Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
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
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
When it is required to get the character repetitions of a given character, a method is defined that uses the index value to print the repetitions.ExampleBelow is a demonstration of the samedef to_string(my_list): return ''.join(my_list) def lex_recurrence(my_string, my_data, last_val, index_val): length = len(my_string) for i in range(length): my_data[index_val] = my_string[i] if index_val==last_val: print(to_string(my_data)) else: ... Read More
When it is required to group the contiguous elements of a string that are present in a list, a method is defined that uses ‘groupby’, and ‘yield’.ExampleBelow is a demonstration of the samefrom itertools import groupby def string_check(elem): return isinstance(elem, str) def group_string(my_list): for key, grp in groupby(my_list, key=string_check): if key: yield list(grp) else: yield from ... Read More
To get the datatypes of columns, use the info() method. Let us first import the required library −import pandas as pdCreate a DataFrame with 2 columns having different datatypes −dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, 3, 8, 2, 9, 6] } )Get the complete information about datatypes −dataFrame.info()ExampleFollowing is the complete code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, 3, ... Read More
To select columns with specific datatypes, use the select_dtypes() method and the include parameter. At first, create a DataFrame with 2 columns −dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, 3, 8, 2, 9, 6] } )Now, select the 2 columns with their respective specific datatype −column1 = dataFrame.select_dtypes(include=['object']).columns column2 = dataFrame.select_dtypes(include=['int64']).columnsExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Roll Number": [ 5, 10, ... Read More
To add a zero column to a Pandas DataFrame, use the square bracket and set it to 0. At first, import te required library −import pandas as pdCreate a DataFrame with 3 columns −dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Marc', 'Scarlett', 'Kat', 'John'], "Result": ['Pass', 'Fail', 'Pass', 'Fail', 'Pass', 'Pass', 'Pass'], "Roll Number": [ 5, 10, 3, 8, 2, 9, 6] } )Create a new column with zero entries −dataFrame['ZeroColumn'] = 0 ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { ... Read More
When it is required to find the element frequencies in the percentage range, the ‘Counter’ is used along with a simple iteration technique.ExampleBelow is a demonstration of the samefrom collections import Counter my_list = [56, 34, 78, 90, 11, 23, 6, 56, 79, 90] print("The list is :") print(my_list) start, end = 13, 60 my_freq = dict(Counter(my_list)) my_result = [] for element in set(my_list): percent = (my_freq[element] / len(my_list)) * 100 if percent >= start and percent
To open a list, we can use append() method. With that, we can also use loc() method. At first, let us import the required library −import pandas as pdFollowing is the data in the form of lists of team rankings −Team = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], ['New Zealand', 4 , 65], ['South Africa', 5, 50]]Creating a DataFrame with the above data and adding columns −dataFrame = pd.DataFrame(Team, columns=['Country', 'Rank', 'Points']) Let’s say the following is the row to be appended −myList = [["Sri Lanka", 6, 40]]Append the above row in the form of list − ... Read More
When it is required to get the specific number of power of a number, the ‘**’ operator is used along with list comprehension.ExampleBelow is a demonstration of the samen = 4 print("The value n is : ") print(n) k = 5 print("The value of k is : ") print(k) result = [n ** index for index in range(0, k)] print("The square values of N till K : " ) print(result)OutputThe value n is : 4 The value of k is : 5 The square values of N till K : [1, 4, 16, 64, 256]ExplanationThe values for ‘n’ ... Read More