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

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

In the OSI (Open System Interconnection) model, the transport layer is one of the seven layers and it is responsible for the end to end communication between the sender and receiver over the internet. It provides logical communication between the sender and receiver and ensures the end to end delivery of the packet.The transport layer main protocols are as follows −TCP (Transmission Control Protocol)UDP (User Datagram Protocol)SCTP (Stream Control Transmission Protocol)RDP (Reliable Data Protocol)RUDP (Reliable User Datagram Protocol)Responsibilities of the transport layerThe responsibilities of the transport layer are as follows −It provides a process to process delivery or end to ... Read More

To generate dates in a range, use the date _range() method. At first, import the required pandas library with an alias −import pandas as pdNow, let’s say you need to generate dates in arrange, therefore for this, mention the date from where you want to begin. Here, we have mentioned 1st June 2021 and period of 60 days −dates = pd.date_range('6/1/2021', periods=60) ExampleFollowing is the complete code − import pandas as pd # generate dates in a range # period is 60 i.e. 60 days from 1st June 2021 dates = pd.date_range('6/1/2021', periods=60) print"Displaying dates in a range...", ... Read More

To convert string data to actual dates i.e. datetime type, use the to_datetime() method. At first, let us create a DataFrame with 3 categories, one of the them is a date string −dataFrame = pd.DataFrame({ 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Stationery'], 'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Chairs'], 'Date_of_Purchase': ['10/07/2021', '20/04/2021', '25/06/2021', '15/02/2021'], }) Convert date strings to actual dates using to_datetime() −dataFrame['Date_of_Purchase'] = pd.to_datetime(dataFrame['Date_of_Purchase'])ExampleFollowing is the complete code −import pandas as pd # create a dataframe dataFrame = pd.DataFrame({ 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Stationery'], 'Product Name': ['Keyboard', 'Charger', 'SmartTV', ... Read More

To compute last of group values, use the groupby.last() method. At first, import the required library with an alias −import pandas as pd;Create a DataFrame with 3 columns −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'BMW', 'Tesla', 'Lexus', 'Tesla'], "Place": ['Delhi', 'Bangalore', 'Pune', 'Punjab', 'Chandigarh', 'Mumbai'], "Units": [100, 150, 50, 80, 110, 90]    } ) Now, group DataFrame by a column −groupDF = dataFrame.groupby("Car")Compute last of group values and resetting index −res = groupDF.last() res = res.reset_index()ExampleFollowing is the complete code. The last occurrence of repeated values are displayed i.e. last of group values ... Read More

To filter on the basis of sum of columns, we use the loc() method. Here, in our example, we sum the marks of each student to get the student column with marks above 400 i.e. 80%.At first, create a DataFrame with student records. We have marks records of 3 students i.e 3 columns −dataFrame = pd.DataFrame({ 'Jacob_Marks': [95, 90, 75, 85, 88], 'Ted_Marks': [60, 50, 65, 85, 70], 'Jamie_Marks': [77, 76, 65, 45, 50]}) Filtering on the basis of columns. Fetching student with total marks above 400 −dataFrame = dataFrame.loc[:, dataFrame.sum(axis=0) > 400]ExampleFollowing is the complete ... Read More