Advanced Data Wrangling with Pandas

1. Introduction

Pandas is an essential library in Python for data manipulation and analysis. This lesson covers advanced techniques in data wrangling, which includes importing, cleaning, transforming, and aggregating data.

2. Data Importing

Importing data is the first step in data wrangling. Pandas provides several functions to read data from various formats.

import pandas as pd

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

# Read Excel file
df_excel = pd.read_excel('data.xlsx')

3. Data Cleaning

Data cleaning is crucial for accurate analysis. Common tasks include handling missing values, removing duplicates, and correcting data types.

# Handling missing values
df.fillna(0, inplace=True)

# Removing duplicates
df.drop_duplicates(inplace=True)

# Changing data type
df['column_name'] = df['column_name'].astype('int')

4. Data Transformation

This step involves reshaping the data to make it suitable for analysis. Techniques include filtering, sorting, and applying functions.

# Filtering rows
filtered_df = df[df['column_name'] > 10]

# Sorting data
sorted_df = df.sort_values(by='column_name', ascending=False)

# Applying a function
df['new_column'] = df['existing_column'].apply(lambda x: x * 2)

5. Data Aggregation

Aggregation helps summarize data. Use groupby to aggregate data by certain criteria.

6. Merging and Joining

Combining datasets is essential for comprehensive analysis. Use merge and join functions to accomplish this.

# Merging DataFrames
merged_df = pd.merge(df1, df2, on='key_column', how='inner')

7. Best Practices

Follow these best practices to enhance your data wrangling process:

• Use descriptive column names.
• Document your data cleaning steps.
• Test your transformations with small datasets.
• Keep your data pipeline modular.

8. FAQ