Introduction
  • What is Data analysis
  • Claim your Free Gift
  • Course FAQ
  • Join Online Classroom
  • Introduction to Pandas
  • Course FAQ
  • How to get Certificate
  • Pandas
Installation and IDE
  • Different ways of installation
  • Download and Install anaconda + Pandas
  • Troubleshooting : 'conda' is not recognized as an internal or external command
  • Anaconda + Conda Command
  • Conda Cheatsheet
  • anaconda, conda & pandas Update
  • Getting started with Jupyter Lab
  • Jupyter Notebook cheatsheet
  • Import Library
  • Pandas Version
  • Installation
Code Download
  • Python Code
Python Crash Course [Optional]
  • Introduction
  • Python Basics - I
  • Data types, Numbers, String
  • Python Basics - II
  • Loops & Decision making
  • Lists and tuples
  • Lists and tuples
  • Dictionary and set
  • Dictionary and set
  • Functions
  • Python - 1
  • Python - 2
Python Exercises
  • Exercise Overview
  • Solutions
Numpy
  • Creating NumPy array
  • Numpy indexing and selection, Functions
  • Some more Numpy Functions
  • Linear algebra with NumPy
  • List vs NumPy Array
  • Views vs Copy - Numpy Array
  • Insert, Append and Delete NumPy array
  • Split, Concatenate, Tile and Repeat array
  • NumPy
Series : Pandas
  • Series
  • Introduction to Series
  • Create Series from Python Object
  • Create Series from CSV file
  • Create Series Object
  • Series attributes & methods
  • Series attributes & methods
  • Label indexing
  • Label indexing
  • inplace parameter, sort_values & sort_index
  • inplace parameter, sort_values & sort_index
  • Apply Python built in function on Series
  • Extract Value from Series
  • Extract Value from Series
  • .value_counts() Method
  • .apply() and .map() method
  • .apply() and .map() method
  • Series
Data Frame : Pandas
  • Introduction to Data Frame
  • Create Data Frame - random data + from File
  • Data frame attributes and methods
  • Adding new column
  • Select one or more than one column
  • Broadcasting operation
  • Drop missing row or column
  • Filtering Data with one condition
  • Filtering Data with multiple condition
  • Filtering Data with .isin() method
  • Filtering Data with .between() method
  • unique() & nunique() method
  • sorting values
  • sort index and inplace parameter
  • .loc() and .iloc() method
  • .ix() method
  • .astype() method - optimize memory requirement
  • set_index() : change index column
  • .apply() method on single column
  • .apply() method on multiple column
  • Fetch random sample
Pandas Exercise
  • Exercise Overview : Google App store dataset
  • Pandas Exercise Solution - I
  • Pandas Exercise Solution - II
Panel : Pandas
  • Warning - Panel Data type
Pandas Options
  • max_rows , max_columns
  • precision
Visualize Data with Pandas