StatSnap Tutorial

1. Installation

First, you need to install StatSnap. You can do this via pip:

pip install statsnap

2. Importing Libraries

Start by importing the necessary libraries:

import statsnap as ss
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

3. Loading Data

You can load your dataset using Pandas. For this example, let’s create a sample DataFrame.

# Creating a sample dataset
data = {
    'A': np.random.rand(100),
    'B': np.random.rand(100),
    'C': np.random.rand(100)
}
df = pd.DataFrame(data)

4. Descriptive Statistics

StatSnap can help you generate descriptive statistics easily:

# Generate descriptive statistics
desc_stats = ss.describe(df)
print(desc_stats)

5. Visualizing Data

You can create various plots using StatSnap. Here’s how to create a histogram and a scatter plot.

Histogram:

# Creating a histogram of column 'A'
ss.histogram(df['A'], bins=10, title='Histogram of A', xlabel='A values', ylabel='Frequency')
plt.show()

Scatter Plot:

# Creating a scatter plot between columns 'A' and 'B'
ss.scatter(df['A'], df['B'], title='Scatter Plot of A vs B', xlabel='A values', ylabel='B values')
plt.show()

6. Correlation Matrix

You can visualize the correlation matrix to understand the relationships between variables.

# Calculate and plot the correlation matrix
correlation_matrix = df.corr()
ss.heatmap(correlation_matrix, title='Correlation Matrix')
plt.show()

7. Saving Results

You may want to save your statistics or plots for further use:

# Save descriptive statistics to a CSV file
desc_stats.to_csv('descriptive_statistics.csv')

# Save a plot
plt.savefig('scatter_plot.png')