Data Literacy, Pandas Library & Exploration Data Analysis(EDA)

In this section we will cover the topics such as; how to handle and process the data, how to read it, how to visualize the data, what can we deduct from a dataset after the processes that we made and at the end we apply EDA(Exploration Data Analysis) on a valid dataset.

The Dataset and the EDA notebook for this section:

• 🐾 Cats Dataset
• 🗒️ EDA Notebook

Currently Covered Topics:

• Data Types
• Measures of Central Tendency
  • Measures of Central Tendency In Practice
• Measures of Dispersion
  • Measures of Dispersion In Practice
• Essentials of Pandas
  • Data Frames and Series
  • Indexing a Data Frame
  • Selecting a Specific Part of a Data Frame (iloc and loc)
  • Modifying Columns and Adding New Columns
  • Resetting the Data Frame
  • cut and qcut Methods of Pandas
  • Deleting Rows and Columns
  • Missing Values
  • Groupby Method
  • Pivot Tables in Pandas
  • Apply and Lambda in Pandas
  • Concat, Join and Merge Methods
• Exploratory Data Analysis
  • Heading of the Dataset
  • Dataset Story
  • Required Libraries & Setting Pandas Display Options
  • Importing the Dataset
  • General Information About the Dataset
  • Separating and Inspecting of Categorical and Numerical Features
  • Distribution of Categorical Features
  • Distribution of Numerical Features
  • Feature Distributions by Comparisons
  • Correlation Analysis

Future Topics: (COMPLETED)

• Continuation of Pandas
  • Groupby Method
  • Pivot Tables in Pandas
  • Apply and Lambda in Pandas
  • Concat, Join and Merge Methods
• Exploratory Data Analysis
  • General Information About the Data Frame
  • Analysis of Categorical and Numerical Variables
  • Defining the Target and Analysis of Target
  • Correlation Analysis