Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Server Side Programming Articles
Page 336 of 2109
Python – Cumulative Row Frequencies in a List
When you need to count how many times specific elements appear in each row of a nested list, you can use Python's Counter class from the collections module combined with list comprehension to calculate cumulative row frequencies. What are Cumulative Row Frequencies? Cumulative row frequencies refer to counting the total occurrences of specified elements within each row of a nested list. For example, if you want to find how many times elements [19, 2, 71] appear in each row, you sum their individual frequencies per row. Example Here's how to calculate cumulative row frequencies using Counter ...
Read MorePython Program To Get Minimum Element For String Construction
When you need to find the minimum number of strings required to construct a target string, you can use Python's combinations from itertools along with set operations. This approach finds the smallest subset of strings that contains all characters needed for the target string. Example Below is a demonstration of finding the minimum elements needed ? from itertools import combinations my_list = ["python", "is", "fun", "to", "learn"] print("The list is :") print(my_list) my_target_str = "onis" my_result = -1 my_set_string = set(my_target_str) complete_val = False for value in range(0, len(my_list) + 1): ...
Read MorePython - Filter Pandas DataFrame with numpy
The numpy.where() method can be used to filter Pandas DataFrame by returning indices that meet specified conditions. This approach is particularly useful when you need to apply multiple filtering conditions simultaneously. Setup First, let's import the required libraries and create a sample DataFrame ? import pandas as pd import numpy as np # Create a DataFrame with product records dataFrame = pd.DataFrame({ "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900] }) print("Original DataFrame:") print(dataFrame) ...
Read MorePython - Summing all the rows of a Pandas Dataframe
To sum all the rows of a DataFrame, use the sum() function with axis=1. Setting the axis parameter to 1 adds values across columns for each row, while axis=0 (default) would sum down columns. Creating a Sample DataFrame Let's start by creating a DataFrame with stock data ? import pandas as pd dataFrame = pd.DataFrame({ "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900] }) print("DataFrame...") print(dataFrame) DataFrame... Opening_Stock Closing_Stock 0 ...
Read MorePython Pandas - How to append rows to a DataFrame
To append rows to a DataFrame, use the concat() method (recommended) or the deprecated append() method. Here, we will create two DataFrames and append one after the other. Using concat() Method (Recommended) The concat() method is the modern approach for appending DataFrames ? import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'] }) print("DataFrame1 ...", dataFrame1) print("DataFrame1 length =", len(dataFrame1)) # Create DataFrame2 dataFrame2 = pd.DataFrame({ "Car": ['Mercedes', 'Tesla', 'Bentley', 'Mustang'] }) print("DataFrame2 ...", dataFrame2) print("DataFrame2 length =", ...
Read MorePython Pandas - Create a subset by choosing specific values from columns based on indexes
To create a subset by choosing specific values from columns based on indexes, use the iloc() method. The iloc() method allows you to select data by integer position, making it perfect for choosing specific rows and columns from a DataFrame. Creating a DataFrame Let us first create a sample DataFrame with product records ? import pandas as pd dataFrame = pd.DataFrame({ "Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900] }) print("DataFrame...", dataFrame) ...
Read MorePython Pandas - Subset DataFrame by Column Name
To create a subset of DataFrame by column name, use the square brackets. Use the DataFrame with square brackets (indexing operator) and the specific column name like this ? dataFrame['column_name'] Syntax # Single column - returns a Series dataFrame['column_name'] # Multiple columns - returns a DataFrame dataFrame[['col1', 'col2']] Example - Single Column Subset Let us create a DataFrame and extract a single column ? import pandas as pd # Create a Pandas DataFrame with Product records dataFrame = pd.DataFrame({ "Product": ["SmartTV", "ChromeCast", "Speaker", ...
Read MorePython – Assign Alphabet to each element
When working with integer lists, you may need to assign alphabet letters to each element based on their sorted order. Python's string.ascii_lowercase module provides lowercase letters, and we can map them to unique values using list comprehension. Syntax import string string.ascii_lowercase[index] # Returns letter at given index Example Below is a demonstration that assigns alphabets to unique elements based on their sorted order − import string my_list = [11, 51, 32, 45, 21, 66, 12, 58, 90, 0] print("The list is:") print(my_list) print("The list after sorting is:") my_list.sort() ...
Read MorePython – Dictionaries with Unique Value Lists
When working with a list of dictionaries, you may need to extract all unique values from across all dictionaries. Python provides an elegant solution using set operations and list comprehensions to eliminate duplicates efficiently. Example Below is a demonstration of extracting unique values from dictionary lists ? my_dictionary = [{'Python': 11, 'is': 22}, {'fun': 11, 'to': 33}, {'learn': 22}, {'object': 9}, {'oriented': 11}] print("The dictionary is:") print(my_dictionary) my_result = list(set(value for element in my_dictionary for value in element.values())) print("The resultant list is:") print(my_result) print("The resultant list after sorting is:") my_result.sort() print(my_result) ...
Read MorePython – Get Matrix Mean
When working with matrices in Python, calculating the mean (average) of all elements is a common operation. The NumPy package provides the mean() method to efficiently compute the mean of matrix elements. Basic Matrix Mean Here's how to calculate the mean of all elements in a matrix ? import numpy as np my_matrix = np.matrix('24 41; 35 25') print("The matrix is:") print(my_matrix) my_result = my_matrix.mean() print("The mean is:") print(my_result) The matrix is: [[24 41] [35 25]] The mean is: 31.25 Mean Along Specific Axis You can calculate ...
Read More