To calculate the standard deviation, use the std() method of the Pandas. At first, import the required Pandas library −import pandas as pdNow, create a DataFrame with two columns −dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],       "Units": [100, 150, 110, 80, 110, 90] } ) Finding the standard deviation of “Units” column value using std() −print"Standard Deviation of Units column from DataFrame1 = ", dataFrame1['Units'].std()In the same way, we have calculated the standard deviation from the 2nd DataFrame.ExampleFollowing is the complete code −# # Python - Calculate the ... Read More

To select final periods of time series based on a date offset, use the last() method. At first, set the date index with periods and freq. Freq is for frequency −i = pd.date_range('2021-07-15', periods=5, freq='3D')Now, create a DataFrame with above index −dataFrame = pd.DataFrame({'k': [1, 2, 3, 4, 5]}, index=i) Fetch rows from last 4 days i.e. 4D −dataFrame.last('4D')ExampleFollowing is the complete code −import pandas as pd # date index set with 5 periods and frequency of 3 days i = pd.date_range('2021-07-15', periods=5, freq='3D') # creating DataFrame with above index dataFrame = pd.DataFrame({'k': [1, 2, 3, 4, 5]}, ... Read More

To remove leading or trailing whitespace, use the strip() method. At first, create a DataFrame with 3 columns “Product Category”, “Product Name” and “Quantity” −dataFrame = pd.DataFrame({ 'Product Category': [' Computer', ' Mobile Phone', 'Electronics ', 'Appliances', ' Furniture', 'Stationery'], 'Product Name': ['Keyboard', 'Charger', ' SmartTV', 'Refrigerators', ' Chairs', 'Diaries'], 'Quantity': [10, 50, 10, 20, 25, 50]})Removing whitespace from more than one column −dataFrame['Product Category'].str.strip() dataFrame['Product Name'].str.strip()ExampleFollowing is the complete code −import pandas as pd # create a dataframe with 3 columns dataFrame = pd.DataFrame({    'Product Category': [' Computer', ' Mobile Phone', 'Electronics ', 'Appliances', ... Read More

When it is required to convert a matrix into a string, a simple list comprehension along with the ‘join’ method is used.ExampleBelow is a demonstration of the samemy_list = [[1, 22, "python"], [22, "is", 1], ["great", 1, 91]] print("The list is :") print(my_list) my_list_1, my_list_2 = ", ", " " my_result = my_list_2.join([my_list_1.join([str(elem) for elem in sub]) for sub in my_list]) print("The result is :") print(my_result)OutputThe list is : [[1, 22, 'python'], [22, 'is', 1], ['great', 1, 91]] The result is : 1, 22, python 22, is, 1 great, 1, 91ExplanationA list of list is defined ... Read More

To compare specific timestamps, use the index number in the square brackets. At first, import the required library −import pandas as pdCreate a DataFrame with 3 columns. We have two date columns with timestamp −dataFrame = pd.DataFrame(    {       "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW"],       "Date_of_Purchase": [          pd.Timestamp("2021-06-10"),          pd.Timestamp("2021-07-11"),          pd.Timestamp("2021-06-25"),          pd.Timestamp("2021-06-29"),          pd.Timestamp("2021-03-20"),       ],       "Date_of_Service": [           pd.Timestamp("2021-11-05"),           pd.Timestamp("2021-12-03"), ... Read More

When it is required to replace list elements within a range with a given number, list slicing is used.ExampleBelow is a demonstration of the samemy_list = [42, 42, 18, 73, 11, 28, 29, 0, 10, 16, 22, 53, 41] print("The list is :") print(my_list) i, j = 4, 8 my_key = 9 my_list[i:j] = [my_key] * (j - i) print("The result is:") print(my_list)OutputThe list is : [42, 42, 18, 73, 11, 28, 29, 0, 10, 16, 22, 53, 41] The result is: [42, 42, 18, 73, 9, 9, 9, 9, 10, 16, 22, 53, 41]ExplanationA list ... Read More

When it is required to assign each list element value equal to its magnitude order, the ‘set’ operation, the ‘zip’ method and a list comprehension are used.ExampleBelow is a demonstration of the samemy_list = [91, 42, 27, 39, 24, 45, 53] print("The list is : ") print(my_list) my_ordered_dict = dict(zip(list(set(my_list)), range(len(set(my_list))))) my_result = [my_ordered_dict[elem] for elem in my_list] print("The result is: ") print(my_result)OutputThe list is : [91, 42, 27, 39, 24, 45, 53] The result is: [0, 2, 6, 1, 5, 3, 4]ExplanationA list is defined and is displayed on the console.The unique elements of the ... Read More

When it is required to filter supersequence strings, a simple list comprehension is used.ExampleBelow is a demonstration of the samemy_list = ["Python", "/", "is", "alwaysgreat", "to", "learn"] print("The list is :") print(my_list) substring = "ys" my_result = [sub for sub in my_list if all(elem in sub for elem in substring)] print("The resultant string is :") print(my_result)OutputThe list is : ['Python', '/', 'is', 'alwaysgreat', 'to', 'learn'] The resultant string is : ['alwaysgreat']ExplanationA list is defined and is displayed on the console.A substring is defined.The list comprehension is used to iterate through the elements using the ‘all’ clause.This ... Read More

When it is required to find the maximum difference across the lists, the ‘abs’ and the ‘max’ methods are used.ExampleBelow is a demonstration of the samemy_list_1 = [7, 9, 1, 2, 7] my_list_2 = [6, 3, 1, 2, 1] print("The first list is :") print(my_list_1) print("The second list is :") print(my_list_2) my_result = max(abs(my_list_2[index] - my_list_1[index]) for index in range(0, len(my_list_1) - 1)) print("The maximum difference among the lists is :") print(my_result)OutputThe first list is : [7, 9, 1, 2, 7] The second list is : [6, 3, 1, 2, 1] The maximum difference ... Read More

When it is required to remove positional rows, a simple iteration and the ‘pop’ method is used.ExampleBelow is a demonstration of the samemy_list = [[31, 42, 2], [1, 73, 29], [51, 3, 11], [0, 3, 51], [17, 3, 21], [1, 71, 10], [0, 81, 92]] print("The list is :") print(my_list) my_index_list = [1, 2, 5] for index in my_index_list[::-1]: my_list.pop(index) print("The output is :") print(my_list)OutputThe list is : [[31, 42, 2], [1, 73, 29], [51, 3, 11], [0, 3, 51], [17, 3, 21], [1, 71, 10], [0, 81, 92]] The output is : [[31, ... Read More