Pandas - Statistics

Lecture 16:- Pandas - Statistics

In Pandas, you can perform a variety of statistical operations on DataFrames to gain insights into your data. Pandas provides numerous statistical functions that allow you to calculate measures such as mean, median, standard deviation, correlation, and more. Here are some common statistical operations you can perform in Pandas:

Summary Statistics: You can use the describe() method to get a summary of the statistical measures for each numerical column in the DataFrame, including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum.

Mean and Median: You can calculate the mean and median of specific columns using the mean() and median() methods.

Standard Deviation and Variance: You can compute the standard deviation and variance of specific columns using the std() and var() methods.

Correlation: To calculate the correlation between columns, you can use the corr() method. It returns the correlation coefficients between all numerical columns in the DataFrame.

Grouping and Aggregation: You can group your data based on one or more columns and then apply various aggregation functions to summarize the data for each group. For example, you can use groupby() with functions like sum(), mean(), min(), max(), etc.

These are just a few examples of the statistical operations available in Pandas. The library provides a rich set of functions for data analysis and exploration, making it a powerful tool for working with structured data. Pandas' statistics capabilities are widely used in data science, machine learning, and data analysis tasks.

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import pandas a

s pd # Assuming you have a DataFrame named 'df' groupby_city = df.groupby('City')['Age'].mean() # Output print(groupby_city)

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import pandas as pd # Assuming you have a DataFrame named 'df' correlation_matrix = df.corr() # Output print(correlation_matrix)

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import pandas as pd # Assuming you have a DataFrame named 'df' std_age = df['Age'].std() var_age = df['Age'].var() # Output print("Standard Deviation of Age:", std_age) print("Variance of Age:", var_age)

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import pandas as pd # Assuming you have a DataFrame named 'df' mean_age = df['Age'].mean() median_age = df['Age'].median() # Output print("Mean Age:", mean_age) print("Median Age:", median_age)

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import pandas as pd # Assuming you have a DataFrame named 'df' summary_stats = df.describe() # Output print(summary_stats)