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

To open a list, we can use append() method. With that, we can also use loc() method. At first, let us import the required library −import pandas as pdFollowing is the data in the form of lists of team rankings −Team = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], ['New Zealand', 4 , 65], ['South Africa', 5, 50]]Creating a DataFrame with the above data and adding columns −dataFrame = pd.DataFrame(Team, columns=['Country', 'Rank', 'Points']) Let’s say the following is the row to be appended −myList = [["Sri Lanka", 6, 40]]Append the above row in the form of list − ... Read More

When it is required to remove words that are common in both the strings, a method is defined that takes two strings. The strings are spit based on spaces and list comprehension is used to filter out the result.ExampleBelow is a demonstration of the samedef common_words_filter(my_string_1, my_string_2):    my_word_count = {}    for word in my_string_1.split(): my_word_count[word] = my_word_count.get(word, 0) + 1 for word in my_string_2.split(): my_word_count[word] = my_word_count.get(word, 0) + 1 return [word for ... Read More

When it is required to extract elements from a List in a Set, a simple ‘for’ loop and a base condition can be used.ExampleBelow is a demonstration of the samemy_list = [5, 7, 2, 7, 2, 4, 9, 8, 8] print("The list is :") print(my_list) search_set = {6, 2, 8} my_result = [] for element in my_list: if element in search_set: my_result.append(element) print("The result is :") print(my_result)OutputThe list is : [5, 7, 2, 7, 2, 4, 9, 8, 8] The result is : [2, 2, 8, ... Read More

To concatenate DataFrames, use the concat() method, but to ignore duplicates, use the drop_duplicates() method.Import the required library −import pandas as pdCreate DataFrames to be concatenated −# Create DataFrame1 dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Jaguar', 'Audi', 'Mustang'], "Units": [100, 150, 110, 80]    } ) # Create DataFrame2 dataFrame2 = pd.DataFrame(    {       "Car": ['Tesla', 'Jaguar', 'Mercedes', 'Mustang'], "Units": [120, 150, 180, 80] } ) Now, let us concatenate DataFrame and remove duplicates −concatRes = pd.concat([dataFrame1, dataFrame2]).drop_duplicates()ExampleFollowing is the code −import pandas as pd # ... Read More

Median separates the higher half from the lower half of the data. Use the fillna() method and set the median to fill missing columns with median. At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a DataFrame with 2 columns. We have set the NaN values using the Numpy np.NaN −dataFrame = pd.DataFrame(    {       "Car": ['Lexus', 'BMW', 'Audi', 'Bentley', 'Mustang', 'Tesla'], "Units": [100, 150, np.NaN, 80, np.NaN, np.NaN] } )Find median of the column values with NaN i.e, for Units columns here. ... Read More