Introduction to Pandas

Pandas is an open-source library in Python 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 top of the NumPy library of Python. Pandas is fast and it has high performance & productivity for users.

History of Pandas Library

Pandas were initially developed by Wes McKinney in 2008 while he was working at AQR Capital Management. He convinced the AQR to allow him to open source the Pandas. Another AQR employee, Chang She, joined as the second major contributor to the library in 2012. Over time many versions of pandas have been released. The latest version of the pandas is 1.5.3, released on Jan 18, 2023.

What we will learn in this article:
Why Use Pandas
What can you do using Pandas
Getting Started
Installing Pandas
Importing Pandas
Pandas Data Structures
Series
DataFrame
How to run Pandas Program in Python

Why Use Pandas?

Fast and efficient for manipulating and analyzing data.
Data from different file objects can be easily loaded.
Flexible reshaping and pivoting of data sets
Provides time-series functionality.

What can you do using Pandas?

Pandas are generally used for data science but have you wondered why? This is because pandas are used in conjunction with other libraries that are used for data science. It is built on the top of the NumPy library which means that a lot of structures of NumPy are used or replicated in Pandas. The data produced by Pandas are often used as input for plotting functions of Matplotlib, statistical analysis in SciPy, and machine learning algorithms in Scikit-learn. Here is a list of things that we can do using Pandas.

Data set cleaning, merging, and joining.
Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data.
Columns can be inserted and deleted from DataFrame and higher dimensional objects.
Powerful group by functionality for performing split-apply-combine operations on data sets.
Data Visulaization

Getting Started

Installing Pandas

The first step of working in pandas is to ensure whether it is installed in the system or not. If not then we need to install it in our system using the pip command. Type the cmd command in the search box and locate the folder using the cd command where python-pip file has been installed. After locating it, type the command:

pip install pandas

For more reference take a look at this article on installing pandas follows.

Importing Pandas

After the pandas have been installed into the system, you need to import the library. This module is generally imported as follows:

import pandas as pd

Here, pd is referred to as an alias to the Pandas. However, it is not necessary to import the library using the alias, it just helps in writing less amount code every time a method or property is called.

Pandas Data Structures

Pandas generally provide two data structures for manipulating data, They are:

Series
DataFrame

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. The object supports both integer and label-based indexing and provides a host of methods for performing operations involving the index.

Series Data Frame

Note: For more information, refer to Python | Pandas Series

Creating a Series

In the real world, a Pandas Series will be created by loading the datasets from existing storage, storage can be SQL Database, CSV file, or an Excel file. Pandas Series can be created from lists, dictionaries, and from scalar values, etc.

Example:

Python3

import pandas as pd 
import numpy as np
  
# Creating empty series 
ser = pd.Series() 
print("Pandas Series: ", ser) 
  
# simple array 
data = np.array(['g', 'e', 'e', 'k', 's']) 
    
ser = pd.Series(data) 
print("Pandas Series:\n", ser)

Output:

Pandas Series: Series([], dtype: float64)
Pandas Series:
0    g
1    e
2    e
3    k
4    s
dtype: object

Note: For more information, refer to Creating a Pandas Series

DataFrame

Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.

pandas-dataframe

Note: For more information, refer to Python | Pandas DataFrame

Creating Data Frame

In the real world, a Pandas DataFrame will be created by loading the datasets from existing storage, storage can be SQL Database, CSV file, or an Excel file. Pandas DataFrame can be created from lists, dictionaries, and from a list of dictionaries, etc.

Example:

Python3

import pandas as pd 
    
# Calling DataFrame constructor 
df = pd.DataFrame() 
print(df)
  
# list of strings 
lst = ['Geeks', 'For', 'Geeks', 'is', 'portal', 'for', 'Geeks'] 
    
# Calling DataFrame constructor on list 
df = pd.DataFrame(lst) 
print(df)

Output:

Empty DataFrame
Columns: []
Index: []
        0
0   Geeks
1     For
2   Geeks
3      is
4  portal
5     for
6   Geeks

Note: For more information, refer to Creating a Pandas DataFrame

How to run Pandas Program in Python?

Pandas program can be run from any text editor but it is recommended to use Jupyter Notebook for this as Jupyter gives the ability to execute code in a particular cell rather than executing the entire file. Jupyter also provides an easy way to visualize pandas data frames and plots.