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Standard deviation measures how spread out values are in a dataset and indicates how far individual values are from the arithmetic mean. In Pandas, you can calculate the standard deviation of specific columns using the std() function. When working with DataFrames, you often need to find the standard deviation of particular numeric columns. The std() function can be applied to individual columns by indexing the DataFrame with the column name. Example Let's create a DataFrame and calculate the standard deviation of specific columns ? import pandas as pd my_data = { ... Read More
Decision trees are one of the most intuitive and widely-used algorithms in machine learning for classification tasks. They work by recursively splitting the dataset based on feature values to create a tree-like model that makes predictions by following decision paths from root to leaf nodes. How Decision Trees Work A decision tree splits the input space into regions based on feature values. Each internal node represents a decision based on a feature, while leaf nodes contain the final prediction. The algorithm uses measures like Gini impurity to determine the best splits that maximize information gain. The tree ... Read More
Viewing pixel values of an image is a fundamental step in image processing and computer vision tasks. Scikit-image provides convenient functions to read images and extract pixel data, which can then be converted to a pandas DataFrame for analysis. Images are stored as multi-dimensional arrays where each pixel has intensity values. For RGB images, each pixel contains three values (Red, Green, Blue), while grayscale images have single intensity values per pixel. Reading and Displaying an Image First, let's read an image and display its basic properties ? from skimage import io, data import pandas as ... Read More
Data pre-processing refers to the task of gathering data from various resources into a common format. Since real-world data is never ideal, images may have alignment issues, clarity problems, or incorrect sizing. The goal of pre-processing is to remove these discrepancies. To get the resolution of an image, we use the shape attribute. After reading an image, pixel values are stored as a NumPy array. The shape attribute returns the dimensions of this array, representing the image resolution. Reading and Getting Image Resolution Let's see how to upload an image and get its resolution using scikit-image library ... Read More
Sometimes, you may need to calculate the mean values of specific columns or all columns containing numeric data in a pandas DataFrame. The mean() function automatically identifies and computes the mean for numeric columns only. The term mean refers to finding the sum of all values and dividing it by the total number of values in the dataset (also called the arithmetic average). Basic Example Let's create a DataFrame with mixed data types and calculate the mean of numeric columns − import pandas as pd # Create a DataFrame with mixed data types data ... Read More
Sometimes, it may be required to get the sum of a specific column in a Pandas DataFrame. This is where the sum() function can be used to perform column-wise calculations. The column whose sum needs to be computed can be accessed by column name or index. Let's explore different approaches to calculate the sum of a specific column. Creating a Sample DataFrame First, let's create a DataFrame with sample data ? import pandas as pd my_data = { 'Name': pd.Series(['Tom', 'Jane', 'Vin', 'Eve', 'Will']), 'Age': pd.Series([45, ... Read More
A Pandas DataFrame is a two-dimensional data structure where data is stored in tabular format with rows and columns. It can be visualized as an SQL table or Excel sheet. The pop() function provides an efficient way to delete a column while simultaneously returning its values. Syntax DataFrame.pop(item) Parameters: item − The column name to be removed Returns: The removed column as a Series Example Let's create a DataFrame and delete a column using the pop() function ? import pandas as pd my_data = { ... Read More
A DataFrame is a two-dimensional data structure where data is stored in tabular format with rows and columns. It can be visualized as an SQL table or Excel sheet. There are several methods to delete columns from a DataFrame in Python pandas. Using the del Operator The del operator permanently removes a column from the DataFrame ? import pandas as pd my_data = { 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'London', 'Paris'], 'Salary': [50000, ... Read More
A DataFrame is a two-dimensional data structure where data is stored in tabular format with rows and columns. It can be visualized as an SQL table or Excel sheet representation. You can create a DataFrame using the following constructor ? pd.DataFrame(data, index, columns, dtype, copy) The parameters data, index, columns, dtype, and copy are optional. Creating DataFrame from List of Dictionaries When you pass a list of dictionaries to DataFrame, the dictionary keys become column names by default. Each dictionary represents a row of data ? import pandas as pd ... Read More
When accessing pandas Series elements using custom index values, you use the syntax series_name['index_value']. If the specified index exists, the corresponding data is returned. However, if the index is not present in the series, pandas raises a KeyError. Example: Accessing Non-existent Index Let's see what happens when we try to access an index that doesn't exist ? import pandas as pd my_data = [34, 56, 78, 90, 123, 45] my_index = ['ab', 'mn', 'gh', 'kl', 'wq', 'az'] my_series = pd.Series(my_data, index=my_index) print("The series contains following elements:") print(my_series) print("Attempting to access non-existent index 'mm':") print(my_series['mm']) ... Read More