Sort a dataframe based on a column in Python

Sorting the dataframe based on a column requires pandas which is An open-source library called Python Pandas is described as offering high-performance data processing in Python. For both professionals and beginners, this tutorial is made.

Wes McKinney created it in 2008 and used Python to analyze data. Our tutorial covers the fundamental and advanced Python Pandas principles, including Numpy, Data Operation, and Time Series.

Pandas:

An open-source library called Pandas offers high-performance database operations in Python. The name Pandas originates from Panel Data, which denotes econometric techniques from multidimensional data. Wes McKinney created it in 2008 and uses it for data gathering in Python. Data analysis necessitates extensive processing, including cleansing, merging, and other operations. Numerous tools, including Python language, Scipy, Cython, and Panda, are available for quick data processing. However, working with Panda bears is quicker, easier, and more creative than with other tools. Therefore, we favor them.

Given that Pandas is constructed on top of a Numpy package, Numpy is necessary to use Pandas. Python was capable of handling data preparation before Pandas, but it only offered a few tools for data analysis. Pandas entered the scene and improved data analysis capabilities. It can carry out the five crucial steps—load, modify, prepare, model, and analyze—necessary for storing and analyzing data, regardless of where it came from.

Sorting methods in Pandas:

The most effective way to learn and practice Python data analysis fundamentals is with Pandas sort techniques. Data analysis is frequently carried out using spreadsheets, SQL, and Pandas. Pandas can manage many data and have the ability to do extremely efficient data transformations. In this lesson, we'll go through how to utilize the functions. Sort values() and. sort index()let users effectively sort the data in a DataFrame.

Introduction:

The data structure with a labeled axis for both columns and rows is known as a DataFrame. The DataFrame can be sorted by column or row values and column or row index. Data location in the user's Data Frame is indicated numerically by indices in both the rows and columns. The user can obtain data from particular rows or columns using the DataFrame's index position. By default, the index number is zero. However, the user can explicitly choose their index.

Sorting through various types is effectively possible in the DataFrame:

• By label
• By Actual value

CODE:

import pandas as pd  
import numpy as np  
info=pd.DataFrame(np.random.randn(8,2),index=[8,7,6,5,3,4,1,2],columns=['c1','c2'])  
print(info)  

OUTPUT:

By Label:

The sort index() method can be used to sort the DataFrame. The axis parameters and sorting order can be given to accomplish this. By default, row labels are sorted in ascending order.

CODE:

import pandas as pd  
import numpy as np  
info=pd.DataFrame(np.random.randn(8,2),index=[1,2,5,4,8,7,3,6],columns = ['col3','col4'])  
info2=info.sort_index()  
print(info2)  

OUTPUT:

Order of sorting:

By providing a Boolean to the ascending argument, it is possible to modify the sorting order. The panda’s method sort values() is used to sort a data frame. The data frame can be sorted using Pandas sort values() in increasing or descending order.

A fantastic place to start or practise using Python for basic data analysis is with pandas sort algorithms. Most frequently, spreadsheet, SQL, or pandas are used for data analysis. The fact that pandas can manage a lot of data and has highly effective data manipulation skills is one of its primary advantages.

CODE:

import pandas as pd  
import numpy as np  
info= pd.DataFrame(np.random.randn(8,2),index=[1,4,7,2,5,3,8,6],columns = ['col5','col6'])  
info_2 = info.sort_index(ascending=False)  
print(info)  

OUTPUT:

Sort the Columns:

We can order the column labels concerning the axis parameter with values of 0 or 1. When axis=0, it sorts by row by default. The DataFrame will be sorted by the column labels if the additional parameter axis is set to 1 when using the sort index() function. Instead of sorting the data itself, the axis labels are subjected to the algorithm.

CODE:

import pandas as pd  
import numpy as np  
data = pd.DataFrame(np.random.randn(8,2),index=[1,4,8,2,6,7,5,3],columns = ['col4','col5'])  
data =info.sort_index(axis=1)  
print(data)  

OUTPUT:

By Actual value:

It is an additional kind that the DataFrame can sort through. Sort values() is a technique for sorting by values, similar to index sorting. Additionally, it offers a feature that allows us to specify the DataFrame's column name that will be used to sort the values. By passing the "by" argument, it is accomplished.

CODE:

import pandas as pd  
import numpy as np  
data = pd.DataFrame({'c1':[7,1,8,3,5,3,6],'c2':[9,8,7,6,5,4,18]})  
data = data.sort_values(by='c2')  
print(data)  

OUTPUT: