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Program 1

The document outlines a Python script that generates a synthetic dataset with numerical and categorical columns, performs numerical analysis, and visualizes the data using histograms, boxplots, bar charts, and pie charts. It includes calculations for mean, median, mode, standard deviation, variance, and outlier detection using the IQR method. Additionally, it provides frequency counts for the categorical data and visual representations of these counts.

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iamnotnoob8888
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5 views1 page

Program 1

The document outlines a Python script that generates a synthetic dataset with numerical and categorical columns, performs numerical analysis, and visualizes the data using histograms, boxplots, bar charts, and pie charts. It includes calculations for mean, median, mode, standard deviation, variance, and outlier detection using the IQR method. Additionally, it provides frequency counts for the categorical data and visual representations of these counts.

Uploaded by

iamnotnoob8888
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as TXT, PDF, TXT or read online on Scribd
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import pandas as pd

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

# Generate synthetic dataset


np.random.seed(42)
data = {
'numerical_column': np.append(np.random.randint(20, 80, 95), [150, 160, 170, 5,
10]),
'categorical_column': np.random.choice(['A', 'B', 'C', 'D'], 100)
}
df = pd.DataFrame(data)
df.to_csv('Sample_Data.csv', index=False) # Optional: Save to CSV
df = pd.read_csv('Sample_Data.csv') # Load the dataset

# Numerical analysis
num = df['numerical_column'].dropna()
print("Dataset Preview:\n", df.head())
print(f"\n--- Numerical Stats for 'numerical_column' ---")
print(f"Mean: {num.mean():.2f} | Median: {num.median()} | Mode:
{num.mode().values}")
print(f"Std Dev: {num.std():.2f} | Variance: {num.var():.2f} | Range: {num.max() -
num.min()}")

# Plot histogram & boxplot


plt.figure(figsize=(10, 4))
sns.histplot(num, bins=20, kde=True).set(title='Histogram')
plt.figure()
sns.boxplot(x=num).set(title='Boxplot')
plt.show()

# Outlier detection using IQR


Q1, Q3 = num.quantile([0.25, 0.75])
IQR = Q3 - Q1
outliers = num[(num < Q1 - 1.5 * IQR) | (num > Q3 + 1.5 * IQR)]
print(f"\nOutliers Detected:\n{outliers.values}")

# Categorical analysis
cat_counts = df['categorical_column'].value_counts()
print(f"\n--- Category Frequencies ---\n{cat_counts}")

# Bar & pie chart


plt.figure(figsize=(10, 4))
cat_counts.plot(kind='bar', color='lightgreen', title='Bar Chart')
plt.xlabel('Category')
plt.ylabel('Count')
plt.tight_layout()
plt.figure()
cat_counts.plot(kind='pie', autopct='%1.1f%%', title='Pie Chart')
plt.ylabel('')
plt.tight_layout()
plt.show()

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