Using Pandas to Find Statistics of Given Data

Objective: 

To use the pandas library in Python to compute statistical measures such as mean, median, and standard deviation, and to visualize the data using a histogram.

Prerequisites:

• Basic knowledge of Python

Lab Tasks:

Write a new Python script file as PandasTest.py and start by importing the requisite libraries. 

import pandas as pd

import matplotlib.pyplot as plt

Dataset: 

The following are weight values (in pounds) for 20 people:

164, 158, 172, 153, 144, 156, 189, 163, 134, 159, 143, 176, 177, 162, 141, 151, 182, 185, 171, 152

Create a Pandas DataFrame:

# Create a pandas Series with given data

weights = pd.Series([164, 158, 172, 153, 144, 156, 189, 163, 134, 159,

                     143, 176, 177, 162, 141, 151, 182, 185, 171, 152])

Compute Statistical Measures:

# Calculate mean

mean_weight = weights.mean()

print(f"Mean: {mean_weight}")

# Calculate median

median_weight = weights.median()

print(f"Median: {median_weight}")

# Calculate standard deviation

std_dev_weight = weights.std()

print(f"Standard Deviation: {std_dev_weight}")

Step 3: Plot a Histogram

# Plot histogram

plt.hist(weights, bins=5, color='blue', edgecolor='black')

plt.xlabel('Weight (lbs)')

plt.ylabel('Frequency')

plt.title('Histogram of Weights')

plt.show()

Expected Output:

• The computed values of mean, median, and standard deviation.

• A histogram displaying the distribution of the given weights.

Conclusion: 

This lab demonstrates how pandas can be used to perform statistical analysis efficiently. The histogram visualization helps understand the data distribution.