Programming Articles - Page 1082 of 3363

When it is required to count the frequency of a sub-list in a given list, a list comprehension and the ‘len’ method along with the ‘if’ condition are used.ExampleBelow is a demonstration of the same −my_list = [23, 33, 45, 67, 54 , 43, 33, 45, 67, 83, 33, 45, 67, 90, 0] print("The list is : " ) print(my_list) sub_list = [33, 45, 67, 90] print("The sub-list is : " ) print(sub_list) my_result = len([sub_list for index in range(len(my_list)) if my_list[index : index + len(sub_list)] == sub_list]) print("The resultant list is : ") print(my_result)OutputThe list ... Read More

When it is required to segregate elements based on a delimiter, ExampleBelow is a demonstration of the same −my_list = ["89@21", "58@51", "19@61", "11@10", "32@65", "34@45", "87@90", "32@21", '1@345'] print("The list is : " ) print(my_list) print("The list after sorting is :") my_list.sort() print(my_list) my_delimiter = "@" print("The delimiter is :") print(my_delimiter) result_before_delim, result_after_delim = [ele.split(my_delimiter)[0] for ele in my_list], [ele.split(my_delimiter)[1] for ele in my_list] print("The result containing elements before delimiter is : ") print(result_before_delim) print("The result containing elements after delimiter is : ") print(result_after_delim)OutputThe list is : ['89@21', '58@51', '19@61', '11@10', '32@65', '34@45', '87@90', ... Read More

When it is required to extract elements that are in between multiple specific range of index, the ‘extend’ method, a simple iteration and indexing are used.ExampleBelow is a demonstration of the same −my_list = [13, 21, 81, 10, 13, 17, 22, 18, 11, 90, 0] print("The list is : ") print(my_list) print("The list after sorting is : " ) my_list.sort() print(my_list) range_list = [(2, 4), (7, 8), (1, 2), (2, 7)] my_result = [] for element in range_list: my_result.extend(my_list[element[0] : element[1] + 1]) print("The resultant list is : ") print(my_result) print("The result ... Read More

When it is required to sort the elements of a list based on the digits, the ‘max’, ‘max’ method are used. We will also use the ‘sorted’ method, the ‘lambda’ function and ‘ljust’.ExampleBelow is a demonstration of the same −my_list = [4344, 2611, 122, 541, 33, 892, 48292, 460, 390, 120, 10, 2909, 11239, 1] print("The list is : " ) print(my_list) print("The list after sorting is : " ) my_list.sort() print(my_list) my_temp_val = map(str, my_list) my_max_length = max(map(len, my_temp_val)) my_result = sorted(my_list, key = lambda index : (str(index).ljust(my_max_length, 'a'))) print("The resultant list is ... Read More

When it is required to randomly insert elements K times, the ‘random’ package and methods from the random package along with a simple iteration is used.ExampleBelow is a demonstration of the same −import random my_list = [34, 12, 21, 56, 8, 9, 0, 3, 41, 11, 90] print("The list is : " ) print(my_list) print("The list after sorting is : " ) my_list.sort() print(my_list) to_add_list = ["Python", "Object", "oriented", "language", 'cool'] K = 3 print("The value of K is ") print(K) for element in range(K): index = random.randint(0, len(my_list)) ... Read More

When it is required to find all the combinations in a list with a specific condition, a simple iteration, the ‘isinstance’ method, the ‘append’ method and indexing are used.ExampleBelow is a demonstration of the same −print("Method definition begins") def merge_the_vals(my_list_1, my_list_2, K): index_1 = 0 index_2 = 0 while(index_1 < len(my_list_1)): for i in range(K): yield my_list_1[index_1] index_1 += 1 ... Read More

To query the columns of a Pandas DataFrame, use the query(). We are querying to filter records. At first, let us create a Pandas DataFramedataFrame = pd.DataFrame({"Product": ["SmartTV", "PenDrive", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Using query() to query columns with conditions −print(dataFrame.query('Opening_Stock >=500 & Closing_Stock < 1000 & Product.str.startswith("P").values'))ExampleFollowing is the complete code −import pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "PenDrive", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print"DataFrame...", dataFrame # using query() to query columns print"Querying columns to filter records..." print(dataFrame.query('Opening_Stock >=500 & Closing_Stock ... Read More

To select multiple rows from a DataFrame, set the range using the : operator. At first, import the require pandas library with alias −import pandas as pdNow, create a new Pandas DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b'])Select multiple rows using the : operator −dataFrame[0:2]ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b']) # DataFrame print"DataFrame...", dataFrame # select rows with loc print"Select rows by passing label..." print(dataFrame.loc['z']) ... Read More

To select a column from a DataFrame, just fetch it using square brackets. Mention the column to select in the brackets and that’s it, for exampledataFrame[‘ColumnName’]At first, import the required library −import pandas as pdNow, create a DataFrame. We have two columns in it −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )To select only a single column, mention the column name using the square bracket as shown below. Here, our ... Read More

To merge Pandas DataFrame, use the merge() function. The outer join is implemented on both the DataFrames by setting under the “how” parameter of the merge() function i.e. −how = “outer”At first, let us import the pandas library with an alias −import pandas as pdLet us create DataFrame1 −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Let us now create DataFrame2 −dataFrame2 = pd.DataFrame( { ... Read More