Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Economics & Finance
Filtering a Pandas DataFrame by time allows you to extract records that meet specific date or time conditions. Use the loc indexer with datetime comparisons to filter rows based on date ranges. Basic Time Filtering with loc First, create a DataFrame with date information ? import pandas as pd # Create dictionary with car purchase data data = { 'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_Purchase': ['2021-07-10', '2021-08-12', '2021-06-17', '2021-03-16', '2021-05-19', '2021-08-22'] } # Create DataFrame dataFrame = pd.DataFrame(data) print("Original DataFrame:") print(dataFrame) ... Read More
When it is required to remove palindromic elements from a list, list comprehension and the 'not' operator are used. A palindromic number reads the same forwards and backwards, like 121 or 9. Example Below is a demonstration of removing palindromic elements from a list − my_list = [56, 78, 12, 32, 4, 8, 9, 100, 11] print("The list is:") print(my_list) my_result = [elem for elem in my_list if int(str(elem)[::-1]) not in my_list] print("The result is:") print(my_result) Output The list is: [56, 78, 12, 32, 4, 8, 9, 100, 11] ... Read More
Pandas groupby() operations allow you to split data into groups and count rows in each group using size(). This is useful for analyzing data distribution and finding group frequencies. Creating the DataFrame First, let's create a sample DataFrame with product data ? import pandas as pd # Create a DataFrame dataFrame = pd.DataFrame({ 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'], 'Quantity': [10, 50, 10, 20, 25, 50], 'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone'] }) print("DataFrame:") print(dataFrame) ... Read More
When working with strings, you might need to find all substrings of a specific length that contain exactly K distinct characters. This can be achieved by iterating through the string and using Python's set() method to count unique characters in each substring. Syntax The general approach involves: for i in range(len(string) - n + 1): substring = string[i:i+n] if len(set(substring)) == k: # Add to result Example Below is a demonstration that finds all 2-character substrings with ... Read More
When working with lists of dictionaries, you often need to merge them while handling duplicate keys. This process involves iterating through corresponding dictionaries and adding keys that don't already exist. Basic Dictionary List Merging Here's how to merge two lists of dictionaries, keeping original values for duplicate keys ? list1 = [{"aba": 1, "best": 4}, {"python": 10, "fun": 15}, {"scala": "fun"}] list2 = [{"scala": 6}, {"python": 3, "best": 10}, {"java": 1}] print("First list:") print(list1) print("Second list:") print(list2) # Merge dictionaries at corresponding positions for i in range(len(list1)): existing_keys = list(list1[i].keys()) ... Read More
To sort a Pandas DataFrame in descending order according to element frequency, you need to group the data, count occurrences, and use sort_values() with ascending=False. Basic Syntax The key is combining groupby(), count(), and sort_values() ? df.groupby(['column']).count().sort_values(['count_column'], ascending=False) Creating Sample Data Let's create a DataFrame with car data to demonstrate frequency sorting ? import pandas as pd # Create DataFrame with duplicate car entries dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'BMW', 'Mustang', 'Mercedes', 'Lexus'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 2000], ... Read More
Use pop() to remove a column and insert() to place it at the first position in a Pandas DataFrame. This technique allows you to reorder columns efficiently without creating a new DataFrame. Creating the DataFrame First, let's create a sample DataFrame with three columns ? 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'], ... Read More
In this tutorial, we will learn how to display only non-duplicate values from a Pandas DataFrame. We'll use the duplicated() method combined with the logical NOT operator (~) to filter out duplicate entries. Creating a DataFrame with Duplicates First, let's create a DataFrame containing duplicate values ? import pandas as pd # Create DataFrame with duplicate student names dataFrame = pd.DataFrame({ "Student": ['Jack', 'Robin', 'Ted', 'Robin', 'Scarlett', 'Kat', 'Ted'], "Result": ['Pass', 'Fail', 'Pass', 'Fail', 'Pass', 'Pass', 'Pass'] }) print("Original DataFrame:") print(dataFrame) Original DataFrame: ... Read More
Reshaping data in Pandas involves transforming data structure to make it more suitable for analysis. One common approach is categorizing text values into numerical form using the map() function with a dictionary. Basic DataFrame Creation First, let's create a DataFrame with student names and their results ? import pandas as pd # Create DataFrame with student results dataFrame = pd.DataFrame({ "Student": ['Jack', 'Robin', 'Ted', 'Scarlett', 'Kat'], "Result": ['Pass', 'Fail', 'Fail', 'Pass', 'Pass'] }) print("Original DataFrame:") print(dataFrame) Original DataFrame: Student Result ... Read More
Use the get_dummies() method to convert categorical DataFrame to binary data. This process creates binary columns for each unique category, where 1 indicates the presence of that category and 0 indicates absence. Creating a Sample DataFrame Let's start by creating a DataFrame with categorical data ? import pandas as pd # Create DataFrame with categorical data dataFrame = pd.DataFrame( { "Student": ['Jack', 'Robin', 'Ted', 'Scarlett', 'Kat'], "Result": ['Pass', 'Fail', 'Fail', 'Pass', 'Pass'] ... Read More