Python Pandas Tutorial
Python Pandas is defined as an open-source library that provides high-performance data manipulation in Python. This tutorial is designed for both beginners and professionals.
It is used for data analysis in Python and developed by Wes McKinney in 2008. Our Tutorial provides all the basic and advanced concepts of Python Pandas, such as Numpy, Data operation and Time Series
Python Pandas Introduction
Pandas is defined as an open-source library that provides high-performance data manipulation in Python. The name of Pandas is derived from the word Panel Data, which means an Econometrics from Multidimensional data. It is used for data analysis in Python and developed by Wes McKinney in 2008.
Data analysis requires lots of processing, such as restructuring, cleaning or merging, etc. There are different tools are available for fast data processing, such as Numpy, Scipy, Cython, and Panda. But we prefer Pandas because working with Pandas is fast, simple and more expressive than other tools.
Pandas is built on top of the Numpy package, means Numpy is required for operating the Pandas.
Before Pandas, Python was capable for data preparation, but it only provided limited support for data analysis. So, Pandas came into the picture and enhanced the capabilities of data analysis. It can perform five significant steps required for processing and analysis of data irrespective of the origin of the data, i.e., load, manipulate, prepare, model, and analyze.
Key Features of Pandas
Benefits of Pandas
The benefits of pandas over using other language are as follows:
Python Pandas Data Structure
The Pandas provides two data structures for processing the data, i.e., Series and DataFrame, which are discussed below:
It is defined as a one-dimensional array that is capable of storing various data types. The row labels of series are called the index. We can easily convert the list, tuple, and dictionary into series using "series' method. A Series cannot contain multiple columns. It has one parameter:
Data: It can be any list, dictionary, or scalar value.
Creating Series from Array:
Before creating a Series, Firstly, we have to import the numpy module and then use array() function in the program.
0 P 1 a 2 n 3 d 4 a 5 s dtype: object
Explanation: In this code, firstly, we have imported the pandas and numpy library with the pd and np alias. Then, we have taken a variable named "info" that consist of an array of some values. We have called the info variable through a Series method and defined it in an "a" variable. The Series has printed by calling the print(a) method.
Python Pandas DataFrame
It is a widely used data structure of pandas and works with a two-dimensional array with labeled axes (rows and columns). DataFrame is defined as a standard way to store data and has two different indexes, i.e., row index and column index. It consists of the following properties:
Create a DataFrame using List:
We can easily create a DataFrame in Pandas using list.
0 0 Python 1 Pandas
Explanation: In this code, we have defined a variable named "x" that consist of string values. The DataFrame constructor is being called on a list to print the values.
Before learning Python Pandas, you should have a basic understanding of Computer Programming terminologies and any of the programming languages.
Our Python Pandas Tutorial is designed to help beginners and professionals.
We assure that you will not find any problem in this Python Pandas tutorial. But if there is any mistake, please post the problem in contact form.