In this tutorial you will learn about Python Pandas Series and DataFrame Automation from basics to advance.

Pandas is a Python library used for data manipulation and analysis. It provides two primary data structures to work with - Series and DataFrame.

A Pandas Series is a one-dimensional labeled array that can hold data of any type (integer, float, string, etc.). The labels, called the index, can be integers or strings and provide a way to identify the data. A Series can be created from a list, tuple, dictionary, or even another Series.

A Pandas DataFrame is a two-dimensional labeled data structure with columns of potentially different data types. It is similar to a spreadsheet or SQL table. A DataFrame can be created from a dictionary of Series or a list of dictionaries. For example:

This video tutorial will cover a complete understanding of the following topics:

1.) What are Pandas Series and their indexes?
2.) Creating Pandas Series from different data structures
3.) Creating Pandas DataFrame from Lists, Dictionaries, etc.
4.) How to modify and create indexes of Series and DataFrame?
5.) Creating an empty DataFrame and appending rows and columns
6.) Creating DataFrame from scalar values
7.) Different ways to create Pandas DataFrame and Series.

In a DataFrame, each column is a Series, and the rows are identified by the index. The index can be specified when creating the DataFrame, or Pandas will use a default integer index if none is provided.

Pandas provides a wide range of functions and methods to manipulate and analyze data in Series and DataFrames, including indexing, selecting, filtering, grouping, merging, and more.


Pandas Data Science Playlist: [ Ссылка ]


Last Video:
Python Pandas and Data Automation Tutorial | What is Pandas | Features of Pandas.
[ Ссылка ]

Python Teaser: [ Ссылка ]

Python Playlist: [ Ссылка ]

Python Data Structure Playlist: [ Ссылка ]

Python OOPs Playlist: [ Ссылка ]-

Python Excel Automation: [ Ссылка ]

Python Project: [ Ссылка ]

свернуть