To fetch columns between two DataFrames by Intersection, use the intersection() method. Let us create two DataFrames −# creating dataframe1 dataFrame1 = pd.DataFrame({"Car": ['Bentley', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], }) # creating dataframe2 dataFrame2 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Units_Sold": [ 100, 110, 150, 80, 200, 90] })Fetch common columns −dataFrame2.columns.intersection(dataFrame1.columns) ExampleFollowing is the complete code −import pandas as pd # creating dataframe1 dataFrame1 = pd.DataFrame({"Car": ['Bentley', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, ... Read More

When it is required to determine the index rank of elements in a data structure, a method is defined that takes a list as a parameter. It iteeates over the elements in the list, and performs certain comparisons before changing the values of two variables.ExampleBelow is a demonstration of the samedef find_rank_elem(my_list): my_result = [0 for x in range(len(my_list))] for elem in range(len(my_list)): (r, s) = (1, 1) for j in range(len(my_list)): if ... Read More

When it is required to remove non-increasing elements, a simple iteration is used along with comparison of elements.ExampleBelow is a demonstration of the samemy_list = [5, 23, 45, 11, 45, 67, 89, 99, 10, 26, 7, 11] print("The list is :") print(my_list) my_result = [my_list[0]] for elem in my_list: if elem >= my_result[-1]: my_result.append(elem) print("The result is :") print(my_result)OutputThe list is : [5, 23, 45, 11, 45, 67, 89, 99, 10, 26, 7, 11] The result is : [5, 5, 23, 45, 45, 67, 89, 99] ... Read More

To append a list to a DataFrame using append(), let us first create a DataFrame. The data is in the form of lists of team rankings for our example − # data in the form of list of team rankings Team = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], ['New Zealand', 4 , 65], ['South Africa', 5, 50]] # Creating a DataFrame and adding columns dataFrame = pd.DataFrame(Team, columns=['Country', 'Rank', 'Points'])Let’s say the following is the row to be append −myList = [["Sri Lanka", 6, 40]] Append the above row in the form of list using append() ... Read More

When it is required to get the consecutive ranges of ‘K’ which are greater than ‘N’, the ‘enumerate’ attribute and simple iteration is used.ExampleBelow is a demonstration of the samemy_list = [3, 65, 33, 23, 65, 65, 65, 65, 65, 65, 65, 3, 65] print("The list is :") print(my_list) K = 65 N = 3 print("The value of K is ") print(K) print("The value of N is ") print(N) my_result = [] beg, end = 0, 0 previous = 1 for index, element in enumerate(my_list): if element == K: end = ... Read More

To stack a single-level column, use the datafrem.stack(). At first, let us import the required library −import pandas as pdCreate a DataFrame with single-level column −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b'])Stack the DataFrame using the stack() method −dataFrame.stack() ExampleFollowing is the complete 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 # stack print"Stacking...", dataFrame.stack()OutputThis will produce the following output −DataFrame...     a   b w  10 ... Read More

When it is required to flatten a nested list into a tuple list, a method is defined that takes a list as a parameter, and uses the ‘isinstance’ method to check if an element belongs to a specific type. Depending on this, the output is displayed.ExampleBelow is a demonstration of the samedef convert_nested_tuple(my_list): for elem in my_list: if isinstance(elem, list): convert_nested_tuple(elem) else: my_result.append(elem) return ... Read More

When it is required to create a nested list containing values as the count of list elements, a simple iteration is used.ExampleBelow is a demonstration of the samemy_list = [11, 25, 36, 24] print("The list is :") print(my_list) for element in range(len(my_list)): my_list[element] = [element+1 for j in range(element+1)] print("The resultant list is :") print(my_list)OutputThe list is : [11, 25, 36, 24] The resultant list is : [[1], [2, 2], [3, 3, 3], [4, 4, 4, 4]]ExplanationA list is defined and is displayed on the console.It is iterated over, and it is added to 1 and ... Read More

We will be using the iat attribute to access the last element, since it is used to access a single value for a row/column pair by integer position.Let us first import the required Pandas library −import pandas as pdCreate a Pandas series with numbers −data = pd.Series([10, 20, 5, 65, 75, 85, 30, 100])Now, get the last element using iat() −data.iat[-1]ExampleFollowing is the code −import pandas as pd # pandas series data = pd.Series([10, 20, 5, 65, 75, 85, 30, 100]) print"Series...", data # get the first element print"The first element in the series = ", data.iat[0] ... Read More

When it is required to count the frequency of the matrix row length, it is iterated over and its frequency is added to the empty dictionary or incremented if found again.ExampleBelow is a demonstration of the samemy_list = [[42, 24, 11], [67, 18], [20], [54, 10, 25], [45, 99]] print("The list is :") print(my_list) my_result = dict() for element in my_list: if len(element) not in my_result: my_result[len(element)] = 1 else: my_result[len(element)] += 1 print("The result is :") print(my_result)OutputThe ... Read More