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

Let us understand the symmetric key encryption.Symmetric Key encryptionSymmetric-key encryption algorithms in cryptography use a single key or the same cryptographic keys (secret key) shared between the two parties for both encrypting plain-text and decrypting cipher-text. The keys could be identical or there could be a simple change to go between the two keys.It uses Diffie–Hellman key exchange or other public-key protocol to securely agree upon the sharing and usage of a fresh new secret key for each message.Asymmetric Key encryptionAsymmetric key encryption is an encryption technique using a pair of public and private keys to encrypt and decrypt plain-text ... 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

To find numeric columns in Pandas, we can make a list of integers and then include it into select_dtypes() method. Let's take an example and see how to apply this method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Make a list of data type, i.e., numerics, to select a column.Return a subset of the DataFrame's columns based on the column dtypes.Print the column whose data type is int.Example import pandas as pd df = pd.DataFrame( dict( name=['John', 'Jacob', 'Tom', 'Tim', 'Ally'], ... Read More

To find the maximum value of a column and to return its corresponding row values in Pandas, we can use df.loc[df[col].idxmax()]. Let's take an example to understand it better.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable, col, to find the maximum value of that column.Find the maximum value and its corresponding row, using df.loc[df[col].idxmax()]Print the Step 4 output.Exampleimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 7, 0],       "y": [4, 7, 5, 1],       "z": [9, 3, 5, 1]    } ... Read More

We can use the .corr() method to get the correlation between two columns in Pandas. Let's take an example and see how to apply this method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize two variables, col1 and col2, and assign them the columns that you want to find the correlation of.Find the correlation between col1 and col2 by using df[col1].corr(df[col2]) and save the correlation value in a variable, corr.Print the correlation value, corr.Exampleimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 7, 0],       "y": [4, ... Read More

A regular expression (regex) is a sequence of characters that define a search pattern. To filter rows in Pandas by regex, we can use the str.match() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable regex for the expression. Supply a string value as regex, for example, the string 'J.*' will filter all the entries that start with the letter 'J'.Use df.column_name.str.match(regex) to filter all the entries in the given column name by the supplied regex.Example import pandas as pd df = pd.DataFrame(    dict(       name=['John', 'Jacob', 'Tom', 'Tim', 'Ally'], ... Read More