When it is required to convert a three dimensional matrix into a co-ordinate list, the ‘zip’ method, and a list comprehension are used.ExampleBelow is a demonstration of the same −my_list_1 = [[['He', 'Wi'], ['llo', 'll']], [['Pyt', 'i'], ['hon', 'sFun']], [['Ho', 'g'], ['pe', 'ood']]] print("The list is : ") print(my_list_1) my_list_1.sort() print("The list after sorting is ") print(my_list_1) my_result = [ele for sub_elem_1, sub_elem_2 in my_list_1 for ele in zip(sub_elem_1, sub_elem_2)] print("The resultant list is : ") print(my_result)OutputThe list is : [[['He', 'Wi'], ['llo', 'll']], [['Pyt', 'i'], ['hon', 'sFun']], [['Ho', 'g'], ['pe', 'ood']]] The list after sorting is [[['He', ... Read More

When it is required to perform cross pairing in a list of tuples, the ‘zip’ method, a list comprehension and the ‘==’ operator is used.ExampleBelow is a demonstration of the same −my_list_1 = [('Hi', 'Will'), ('Jack', 'Python'), ('Bill', 'Mills'), ('goodwill', 'Jill')] my_list_2 = [('Hi', 'Band'), ('Jack', 'width'), ('Bill', 'cool'), ('a', 'b')] print("The first list is : " ) print(my_list_1) print("The second list is :") print(my_list_2) my_list_1.sort() my_list_2.sort() print("The first list after sorting is ") print(my_list_1) print("The second list after sorting is ") print(my_list_2) my_result = [(a[1], b[1]) for a, b in zip(my_list_1, my_list_2) if a[0] == b[0]] print("The ... Read More

To group Pandas data frame, we use groupby(). To sort grouped data frames in ascending or descending order, use sort_values(). The size() method is used to get the data frame size. Steps Involved The steps included in sorting the panda's data frame by its group size are as follows. Importing the panda's library and Creating a Pandas dataframe. Grouping the columns by using the groupby() function and sorting the ... Read More

To group columns in Pandas dataframe, use the groupby(). At first, let us create Pandas dataframe −dataFrame = pd.DataFrame(    {       "Car": ["Audi", "Lexus", "Audi", "Mercedes", "Audi", "Lexus", "Mercedes", "Lexus", "Mercedes"],       "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]    } )Let us now group according to Car column −res = dataFrame.groupby("Car")After grouping, we will use functions to find the means Registration prices (Reg_Price) of grouped car names −res.mean()This calculates mean of the Registration price according to column Car.ExampleFollowing is the code −import pandas as pd # dataframe with one of ... Read More

To display the index of dataframe in the form of multiindex, use the dataframe.index(). At first, let us create a dictionary of lists −# dictionary of lists d = {'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_purchase': ['2020-10-10', '2020-10-12', '2020-10-17', '2020-10-16', '2020-10-19', '2020-10-22'] }Create a DataFrame from the above dictionary of lists −dataFrame = pd.DataFrame(d)Now, set index column “Car” and display the index −dataFrame.set_index(["Car"], inplace=True, append=True, drop=False) print"Multiindex...", dataFrame.indexExampleFollowing is the code −import pandas as pd # dictionary of lists d = {'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_purchase': ['2020-10-10', ... Read More

We will group Pandas DataFrame using the groupby(). Select the column to be used using the grouper function. We will group day-wise and calculate sum of Registration Price with day interval for our example shown below for Car Sale Records.Set the frequency as an interval of days in the groupby() grouper method, that means, if the freq is 7D, that would mean data grouped by interval of 7 days of every month till the last date given in the date column.At first, let’s say the following is our Pandas DataFrame with three columns −import pandas as pd # dataframe ... Read More

To replace values of a DataFrame with the value of another DataFrame, use the replace() method n Pandas.At first, let us first create a DataFrame −dataFrame1 = pd.DataFrame({"Car": ["Audi", "Lamborghini"], "Place": ["US", "UK"], "Units": [200, 500]})Let us create another DataFrame −dataFrame2 = pd.DataFrame({"Car": ["BMW", "Lexus"], "Place": ["India", "Australia"], "Units": [800, 1000]})Next, get a value from DataFrame2 and replace with a value from DataFrame1 −# get value from 2nd DataFrame i = dataFrame2['Car'][1] # replacing with a value from the 1st DataFrame j = dataFrame1['Car'][0]Finally, use the replace() method to replace the value of one DataFrame with value of another ... Read More

We want to replace the negative values with latest preceding positive value. With that, if there’s no positive preceding value, then the value should update to 0.InputFor example, the input is −DataFrame:   One  two 0  -2   -3 1   4   -7 2   6    5 3   0   -9OutputThe output should be −   One two 0   0   0 1   7   0 2   4   2 3   0   2Data Frame masking is used to replace negative values. To fill the missing values, we used forward fill. At first, let ... Read More

To drop specific rows rom multiindex dataframe, use the drop() method. At first, let us create a multi-index array −arr = [np.array(['car', 'car', 'car', 'bike', 'bike', 'bike', 'truck', 'truck', 'truck']),    np.array(['valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC'])]Next, create multiindex dataframe and set index also −dataFrame = pd.DataFrame(    np.random.randn(9, 3), index=arr, columns=['Col 1', 'Col 2', 'Col 3']) dataFrame.index.names = ['level 0', 'level 1']Now, drop specific row −dataFrame.drop(('car', 'valueA'), axis=0, inplace=True)ExampleFollowing is the code −import numpy as np import pandas as pd # multiindex array arr = [np.array(['car', 'car', 'car', 'bike', 'bike', 'bike', 'truck', 'truck', 'truck']), ... Read More

Let us see how to find the sum of negative and positive values. At first, create a dataframe with positive and negative values −dataFrame = pd.DataFrame({'Place': ['Chicago', 'Denver', 'Atlanta', 'Chicago', 'Dallas', 'Denver', 'Dallas', 'Atlanta'], 'Temperature': [-2, 30, -5, 10, 30, -5, 20, -10]})Next, use groupby to group on the basis of Place column −groupRes = dataFrame.groupby(dataFrame['Place'])Use lambda function to return the positive and negative values. We have also added the positive and negative values individually −# lambda function def plus(val): return val[val > 0].sum() def minus(val): return val[val < 0].sum()ExampleFollowing is the complete code ... Read More