In today’s world, we are surrounded by data that comes in many different shapes and forms. It is impossible for humans to manage large volumes of data manually. For this reason, to analyze or categorize this volume of data, we need special tools that can make data analysis easier for us.

We all know that the Python programming language is a versatile language. One of the reasons for Python’s popularity is the libraries used in data science and machine learning. Among all these libraries, Pandas is one of the most prominent Python libraries. In this article, we will introduce the Pandas library.

What are Pandas?

Maybe the name Pandas reminds you of panda bears. But in fact, “Pandas” was created from the combination of two words, “Panel data” and “Python Data Analysis”. Pandas is one of the famous Python programming language libraries in data science. The Pandas library contains functions used in data analysis. For example, selecting a specific column or row, categorizing and sorting, merging different data, etc.

“Data science is a branch of computer science that studies how to store, use and analyze data to extract information from it.”

Pandas perform data processing and analysis in five steps: load, prepare, manipulate, model, and analyze.

Pandas is a free and open-source library. Many people believe that this library from Python is the main competitor of the R programming language, which is used specifically in statistics and data analysis. However, it is good to know that data analysis is easier in Pandas than in R.

Benefits of the Pandas library


  • The Pandas library speeds up the manipulation and analysis of complex data quickly and efficiently.
  • This tool allows resizing data.
  • Pandas is a must-have tool for data professionals. For this reason, it has a large community.
  • Pandas support different data types.
  • Data analysts can easily integrate data sets.

Data structures in pandas


Series in Pandas means a one-dimensional array (column) capable of storing all types of data (integers, strings, decimal numbers, Python objects, etc.). We can easily convert a list, tuple and dictionary into series using the series () method. The rows in the series are identified by an index.

Features of the series

It is not possible to name them in the series column.

It is possible to convert series to data frames and vice versa. That is, two or more series can be combined and create a data frame. Also, a data frame containing several columns can be converted into several series of one column.

Data Frame

Data frames in Pandas are actually two-dimensional arrays that store data in a table with specified columns and rows.

Data Frame properties

Rows and columns in the data frame can be named.

Data frame supports heterogeneous datasets.

In the data frame, arithmetic operations can be performed on the data.

Data frame supports CSV, Excel, JSON and SQL data.

In the data frame, it is possible to access the lost data.

Install Pandas

Via Anaconda

There are several different ways to install Pandas on your computer. The recommended method in the Pandas documentation is to install it on Anaconda. Anaconda also includes other popular SciPy packages such as NumPy, Matplotlib, and IPython, all of which work well with Pandas.

Pip Install

The second way to install pandas is to use pip, which allows you to install individual packages on your computer using the pip install command in the terminal.

Features of Pandas

Read and write data in the table

Pandas supports various file formats or data sources such as csv, excel, sql, json, parket, etc. Importing data from each of these data sources is done by a function with the read_* prefix. Similarly, the to_* prefix can also be used to store data.

Select or filter a subset of the table

Pandas has the ability to select or filter specific rows or columns of a table.

Draw a diagram with Pandas

Pandas displays data as custom graphs using the powerful Matplotlib library. You can draw different types of graphs such as scatter, bar, pie, etc.

Adding columns to the Data Frame

With Pandas, you can add a new column based on the existing columns in the data frame.

Data summarization

In Pandas, commonly used and basic operators in statistics such as average, median, minimum, maximum, etc. can be easily calculated. You can apply these operators to all or part of the data.

Change table layout

There are several different ways to transform tables. You can do this using the melt () and pivot () functions.

Combine data from multiple tables

Pandas has the ability to combine data from multiple tables in rows and columns.

The possibility of managing time series data or Time series data

Pandas supports time series data very well and provides good tools for working with this data.

Working with textual data

Data is not just about numbers. Pandas provides a wide range of functions for cleaning textual data and extracting useful information from them.

Pandas is not only limited to the features we mentioned in this section. The official Pandas website provides a help file for working with Pandas.

Why should we learn Pandas?

If you deal with a lot of data, or want to do data science, Pandas is a must-have tool. Pandas allow us to analyze big data and draw conclusions based on statistical theories.

Pandas can clean messy datasets and make them readable and relevant.

One of the key elements in data science and machine learning is being able to effectively manipulate and evaluate the content of your data. Pandas not only provide a flexible way to manage data, but more importantly they allow you to clearly analyze patterns between data.

As mentioned, Pandas is one of the Python programming language libraries. As a result, if you are already familiar with the Python language, you can easily work with this library. If you are not familiar with Python, don’t worry. Because the Python programming language is one of the easiest programming languages to learn, which, at the same time, provides great capabilities to programmers. As a result, to work with pandas, it is suggested that you first participate in the Python training course and then continue to learn more deeply on the data science or machine learning training course.

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