Python Pandas – Introduction

Pandas is one of the important package for data analysis. Pandas is an open-source, BSD-licensed Python library providing high performance and easy to use powerful data structures and data analysis tools.  Pandas makes data manipulating much easier. Pandas is extensively used in machine learning. This tutorial have explained the various features of pandas with examples.

Installation

The following command is used to install Pandas in your local machine.

pip install pandas

Data Structure

Pandas library implements mainly two data structures: Series and DataFrame

1)  Series :

Series is one-dimensional labeled array capable of holding any data type such as integers, strings, floating-point numbers, Python objects, etc. A Series accepts three types of input:

  • a Python dict
  • an ndarray
  • a scalar value (like 5)

 

2) DataFrame :

DataFrame is a 2-dimensional labeled data structure with columns of different types. DataFrame is the most widely used data structure. A DataFrame can accept many kinds of input:

  • Dict of 1D ndarrays, lists, dicts, or Series
  • 2-D numpy.ndarray
  • Structured or record ndarray
  • ASeries
  • Another DataFrame

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Python Pandas Tutorials

Pandas – How to remove DataFrame columns with constant (same) values?

Pandas – How to remove DataFrame columns with only one distinct value?

Pandas – Count unique values for each column of a DataFrame

Pandas – Count missing values (NaN) for each columns in DataFrame

Pandas – MultiIndex

Pandas – Applymap

Pandas – Apply

Pandas – Map

Pandas – Missing Data

Difference between Merge, join, and concatenate

Pandas – Join

pandas : Handling Duplicate Data

Pandas : Handling Categorical Data

Pandas : Data Types

Appending a row to DataFrame

Python Pandas – Merge

Python Pandas – Concatenation & append

Python Pandas – GroupBy

Python Pandas – Visualization

Python Pandas – Options and Customization

Python Pandas – Descriptive Statistics

Python Pandas – Basic functions

Python Pandas – DataFrame

Python Pandas – Series