Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
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
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 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
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
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
We will consider an example of Car Sale Records and group month-wise to calculate the sum of Registration Price of car monthly. To sum, we use the sum() method.At first, let’s say the following is our Pandas DataFrame with three columns −dataFrame = pd.DataFrame( { "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"], "Date_of_Purchase": [ pd.Timestamp("2021-06-10"), pd.Timestamp("2021-07-11"), pd.Timestamp("2021-06-25"), ... Read More
Let us understand what an ultra-dense network is.Ultra-Dense NetworkThe dense networks deliver the best of the user experience to the users. This type of network allows the users to get the best result by the use of the densification. The density here can be the absolute or relative density in the networks.The user density networks increase the density too potentially beyond its range to fulfill the customer demand. In the user defined network, the capacity grows as the capacity of the base station also increases. This type of network provides users with the best of experience with the densification approach.In ... Read More