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Hi everyone, welcome to all in **What is pandas in Python Tutorial**. Here you will learn a very important and popular data science library i.e., pandas.

Pandas is an essential library for data analysis. So it is very popular among data scientists. Data science is a very hot field of the programming world. So If you are deciding to build a bright career in data science then you must have to learn pandas library.

In this tutorial, you will know a general introduction of the pandas library in python. After finishing this article, you will have the following knowledge of pandas.

- Introduction of pandas
- Key features of pandas
- Why pandas is used for data analysis
- How to install pandas
- Working with pandas in python
- Pandas data structure and many more

So without wasting the time let’s explore this awesome python library.

**What Is Pandas In Python – A General Introduction**

**Introduction **

Pandas is a popular python based **open-source**, BSD licensed library for data analysis. It provides high performance, easy to use data structures and data analysis tools for the python programming language.

The name pandas is derived from the word **panel data** which is an econometric term for data sets.

Pandas can do various tasks such as computing tasks like finding mean, median and mode of data as well as handling large CSV files. We can say pandas is the **backbone** of data science.

It provides various data structures such as** series, panel** and** dataframe** which are very helpful to manipulate data sets and time series.

**What Kind Of Data Does Pandas Take ?**

Pandas can work on different kinds of data which are following.

- Arbitrary Mixed data
- Tabular data
- Ordered and unordered data
- Any other form of observational and statistical data set

**History Of Pandas**

The developer of pandas is **Wes McKinney**, an employee of **AQR** capital management. When AQR had needed a high performance, flexible tool to perform qualitative analysis on financial data then Wes McKinney started working on pandas in **2008.**

Before leaving AQR he was able to convince management to allow him to open source the library.

In **2012, Chang She**, another AQR employee, joined as the second major contributor to the library.

Over the time many versions of pandas have been released and the latest version is **1.1.3**.

**Goals Of Pandas**

Pandas has following goals –

- To be the fundamental high level building block for doing practical, real world data analysis in python.
- Becoming the most powerful and flexible open source data analysis/manipulation tool available in any language.

**Key Features Of Pandas**

If we talk about features of pandas then there is a huge list, some of them are listed below.

- Alignment and indexing
- Multiple file format supported
- Great handling of data
- Cleaning up data
- Handling missing data
- Optimized performance
- Input and output tools
- Merging and joining of datasets
- A lot of time series
- Python supported
- Grouping of data
- Visualizing the data
- Perform mathematical operations on data

**Applications Of Pandas**

Pandas has various applications. There are lots of areas where we can use pandas. So here, i have listed some mind blowing applications of pandas.

- Advertising
- Natural Language Processing
- Analytics
- Economics
- Statistics
- Neuroscience
- Big Data
- Data Science
- Stock Prediction
- Recommendation systems

**Why Pandas Are Used For Data Science?**

So, are you thinking why pandas are so popular for data science or what makes it fitted in data science toolkit? Yeah you are thinking right, so let’s explore the logic behind this.

Some of the reasons of popularity of pandas for data science are following-

- Pandas is built on the top of the
**NumPy**package, that means a lot of the structure of NumPy is used or replicated in pandas. - One can easily use data in pandas for plotting functions from
**Matplotlib**, machine learning algorithm in**Scikit-learn**and statistical analysis in**SciPy**. - It is simple to use, hiding all the complex and abstract computations behind.
- Pandas is very fast for data analysis.
- It has eloquent syntax and rich functionality.

Python could be used mainly for data munging and preparation and not for data analysis before development of pandas. But pandas solved this issue.

**Data Analysis Using Pandas – Installing And Importing Pandas**

So now you will learn how to use pandas for data analysis. Are you ready for this? Yes then keep reading !!!

In this section you will learn to install and import pandas, so let’s get started.

**How to install pandas?**

You can install pandas in windows in two ways.

**Installing Pandas Using pip**

To install pandas using pip, you have to run the following command.

`pip install pandas`

**Installing Pandas Using Anaconda**

If you have anaconda, then run the following command to install pandas module.

```
conda install pandas
```

**Installing Pandas In Jupyter Notebook**

If you work on jupyter notebook then run following cell to install pandas.

`!pip install pandas`

The ! at the beginning runs cells as if they were in a terminal.

**How to import pandas?**

After installing pandas, you need to import this. So run the following line to import pandas.

`import pandas as pd`

Here, **pd** is referred to as an **alias** of pandas. We generally use aliases just because it helps in writing less amount of code every time a method or property is called otherwise it is not necessary.

Till now you have learned just the basics of pandas. And now it’s time to move towards the practical part of pandas. So let’s start to learn how to implement pandas practically.

**Core Components Of Pandas – What Are Pandas Data Structure **

Pandas provide primarily two data structures for data analysis which are following.

- Series
- Dataframe

Now let’s learn about them one-by-one.

**Pandas Series**

Pandas series is a one dimensional array that is capable of holding any type of data. It only contains homogeneous data.

Series are similar to** NumPy** arrays, except that we can give them a named or datetime index instead of just a numerical index.

**Key Points To Remember :**

- It is capable of storing homogeneous data.
- It’s size is immutable(can’t be changed).
- Data in series are mutable.

You can create a series from lists, dictionaries etc. You will explore all of them in the upcoming tutorial.

**Pandas Dataframe**

Pandas dataframe is a two-dimensional data structure that contains heterogeneous data.

Dataframes are very similar to **MS Excel sheet, Google sheets** and any other **spreadsheet** software.

It can be created from a dictionary, lists, a list of dictionaries etc. I will cover all these ways in the next tutorials.

**Key Points To Remember :**

- It’s size is mutable.
- Data in dataframes are mutable.
- It contains heterogeneous data.

## Conclusion

So friends you have seen importance of pandas. Now you have a general idea about what is pandas in python. I hope you found this tutorial very helpful. If so then don’t hesitate to share with your friends. In the upcoming tutorials you will learn more about pandas. So till then stay tuned with Dggul AI Tutorial.