Learn Top Most Python Topics By Codersarts - Part 1 : Pandas

Learn Top Most Python Topics By Codersarts

Hello developers,

Now Codersarts launch the new python study package series, in which here, we will try to covers all python related topics which is related to python and data- science. This series is designed as per his official document and make it easy to understand for both programmers and non-programers.

Let's will start topic "Pandas", and next part we will covers other topic like "Numpy", "SciPy", "Matplotlib", etc.

Table of content

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• Pandas - Introduction

• Pandas - Installation Guide

• Pandas - Data Structure

• Pandas - Series

• Pandas - Data Frames

• Pandas - Frequencies

• Pandas - Panel

• Pandas - DateTimeIndex

• Pandas - Indexing and Selecting Data

• Pandas -Window

• Pandas - Aggregations

• Pandas - Missing Data

• Pandas - GroupBy

• Pandas - Merging/Joining

• Pandas - Sorting

• Pandas - Concatenation

• Pandas - Function

Pandas - Introduction

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Pandas is an open source open-source python Library providing high-performance data manipulation and analysis tool using its powerful data structures in Python programming language.

Pandas - Installation Guide

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We can install it using below commands

Via PyPI: Open cmd and install it

And then import

Test it By Running on Jupyter Notebook

Pandas - Data Structure

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Pandas divided into three types of Data Structure

• Series (Dimensions - 1)

• DataFrame (Dimensions - 2)

• Panel (Dimensions - 2)

In this higher dimensions is container of lower dimensions, means Panel is container of DataFrame, and DataFrame is container of Series.

We will discuss all three types of data structure separately in this blog so continue blog to read all about Data Structures.

Pandas - Series

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It is a one-dimensional labeled array.

Holding any types of data like -

• integer

• string

• float, etc.

pandas.Series : It can be created using the following constructor

How to create series: Pandas series can be created using various methods which is given below

• Array

• Dict

• Scalar value or constant

Example

Create Empty series

Run on Jupyter notebook

Creating Series using array: You can create ndarray using with the help of below example

Run on Jupyter notebook

Creating using by default: You can also create it by default using own indexing values.

Run on Jupyter notebook

Creating using dict: It directly passed a input when no index is supplied, here we can understand it with the help of below example

Run on Jupyter notebook

We can also run it using assign default indexing values as per our requirements

p = pd.Series(data,index=['b','c','d','a'])

Then output look like that

b 6.0

c 7.0

d NaN

a 5.0

Pandas - Data Frames

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It is the two-dimensional, and tabular data structure with labeled axes (rows and columns), which is used to managing data.

Syntax:

pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)

Example1:

Run on Jupyter notebook

Example2:

Run on Jupyter notebook

Creating Using ndarray

Run on Jupyter notebook

Dataframes use with different types of attributes(like .loc, .iloc, etc) and methods(like, add, append, aggregate, etc) to manage data if you want to read complete all about attribute and methods the click here

Pandas - Panel

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Python Pandas Panel is an important container for data which is 3-dimensional.

Panel Parameters

• data

• items

• major-axis

• minor-axis

• copy

• dtype

Create an Empty Panel: Follow the given below code to create empty panel

Creating Empty panel:

Example1:

Run on Jupyter notebook

Example2:

Run on Jupyter notebook

How to selecting data from panel

Select the data from the panel using −

• Items

• Major_axis

• Minor_axis

Example

Using items -

Run on Jupyter notebook

Thanks for reading codersarts blog, we will continue remaining topic in next part - 2 of this series.

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