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Page 316 of 2109
Python program to mask a list using values from another list
When working with data analysis or filtering tasks, you often need to create a binary mask from one list based on values present in another list. Python provides several approaches to accomplish this task efficiently. What is List Masking? List masking creates a binary list (containing 0s and 1s) where 1 indicates the element exists in the reference list and 0 indicates it doesn't. This technique is commonly used in data filtering and boolean indexing. Using List Comprehension The most Pythonic approach uses list comprehension to create a mask ? my_list = [5, 6, ...
Read MoreHow to append a list to a Pandas DataFrame using loc in Python?
The DataFrame.loc accessor is used to access a group of rows and columns by label or a boolean array. We can use it to append a list as a new row to an existing DataFrame by specifying the next available index position. Creating the Initial DataFrame Let us first create a DataFrame with team ranking data ? import pandas as pd # Data in the form of list of team rankings team_data = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], ...
Read MorePython Pandas - Filling missing column values with mode
Mode is the value that appears most frequently in a dataset. In Pandas, you can fill missing values with the mode using the fillna() method combined with mode(). This is useful when you want to replace NaN values with the most common value in a column. Syntax dataframe.fillna(dataframe['column'].mode()[0], inplace=True) Creating DataFrame with Missing Values Let's start by importing the required libraries and creating a DataFrame with some missing values − import pandas as pd import numpy as np # Create DataFrame with NaN values dataFrame = pd.DataFrame({ ...
Read MorePython - Search DataFrame for a specific value with pandas
Searching a DataFrame for specific values is a common operation in data analysis. Pandas provides several methods to locate and retrieve rows based on specific criteria using boolean indexing and iloc. Creating the DataFrame First, let's create a sample DataFrame with car information ? import pandas as pd # Creating DataFrame with car data dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], "Units_Sold": ...
Read MorePython - Add a prefix to column names in a Pandas DataFrame
A Pandas DataFrame allows you to add prefixes to all column names using the add_prefix() method. This is useful for distinguishing columns when merging DataFrames or organizing data. Syntax DataFrame.add_prefix(prefix) Parameters: prefix − String to add before each column name Creating a DataFrame First, let's create a DataFrame with car data ? import pandas as pd # Create DataFrame dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], ...
Read MorePython - Reverse the column order of the Pandas DataFrame
To reverse the column order in a Pandas DataFrame, use the slice notation [::-1] with dataFrame.columns. This creates a new DataFrame with columns in reverse order without modifying the original data. Syntax dataFrame[dataFrame.columns[::-1]] Step-by-Step Process Import Required Library import pandas as pd Create a DataFrame import pandas as pd # Create a DataFrame with 4 columns dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, ...
Read MoreFetch only capital words from DataFrame in Pandas
In Pandas, you can extract only capital words from a DataFrame using regular expressions. The re module provides pattern matching capabilities to identify words containing uppercase letters. Setting Up the Data First, let's create a sample DataFrame with mixed case words ? import re import pandas as pd # Create sample data with mixed case words data = [['computer', 'mobile phone', 'ELECTRONICS', 'electronics'], ['KEYBOARD', 'charger', 'SMARTTV', 'camera']] df = pd.DataFrame(data, columns=['Col1', 'Col2', 'Col3', 'Col4']) print("Original DataFrame:") print(df) Original DataFrame: ...
Read MorePython - Display True for infinite values in a Pandas DataFrame
When working with numerical data in Pandas, you may encounter infinite values. You can identify and display True for infinite values using the isin() method or np.isinf() function. Creating a DataFrame with Infinite Values First, let's create a DataFrame containing some infinite values using np.inf − import pandas as pd import numpy as np # Create DataFrame with infinite values data = {"Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000, 900, np.inf]} dataFrame = pd.DataFrame(data) print("DataFrame...") print(dataFrame) DataFrame... Reg_Price 0 7000.506 1 ...
Read MorePython Pandas – Check and Display row index with infinity
When working with Pandas DataFrames, you may need to identify rows containing infinity values. This is useful for data cleaning and analysis. Python provides np.isinf() and any() methods to check and display row indexes with infinity values. Required Libraries First, import the necessary libraries ? import pandas as pd import numpy as np Creating DataFrame with Infinity Values Create a DataFrame containing infinity values using np.inf ? import pandas as pd import numpy as np # Create dictionary with infinity values data = {"Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000, ...
Read MorePython Pandas – Count the Observations
In Pandas, you can count the observations (rows) within groups using the groupby() method combined with count(). This is useful for analyzing the frequency of categories in your data. Creating a Sample DataFrame Let's start by creating a DataFrame with product information ? import pandas as pd # Create a DataFrame with product data dataFrame = pd.DataFrame({ 'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone'], 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'], 'Quantity': [10, 50, 10, 20, 25, 50] ...
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