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To annotate Seaborn pairplots, we can use the fig.text() method.StepsImport Seaborn, Pandas, Numpy, and Pyplot packages.Set the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe of two-dimensional, size-mutable, potentially heterogeneous tabular data.Plot pairwise relationships in a dataset, using sns.pairplot().Add an annotated text using fig.text() method.To display the figure, use show() method.Exampleimport seaborn as sns import pandas as pd import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame( np.random.random((4, 4)), columns=["a", "b", "c", "d"] ) pp = ... Read More
To rename multiple column headers, use the rename() method and set the dictionary in the columns parameter. At first, let us create a DataFrame −dataFrame = pd.DataFrame({"Car": ['BMW', 'Mustang', 'Tesla', 'Mustang', 'Mercedes', 'Tesla', 'Audi'], "Cubic Capacity": [2000, 1800, 1500, 2500, 2200, 3000, 2000], "Reg Price": [7000, 1500, 5000, 8000, 9000, 6000, 1500], "Units Sold": [ 200, 120, 150, 120, 210, 250, 220] })Creating a dictionary to rename columns. The key and value pairs as old name and new name −dictionary = {'Car': 'Car Name', 'Cubic Capacity': 'CC', 'Reg Price': 'Registration Price', 'Units Sold': 'Units Purchased' }Use rename() and set the ... Read More
To filter rows based on column values, we can use the query() function. In the function, set the condition through which you want to filter records. At first, import the required library −import pandas as pdFollowing is our data with Team Records −Team = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], ['New Zealand', 4 , 65], ['South Africa', 5, 50], ['Bangladesh', 6, 40]]Create a DataFrame from above and add columns as well −dataFrame = pd.DataFrame(Team, columns=['Country', 'Rank', 'Points']) Use query() to filter records with “Rank” equal to 5 −dataFrame.query("Rank == 5"))ExampleFollowing is the complete code −import pandas as ... Read More
When it is required to convert a list of list to a list of set, the ‘map’, ‘set’, and ‘list’ methods are used.ExampleBelow is a demonstration of the samemy_list = [[2, 2, 2, 2], [1, 2, 1], [1, 2, 3], [1, 1], [0]] print("The list of lists is: ") print(my_list) my_result = list(map(set, my_list)) print("The resultant list is: ") print(my_result)OutputThe list of lists is: [[2, 2, 2, 2], [1, 2, 1], [1, 2, 3], [1, 1], [0]] The resultant list is: [set([2]), set([1, 2]), set([1, 2, 3]), set([1]), set([0])]ExplanationA list of list is defined and is displayed ... Read More
When it is required to get all the subset having a specific sum ‘s’, a method is defined that iterates through the list and gets all combinations of the list, and if it matches the sum, it is printed on the console.ExampleBelow is a demonstration of the samefrom itertools import combinations def sub_set_sum(size, my_array, sub_set_sum): for i in range(size+1): for my_sub_set in combinations(my_array, i): if sum(my_sub_set) == sub_set_sum: print(list(my_sub_set)) my_size = 6 my_list = [21, 32, 56, 78, 45, 99, 0] ... Read More
When it is required to find all the strings that are substrings of a given list of strings, the ‘set’ and ‘list’ attributes are used.ExampleBelow is a demonstration of the samemy_list_1 = ["Hi", "there", "how", "are", "you"] my_list_2 = ["Hi", "there", "how", "have", "you", 'been'] print("The first list is :") print(my_list_1) print("The second list is :") print(my_list_2) my_result = list(set([elem_1 for subset_1 in my_list_1 for elem_1 in my_list_2 if elem_1 in subset_1])) print("The result is :") print(my_result)OutputThe first list is : ['Hi', 'there', 'how', 'are', 'you'] The second list is : ['Hi', 'there', 'how', 'have', 'you', 'been'] The ... Read More
Yes, we can use the & operator to find the common columns between two DataFrames. At first, let us create two DataFrames −# creating dataframe1 dataFrame1 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], }) print("Dataframe1...", dataFrame1) # creating dataframe2 dataFrame2 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Units_Sold": [ 100, 110, 150, 80, 200, 90] })Get the common columns using the & operator −res = dataFrame1.columns & dataFrame2.columns ExampleFollowing is the code −import pandas as pd # creating dataframe1 dataFrame1 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], ... Read More
When it is required to print the sorted numbers that are formed by merging the elements of an array, a method can be defined that first sorts the number and converts the number to an integer. Another method maps this list to a string, and is sorted again.ExampleBelow is a demonstration of the samedef get_sorted_nums(my_num): my_num = ''.join(sorted(my_num)) my_num = int(my_num) print(my_num) def merged_list(my_list): my_list = list(map(str, my_list)) my_str = ''.join(my_list) get_sorted_nums(my_str) my_list = [7, 845, 69, 60, ... Read More
When it is required to get all the unique keys from a list of dictionary, the dictionary values are iterated over and converted into a set. This is converted to a list and displayed on the console.ExampleBelow is a demonstration of the samemy_list = [{'hi' : 11, 'there' : 28}, {'how' : 11, 'are' : 31}, {'you' : 28, 'Will':31}] print("The list is:") print(my_list) my_result = list(set(value for dic in my_list for value in dic.values())) print("The result is :") print(my_result)OutputThe list is: [{'there': 28, 'hi': 11}, {'how': 11, 'are': 31}, {'Will': 31, 'you': 28}] The result is : ... Read More
When it is required to print all the distinct uncommon digits that are present in two numbers, a method is defined that takes two integers as parameters. The method ‘symmetric_difference’ is used to get the uncommon digits.ExampleBelow is a demonstration of the samedef distinct_uncommon_nums(val_1, val_2): val_1 = str(val_1) val_2 = str(val_2) list_1 = list(map(int, val_1)) list_2 = list(map(int, val_2)) list_1 = set(list_1) list_2 = set(list_2) my_list = list_1.symmetric_difference(list_2) my_list = list(my_list) my_list.sort(reverse ... Read More