Why Do Data Analysis? 10 Practical Python Data Analysis Scenarios with Code Examples

Data analysis plays a crucial role in modern society by providing insights into business performance, uncovering problems and opportunities, supporting evidence‑based decision making, optimizing operations, enabling forecasting and planning, and enhancing competitiveness.

The following ten practical Python scenarios demonstrate how to apply data‑analysis techniques using pandas, matplotlib, and scikit‑learn.

1. Data Reading and Inspection:

import pandas as pd

# Read CSV file

data = pd.read_csv('data.csv')

# View first rows

print(data.head())

# Basic statistical summary

print(data.describe())

2. Data Cleaning and Processing:

# Drop missing values

data.dropna()

# Fill missing values

data.fillna(0)

# Remove duplicate rows

data.drop_duplicates()

3. Data Filtering:

# Filter by a single condition

filtered_data = data[data['column'] > 10]

# Filter by multiple conditions

filtered_data = data[(data['column1'] > 10) & (data['column2'] == 'value')]

4. Grouping and Aggregation:

# Group by a column and compute mean

grouped_data = data.groupby('column')['column2'].mean()

# Group by multiple columns and compute sum

grouped_data = data.groupby(['column1', 'column2'])['column3'].sum()

5. Data Visualization:

import matplotlib.pyplot as plt

# Bar chart

plt.bar(data['column1'], data['column2'])

# Scatter plot

plt.scatter(data['column1'], data['column2'])

# Line plot

plt.plot(data['column1'], data['column2'])

6. Statistical Analysis:

# Mean of a column

mean_value = data['column'].mean()

# Median of a column

median_value = data['column'].median()

# Standard deviation

std_value = data['column'].std()

7. Data Transformation:

# Apply function to a column

data['new_column'] = data['column'].apply(lambda x: x * 2)

# Replace values in a column

data['column'].replace({'value1': 'new_value1', 'value2': 'new_value2'}, inplace=True)

8. Time‑Series Analysis:

# Convert to datetime

data['date_column'] = pd.to_datetime(data['date_column'])

# Set datetime column as index

data.set_index('date_column', inplace=True)

# Resample to daily, weekly, monthly sums

daily_data = data.resample('D').sum()

weekly_data = data.resample('W').sum()

monthly_data = data.resample('M').sum()

9. Data Export:

# Export to CSV

data.to_csv('output.csv', index=False)

# Export to Excel

data.to_excel('output.xlsx', index=False)

10. Machine Learning Application:

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

# Split dataset

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and train model

model = LinearRegression()

model.fit(X_train, y_train)

# Predict on test set

predictions = model.predict(X_test)

These code snippets provide a hands‑on foundation for performing Python data analysis, from data ingestion and cleaning to visualization and simple machine‑learning modeling.