Today in this article, we want to talk about the pandas’ library, one of the famous libraries in the Python language.

This library is an essential tool for data analysts and data science people. Also, this library can have many uses for ordinary people.

If you intend to enter the field of science, learning about pandas is a must. And even as a regular Python developer, you need to use Pandas.

What are the uses of the Pandas library?

It is time-consuming and difficult to list the uses of this library because of its many benefits, so it is easier to list its weaknesses!

You can clean and sort your data using the pandas’ library.

For example, suppose you want to analyze the information stored in a CSV file; pandas read the data in this file and convert it into a data frame.

Which is a table and then allows you to perform various operations on it:

• Perform statistical calculations on data
• A look at how the data is distributed in a column
• Investigating the possibility of the columns being dependent on each other
• Clear data
• Collaboration with other significant packages, such as Matplotlib, for data visualization

What is the place of the pandas’ library in data science?

The pandas’ library plays an essential role in data science. Wes McKinney created pandas on the Numpy package, so many Numpy structures are repeated in pandas.

Data generated or calculated in Pandas are usually transferred to packages such as SciPy for more advanced statistical analysis.

Also, these data are usually used in libraries such as matplotlib.

The pandas’ library plays a significant role in data science.

Various versions of Pandas library

So far, various versions of the Pandas library have been released, some of which have significant changes compared to their previous versions, and in some of them, only a few minor bugs have been fixed.

The last version of this library is 0.24, which Wes McKinney released in March 2019.

In Pandas 0.24, in addition to debugging, there have been changes to the “Application Programming Interface” and its type of plugins. Overall, this version has significant improvements compared to its predecessor.

Data structures in Pandas

Pandas have two main structures for data storage, which are:

• Series
• DataFrame

Series

A series is similar to a one-dimensional array. Series can store data of any type. You can change the values placed in series, But the pandas’ series size is immutable.

The index of 0 will be assigned to the first element in the series, and the index of the last component of the series is equal to N-1, where N is the total number of features in the series.

To build Pandas Series, you must first import the package using Python‘s import command.

DataFrame

The DataFrame data structure in Pandas can be considered as a table.

A DataFrame organizes data into rows and columns and creates a two-dimensional data structure from them.

Columns can contain values of various types, and at the same time, You can change the size of the DataFrame so The user can edit it.

To build a DataFrame, you can start from scratch or convert data structures like Numpy arrays into a DataFrame.

Conclusion

Pandas is a handy library, especially for data science. Various functions of Pandas significantly help to simplify the data preprocessing process.

In this article, we describe what pandas are and discuss their features and usage.

We hope you enjoy this article and would like you to share your thoughts with us in the comments.