When it is required to convert a list into matrix with the size of every row increasing by a number, the ‘//’ operator and a simple iteration is used.ExampleBelow is a demonstration of the samemy_list = [42, 45, 67, 89, 99, 10, 23, 12, 31, 43, 60, 1, 0] print("The list is :") print(my_list) my_key = 3 print("The value of key is ") print(my_key) my_result = [] for index in range(0, len(my_list) // my_key): my_result.append(my_list[0: (index + 1) * my_key]) print("The resultant matrix is :") print(my_result)OutputThe list is : [42, 45, 67, ... Read More

When it is required to sort tuples by frequency of their absolute difference, the lambda function, the ‘abs’ method and the ‘sorted’ method are used.ExampleBelow is a demonstration of the samemy_list = [(11, 26), (21, 33), (90, 11), (26, 21), (32, 18), (25, 37)] print("The list is :") print(my_list) my_diff_list = [abs(x - y) for x, y in my_list] my_result = sorted(my_list, key = lambda sub: my_diff_list.count(abs(sub[0] - sub[1]))) print("The resultant list is :") print(my_result)OutputThe list is : [(11, 26), (21, 33), (90, 11), (26, 21), (32, 18), (25, 37)] The resultant list is : [(11, ... Read More

When it is required to remove the first diagonal elements from a square matrix, the ‘enumerate’ and list comprehension is used.ExampleBelow is a demonstration of the samemy_list = [[45, 67, 85, 42, 11], [78, 99, 10, 13, 0], [91, 23, 23, 64, 23], [91, 11, 22, 14, 35]] print("The list is :") print(my_list) my_result = [] for index, element in enumerate(my_list): my_result.append([ele for index_1, ele in enumerate(element) if index_1 != index]) print("The resultant matrix is :") print(my_result)OutputThe list is : [[45, 67, 85, 42, 11], [78, 99, 10, 13, 0], [91, 23, 23, ... Read More

When it is required to extract strings with atleast a given number of characters from the other list, a list comprehension is used.ExampleBelow is a demonstration of the samemy_list = ["Python", "is", "fun", "to", "learn"] print("The list is :") print(my_list) my_char_list = ['e', 't', 's', 'm', 'n'] my_key = 2 print("The value of key is ") print(my_key) my_result = [element for element in my_list if sum(ch in my_char_list for ch in element) >= my_key] print("The resultant list is :") print(my_result)OutputThe list is : ['Python', 'is', 'fun', 'to', 'learn'] The value of key is 2 The ... Read More

To get the datatype and DataFrame columns information, use the info() method. Import the required library with an alias −import pandas as pd;Create a DataFrame with 3 columns −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Audi', 'BMW', 'Lexus', 'Tesla', 'Lexus', 'Mustang'], "Place": ['Delhi', 'Bangalore', 'Hyderabad', 'Chandigarh', 'Pune', 'Mumbai', 'Jaipur'], "Units": [100, 150, 50, 110, 90, 120, 80] } ) Get the datatype and other info about the DataFrame −dataFrame.info()ExampleFollowing is the code −import pandas as pd; # create a DataFrame dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Audi', 'BMW', ... Read More

When it is required to convert a matrix to a dictionary value list, a simple dictionary comprehension can be used.ExampleBelow is a demonstration of the samemy_list = [[71, 26, 35], [65, 56, 37], [89, 96, 99]] print("The list is :") print(my_list) my_result = {my_index + 1 : my_list[my_index] for my_index in range(len(my_list))} print("The result is:") print(my_result)OutputThe list is : [[71, 26, 35], [65, 56, 37], [89, 96, 99]] The result is: {1: [71, 26, 35], 2: [65, 56, 37], 3: [89, 96, 99]}ExplanationA list of list is defined and is displayed on the console.A dictionary comprehension is ... Read More

When it is required to create N lists randomly that are K in size, a method is defined that shuffles the values and yields the output.ExampleBelow is a demonstration of the samefrom random import shuffle def gen_random_list(my_val, K): while True: shuffle(my_val) yield my_val[:K] my_list = [12, 45, 76, 32, 45, 88, 99, 0, 1] print("The list is ") print(my_list) K, N = 4, 5 print("The value of K is ") print(K) print("The value of N is ") print(N) my_result = [] ... Read More

There are several ways to extract unique values from a column in a data frame using Python Pandas, including unique() and nunique(). The panda's library in Python is mostly used for data analysis and manipulation to locate unique values in a data frame column. Some common methods to get unique values from a column are as follows: unique(): This method will return the unique values of a Series or DataFrame column as a NumPy array. ... Read More

To count distinct, use nunique in Pandas. We will groupby a column and find sun as well using Numpy sum().At first, import the required libraries −import pandas as pd import numpy as npCreate a DataFrame with 3 columns. The columns have duplicate values −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Audi', 'BMW', 'Lexus', 'Lexus'], "Place": ['Delhi', 'Bangalore', 'Delhi', 'Chandigarh', 'Chandigarh'], "Units": [100, 150, 50, 110, 90] } )Count distinct in aggregation agg() with nunique. Calculating the sum for counting, we are using numpy sum() −dataFrame = dataFrame.groupby("Car").agg({"Units": np.sum, "Place": pd.Series.nunique})ExampleFollowing is the code −import ... Read More

To remove duplicate values from a Pandas DataFrame, use the drop_duplicates() method. At first, create a DataFrame with 3 columns −dataFrame = pd.DataFrame({'Car': ['BMW', 'Mercedes', 'Lamborghini', 'BMW', 'Mercedes', 'Porsche'], 'Place': ['Delhi', 'Hyderabad', 'Chandigarh', 'Delhi', 'Hyderabad', 'Mumbai'], 'UnitsSold': [95, 70, 80, 95, 70, 90]})Remove duplicate values −dataFrame = dataFrame.drop_duplicates() ExampleFollowing is the complete code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame({'Car': ['BMW', 'Mercedes', 'Lamborghini', 'BMW', 'Mercedes', 'Porsche'], 'Place': ['Delhi', 'Hyderabad', 'Chandigarh', 'Delhi', 'Hyderabad', 'Mumbai'], 'UnitsSold': [95, 70, 80, 95, 70, 90]}) print"Dataframe...", dataFrame # counting frequency of column Car count = dataFrame['Car'].value_counts() print"Count in column ... Read More