When it is required to flatten a nested list into a tuple list, a method is defined that takes a list as a parameter, and uses the ‘isinstance’ method to check if an element belongs to a specific type. Depending on this, the output is displayed.ExampleBelow is a demonstration of ... Read More

We will be using the iat attribute to access the last element, since it is used to access a single value for a row/column pair by integer position.Let us first import the required Pandas library −import pandas as pdCreate a Pandas series with numbers −data = pd.Series([10, 20, 5, 65, ... Read More

When it is required to create a nested list containing values as the count of list elements, a simple iteration is used.ExampleBelow is a demonstration of the samemy_list = [11, 25, 36, 24] print("The list is :") print(my_list) for element in range(len(my_list)): my_list[element] = [element+1 for j ... Read More

When it is required to count the frequency of the matrix row length, it is iterated over and its frequency is added to the empty dictionary or incremented if found again.ExampleBelow is a demonstration of the samemy_list = [[42, 24, 11], [67, 18], [20], [54, 10, 25], [45, 99]] ... Read More

When it is required to get the character repetitions of a given character, a method is defined that uses the index value to print the repetitions.ExampleBelow is a demonstration of the samedef to_string(my_list): return ''.join(my_list) def lex_recurrence(my_string, my_data, last_val, index_val): length = len(my_string) ... Read More

When it is required to group the contiguous elements of a string that are present in a list, a method is defined that uses ‘groupby’, and ‘yield’.ExampleBelow is a demonstration of the samefrom itertools import groupby def string_check(elem):    return isinstance(elem, str) def group_string(my_list):       ... Read More

To get the datatypes of columns, use the info() method. Let us first import the required library −import pandas as pdCreate a DataFrame with 2 columns having different datatypes −dataFrame = pd.DataFrame(    {       "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, ... Read More

To select columns with specific datatypes, use the select_dtypes() method and the include parameter. At first, create a DataFrame with 2 columns −dataFrame = pd.DataFrame(    {       "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, 3, 8, 2, 9, 6] ... Read More

To add a zero column to a Pandas DataFrame, use the square bracket and set it to 0. At first, import te required library −import pandas as pdCreate a DataFrame with 3 columns −dataFrame = pd.DataFrame(    {       "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Result": ... Read More

When it is required to find the element frequencies in the percentage range, the ‘Counter’ is used along with a simple iteration technique.ExampleBelow is a demonstration of the samefrom collections import Counter my_list = [56, 34, 78, 90, 11, 23, 6, 56, 79, 90] print("The list is :") ... Read More