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What is Pandas in Python

Last Updated on December 26, 2023 by Ankit Kochar

Pandas is a powerful and versatile data manipulation and analysis library for Python. Developed by Wes McKinney in 2008, Pandas is widely used in the field of data science, machine learning, and analytics. Named after the term "Panel Data," Pandas provides data structures that are efficient for handling and analyzing structured data, making it an indispensable tool for data professionals.
At its core, Pandas revolves around two primary data structures: Series and DataFrame. Series is a one-dimensional labeled array, while DataFrame is a two-dimensional table with labeled axes (rows and columns). These structures allow users to perform a wide array of data operations, such as cleaning, filtering, grouping, and aggregation.
Whether you’re a beginner exploring data analysis or an experienced data scientist working on complex projects, Pandas simplifies the process of working with structured data, enabling you to focus on extracting meaningful insights from your datasets.

Why is Pandas Important?

Pandas is an essential Python library created in 2008 by Wes Kinney and released as an open-source project in the year afterwards, in 2009, that can be used for cleaning, processing, manipulation, and visualization of data. It consists of handy data structures like data including Series and Dataframes that revolve around the purpose of storing and manipulating.

A series is a one-dimensional labelled array that is able to hold any type of Python object whether it belongs to integers, floats, strings, or even boolean values. On the other hand, a DataFrame is a two-dimensional labelled data structure with columns of various types, or in simpler words, it can be assumed as a table consisting of rows and columns where each column can be assumed as a Series. Be it any profession that deals with data, a good knowledge of Pandas becomes a necessity to excel in the field.

The source of files for data application can be CSV files, Excel spreadsheets, SQL databases, etc as all these can be imported or created in pandas using the DataFrame with valid operations performed on them.

Getting Started with Pandas

To get started with Pandas, one must ensure that Python is already installed on the user’s machine, in case this step is already fulfilled, you can head over to the terminal and run the command pip install pandas.

Once the installation is completed, open Python in your terminal and write import pandas as pd to ensure that python is successfully installed. Keep in mind that Python does not give errors on interpretation, so if you don’t get any errors, pandas are installed.

It is recommended to install a notebook, preferably Jupyter Notebook to work on data science and data analysis projects. However, there are options available to do on the go like Google Colab with E-Mail Authentication.

Operations in Pandas in Python

Now we have covered some stepping stones to answer what is pandas in python, let us move forward to see how we can be able to perform various operations using pandas.

We can create a DataFrame as easily as by just importing the pandas library, creating a dictionary in python, and then turning it into DataFrame with a pandas-enabled function. Given below is an example.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'Dave'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'Paris', 'London', 'Tokyo']}


       Name    Age      City
   0  Alice      25     New York
   1  Bob        30     Paris
   2  Charlie   35     London
   3  Dave      40    Tokyo

But there’s much more to that as pandas is also capable of operations such as loading and manipulation of data, cleaning, preprocessing and analysis of data.


# the date column to a datetime object
df['Date'] = pd.to_datetime(df['Date'])
import pandas as pd

# load the CSV file into a DataFrame
df = pd.read_csv('sales_data.csv')

# view the first few rows of the DataFrame

# check for missing values

# fill in missing values with 0
df.fillna(0, inplace=True)

# convert
# extract the year and month from the date column
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month

# group the data by year and month and calculate the total sales
monthly_sales = df.groupby(['Year', 'Month'])['Sale Amount'].sum()

# plot the monthly sales data as a line chart
monthly_sales.plot(kind='line', xlabel='Month', ylabel='Total Sales', title='Monthly Sales')

We first load the CSV file into a DataFrame using pd.read_csv(). We then use df.head() to view the first few rows of the DataFrame and df.isnull().sum() to check for missing values. Since there are missing values, we fill them in with 0 using df.fillna(). We then convert the date column to a datetime object using pd.to_datetime() and extract the year and month from the date column using df[‘Date’].dt.year and df[‘Date’].dt.month, respectively. We group the data by year and month and calculate the total sales using df.groupby() and .sum(). Finally, we plot the monthly sales data as a line chart using monthly_sales.plot().

Pandas vs SQL

One of the questions that have left many data enthusiasts curious is if SQL is already used for a such cause then what pandas offer better or their strikingly similar manipulation techniques to obtain data insights.

Pandas is a data manipulation library while SQL is used for managing or querying relational databases. Overall, it is proven that Pandas are suitable for medium-level datasets that require less memory while SQL is preferred for larger datasets stored in databases. Hence the choice of technology depends on what is your need such data manipulation makes pandas the choice but performing aggregate functions on a huge set of data makes SQL the better option.

Applications of Pandas

As we have covered in this article on what is pandas in python to some extent. Let us look at the applications of pandas with the most common use cases being:-

  1. Machine Learning
    Pandas is extensively used in machine-learning algorithms by cleaning and pre-processing and cleaning as well as used for the analysis of the machine-learning models.

  2. Financial Analysis
    Pandas has a major contribution to Python emerging as the programming language used in the finance sector. Frequent operations like calculating the volatility, returns and other indicators are done using Pandas.

  3. Time Series Analysis
    Pandas has important time series analysis functions for techniques like resampling, time zone handling and rolling windows.

  4. Cleaning and Analysis of Data
    Data consisting of missing values, duplicate values, and denormalized data can be handled using the Pandas library in Python. Inferential, Descriptive and Correlation Analysis can be performed using Pandas.

In conclusion, Pandas stands as a cornerstone in the Python ecosystem for data manipulation and analysis. Its intuitive and powerful data structures, combined with a plethora of functions and methods, make it an invaluable tool for anyone dealing with structured data. Whether you are cleaning messy datasets, exploring trends, or preparing data for machine learning models, Pandas provides a robust and efficient solution.
As the field of data science continues to evolve, Pandas remains a reliable and essential library that empowers users to unlock the potential of their data. Its active community, regular updates, and seamless integration with other Python libraries contribute to its enduring popularity and effectiveness.

Frequently Asked Questions Related to Pandas in Python

Below are some of the FAQs related to Pandas in Python:

Q1: What is the difference between a Series and a DataFrame in Pandas?
In Pandas, a Series is a one-dimensional labeled array, similar to a column in a spreadsheet. On the other hand, a DataFrame is a two-dimensional table with labeled axes, consisting of multiple Series arranged in a tabular fashion. In simpler terms, a DataFrame is a collection of Series.

Q2: How can I install Pandas in Python?
You can install Pandas using the following command in your Python environment:

pip install pandas

Q3: Can Pandas handle missing data?
Yes, Pandas provides various methods for handling missing data, such as dropping missing values or filling them with a specified value or a value computed from the data.

Q4: What file formats does Pandas support for reading and writing data?
Pandas supports a variety of file formats, including CSV, Excel, HDF5, SQL, JSON, and more. You can use functions like read_csv(), read_excel(), to_csv(), and others to work with different file types.

Q5: Is Pandas suitable for large datasets?
Yes, Pandas is designed to handle large datasets efficiently. However, for extremely large datasets that may not fit into memory, users often combine Pandas with other libraries like Dask or use database systems for distributed computing.

Q6: Can I customize the index in a Pandas DataFrame?
Absolutely. You can set a custom index for a DataFrame using the set_index() method, and you can reset the index using reset_index(). This allows for flexibility in organizing and accessing your data.

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