Python for Data Analysis

Python for Data Analysis

Python is widely used for data analysis, thanks to its rich ecosystem of libraries for data manipulation, visualization, and statistical analysis.

1. Key Libraries for Data Analysis

2. Loading and Inspecting Data

import pandas as pd

# Load CSV file
df = pd.read_csv('data.csv')

# Inspect top rows
print(df.head())

# Check columns and data types
print(df.info())

# Basic statistics
print(df.describe())

3. Data Cleaning and Preparation

# Handling missing values
df.dropna(inplace=True)           # remove rows with missing values
df.fillna(0, inplace=True)        # fill missing values

# Renaming columns
df.rename(columns={'OldName':'NewName'}, inplace=True)

# Filtering data
filtered = df[df['Age'] > 30]

# Creating new columns
df['Salary_in_k'] = df['Salary'] / 1000

4. Data Aggregation and Grouping

# Group by a column
grouped = df.groupby('Department')['Salary'].mean()
print(grouped)

# Pivot table
pivot = df.pivot_table(values='Salary', index='Department', columns='Gender', aggfunc='mean')
print(pivot)

5. Data Visualization

import matplotlib.pyplot as plt
import seaborn as sns

# Line plot
plt.plot(df['Year'], df['Revenue'])
plt.show()

# Histogram
plt.hist(df['Age'], bins=10)
plt.show()

# Boxplot
sns.boxplot(x='Department', y='Salary', data=df)
plt.show()

# Heatmap
sns.heatmap(df.corr(), annot=True)
plt.show()

6. Exporting Data

# Export to CSV
df.to_csv('cleaned_data.csv', index=False)

# Export to Excel
df.to_excel('cleaned_data.xlsx', index=False)

7. Key Points

• Python is powerful for data cleaning, exploration, and visualization.

• Pandas is the core library for tabular data.

• NumPy handles numerical operations efficiently.

• Matplotlib and Seaborn are essential for plotting and visualization.

• Combining statistical libraries like SciPy and Statsmodels enables advanced analysis.

• Python integrates well with databases, Excel, and big data tools.