To find the common rows between two DataFrames, use the merge() method. Let us first create DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, ... Read More

To check if any specific column of two DataFrames are equal or not, use the equals() method. Let us first create DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], ... Read More

To calculate the mean of column values, use the mean() method. At first, import the required Pandas library −import pandas as pdNow, create a DataFrame with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], ... Read More

To create a pipeline in Pandas, we need to use the pipe() method. At first, import the required pandas library with an alias −import pandas as pdNow, create a DataFrame −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], ... Read More

To concatenate multiindex into single index, at first, let us import the required Pandas and Numpy libraries with their respective aliases −import pandas as pd import numpy as np Create Pandas series −d = pd.Series([('Jacob', 'North'), ('Ami', 'East'), ('Ami', 'West'), ('Scarlett', 'South'), ('Jacob', 'West'), ('Scarlett', 'North')])Now, use the Numpy ... Read More

To typecast pandas into Set, use the set(). At first, let us create a DataFrame −dataFrame = pd.DataFrame( { "EmpName": ['John', 'Ted', 'Jacob', 'Scarlett', 'Ami', 'Ted', 'Scarlett'], "Zone": ['North', 'South', 'South', 'East', 'West', 'East', 'North'] ... Read More

To find the uncommon rows between two DataFrames, use the concat() method. Let us first import the required library with alias −import pandas as pdCreate DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], ... Read More

To query the columns of a Pandas DataFrame, use the query(). We are querying to filter records. At first, let us create a Pandas DataFramedataFrame = pd.DataFrame({"Product": ["SmartTV", "PenDrive", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Using query() to query columns with conditions −print(dataFrame.query('Opening_Stock >=500 & ... Read More

To select multiple rows from a DataFrame, set the range using the : operator. At first, import the require pandas library with alias −import pandas as pdNow, create a new Pandas DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b'])Select multiple rows ... Read More

To select a column from a DataFrame, just fetch it using square brackets. Mention the column to select in the brackets and that’s it, for exampledataFrame[‘ColumnName’]At first, import the required library −import pandas as pdNow, create a DataFrame. We have two columns in it −dataFrame = pd.DataFrame( ... Read More