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Part5-Pandas-Introduction.md

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Introduction to Pandas

Pandas is an open-source library that is made mainly for working with relational or labeled data both easily and intuitively. It provides various data structures and operations for manipulating numerical data and time series. This library is built on the top of the NumPy library. Pandas is fast and it has high-performance & productivity for users. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.

Key Features of Pandas

  • Fast and efficient DataFrame object with default and customized indexing.
  • Tools for loading data into in-memory data objects from different file formats.
  • Data alignment and integrated handling of missing data.
  • Data alignment and integrated handling of missing data.
  • Label-based slicing, indexing and subsetting of large data sets.
  • Columns from a data structure can be deleted or inserted.
  • Group by data for aggregation and transformations.
  • High performance merging and joining of data.
  • Time Series functionality.

How to Install Python Pandas on Windows and Linux?

Pandas programs can be written on any plain text editor like notepad, notepad++, or anything of that sort and saved with a .py extension. To begin with, writing Pandas Codes and performing various intriguing and useful operations, one must have Python installed on their System. This can be done by following the step by step instructions provided below:

What if Python already exists? Let’s check

To check if your device is pre-installed with Python or not, just go to the Command line. Now run the following command:

python --version

If Python is already installed, it will generate a message with the Python version available.

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To install Python, please visit: How to Install Python on Windows or Linux

Downloading & Installling Pandas

Pandas can be installed in multiple ways on Windows and on Linux. Various different ways are listed below:

Windows

Python Pandas can be installed on Windows in two ways:
* Using Pip
* Using Anaconda

Install Pandas using pip
PIP is a package management system used to install and manage software packages/libraries written in Python. These files are stored in a large “on-line repository” termed as Python Package Index (PyPI). Pandas can be installed using PIP by the use of the following command:

pip install pandas

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Install Pandas using Anaconda
Anaconda is open-source software that contains Jupyter, spyder, etc that are used for large data processing, data analytics, heavy scientific computing. If your system is not pre-equipped with Anaconda Navigator, you can learn how to install Anaconda Navigator on Windows or Linux

Steps to Install Pandas using Anaconda Navigator:

Step 1: Search for Anaconda Navigator in Start Menu and open it.
Step 2: Click on the Environment tab and then click on the create button to create a new Pandas Environment.
Step 3: Give a name to your Environment, e.g. Pandas and then choose a python version to run in the environment. Now click on the Create button to create Pandas Environment.
Step 4: Now click on the Pandas Environment created to activate it.
Step 5: In the list above package names, select All to filter all the packages.
Step 6: Now in the Search Bar, look for ‘Pandas‘. Select the Pandas package for Installation.
Step 7: Now Right Click on the checkbox given before the name of the package and then go to ‘Mark for specific version installation‘. Now select the version that you want to install.
Step 8: Click on the Apply button to install the Pandas Package.
Step 9: Finish the Installation process by clicking on the Apply button.
Step 10: Now to open the Pandas Environment, click on the Green Arrow on the right of package name and select the Console with which you want to begin your Pandas programming.

Linux

To install Pandas on Linux, just type the following command in the Terminal Window and press Enter. Linux will automatically download and install the packages and files required to run Pandas Environment in Python:

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