To rename multiple column headers, use the rename() method and set the dictionary in the columns parameter. At first, let us create a DataFrame −dataFrame = pd.DataFrame({"Car": ['BMW', 'Mustang', 'Tesla', 'Mustang', 'Mercedes', 'Tesla', 'Audi'], "Cubic Capacity": [2000, 1800, 1500, 2500, 2200, 3000, 2000], "Reg Price": [7000, 1500, 5000, 8000, 9000, 6000, 1500], "Units Sold": [ 200, 120, 150, 120, 210, 250, 220] })Creating a dictionary to rename columns. The key and value pairs as old name and new name −dictionary = {'Car': 'Car Name', 'Cubic Capacity': 'CC', 'Reg Price': 'Registration Price', 'Units Sold': 'Units Purchased' }Use rename() and set the ... 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] } )Now, select the 2 columns with their respective specific datatype −column1 = dataFrame.select_dtypes(include=['object']).columns column2 = dataFrame.select_dtypes(include=['int64']).columnsExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame(    {       "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, ... 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, 3, 8, 2, 9, 6] } )Get the complete information about datatypes −dataFrame.info()ExampleFollowing is the complete code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame(    {       "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, 3, ... 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) for i in range(length): my_data[index_val] = my_string[i] if index_val==last_val: print(to_string(my_data)) else: ... 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):       for key, grp in groupby(my_list, key=string_check):          if key: yield list(grp) else: yield from ... 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": ['Pass', 'Fail', 'Pass', 'Fail', 'Pass', 'Pass', 'Pass'], "Roll Number": [ 5, 10, 3, 8, 2, 9, 6] } )Create a new column with zero entries −dataFrame['ZeroColumn'] = 0 ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame(    {     ... 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 :") print(my_list) start, end = 13, 60 my_freq = dict(Counter(my_list)) my_result = [] for element in set(my_list):    percent = (my_freq[element] / len(my_list)) * 100    if percent >= start and percent

When it is required to get the specific number of power of a number, the ‘**’ operator is used along with list comprehension.ExampleBelow is a demonstration of the samen = 4 print("The value n is : ") print(n) k = 5 print("The value of k is : ") print(k) result = [n ** index for index in range(0, k)] print("The square values of N till K : " ) print(result)OutputThe value n is : 4 The value of k is : 5 The square values of N till K : [1, 4, 16, 64, 256]ExplanationThe values for ‘n’ ... Read More

When it is required to find duplicate sets in a list of sets, the ‘Counter’ and ‘frozenset’ are used.ExampleBelow is a demonstration of the samefrom collections import Counter my_list = [{4, 8, 6, 1}, {6, 4, 1, 8}, {1, 2, 6, 2}, {1, 4, 2}, {7, 8, 9}] print("The list is :") print(my_list) my_freq = Counter(frozenset(sub) for sub in my_list) my_result = [] for key, value in my_freq.items():    if value > 1 : my_result.append(key) print("The result is :") print(my_result)OutputThe list is : [{8, 1, 4, 6}, {8, 1, ... Read More

When it is required to add custom borders to the matrix, a simple list iteration can be used to add the required borders to the matrix.ExampleBelow is a demonstration of the samemy_list = [[2, 5, 5], [2, 7, 5], [4, 5, 1], [1, 6, 6]] print("The list is :") print(my_list) print("The resultant matrix is :") border = "|" for sub in my_list: my_temp = border + " " for ele in sub: my_temp = my_temp + str(ele) + " " ... Read More