Duckdb vs pandas vs polars for python devs

certainly! let's explore duckdb, pandas, and polars, three popular libraries for data manipulation and analysis in python. each library has its strengths and use cases, and this tutorial will help you understand their differences, performance, and provide code examples for each.

### 1. overview of libraries

#### 1.1. pandas
- **description**: pandas is a powerful open-source data analysis and manipulation library for python. it provides data structures like series and dataframes, which are essential for handling structured data.
- **use cases**: data cleaning, transformation, analysis, and visualization.

#### 1.2. duckdb
- **description**: duckdb is an in-process sql olap database management system. it is designed for fast analytical query processing and integrates well with pandas and other python libraries.
- **use cases**: sql querying on large datasets, analytical workloads, and integration with other data sources.

#### 1.3. polars
- **description**: polars is a fast dataframe library implemented in rust and designed for performance. it provides a similar api to pandas but is optimized for speed and memory efficiency.
- **use cases**: high-performance data manipulation, particularly with large datasets.

### 2. code examples

let's explore how to perform similar operations using pandas, duckdb, and polars.

#### 2.1. setup

first, ensure you have the required libraries installed. you can install them using pip:

#### 2.2. sample data

we'll create a sample dataframe for our examples.

### 3. performing operations

#### 3.1. pandas

let's calculate the average salary by age.

#### 3.2. duckdb

now, let's perform the same operation using duckdb.

#### 3.3. polars

finally, let's do the same using polars.

### 4. performance comparison

#### 4.1. pandas
pandas is great for small to medium-sized datasets and offers many functionalities. however, it might struggle with larger datasets due to memory constraints and performance bottlenecks.

#### 4.2. duc ...

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