duckdb vs pandas vs polars for python devs

sure! let's dive into an informative tutorial comparing duckdb, pandas, and polars for data manipulation in python. each of these libraries has its own strengths and use cases. we will explore their features, performance, and provide code examples for common operations.

overview of the libraries

1. **pandas**:
- a widely-used library for data manipulation and analysis.
- provides easy-to-use data structures like series and dataframe.
- great for small to moderately large datasets that fit into memory.

2. **polars**:
- a fast dataframe library implemented in rust.
- designed for performance and efficiency, especially with larger datasets.
- supports lazy evaluation, which can optimize query performance.

3. **duckdb**:
- an in-process sql olap database management system.
- allows sql queries to be run on dataframes.
- optimized for analytical workloads and can handle large datasets efficiently.

installation

to use these libraries, you can install them via pip:

basic operations

let’s compare basic operations like reading a csv file, filtering, and aggregation across the three libraries.

1. importing libraries

2. reading data

for demonstration, let's create a sample csv file:

using pandas

using polars

using duckdb

3. filtering data

let’s filter rows where the age is greater than 30.

using pandas

using polars

using duckdb

4. aggregation

now, let's calculate the average salary grouped by age.

using pandas

using polars

using duckdb

performance comparison

to compare performance, we can use larger datasets:

conclusion

- **pandas** is great for small to medium datasets, offering a rich set of features and a familiar api.
- **polars** excels with larger datasets due to its performance optimizations and can perform operations in a lazy manner, which can improve efficiency.
- **duckdb** combines the benefits of sql with dataframe operations, making it a powerful tool for analytical ...

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DuckDB
Pandas
Polars
Python DataFrames
Data Analysis
In-Memory Processing
SQL Integration
Performance Comparison
Data Science
Data Manipulation
ETL Processes
Data Wrangling
Memory Efficiency
Query Optimization
Big Data Handling