python pandas statistical functions

Pandas is a powerful data manipulation and analysis library for Python. It provides easy-to-use data structures and functions for efficiently manipulating large datasets. In this tutorial, we will explore some of the statistical functions provided by Pandas to analyze and summarize data.
If you haven't installed Pandas yet, you can install it using the following command:
Now, let's get started with the tutorial!
For this tutorial, let's create a sample DataFrame to work with:
This will create a DataFrame with columns 'Name', 'Age', 'Salary', and 'Experience'.
This will display summary statistics for numerical columns, including count, mean, std (standard deviation), min, 25%, 50%, 75%, and max.
This will display the correlation matrix, which shows the correlation between numerical columns.
This will group the DataFrame by the 'Age' column and calculate the mean for each group.
This will show the count of each unique value in the 'Name' column.
This will apply the double_salary function to each element in the 'Salary' column and create a new column 'Doubled_Salary'.
Pandas provides a wide range of statistical functions for data analysis. This tutorial covered only a subset of these functions. Experiment with these examples and explore more Pandas documentation to enhance your data analysis skills. Happy coding!
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