When it is required to find the cube of each list element, a simple iteration and the ‘append’ method are used.ExampleBelow is a demonstration of the samemy_list = [45, 31, 22, 48, 59, 99, 0] print("The list is :") print(my_list) my_result = [] for i in my_list: my_result.append(i*i*i) print("The resultant list is :") print(my_result)OutputThe list is : [45, 31, 22, 48, 59, 99, 0] The resultant list is : [91125, 29791, 10648, 110592, 205379, 970299, 0]ExplanationA list is defined and is displayed on the console.An empty list is defined.The original list is iterated over.Every element ... Read More

When it is required to print all the words occurring in a sentence exactly K times, a method is defined that uses the ‘split’ method, ‘remove’ method and the ‘count’ methods. The method is called by passing the required parameters and output is displayed.ExampleBelow is a demonstration of the samedef key_freq_words(my_string, K):    my_list = list(my_string.split(" "))    for i in my_list:       if my_list.count(i) == K:          print(i)          my_list.remove(i) my_string = "hi there how are you, how are u" K = 2 print("The string is :") print(my_string) print"The repeated ... Read More

When it is required to customize the space size padding in a list of strings, an empty list, an iteration and the ‘append’ method is used.ExampleBelow is a demonstration of the samemy_list = ["Python", "is", "great"] print("The list is :") print(my_list) lead_size = 3 trail_size = 2 my_result = [] for elem in my_list: my_result.append((lead_size * ' ') + elem + (trail_size * ' ')) print("The result is :") print(my_result)OutputThe list is : ['Python', 'is', 'great'] The result is : [' Python ', ' is ', ' great ']ExplanationA list is defined and ... Read More

When it is required to find the first occurrence of one list in another list, the ‘set’ attribute and the ‘next’ method is used.ExampleBelow is a demonstration of the samemy_list_1 = [23, 64, 34, 77, 89, 9, 21] my_list_2 = [64, 10, 18, 11, 0, 21] print("The first list is :") print(my_list_1) print("The second list is :") print(my_list_2) my_list_2 = set(my_list_2) my_result = next((ele for ele in my_list_1 if ele in my_list_2), None) print("The result is :") print(my_result)OutputThe first list is : [23, 64, 34, 77, 89, 9, 21] The second list is : [64, 10, 18, ... Read More

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

To calculate the variance of column values, use the var() method. At first, import the required Pandas library −import pandas as pdCreate a DataFrame with two columns −dataFrame1 = pd.DataFrame(    { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],       "Units": [100, 150, 110, 80, 110, 90] } ) Finding Variance of "Units" column values using var() function −print"Variance of Units column from DataFrame1 = ", dataFrame1['Units'].var()In the same way, we have calculated the Variance from the 2nd DataFrame.ExampleFollowing is the complete code −import pandas as pd ... Read More

To reset index after group by, at first group according to a column using groupby(). After that, use reset_index().At first, import the required library −import pandas as pdCreate a DataFrame with 2 columns −dataFrame = pd.DataFrame( { "Car": ["Audi", "Lexus", "Audi", "Mercedes", "Audi", "Lexus", "Mercedes", "Lexus", "Mercedes"], "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350] } ) Group according to Car column −resDF = dataFrame.groupby("Car").mean()Now, reset index after grouping −resDF.reset_index() ExampleFollowing is the code − import pandas as ... Read More

To sum only specific rows, use the loc() method. Mention the beginning and end row index using the : operator. Using loc(), you can also set the columns to be included. We can display the result in a new column.At first, let us create a DataFrame. We have Product records in it, including the Opening and Closing Stock −dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Sum of some rows i.e. 1st two rows. Column names also mentioned in the loc() i.e. Opening_Stock and Closing_Stock. We are displaying result in a new ... Read More

To calculate the median of column values, use the median() method. 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 median of a single column “Units” using median() −print"Median of Units column from DataFrame1 = ", dataFrame1['Units'].median() In the same way, we have calculated the median value from the 2nd DataFrame.ExampleFollowing is the complete code ... Read More

To find the common rows between two DataFrames, use the merge() method. Let us first create DataFrame1 with two columns −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Create DataFrame2 with two columns −dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 250, 150, 80, 130, 90] } )To find the common ... Read More