Python Articles - Page 376 of 829

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'], "Units": [100, 150, 110, 80, 110, 90] } )Create DataFrame2 with two columns −dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] ... 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'], "Units": [100, 150, 110, 80, 110, 90] } )Finding the mean of a single column “Units” using mean() −print"Mean of Units column from DataFrame1 = ", dataFrame1['Units'].mean()In the same way, we have calculated the mean value from the 2nd DataFrame.ExampleFollowing is the complete code −import pandas ... 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'], "Units": [100, 150, 110, 80, 110, 90] } ) Create a pipeline and call the upperFunc() custom function to convert column names to uppercase −pipeline = dataFrame.pipe(upperFunc)Following is the upperFun() to convert column names to uppercase −def upperFunc(dataframe): # Converting ... 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 arrange() method −dataFrame = pd.Series(np.arange(1, 7), index=d) Let us now map and join −dataMap = dataFrame.index.map('_'.join)ExampleFollowing is the code −import pandas as pd import numpy as np # pandas series d = pd.Series([('Jacob', 'North'), ('Ami', 'East'), ('Ami', 'West'), ('Scarlett', 'South'), ('Jacob', 'West'), ('Scarlett', 'North')]) dataFrame = pd.Series(np.arange(1, 7), ... 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'] } ) Typecast pandas to set and then take set union −set(dataFrame.EmpName) | set(dataFrame.Zone)ExampleFollowing is the complete code − import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { "EmpName": ['John', 'Ted', 'Jacob', 'Scarlett', 'Ami', ... Read More

Let us begin by learning about the digital certificate.Digital CertificateIt is basically a certificate issued digitally, issued to verify a user's authenticity i.e., verifying the user sending a message is who he or she claims to be, and also to provide the receiver with the means to encode a reply.Whoever wants to or an individual who wants to send encrypted messages applies for a digital certificate from a Certificate Authority (CA).Need of digital certificateThe digital certificate allows entities to share their public key in an authenticated way. They are used in initializing and establishing secure SSL (Secure Sockets Layer) connections ... 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'], "Reg_Price": [1000, 1500, 1100, 800, 1100, 900] } )Create DataFrame2 with two columns −dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Reg_Price": [1000, 1300, ... Read More

We can use the shift() method in Pandas to shift the columns of a DataFrame without having to rewrite the whole DataFrame. shift() takes the following parametersshift(self, periods=1, freq=None, axis=0, fill_value=None)periods  Number of periods to shift. It can take a negative number too.axis  It takes a Boolean value; 0 if you want to shift index and 1 if you want to shift columnfill_value  It will replace the missing value.Let's take an example and see how to use this shift() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Select a column and shift it by using df["column_name]=df.column_name.shift()Print ... Read More

To append the rows of one dataframe with the rows of another, we can use the Pandas append() function. With the help of append(), we can append columns too. Let's take an example and see how to use this method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df1.Print the input DataFrame, df1.Create another DataFrame, df2, with the same column names and print it.Use the append method, df1.append(df2, ignore_index=True), to append the rows of df2 with df2.Print the resultatnt DataFrame.Exampleimport pandas as pd df1 = pd.DataFrame({"x": [5, 2], "y": [4, 7], "z": [9, 3]}) df2 = pd.DataFrame({"x": [1, 3], "y": ... Read More

To get the nth row in a Pandas DataFrame, we can use the iloc() method. For example, df.iloc[4] will return the 5th row because row numbers start from 0.StepsMake two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print input DataFrame, df.Initialize a variable nth_row.Use iloc() method to get nth row.Print the returned DataFrame.Exampleimport pandas as pd df = pd.DataFrame( dict( name=['John', 'Jacob', 'Tom', 'Tim', 'Ally'], marks=[89, 23, 100, 56, 90], subjects=["Math", "Physics", "Chemistry", "Biology", "English"] ) ) ... Read More