Sample_Py

A Python project implementing various sampling methods for data analysis.

Overview

This project provides a collection of functions for performing different types of sampling on datasets. The sampling methods include:

• Simple Random Sampling
• Stratified Sampling
• Systematic Sampling
• Cluster Sampling
• Multi-Stage Sampling
• Weighted Sampling
• Reservoir Sampling
• Bootstrap Sampling
• Temporal Sampling
• Spatial Sampling

Table of Contents

• Installation
• Usage
  • Simple Random Sampling
  • Stratified Sampling
  • Systematic Sampling
  • Cluster Sampling
  • Multi-Stage Sampling
  • Weighted Sampling
  • Reservoir Sampling
  • Bootstrap Sampling
  • Temporal Sampling
  • Spatial Sampling
• Testing
• Contributing
• License
• Contact

Installation

To install the project, clone the repository and install the dependencies using pip:

git clone https://github.com/elkronos/sample_py.git
cd sample_py
pip install -r requirements.txt

Usage

Here are examples of how to use the sampling functions provided in this project:

Simple Random Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform simple random sampling
sampled_data = sampling_methods.simple_random_sampling(data, sample_size=100, seed=42)

print(sampled_data)

Stratified Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform stratified sampling based on a 'category' column
stratified_sampled_data = sampling_methods.stratified_sampling(
    data, 
    strata_column='category', 
    sample_size=50, 
    seed=42
)

print(stratified_sampled_data)

Systematic Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform systematic sampling with an interval of 10
systematic_sampled_data = sampling_methods.systematic_sampling(data, interval=10, seed=42)

print(systematic_sampled_data)

Cluster Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform cluster sampling based on a 'cluster_id' column
cluster_sampled_data = sampling_methods.cluster_sampling(
    data, 
    cluster_column='cluster_id', 
    num_clusters=5, 
    seed=42
)

print(cluster_sampled_data)

Multi-Stage Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform multi-stage sampling: first by clusters, then sample within clusters
multi_stage_sampled_data = sampling_methods.multi_stage_sampling(
    data, 
    cluster_column='cluster_id', 
    num_clusters=5, 
    stage_two_sample_size=10, 
    seed=42
)

print(multi_stage_sampled_data)

Weighted Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform weighted sampling based on a 'weights' column
weighted_sampled_data = sampling_methods.weighted_sampling(
    data, 
    weights_column='weights', 
    sample_size=100, 
    seed=42
)

print(weighted_sampled_data)

Reservoir Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data and convert it to a data stream (list of records)
data = pd.read_csv('your_dataset.csv')
data_stream = data.to_dict('records')

# Perform reservoir sampling
reservoir_sample = sampling_methods.reservoir_sampling(data_stream, sample_size=100, seed=42)

print(reservoir_sample)

Bootstrap Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform bootstrap sampling to generate 10 bootstrap samples
bootstrap_samples = sampling_methods.bootstrap_sampling(data, num_samples=10, sample_size=100, seed=42)

for i, sample in enumerate(bootstrap_samples):
    print(f"Bootstrap Sample {i+1}")
    print(sample)

Temporal Sampling

import pandas as pd
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Perform temporal sampling on a time-based column 'timestamp'
temporal_sampled_data = sampling_methods.temporal_sampling(
    data, 
    time_column='timestamp', 
    start_time=pd.Timestamp('2021-01-01'), 
    end_time=pd.Timestamp('2021-12-31'), 
    interval=7, 
    sample_size=10, 
    seed=42
)

print(temporal_sampled_data)

Spatial Sampling

import pandas as pd
from shapely.geometry import Polygon
from sampling import sampling_methods

# Load your data
data = pd.read_csv('your_dataset.csv')

# Define a region as a polygon
region = Polygon([(-10, -10), (-10, 10), (10, 10), (10, -10)])

# Perform spatial sampling based on latitude and longitude columns
spatial_sampled_data = sampling_methods.spatial_sampling(
    data, 
    latitude_column='lat', 
    longitude_column='lon', 
    region=region, 
    sample_size=100, 
    seed=42
)

print(spatial_sampled_data)

Testing

To run the tests, use the following command:

pytest

Contributing

If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch-name

3. Make your changes and commit them:

   git commit -m 'Add some feature'

4. Push to the branch:

   git push origin feature-branch-name

5. Submit a pull request.

Please ensure your code adheres to the project's coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

If you have any questions or suggestions, feel free to reach out to:

• [email protected]