semvec/pypi-packages huggingface dataset:
Aadhaar-extractor 0.0.5
Detects and extracts the features in a aadhaar card
Requires Python: >3.8

# Use to extract details from a image of a aadhaar card

 Trained using YOLOv8

 [Dataset](https://universe.roboflow.com/akash-k-p-gs9iu/aadhaar-card-details-extraction-3/dataset/7)

Check out the Github [Repo](https://github.com/Akash-k-p/Aadhaar_Extractor/) for more info



> Note : Requires external installation of tesseract , and setting the environment variable of the same


## How to use :

```
from Aadhaar_extractor.Extractor import AadhaarExtractor

#create a object of the AadhaarExtractor class by passing the image of the aadhaar card as argument


obj = AadhaarExtractor("aadhaar.jpg")

#use obj.extract() for extracting the data 

extractedData = obj.extract()

```


The extract() method will return , a list of all the fields detected .

This module currently detects five fields:

1. Aadhaar Number (aadhaar_no)
2. Date of Birth (dob)
3. Gender (gender)
4. Name (name)
5. Address (address)

Each field is member of the list(detected field), has four fields in the following order

1. The coordinates of the detected field in x1,y1,x2,y2 format (type=list)

2. The name of the field

3. Confidence of detection 

4. Extracted text from the detected field

**Note:All the data used to train , were publicly available in the internet**

***This project is far from complete, and can be made much better, feel free to contribute***
pythonCSVutils 0.1.1
A Python module for processing CSV files efficiently

# PythonCSVUtils
`pythonCSVutils` is a lightweight Python library that provides utility functions for working with CSV files. It allows you to read, write, filter, sort, update, select, group, aggregate, merge, and manipulate CSV data with ease.

## Installation
You can install pythonCSVutils via pip:
```bash
pip install pythonCSVutils
```
## Usage
Import the necessary functions from the library:

```python
from pythonCSVutils import read_csv, write_csv, filter_rows, sort_rows, update_rows, select_columns, read_csv_header, append_rows, group_by, aggregate, merge, drop_columns

#Reading and Writing CSV Files

data = read_csv('data.csv')

#Write data to a CSV file

write_csv('output.csv', data)

#Filter rows based on a condition

filtered_data = filter_rows(data, lambda row: row['age'] > 30)

#Sort rows based on a key

sorted_data = sort_rows(data, 'age', reverse=True)

#Update rows by modifying a specific column

updated_data = update_rows(data, 'age', lambda row: row['age'] + 1)

#Select specific columns from the data

selected_data = select_columns(data, ['name', 'age'])

#Read the header of a CSV file

header = read_csv_header('data.csv')

#Append rows to an existing CSV file

append_rows('data.csv', new_rows)

#Group data by a specific key

grouped_data = group_by(data, 'gender')

#Aggregate data based on a grouping key, an aggregation key, and an aggregation function

aggregated_data = aggregate(data, 'gender', 'age', sum)

#Merge two datasets based on a common key

merged_data = merge(data1, data2, 'id')

#Drop specific columns from the data

dropped_data = drop_columns(data, ['email', 'phone'])
```

## License
This project is licensed under the MIT License.