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Pandas in Python refer to an open-source library that delivers high-performance, easy-to-use data structures and different data analysis tools. Python Panda is employed in a wide range of fields, including finance, academics, statistics, and more. It is well-suited for different types of data, including statistical data sets, unlabeled data, ordered and unordered time series data, and more. Dive into this article to learn more about Pandas in Python.
Table of Content:
Python Panda is a core library for data analysis. It is a base package with additional functionality from various other packages. Pandas in Python provide the ability to organize structured data into an array so that it can be managed easily.
Python Panda is useful for performing the following tasks:
Therefore, the importance of Python Panda in data analysis stems from its potential to make data sets more accessible and comprehensible.
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A few key features of Pandas in Python are as follows:
Learn: What Is a List in Python: Functions with Examples
The two primary data structures in Python Panda include series and DataFrame
It is a one-dimensional array that contains different types of data. The row labels in a series are referred to as the index. A series cannot have multiple columns and comes with only one parameter.
To create a series, you will have to import the numpy module and use the array() function. The code is as follows:
It is suitable for a two-dimensional array with labeled rows and columns. Python Pandas DataFrame is widely used and comes with the row index and column index. The Pandas DataFrame in Python comes with the following features:
The columns can be heterogeneous with int, bool, and others
It can be defined as a dictionary of Series structures with indexed rows and columns.
You can easily make a DataFrame in Python with the help of a list. The code for creating a DataFrame is as follows:
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You will come across two types of sorting techniques in Python Panda. They are as follows:
The DataFrame can be sorted with the help of the sort_index() method. The code is as follows:
By default, sorting on row labels takes place in ascending order.
You can use the sort_values() method to perform sorting according to values. The code is as follows:
The groupby function in Python Panda can perform one of the following functions on original data:
The syntax for dataframe.groupby() is as follows:
If you are using Pandas in Python, you will be able to merge two DataFrames in the following way:
A few tips and tricks for using Pandas in Python are as follows:
Pandas in Python comes with a testing module that offers various convenient functions. Pandas in Python lets you use these toy data structures for the purpose of testing assertions, benchmarking, experimenting, and more.
Built on the Numpy library, Python Panda is valuable for data analysis, machine learning, and more. It comes with two types of data structures, including the series and DataFrames. Moreover, Pandas in Python can be used with a wide variety of libraries applicable to data science.
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