Pandas for Data Science

Go to main | Course Page

Section 1: Introduction to Pandas

  1. Introduction to Pandas
  2. How to Get Queries Resolved
  3. Download Resources
  4. Need For DataFrame
  5. Creating DataFrame
  6. Mini Challenge
  7. Series
  8. Mini Challenge
  9. Complete ML Mastery
  10. Assignment
  11. Assignment Solution

Section 2: Setup Environment

  1. Python Setup Local Installation
  2. Python Setup Google Colab

Section 3: Inspecting Dataframes and Must know operations

  1. Course Review
  2. Inspecting Dataframes
  3. Renaming Columns
  4. Pandas Summary
  5. Essential Operations
  6. Display Options
  7. Assignment
  8. Assignment Solution

Section 4: Conditional filtering and sorting

  1. Extracting Specific Part of Data - Part 1
  2. Extracting Specific Part of Data - Part 2
  3. Mini Challenge
  4. at and iat
  5. Mini Challenge
  6. Filtering Data That Satisfy Conditions
  7. Membership Filtering
  8. Query and Eval
  9. Removing Duplicates
  10. Sorting
  11. Map and Applymap
  12. Assignment
  13. Assignment Solution

Section 5: Data preparation and transformation

  1. Apply a function rowwise or columnwise
  2. Scaling and Standardization
  3. Make Index as a Dataframe Column
  4. Discretization and Binning
  5. Course Review
  6. Assignment
  7. Assignment Solution

Section 6: Useful tips and tricks

  1. Random Sampling
  2. Dummy Variables
  3. Categorical Data Part-1
  4. Categorical Data Part-2
  5. Method Chaining
  6. Efficiently Read Data From Multiple Files
  7. Assignment
  8. Assignment Solution

Section 7: Data grouping and aggregation

  1. Group by Mechanism
  2. Mini Challenge
  3. Iterating Between Groups
  4. Transform
  5. Course Review
  6. Assignment
  7. Assignment Solution

Section 8: Reshaping and pivoting data

  1. Cross Tabulation
  2. Pivoting
  3. Wide to long and back
  4. Assignment
  5. Assignment Solution

Section 9: Combining dataframes

  1. Joining Dataframes
  2. Types of Joins
  3. Concatenating Dataframes
  4. Course Review
  5. Assignment
  6. Assignment Solution

Section 10: Data cleaning and transformations

  1. Representing Missing Values
  2. Threshold Based Dropping
  3. Approaches To Filling Missing Data
  4. Interpolation
  5. Assignment
  6. Assignment Solution

Section 11: Practical tips and tricks

  1. Compressed File Formats
  2. Sparse Datatype
  3. Combining Categories
  4. Split Contents of a Column
  5. Insert Column at Specific Location
  6. Select using both Position and Lab
  7. Styling Dataframes
  8. Comprehensive Profile Report
  9. Interactive Plots
  10. Third Party Data
  11. Interactive Data Analysis
  12. Course Review

Section 12: Optimizing dataframes

  1. Optimizing dataframes

Section 13: Handling Large Data

  1. Sampling On Load
  2. Efficient File Formats
  3. HDF5
  4. Chunking
  5. Load to Database
  6. Course Review

Section 14: Making Pandas Faster

  1. Faster Pandas
  2. Numba
  3. Dask Part-1
  4. Dask Part-2
  5. Modin
  6. Swifter
  7. Vaex
  8. Cython
  9. Cythonize Pandas Code
  10. Cythonize apply

Section 15: Data Visualization

  1. Matplotlib Part 1 - Getting Started
  2. Matplotlib Part 2 - Plot Components
  3. Matplotlib Part 3 - Subplots
  4. Matplotlib Part 4 - Annotations
  5. Dual Axis Line Plots
  6. Bar Charts
  7. Histogram and Density Plots
  8. Regression Plots
  9. Pair Plots
  10. Course Review
  11. Assignment
  12. Assignment Solution
Report abuse