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Lecture 02 - Course structure and installation.ipynb
Lecture 02 - Course structure and installation.ipynb
 
 
Lecture 03 - Reading data.ipynb
Lecture 03 - Reading data.ipynb
 
 
Lecture 04 - Exploring a DataFrame.ipynb
Lecture 04 - Exploring a DataFrame.ipynb
 
 
Lecture 05 - Data types.ipynb
Lecture 05 - Data types.ipynb
 
 
Lecture 06 - Columns operations.ipynb
Lecture 06 - Columns operations.ipynb
 
 
Lecture 07 - Date manipulation.ipynb
Lecture 07 - Date manipulation.ipynb
 
 
Lecture 08 - String manipulation.ipynb
Lecture 08 - String manipulation.ipynb
 
 
Lecture 09 - Categorical data.ipynb
Lecture 09 - Categorical data.ipynb
 
 
Lecture 10 - Missing values.ipynb
Lecture 10 - Missing values.ipynb
 
 
Lecture 11 - loc and iloc methods.ipynb
Lecture 11 - loc and iloc methods.ipynb
 
 
Lecture 12 - Filtering DataFrames.ipynb
Lecture 12 - Filtering DataFrames.ipynb
 
 
Lecture 13 - Combining DataFrames.ipynb
Lecture 13 - Combining DataFrames.ipynb
 
 
Lecture 14 - Merging DataFrames.ipynb
Lecture 14 - Merging DataFrames.ipynb
 
 
Lecture 15 - Reshaping DataFrames.ipynb
Lecture 15 - Reshaping DataFrames.ipynb
 
 
Lecture 16 - Data analysis.ipynb
Lecture 16 - Data analysis.ipynb
 
 
Lecture 17 - Data visualization.ipynb
Lecture 17 - Data visualization.ipynb
 
 
Lecture 18 - Time series analysis with Pandas.ipynb
Lecture 18 - Time series analysis with Pandas.ipynb
 
 
Lecture 19 - Data cleaning and analysis 1 (obesity dataset).ipynb
Lecture 19 - Data cleaning and analysis 1 (obesity dataset).ipynb
 
 
Lecture 20 - Data cleaning and analysis 2 (customer churn dataset).ipynb
Lecture 20 - Data cleaning and analysis 2 (customer churn dataset).ipynb
 
 
Lecture 21 - Python dictionaries with Pandas.ipynb
Lecture 21 - Python dictionaries with Pandas.ipynb
 
 
Lecture 22 - Pandas pipelines.ipynb
Lecture 22 - Pandas pipelines.ipynb
 
 
Lecture 23 - Styling DataFrames.ipynb
Lecture 23 - Styling DataFrames.ipynb
 
 
Lecture 24 - Functions not to forget.ipynb
Lecture 24 - Functions not to forget.ipynb
 
 
README.md
README.md
 
 

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500 Exercises to Master Python Pandas

1. Introduction

  1. Introduction
  2. Course structure and installation
  3. Reading data
  4. Exploring a DataFrame

2. Data exploration and manipulation

  1. Data types
  2. Column and row operations
  3. Date manipulation
  4. String manipulation
  5. Categorical data

3. Data filtering

  1. Missing values
  2. loc and iloc methods
  3. Filtering DataFrames

4. Combining DataFrames

  1. Combining DataFrames
  2. Merging DataFrames
  3. Reshaping DataFrames

5. Data analysis and visualization

  1. Data analysis
  2. Data visualization
  3. Time series analysis with Pandas

6. Use cases

  1. Data cleaning and analysis 1 (obesity dataset)
  2. Data cleaning and analysis 2 (customer churn dataset)

7. Bonus

  1. Python dictionaries with Pandas
  2. Pandas pipelines
  3. Styling DataFrames
  4. Functions not to forget

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