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Project overview
In this project, you’ll assume the role of a data engineer working with a large dataset of loan data from Lending Club. Your challenge is to analyze this data efficiently given memory constraints. You’ll apply techniques to optimize the DataFrame’s memory usage and process the data in chunks.
Throughout the project, you’ll use Python and pandas to explore the data, identify opportunities to reduce memory usage, and implement changes like converting data types and using categories. You’ll calculate memory usage to quantify improvements. This project lets you apply key data engineering skills to solve a realistic, large-scale data challenge.
Objective: Efficiently analyze a large loan dataset by optimizing DataFrame memory usage and processing the data in chunks using Python and pandas.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Reading CSV data into pandas DataFrames for analysis
- Filtering DataFrame rows using Boolean masks and selecting columns
- Adding new calculated columns to pandas DataFrames
- Downcasting DataFrame data types to optimize memory usage
Projects steps
Step 1: Introduction
Step 2: Exploring the Data in Chunks
Step 3: Optimizing String Columns
Step 4: Optimizing Numeric Columns
Step 5: Next Steps
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