35,23,37
168 | 25/11/2018,38,26,41
169 | 2/12/2018,38,26,42
170 | 9/12/2018,35,26,40
171 | 16/12/2018,30,23,37
172 | 23/12/2018,19,19,28
173 | 30/12/2018,20,22,31
174 | 6/01/2019,30,29,37
175 | 13/01/2019,31,29,37
176 | 20/01/2019,30,28,39
177 | 27/01/2019,29,28,40
178 | 3/02/2019,29,29,41
179 | 10/02/2019,32,28,40
180 | 17/02/2019,35,30,42
181 | 24/02/2019,38,29,41
182 | 3/03/2019,36,28,42
183 | 10/03/2019,39,27,40
184 | 17/03/2019,41,28,41
185 | 24/03/2019,38,30,41
186 | 31/03/2019,41,29,41
187 | 7/04/2019,38,28,41
188 | 14/04/2019,36,27,37
189 | 21/04/2019,36,27,39
190 | 28/04/2019,36,27,39
191 | 5/05/2019,36,28,41
192 | 12/05/2019,37,29,41
193 | 19/05/2019,35,30,39
194 | 26/05/2019,32,29,38
195 | 2/06/2019,27,29,40
196 | 9/06/2019,27,30,42
197 | 16/06/2019,32,31,41
198 | 23/06/2019,28,30,41
199 | 30/06/2019,32,29,40
200 | 7/07/2019,27,31,40
201 | 14/07/2019,23,31,41
202 | 21/07/2019,22,31,40
203 | 28/07/2019,20,31,39
204 | 4/08/2019,20,30,38
205 | 11/08/2019,19,31,39
206 | 18/08/2019,26,35,43
207 | 25/08/2019,27,37,43
208 | 1/09/2019,33,34,40
209 | 8/09/2019,42,36,44
210 | 15/09/2019,44,37,45
211 | 22/09/2019,45,35,46
212 | 29/09/2019,55,34,43
213 | 6/10/2019,53,34,43
214 | 13/10/2019,55,34,44
215 | 20/10/2019,52,32,42
216 | 27/10/2019,48,31,39
217 | 3/11/2019,49,34,44
218 | 10/11/2019,47,33,43
219 | 17/11/2019,46,34,45
220 | 24/11/2019,42,30,41
221 | 1/12/2019,43,34,43
222 | 8/12/2019,43,32,44
223 | 15/12/2019,38,32,42
224 | 22/12/2019,24,26,33
225 | 29/12/2019,23,29,33
226 | 5/01/2020,38,35,42
227 | 12/01/2020,34,35,42
228 | 19/01/2020,31,36,44
229 | 26/01/2020,30,35,43
230 | 2/02/2020,34,36,44
231 | 9/02/2020,37,38,44
232 | 16/02/2020,43,38,46
233 | 23/02/2020,42,38,46
234 | 1/03/2020,48,37,44
235 | 8/03/2020,57,34,39
236 | 15/03/2020,98,29,34
237 | 22/03/2020,100,28,33
238 | 29/03/2020,97,30,35
239 | 5/04/2020,100,32,38
240 | 12/04/2020,94,34,41
241 | 19/04/2020,98,32,41
242 | 26/04/2020,86,33,41
243 | 3/05/2020,86,31,41
244 | 10/05/2020,83,33,40
245 | 17/05/2020,66,33,40
246 | 24/05/2020,57,32,40
247 | 31/05/2020,65,33,41
248 | 7/06/2020,69,33,41
249 | 14/06/2020,61,33,40
250 | 21/06/2020,61,34,41
251 | 28/06/2020,59,35,38
252 | 5/07/2020,55,35,39
253 | 12/07/2020,58,36,40
254 | 19/07/2020,55,36,40
255 | 26/07/2020,43,34,39
256 | 2/08/2020,44,34,37
257 | 9/08/2020,44,35,38
258 | 16/08/2020,41,36,40
259 | 23/08/2020,49,39,40
260 | 30/08/2020,62,37,40
261 | 6/09/2020,74,38,42
262 | 13/09/2020,80,39,39
263 |
--------------------------------------------------------------------------------
/Day14 - Merging, Joining and Combine/Data/SearchTrendData2.csv:
--------------------------------------------------------------------------------
1 | Week,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,11,4
3 | 27/09/2015,11,4
4 | 4/10/2015,11,4
5 | 11/10/2015,10,4
6 | 18/10/2015,10,4
7 | 25/10/2015,11,4
8 | 1/11/2015,11,3
9 | 8/11/2015,12,5
10 | 15/11/2015,12,4
11 | 22/11/2015,10,4
12 | 29/11/2015,11,4
13 | 6/12/2015,12,5
14 | 13/12/2015,11,5
15 | 20/12/2015,9,4
16 | 27/12/2015,9,3
17 | 3/01/2016,14,4
18 | 10/01/2016,12,4
19 | 17/01/2016,11,6
20 | 24/01/2016,12,7
21 | 31/01/2016,11,6
22 | 7/02/2016,11,5
23 | 14/02/2016,10,5
24 | 21/02/2016,12,5
25 | 28/02/2016,11,5
26 | 6/03/2016,13,7
27 | 13/03/2016,13,8
28 | 20/03/2016,12,6
29 | 27/03/2016,12,6
30 | 3/04/2016,12,7
31 | 10/04/2016,11,6
32 | 17/04/2016,12,6
33 | 24/04/2016,12,6
34 | 1/05/2016,12,6
35 | 8/05/2016,12,6
36 | 15/05/2016,13,7
37 | 22/05/2016,12,6
38 | 29/05/2016,11,6
39 | 5/06/2016,11,7
40 | 12/06/2016,10,6
41 | 19/06/2016,10,7
42 | 26/06/2016,10,7
43 | 3/07/2016,10,6
44 | 10/07/2016,9,6
45 | 17/07/2016,9,6
46 | 24/07/2016,9,6
47 | 31/07/2016,9,6
48 | 7/08/2016,9,6
49 | 14/08/2016,8,5
50 | 21/08/2016,10,6
51 | 28/08/2016,10,7
52 | 4/09/2016,11,6
53 | 11/09/2016,11,7
54 | 18/09/2016,12,6
55 | 25/09/2016,12,8
56 | 2/10/2016,13,8
57 | 9/10/2016,16,8
58 | 16/10/2016,14,8
59 | 23/10/2016,14,8
60 | 30/10/2016,14,8
61 | 6/11/2016,14,8
62 | 13/11/2016,14,9
63 | 20/11/2016,14,9
64 | 27/11/2016,15,9
65 | 4/12/2016,16,10
66 | 11/12/2016,15,9
67 | 18/12/2016,13,9
68 | 25/12/2016,12,7
69 | 1/01/2017,14,9
70 | 8/01/2017,15,10
71 | 15/01/2017,14,10
72 | 22/01/2017,15,10
73 | 29/01/2017,14,10
74 | 5/02/2017,15,10
75 | 12/02/2017,15,11
76 | 19/02/2017,16,11
77 | 26/02/2017,15,11
78 | 5/03/2017,16,11
79 | 12/03/2017,17,12
80 | 19/03/2017,15,11
81 | 26/03/2017,17,11
82 | 2/04/2017,17,12
83 | 9/04/2017,16,11
84 | 16/04/2017,16,12
85 | 23/04/2017,18,12
86 | 30/04/2017,17,11
87 | 7/05/2017,16,12
88 | 14/05/2017,18,12
89 | 21/05/2017,18,12
90 | 28/05/2017,15,11
91 | 4/06/2017,14,11
92 | 11/06/2017,15,11
93 | 18/06/2017,14,11
94 | 25/06/2017,15,12
95 | 2/07/2017,14,11
96 | 9/07/2017,15,12
97 | 16/07/2017,16,13
98 | 23/07/2017,18,12
99 | 30/07/2017,23,12
100 | 6/08/2017,18,13
101 | 13/08/2017,17,13
102 | 20/08/2017,17,12
103 | 27/08/2017,17,12
104 | 3/09/2017,23,13
105 | 10/09/2017,21,13
106 | 17/09/2017,20,14
107 | 24/09/2017,20,13
108 | 1/10/2017,20,13
109 | 8/10/2017,22,13
110 | 15/10/2017,21,14
111 | 22/10/2017,26,13
112 | 29/10/2017,24,14
113 | 5/11/2017,25,14
114 | 12/11/2017,24,15
115 | 19/11/2017,22,14
116 | 26/11/2017,25,15
117 | 3/12/2017,25,15
118 | 10/12/2017,23,15
119 | 17/12/2017,19,14
120 | 24/12/2017,17,12
121 | 31/12/2017,20,13
122 | 7/01/2018,21,15
123 | 14/01/2018,30,14
124 | 21/01/2018,29,14
125 | 28/01/2018,27,16
126 | 4/02/2018,24,15
127 | 11/02/2018,24,13
128 | 18/02/2018,24,14
129 | 25/02/2018,24,14
130 | 4/03/2018,23,14
131 | 11/03/2018,24,14
132 | 18/03/2018,23,15
133 | 25/03/2018,21,14
134 | 1/04/2018,21,14
135 | 8/04/2018,23,15
136 | 15/04/2018,24,15
137 | 22/04/2018,23,15
138 | 29/04/2018,22,14
139 | 6/05/2018,25,15
140 | 13/05/2018,23,15
141 | 20/05/2018,22,14
142 | 27/05/2018,21,14
143 | 3/06/2018,21,14
144 | 10/06/2018,19,14
145 | 17/06/2018,17,14
146 | 24/06/2018,19,14
147 | 1/07/2018,18,13
148 | 8/07/2018,19,14
149 | 15/07/2018,19,14
150 | 22/07/2018,20,13
151 | 29/07/2018,20,14
152 | 5/08/2018,20,13
153 | 12/08/2018,19,12
154 | 19/08/2018,18,14
155 | 26/08/2018,20,14
156 | 2/09/2018,21,14
157 | 9/09/2018,22,14
158 | 16/09/2018,22,15
159 | 23/09/2018,23,15
160 | 30/09/2018,21,15
161 | 7/10/2018,22,15
162 | 14/10/2018,23,15
163 | 21/10/2018,24,15
164 | 28/10/2018,23,15
165 | 4/11/2018,21,15
166 | 11/11/2018,25,15
167 | 18/11/2018,22,14
168 | 25/11/2018,24,15
169 | 2/12/2018,24,16
170 | 9/12/2018,22,15
171 | 16/12/2018,23,13
172 | 23/12/2018,17,10
173 | 30/12/2018,18,11
174 | 6/01/2019,22,14
175 | 13/01/2019,24,15
176 | 20/01/2019,22,15
177 | 27/01/2019,23,15
178 | 3/02/2019,23,15
179 | 10/02/2019,24,15
180 | 17/02/2019,25,15
181 | 24/02/2019,23,15
182 | 3/03/2019,23,15
183 | 10/03/2019,23,15
184 | 17/03/2019,23,14
185 | 24/03/2019,24,16
186 | 31/03/2019,23,15
187 | 7/04/2019,23,16
188 | 14/04/2019,21,14
189 | 21/04/2019,23,15
190 | 28/04/2019,24,14
191 | 5/05/2019,23,14
192 | 12/05/2019,24,15
193 | 19/05/2019,22,15
194 | 26/05/2019,21,14
195 | 2/06/2019,21,14
196 | 9/06/2019,22,15
197 | 16/06/2019,24,15
198 | 23/06/2019,22,15
199 | 30/06/2019,21,14
200 | 7/07/2019,22,15
201 | 14/07/2019,23,15
202 | 21/07/2019,22,14
203 | 28/07/2019,22,13
204 | 4/08/2019,21,14
205 | 11/08/2019,21,14
206 | 18/08/2019,22,14
207 | 25/08/2019,22,14
208 | 1/09/2019,22,15
209 | 8/09/2019,25,15
210 | 15/09/2019,25,15
211 | 22/09/2019,26,15
212 | 29/09/2019,23,15
213 | 6/10/2019,24,15
214 | 13/10/2019,24,15
215 | 20/10/2019,23,14
216 | 27/10/2019,22,14
217 | 3/11/2019,25,14
218 | 10/11/2019,24,15
219 | 17/11/2019,27,15
220 | 24/11/2019,24,15
221 | 1/12/2019,24,14
222 | 8/12/2019,25,15
223 | 15/12/2019,22,15
224 | 22/12/2019,17,11
225 | 29/12/2019,19,12
226 | 5/01/2020,25,15
227 | 12/01/2020,25,15
228 | 19/01/2020,24,14
229 | 26/01/2020,24,14
230 | 2/02/2020,24,15
231 | 9/02/2020,25,16
232 | 16/02/2020,26,16
233 | 23/02/2020,26,16
234 | 1/03/2020,25,15
235 | 8/03/2020,23,14
236 | 15/03/2020,19,12
237 | 22/03/2020,19,12
238 | 29/03/2020,19,13
239 | 5/04/2020,21,13
240 | 12/04/2020,22,14
241 | 19/04/2020,21,15
242 | 26/04/2020,20,15
243 | 3/05/2020,22,15
244 | 10/05/2020,22,15
245 | 17/05/2020,21,15
246 | 24/05/2020,20,15
247 | 31/05/2020,21,14
248 | 7/06/2020,20,15
249 | 14/06/2020,21,15
250 | 21/06/2020,20,14
251 | 28/06/2020,20,14
252 | 5/07/2020,19,14
253 | 12/07/2020,21,15
254 | 19/07/2020,21,14
255 | 26/07/2020,20,14
256 | 2/08/2020,20,13
257 | 9/08/2020,21,13
258 | 16/08/2020,22,14
259 | 23/08/2020,21,14
260 | 30/08/2020,21,14
261 | 6/09/2020,24,14
262 | 13/09/2020,21,14
263 |
--------------------------------------------------------------------------------
/Day15 - Summary, Crosstab and Pivot/Data/SearchTrendData_WithMissing.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,,7
31 | 10/04/2016,,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,,15,12,7
69 | 1/01/2017,21,13,20,,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,,,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,,37,,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,,19,30,20,
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,,36,24,15
137 | 22/04/2018,,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,,15
163 | 21/10/2018,42,26,40,24,
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,,24,16
186 | 31/03/2019,,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,,37,43,22,14
208 | 1/09/2019,33,,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,,36,,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,,15
247 | 31/05/2020,65,33,,21,14
248 | 7/06/2020,,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day16 - Date, Categorical and Sparse Data/.ipynb_checkpoints/Day16-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics Covered today\n",
8 | "- Date Functionality and Time Delta\n",
9 | "- Categorical Data\n",
10 | "- Sparse Data\n"
11 | ]
12 | },
13 | {
14 | "cell_type": "code",
15 | "execution_count": null,
16 | "metadata": {},
17 | "outputs": [],
18 | "source": [
19 | "import pandas as pd"
20 | ]
21 | },
22 | {
23 | "cell_type": "markdown",
24 | "metadata": {},
25 | "source": [
26 | "1a. Creating a Date Column"
27 | ]
28 | },
29 | {
30 | "cell_type": "code",
31 | "execution_count": null,
32 | "metadata": {},
33 | "outputs": [],
34 | "source": [
35 | "print(pd.date_range('1/1/2011', periods=5))"
36 | ]
37 | },
38 | {
39 | "cell_type": "markdown",
40 | "metadata": {},
41 | "source": [
42 | "1b. Adding Frequency"
43 | ]
44 | },
45 | {
46 | "cell_type": "code",
47 | "execution_count": null,
48 | "metadata": {},
49 | "outputs": [],
50 | "source": [
51 | "print(pd.date_range('1/1/2011', periods=5, freq='M'))"
52 | ]
53 | },
54 | {
55 | "cell_type": "markdown",
56 | "metadata": {},
57 | "source": [
58 | "1c. Other Frequency Options"
59 | ]
60 | },
61 | {
62 | "cell_type": "code",
63 | "execution_count": null,
64 | "metadata": {},
65 | "outputs": [],
66 | "source": [
67 | "print(pd.date_range('1/1/2011', periods=5, freq='MS'))\n",
68 | "# Show all the Frequency Options from Pandas"
69 | ]
70 | },
71 | {
72 | "cell_type": "markdown",
73 | "metadata": {},
74 | "source": [
75 | "2a. Time Delta"
76 | ]
77 | },
78 | {
79 | "cell_type": "code",
80 | "execution_count": null,
81 | "metadata": {},
82 | "outputs": [],
83 | "source": [
84 | "day1 = pd.to_datetime('today')\n",
85 | "day2 = day1 + pd.Timedelta('1 day')\n",
86 | "print(\"Day 1:\", day1)\n",
87 | "print(\"Day 2:\", day2, day2.day_name())"
88 | ]
89 | },
90 | {
91 | "cell_type": "markdown",
92 | "metadata": {},
93 | "source": [
94 | "2b. Date Operations"
95 | ]
96 | },
97 | {
98 | "cell_type": "code",
99 | "execution_count": null,
100 | "metadata": {},
101 | "outputs": [],
102 | "source": [
103 | "date = pd.Series(pd.date_range('2020-1-1', periods=7, freq='D'))\n",
104 | "to_be_added = pd.Series([pd.Timedelta(days=i) for i in range(7)])\n",
105 | "date_df = pd.DataFrame({'Date': date, 'To_Add': to_be_added})\n",
106 | "print(date_df)\n",
107 | "date_df['Final_Date'] = date_df['Date'] + date_df['To_Add']\n",
108 | "print(date_df)"
109 | ]
110 | },
111 | {
112 | "cell_type": "code",
113 | "execution_count": null,
114 | "metadata": {},
115 | "outputs": [],
116 | "source": [
117 | "date = pd.Series(pd.date_range('2020-1-1', periods=7, freq='D'))\n",
118 | "to_be_added = pd.Series([pd.Timedelta(days=i) for i in range(7)])\n",
119 | "date_df = pd.DataFrame({'Date': date, 'To_Add': to_be_added})\n",
120 | "print(date_df)\n",
121 | "date_df['Final_Date'] = date_df['Date'] - date_df['To_Add']\n",
122 | "print(date_df)"
123 | ]
124 | },
125 | {
126 | "cell_type": "code",
127 | "execution_count": null,
128 | "metadata": {},
129 | "outputs": [],
130 | "source": [
131 | "date = pd.Series(pd.date_range('2020-1-1', periods=7, freq='D'))\n",
132 | "to_be_added = pd.Series([pd.Timedelta(days=i) for i in range(7)])\n",
133 | "date_df['year'] = date_df['Date'].dt.year\n",
134 | "date_df['month'] = date_df['Date'].dt.month\n",
135 | "date_df['day'] = date_df['Date'].dt.day\n",
136 | "print(date_df)"
137 | ]
138 | },
139 | {
140 | "cell_type": "markdown",
141 | "metadata": {},
142 | "source": [
143 | "More date operation Here - https://pandas.pydata.org/pandas-docs/stable/user_guide/timedeltas.html"
144 | ]
145 | },
146 | {
147 | "cell_type": "markdown",
148 | "metadata": {},
149 | "source": [
150 | "3a. Categorical Data"
151 | ]
152 | },
153 | {
154 | "cell_type": "code",
155 | "execution_count": null,
156 | "metadata": {},
157 | "outputs": [],
158 | "source": [
159 | "gender = pd.Series([\"Male\",\"Female\",\"Male\",\"Female\", \"Female\"], dtype=\"category\")\n",
160 | "print(gender)"
161 | ]
162 | },
163 | {
164 | "cell_type": "markdown",
165 | "metadata": {},
166 | "source": [
167 | "3b. Get all categories"
168 | ]
169 | },
170 | {
171 | "cell_type": "code",
172 | "execution_count": null,
173 | "metadata": {},
174 | "outputs": [],
175 | "source": [
176 | "Categories = pd.Categorical(gender)\n",
177 | "print(Categories)\n"
178 | ]
179 | },
180 | {
181 | "cell_type": "markdown",
182 | "metadata": {},
183 | "source": [
184 | "3c. Accessing the objects from the categories"
185 | ]
186 | },
187 | {
188 | "cell_type": "code",
189 | "execution_count": null,
190 | "metadata": {},
191 | "outputs": [],
192 | "source": [
193 | "print(\"Categories:\", Categories[0], \"and\", Categories[1])"
194 | ]
195 | },
196 | {
197 | "cell_type": "markdown",
198 | "metadata": {},
199 | "source": [
200 | "3d. Summary on the Categrical attribute"
201 | ]
202 | },
203 | {
204 | "cell_type": "code",
205 | "execution_count": null,
206 | "metadata": {},
207 | "outputs": [],
208 | "source": [
209 | "print(gender.describe())"
210 | ]
211 | },
212 | {
213 | "cell_type": "markdown",
214 | "metadata": {},
215 | "source": [
216 | "3e. Removing certain categories"
217 | ]
218 | },
219 | {
220 | "cell_type": "code",
221 | "execution_count": null,
222 | "metadata": {},
223 | "outputs": [],
224 | "source": [
225 | "print(gender.cat.remove_categories(\"Male\"))"
226 | ]
227 | },
228 | {
229 | "cell_type": "markdown",
230 | "metadata": {},
231 | "source": [
232 | "4a. Sparse Data - Useful when there is a large number of zero elements"
233 | ]
234 | },
235 | {
236 | "cell_type": "code",
237 | "execution_count": null,
238 | "metadata": {},
239 | "outputs": [],
240 | "source": [
241 | "import pandas as pd\n",
242 | "import numpy as np\n",
243 | "\n",
244 | "\n",
245 | "df = pd.DataFrame(np.random.randn(10000, 4))\n",
246 | "df.iloc[:9998] = np.nan\n",
247 | "sdf = df.astype(pd.SparseDtype(\"float\", np.nan))\n",
248 | "print(type(sdf))"
249 | ]
250 | },
251 | {
252 | "cell_type": "code",
253 | "execution_count": null,
254 | "metadata": {},
255 | "outputs": [],
256 | "source": [
257 | "sdf.dtypes"
258 | ]
259 | },
260 | {
261 | "cell_type": "code",
262 | "execution_count": null,
263 | "metadata": {},
264 | "outputs": [],
265 | "source": [
266 | "sdf.sparse.density"
267 | ]
268 | },
269 | {
270 | "cell_type": "markdown",
271 | "metadata": {},
272 | "source": [
273 | "To Do\n",
274 | "- Read and try from the pandas documentation - https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
275 | "- Explore Categorical data with a kaggle dataset"
276 | ]
277 | },
278 | {
279 | "cell_type": "code",
280 | "execution_count": null,
281 | "metadata": {},
282 | "outputs": [],
283 | "source": []
284 | }
285 | ],
286 | "metadata": {
287 | "kernelspec": {
288 | "display_name": "Python 3",
289 | "language": "python",
290 | "name": "python3"
291 | },
292 | "language_info": {
293 | "codemirror_mode": {
294 | "name": "ipython",
295 | "version": 3
296 | },
297 | "file_extension": ".py",
298 | "mimetype": "text/x-python",
299 | "name": "python",
300 | "nbconvert_exporter": "python",
301 | "pygments_lexer": "ipython3",
302 | "version": "3.8.5"
303 | }
304 | },
305 | "nbformat": 4,
306 | "nbformat_minor": 4
307 | }
308 |
--------------------------------------------------------------------------------
/Day17 - Pandas Visualizations/Data/SearchTrendData.csv:
--------------------------------------------------------------------------------
1 | Week,eLearning,DataScience,MachineLearning,ArtificialIntelligence,DeepLearning
2 | 20/09/2015,32,9,11,11,4
3 | 27/09/2015,35,10,11,11,4
4 | 4/10/2015,38,10,12,11,4
5 | 11/10/2015,37,9,12,10,4
6 | 18/10/2015,38,9,12,10,4
7 | 25/10/2015,38,10,12,11,4
8 | 1/11/2015,37,9,13,11,3
9 | 8/11/2015,35,8,14,12,5
10 | 15/11/2015,35,9,13,12,4
11 | 22/11/2015,31,8,12,10,4
12 | 29/11/2015,35,9,13,11,4
13 | 6/12/2015,33,9,13,12,5
14 | 13/12/2015,30,9,14,11,5
15 | 20/12/2015,17,7,11,9,4
16 | 27/12/2015,17,7,10,9,3
17 | 3/01/2016,24,10,12,14,4
18 | 10/01/2016,26,9,12,12,4
19 | 17/01/2016,26,11,13,11,6
20 | 24/01/2016,28,11,15,12,7
21 | 31/01/2016,28,11,14,11,6
22 | 7/02/2016,28,10,13,11,5
23 | 14/02/2016,30,10,13,10,5
24 | 21/02/2016,30,10,14,12,5
25 | 28/02/2016,30,10,13,11,5
26 | 6/03/2016,30,10,15,13,7
27 | 13/03/2016,27,10,15,13,8
28 | 20/03/2016,27,9,14,12,6
29 | 27/03/2016,29,9,14,12,6
30 | 3/04/2016,29,10,15,12,7
31 | 10/04/2016,29,10,14,11,6
32 | 17/04/2016,29,10,15,12,6
33 | 24/04/2016,28,10,14,12,6
34 | 1/05/2016,26,9,14,12,6
35 | 8/05/2016,28,11,15,12,6
36 | 15/05/2016,27,10,15,13,7
37 | 22/05/2016,27,10,16,12,6
38 | 29/05/2016,25,10,14,11,6
39 | 5/06/2016,25,11,15,11,7
40 | 12/06/2016,25,10,16,10,6
41 | 19/06/2016,21,10,16,10,7
42 | 26/06/2016,20,10,15,10,7
43 | 3/07/2016,17,9,14,10,6
44 | 10/07/2016,19,10,14,9,6
45 | 17/07/2016,18,10,15,9,6
46 | 24/07/2016,17,9,14,9,6
47 | 31/07/2016,18,11,15,9,6
48 | 7/08/2016,16,10,15,9,6
49 | 14/08/2016,15,10,12,8,5
50 | 21/08/2016,19,12,16,10,6
51 | 28/08/2016,21,13,15,10,7
52 | 4/09/2016,26,13,16,11,6
53 | 11/09/2016,29,14,17,11,7
54 | 18/09/2016,32,14,19,12,6
55 | 25/09/2016,33,13,18,12,8
56 | 2/10/2016,36,12,17,13,8
57 | 9/10/2016,38,12,18,16,8
58 | 16/10/2016,36,13,20,14,8
59 | 23/10/2016,35,13,19,14,8
60 | 30/10/2016,34,13,20,14,8
61 | 6/11/2016,32,13,18,14,8
62 | 13/11/2016,30,13,21,14,9
63 | 20/11/2016,28,12,21,14,9
64 | 27/11/2016,30,14,22,15,9
65 | 4/12/2016,30,13,22,16,10
66 | 11/12/2016,28,14,21,15,9
67 | 18/12/2016,22,11,20,13,9
68 | 25/12/2016,17,10,15,12,7
69 | 1/01/2017,21,13,20,14,9
70 | 8/01/2017,29,15,23,15,10
71 | 15/01/2017,26,15,23,14,10
72 | 22/01/2017,25,15,23,15,10
73 | 29/01/2017,25,16,23,14,10
74 | 5/02/2017,28,16,23,15,10
75 | 12/02/2017,27,15,25,15,11
76 | 19/02/2017,29,16,24,16,11
77 | 26/02/2017,31,17,27,15,11
78 | 5/03/2017,30,16,26,16,11
79 | 12/03/2017,29,16,26,17,12
80 | 19/03/2017,31,16,25,15,11
81 | 26/03/2017,32,16,28,17,11
82 | 2/04/2017,30,17,27,17,12
83 | 9/04/2017,28,15,26,16,11
84 | 16/04/2017,26,16,26,16,12
85 | 23/04/2017,30,16,27,18,12
86 | 30/04/2017,28,15,27,17,11
87 | 7/05/2017,26,17,27,16,12
88 | 14/05/2017,26,16,29,18,12
89 | 21/05/2017,24,17,29,18,12
90 | 28/05/2017,25,16,27,15,11
91 | 4/06/2017,25,15,29,14,11
92 | 11/06/2017,24,16,28,15,11
93 | 18/06/2017,20,16,28,14,11
94 | 25/06/2017,19,16,28,15,12
95 | 2/07/2017,21,15,27,14,11
96 | 9/07/2017,19,16,29,15,12
97 | 16/07/2017,18,16,28,16,13
98 | 23/07/2017,17,17,28,18,12
99 | 30/07/2017,16,17,29,23,12
100 | 6/08/2017,15,17,27,18,13
101 | 13/08/2017,15,16,28,17,13
102 | 20/08/2017,18,18,27,17,12
103 | 27/08/2017,19,18,28,17,12
104 | 3/09/2017,24,19,30,23,13
105 | 10/09/2017,27,20,31,21,13
106 | 17/09/2017,32,20,32,20,14
107 | 24/09/2017,34,20,31,20,13
108 | 1/10/2017,35,19,33,20,13
109 | 8/10/2017,35,20,34,22,13
110 | 15/10/2017,36,20,32,21,14
111 | 22/10/2017,34,20,35,26,13
112 | 29/10/2017,33,21,35,24,14
113 | 5/11/2017,33,21,37,25,14
114 | 12/11/2017,31,20,35,24,15
115 | 19/11/2017,29,17,33,22,14
116 | 26/11/2017,29,19,35,25,15
117 | 3/12/2017,30,20,36,25,15
118 | 10/12/2017,30,20,37,23,15
119 | 17/12/2017,24,17,32,19,14
120 | 24/12/2017,16,14,26,17,12
121 | 31/12/2017,18,19,30,20,13
122 | 7/01/2018,29,22,32,21,15
123 | 14/01/2018,27,21,34,30,14
124 | 21/01/2018,27,21,34,29,14
125 | 28/01/2018,26,22,36,27,16
126 | 4/02/2018,27,20,35,24,15
127 | 11/02/2018,27,21,34,24,13
128 | 18/02/2018,30,21,35,24,14
129 | 25/02/2018,33,21,36,24,14
130 | 4/03/2018,34,22,37,23,14
131 | 11/03/2018,34,22,36,24,14
132 | 18/03/2018,37,23,37,23,15
133 | 25/03/2018,36,22,36,21,14
134 | 1/04/2018,30,22,36,21,14
135 | 8/04/2018,33,23,36,23,15
136 | 15/04/2018,32,21,36,24,15
137 | 22/04/2018,33,22,37,23,15
138 | 29/04/2018,31,22,35,22,14
139 | 6/05/2018,31,21,37,25,15
140 | 13/05/2018,32,22,37,23,15
141 | 20/05/2018,30,23,37,22,14
142 | 27/05/2018,29,22,34,21,14
143 | 3/06/2018,30,23,37,21,14
144 | 10/06/2018,25,21,36,19,14
145 | 17/06/2018,24,21,36,17,14
146 | 24/06/2018,24,21,35,19,14
147 | 1/07/2018,24,20,34,18,13
148 | 8/07/2018,21,23,36,19,14
149 | 15/07/2018,21,23,36,19,14
150 | 22/07/2018,18,22,36,20,13
151 | 29/07/2018,17,23,36,20,14
152 | 5/08/2018,18,23,37,20,13
153 | 12/08/2018,17,22,36,19,12
154 | 19/08/2018,19,24,37,18,14
155 | 26/08/2018,23,26,38,20,14
156 | 2/09/2018,28,27,37,21,14
157 | 9/09/2018,34,25,38,22,14
158 | 16/09/2018,39,25,40,22,15
159 | 23/09/2018,38,25,39,23,15
160 | 30/09/2018,44,26,39,21,15
161 | 7/10/2018,43,26,39,22,15
162 | 14/10/2018,45,26,39,23,15
163 | 21/10/2018,42,26,40,24,15
164 | 28/10/2018,39,24,39,23,15
165 | 4/11/2018,37,24,36,21,15
166 | 11/11/2018,41,26,39,25,15
167 | 18/11/2018,35,23,37,22,14
168 | 25/11/2018,38,26,41,24,15
169 | 2/12/2018,38,26,42,24,16
170 | 9/12/2018,35,26,40,22,15
171 | 16/12/2018,30,23,37,23,13
172 | 23/12/2018,19,19,28,17,10
173 | 30/12/2018,20,22,31,18,11
174 | 6/01/2019,30,29,37,22,14
175 | 13/01/2019,31,29,37,24,15
176 | 20/01/2019,30,28,39,22,15
177 | 27/01/2019,29,28,40,23,15
178 | 3/02/2019,29,29,41,23,15
179 | 10/02/2019,32,28,40,24,15
180 | 17/02/2019,35,30,42,25,15
181 | 24/02/2019,38,29,41,23,15
182 | 3/03/2019,36,28,42,23,15
183 | 10/03/2019,39,27,40,23,15
184 | 17/03/2019,41,28,41,23,14
185 | 24/03/2019,38,30,41,24,16
186 | 31/03/2019,41,29,41,23,15
187 | 7/04/2019,38,28,41,23,16
188 | 14/04/2019,36,27,37,21,14
189 | 21/04/2019,36,27,39,23,15
190 | 28/04/2019,36,27,39,24,14
191 | 5/05/2019,36,28,41,23,14
192 | 12/05/2019,37,29,41,24,15
193 | 19/05/2019,35,30,39,22,15
194 | 26/05/2019,32,29,38,21,14
195 | 2/06/2019,27,29,40,21,14
196 | 9/06/2019,27,30,42,22,15
197 | 16/06/2019,32,31,41,24,15
198 | 23/06/2019,28,30,41,22,15
199 | 30/06/2019,32,29,40,21,14
200 | 7/07/2019,27,31,40,22,15
201 | 14/07/2019,23,31,41,23,15
202 | 21/07/2019,22,31,40,22,14
203 | 28/07/2019,20,31,39,22,13
204 | 4/08/2019,20,30,38,21,14
205 | 11/08/2019,19,31,39,21,14
206 | 18/08/2019,26,35,43,22,14
207 | 25/08/2019,27,37,43,22,14
208 | 1/09/2019,33,34,40,22,15
209 | 8/09/2019,42,36,44,25,15
210 | 15/09/2019,44,37,45,25,15
211 | 22/09/2019,45,35,46,26,15
212 | 29/09/2019,55,34,43,23,15
213 | 6/10/2019,53,34,43,24,15
214 | 13/10/2019,55,34,44,24,15
215 | 20/10/2019,52,32,42,23,14
216 | 27/10/2019,48,31,39,22,14
217 | 3/11/2019,49,34,44,25,14
218 | 10/11/2019,47,33,43,24,15
219 | 17/11/2019,46,34,45,27,15
220 | 24/11/2019,42,30,41,24,15
221 | 1/12/2019,43,34,43,24,14
222 | 8/12/2019,43,32,44,25,15
223 | 15/12/2019,38,32,42,22,15
224 | 22/12/2019,24,26,33,17,11
225 | 29/12/2019,23,29,33,19,12
226 | 5/01/2020,38,35,42,25,15
227 | 12/01/2020,34,35,42,25,15
228 | 19/01/2020,31,36,44,24,14
229 | 26/01/2020,30,35,43,24,14
230 | 2/02/2020,34,36,44,24,15
231 | 9/02/2020,37,38,44,25,16
232 | 16/02/2020,43,38,46,26,16
233 | 23/02/2020,42,38,46,26,16
234 | 1/03/2020,48,37,44,25,15
235 | 8/03/2020,57,34,39,23,14
236 | 15/03/2020,98,29,34,19,12
237 | 22/03/2020,100,28,33,19,12
238 | 29/03/2020,97,30,35,19,13
239 | 5/04/2020,100,32,38,21,13
240 | 12/04/2020,94,34,41,22,14
241 | 19/04/2020,98,32,41,21,15
242 | 26/04/2020,86,33,41,20,15
243 | 3/05/2020,86,31,41,22,15
244 | 10/05/2020,83,33,40,22,15
245 | 17/05/2020,66,33,40,21,15
246 | 24/05/2020,57,32,40,20,15
247 | 31/05/2020,65,33,41,21,14
248 | 7/06/2020,69,33,41,20,15
249 | 14/06/2020,61,33,40,21,15
250 | 21/06/2020,61,34,41,20,14
251 | 28/06/2020,59,35,38,20,14
252 | 5/07/2020,55,35,39,19,14
253 | 12/07/2020,58,36,40,21,15
254 | 19/07/2020,55,36,40,21,14
255 | 26/07/2020,43,34,39,20,14
256 | 2/08/2020,44,34,37,20,13
257 | 9/08/2020,44,35,38,21,13
258 | 16/08/2020,41,36,40,22,14
259 | 23/08/2020,49,39,40,21,14
260 | 30/08/2020,62,37,40,21,14
261 | 6/09/2020,74,38,42,24,14
262 | 13/09/2020,80,39,39,21,14
263 |
--------------------------------------------------------------------------------
/Day25 - Visualization Part 3 - Seaborn and Interactive Charts/test.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 | Example
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 |
31 |
32 |
33 |
34 |
35 |
36 |
37 |
38 |
39 |
40 |
41 |
42 |
43 |
46 |
82 |
83 |
84 |
85 |
--------------------------------------------------------------------------------
/Day28 - Relationship Stats and Distribution/.ipynb_checkpoints/Day28-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### Topics to be Covered\n",
8 | "- Relationship Measures: Causal, Covariance and Correlation\n",
9 | "- Distribution: Intro, Discreate and Continous Distribution\n",
10 | "- Example of Normal and Biinomial Distribution"
11 | ]
12 | },
13 | {
14 | "cell_type": "markdown",
15 | "metadata": {},
16 | "source": [
17 | "- Difference between Causal and Correlation on whiteboard\n",
18 | "- Correlation and Covariance: Change the values in the excel and show the results here for correlation and covariance"
19 | ]
20 | },
21 | {
22 | "cell_type": "code",
23 | "execution_count": 5,
24 | "metadata": {},
25 | "outputs": [],
26 | "source": [
27 | "import numpy as np\n",
28 | "import pandas as pd"
29 | ]
30 | },
31 | {
32 | "cell_type": "code",
33 | "execution_count": 6,
34 | "metadata": {},
35 | "outputs": [
36 | {
37 | "name": "stdout",
38 | "output_type": "stream",
39 | "text": [
40 | " Input Output\n",
41 | "0 1000 10\n",
42 | "1 1074 16\n",
43 | "2 1125 21\n",
44 | "3 1197 26\n",
45 | "4 1275 31\n"
46 | ]
47 | }
48 | ],
49 | "source": [
50 | "data = pd.read_csv('../Data/For_Cor_Cov.csv')\n",
51 | "print(data.head())\n"
52 | ]
53 | },
54 | {
55 | "cell_type": "code",
56 | "execution_count": 7,
57 | "metadata": {},
58 | "outputs": [
59 | {
60 | "data": {
61 | "text/html": [
62 | "\n",
63 | "\n",
76 | "
\n",
77 | " \n",
78 | " \n",
79 | " | \n",
80 | " Input | \n",
81 | " Output | \n",
82 | "
\n",
83 | " \n",
84 | " \n",
85 | " \n",
86 | " | Input | \n",
87 | " 3.666234e+06 | \n",
88 | " 410515.140707 | \n",
89 | "
\n",
90 | " \n",
91 | " | Output | \n",
92 | " 4.105151e+05 | \n",
93 | " 45988.313030 | \n",
94 | "
\n",
95 | " \n",
96 | "
\n",
97 | "
\n",
123 | "\n",
136 | "
\n",
137 | " \n",
138 | " \n",
139 | " | \n",
140 | " Input | \n",
141 | " Output | \n",
142 | "
\n",
143 | " \n",
144 | " \n",
145 | " \n",
146 | " | Input | \n",
147 | " 1.000000 | \n",
148 | " 0.999759 | \n",
149 | "
\n",
150 | " \n",
151 | " | Output | \n",
152 | " 0.999759 | \n",
153 | " 1.000000 | \n",
154 | "
\n",
155 | " \n",
156 | "
\n",
157 | "
\n",
41 | "\n",
54 | "
\n",
55 | " \n",
56 | " \n",
57 | " | \n",
58 | " no_of_projects | \n",
59 | " years_of_exp | \n",
60 | " courses | \n",
61 | " salary | \n",
62 | "
\n",
63 | " \n",
64 | " \n",
65 | " \n",
66 | " | 0 | \n",
67 | " 5 | \n",
68 | " 1 | \n",
69 | " 2 | \n",
70 | " 80000 | \n",
71 | "
\n",
72 | " \n",
73 | " | 1 | \n",
74 | " 11 | \n",
75 | " 2 | \n",
76 | " 3 | \n",
77 | " 90000 | \n",
78 | "
\n",
79 | " \n",
80 | " | 2 | \n",
81 | " 15 | \n",
82 | " 2 | \n",
83 | " 3 | \n",
84 | " 97000 | \n",
85 | "
\n",
86 | " \n",
87 | " | 3 | \n",
88 | " 19 | \n",
89 | " 3 | \n",
90 | " 3 | \n",
91 | " 110000 | \n",
92 | "
\n",
93 | " \n",
94 | " | 4 | \n",
95 | " 23 | \n",
96 | " 7 | \n",
97 | " 2 | \n",
98 | " 118000 | \n",
99 | "
\n",
100 | " \n",
101 | " | 5 | \n",
102 | " 25 | \n",
103 | " 11 | \n",
104 | " 2 | \n",
105 | " 150000 | \n",
106 | "
\n",
107 | " \n",
108 | "
\n",
109 | "
\n",
183 | "\n",
196 | "
\n",
197 | " \n",
198 | " \n",
199 | " | \n",
200 | " 0 | \n",
201 | " 1 | \n",
202 | " 2 | \n",
203 | "
\n",
204 | " \n",
205 | " \n",
206 | " \n",
207 | " | 0 | \n",
208 | " 7 | \n",
209 | " 13 | \n",
210 | " 19 | \n",
211 | "
\n",
212 | " \n",
213 | " | 1 | \n",
214 | " 3 | \n",
215 | " 3 | \n",
216 | " 3 | \n",
217 | "
\n",
218 | " \n",
219 | "
\n",
220 | "