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Python Articles
Page 355 of 855
Python - How to rename multiple column headers in a Pandas DataFrame with Dictionary?
In Pandas, you can rename multiple column headers simultaneously using the rename() method with a dictionary. The dictionary maps old column names (keys) to new column names (values). Syntax DataFrame.rename(columns=dictionary, inplace=True) Where dictionary contains old_name: new_name pairs, and inplace=True modifies the original DataFrame. Creating a Sample DataFrame Let's start by creating a DataFrame with car data ? import pandas as pd dataFrame = pd.DataFrame({ "Car": ['BMW', 'Mustang', 'Tesla', 'Mustang', 'Mercedes', 'Tesla', 'Audi'], "Cubic Capacity": [2000, 1800, 1500, 2500, 2200, 3000, 2000], ...
Read MorePython - Select columns with specific datatypes
To select columns with specific datatypes in Pandas, use the select_dtypes() method with the include parameter. This method allows you to filter DataFrame columns based on their data types such as object, int64, float64, etc. Syntax DataFrame.select_dtypes(include=None, exclude=None) Parameters include − List of data types to include exclude − List of data types to exclude Creating a Sample DataFrame Let's start by creating a DataFrame with different data types ? import pandas as pd # Create DataFrame with multiple data types dataFrame = pd.DataFrame( ...
Read MorePython - Character repetition string combinations
When generating string combinations with character repetition, we need to create all possible arrangements where each position can contain any character from the original string. This is essentially generating permutations with repetition using recursive backtracking. Example Below is a demonstration of generating all permutations with repetition − def 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] ...
Read MorePython - Group contiguous strings in List
When working with mixed lists containing both strings and numbers, you might need to group contiguous string elements together while keeping non-string elements separate. Python's itertools.groupby() function combined with a custom key function provides an elegant solution. Understanding the Problem Given a list with mixed data types, we want to group consecutive string elements into sublists while leaving non-string elements as individual items ? Solution Using groupby() We'll create a helper function to identify strings and use groupby() to group contiguous elements ? from itertools import groupby def string_check(elem): ...
Read MorePython - Add a zero column to Pandas DataFrame
To add a zero column to a Pandas DataFrame, you can use square bracket notation to create a new column and assign 0 to all rows. This is useful for initializing placeholder columns or creating default values. Creating the DataFrame First, let's create a sample DataFrame with student data ? import pandas as pd # Create DataFrame 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, ...
Read MorePython - Element frequencies in percent range
When finding elements whose frequencies fall within a specific percentage range, we can use Python's Counter class along with percentage calculations to filter elements based on their occurrence rates. Example Below is a demonstration of finding elements with frequencies between 13% and 60% − from 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)) print("Element frequencies:", my_freq) my_result = [] for element in set(my_list): percent = (my_freq[element] / len(my_list)) * 100 print(f"Element {element}: {percent}%") if percent >= start and percent
Read MorePython Program to Get K initial powers of N
When it is required to get the specific number of powers of a number, the ** operator is used along with list comprehension to generate the first K powers of N. Syntax The basic syntax for calculating powers in Python ? # Using ** operator result = base ** exponent # Using list comprehension for multiple powers powers = [base ** i for i in range(k)] Example Below is a demonstration of getting K initial powers of N ? n = 4 k = 5 print("The value n is:", ...
Read MorePython Program to find Duplicate sets in list of sets
When working with lists of sets, you may need to identify which sets appear more than once. Python provides an efficient solution using Counter from the collections module and frozenset to handle duplicate sets. Why Use frozenset? Sets are mutable and unhashable, so they cannot be directly counted by Counter. frozenset creates an immutable, hashable version of a set that can be used as a dictionary key or counted. Example Below is a demonstration of finding duplicate sets ? from collections import Counter my_list = [{4, 8, 6, 1}, {6, 4, 1, 8}, ...
Read MoreAdd custom borders to a matrix in Python
When it is required to add custom borders to the matrix, a simple list iteration can be used to add the required borders to the matrix. This technique is useful for displaying matrices in a more readable format with visual boundaries. Example Below is a demonstration of the same ? my_list = [[2, 5, 5], [2, 7, 5], [4, 5, 1], [1, 6, 6]] print("The list is :") print(my_list) print("The resultant matrix is :") border = "|" for sub in my_list: my_temp = border + " " ...
Read MoreHow to append a list as a row to a Pandas DataFrame in Python?
To append a list as a row to a Pandas DataFrame in Python, we can use the concat() method (recommended) or the loc[] indexer. Let's first import the required library − import pandas as pd Following 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 − import pandas as pd Team = [['India', 1, 100], ['Australia', ...
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