semvec/pypi-packages huggingface dataset:
correlation-stats-analyzer 0.1.1
A simple tool to compute and interpret Pearson correlation coefficients.
Requires Python: >=3.6

# correlation_stats_analyzer

A lightweight Python package to compute and interpret the strength and direction of correlation between two numerical datasets using the Pearson Correlation Coefficient.

---

## Features

- Compute the Pearson correlation coefficient between two numeric lists.
- Interpret the result in terms of strength and direction (e.g., "Strong Positive").
- Zero dependencies â€“ works with standard Python only.
- Designed for data science beginners, educators, and small-scale analysis scripts.

---

## Installation

```bash
pip install correlation_stats_analyzer
```

## Usage Example

```bash
import correlation_stats_analyzer as csa

x = [10, 20, 30, 40, 50]
y = [12, 22, 29, 41, 52]

r = csa.correlation_r(x, y)

strength, direction = csa.interpret_r(r)

print(f"Pearson r: {r:.4f}")
print(f"Strength: {strength}, Direction: {direction}")
```
biblicaljesus-humans 0.0.1
collection of biblical humans and their family relations
Requires Python: >=3.0

Humans Library

a Python collection of biblical humans and their family relations.
Please note, this is the source code of version 0.0.1, which is the starter file for my "Create Python Package" tutorial on YouTube:
https://youtu.be/9Ii34WheBOA
Quickstart

the library provides different scopes of information
Father Objects

a set of classes that represent biblical fathers, such as Abraham, Isaac and Jacob.

import biblicaljesus_humans

abraham = humans.Abraham()
print(abraham.**dict**)

List of Humans

on a broader scope, the library traces different categories of humans, such as fathers, mothers and children.

import humans

all_mothers = humans.mothers
print(all_mothers)
ffiec-call-reports 0.1.1
A Python library for downloading and processing FFIEC Call Reports
Requires Python: >=3.8
Keywords: ffiec, call-reports, banking, financial-data, xbrl

# FFIEC Call Reports Library

A Python library for downloading and processing FFIEC Call Reports. This library provides a simple interface to download call reports from the FFIEC's Central Data Repository (CDR) and process them into a usable format.

## Installation

```bash
pip install ffiec-call-reports
```

## Quick Start

```python
from ffiec_call_reports import FFIECClient
from datetime import datetime

# Initialize client
client = FFIECClient(username="your_username", passphrase="your_passphrase")

# Download a single call report
report = client.get_call_report(
    rssd_id=1842065,
    period_end_date="2019/03/31"
)

# Get specific metrics
total_assets = report.get_metric("RCFD2170")  # Total Assets
total_loans = report.get_metric("RCFD1400")   # Total Loans

# Download multiple reports
reports = client.get_multiple_call_reports(
    rssd_ids=[1842065, 1842066],
    period_end_date=datetime(2019, 3, 31)
)

# Save to CSV
report.to_csv("call_report.csv")
```

## Features

- Download call reports for single or multiple institutions
- Process XBRL data into pandas DataFrames
- Map metric IDs to human-readable descriptions
- Extract specific metrics easily
- Save reports to CSV format
- Streamlit web interface for easy access

## Web Interface

The library includes a Streamlit web interface for easy access. To run it:

```bash
streamlit run src/app_streamlit.py
```

## Usage Examples

### Basic Usage

```python
from ffiec_call_reports import FFIECClient

# Initialize client
client = FFIECClient(username="your_username", passphrase="your_passphrase")

# Download a report
report = client.get_call_report(1842065, "2019/03/31")

# Get the data as a DataFrame
df = report.to_dataframe()
```

### Working with Multiple Reports

```python
from ffiec_call_reports import FFIECClient, get_mapping_dict, apply_mapping
import pandas as pd

# Initialize client
client = FFIECClient(username="your_username", passphrase="your_passphrase")

# Download multiple reports
reports = client.get_multiple_call_reports(
    rssd_ids=[1842065, 1842066, 1842067],
    period_end_date="2019/03/31"
)

# Combine all reports
all_data = pd.concat([report.to_dataframe() for report in reports])

# Apply mapping
mapping_dict = get_mapping_dict()
mapped_data = apply_mapping(all_data, mapping_dict)
```

### Extracting Specific Metrics

```python
# Get a single metric
total_assets = report.get_metric("RCFD2170")

# Get multiple metrics
metrics = report.get_metrics([
    "RCFD2170",  # Total Assets
    "RCFD1400",  # Total Loans
    "RCFD1410"   # Commercial & Industrial Loans
])
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

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