Introduction to Pandas In Python

In this article, we will learn about pandas. what is pandas, uses of pandas, application of pandas, How to install pandas, and perform some examples for more understanding.

What is Pandas?

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. 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.

Why use Pandas?

Pandas is a useful library in data analysis. It can be used to perform data manipulation and analysis. Through pandas, you get acquainted with your data by cleaning, transforming, and analyzing it.

  • Easily handles missing data.
  • It uses Series for one-dimensional data structure and DataFrame for multi-dimensional data structure.
  • Visualize the data with help from Matplotlib. Plot bars, lines, histograms, bubbles, and more.
  • Store the cleaned, transformed data back into a CSV, other file or database.
  • It provides a flexible way to merge, concatenate or reshape the data.

Applications of Pandas.

One of the applications of Pandas is that it can work with Big data too. Python has a good connection with Hadoop and Spark, allowing Pandas to have access to Big Data. One can easily write to Spark or Hadoop also with the help of Pandas

  • Stock prediction, Neuroscience, Statistics, Big Data
  • Economics, Recommendation System, Advertising

Installation Process

Before we move on with the code for understanding the features of Pandas, let’s get Pandas installed in your system using the following command.

pip install pandas

If you are learning Pandas, I would advise you to use a jupyter notebook for the same. The visualization of data in jupyter notebooks makes it easier to understand what is going on at each step. If you have don’t knowledge about jupyter notebook you follow this blog Jupyter Notebook

There are two sorts of data structures in Pandas:

  1.  Series
  2. Dataframes

Creating Pandas Series:

Series: Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called indexes. Pandas Series is nothing but a column in an excel sheet. Labels need not be unique but must be a hashable type.

Creating Pandas Dataframe:

Dataframes: Pandas DataFrame is a 2-D labeled data structure with columns of a potentially different type. Just like Excel, Pandas DataFrame provides various functionalities to analyze, change, and extract valuable information from the given dataset.

I hope this article helps you and you will like it.

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