semvec/pypi-packages huggingface dataset:
datepy 0.2.0
A robust utility for parsing dates in various formats.

# datepy

[Visit Github](https://github.com/msj121/datepy).

datepy is a versatile Python library for parsing dates from various string formats into standardized RFC 3339 timestamps. It supports a wide range of date formats, including ISO 8601, RFC 822, long date formats, and more, with the ability to extend support through custom formats.

## Features

- **Broad Format Support**: Handles numerous standard and custom date formats out of the box.
- **Customizable**: Easily extendable to parse non-standard date formats.
- **Easy to Use**: Simple and intuitive API.

## Installation

To install datepy, simply use pip:

```
pip install datepy
```

## Usage

datepy is easy to use. Simply import the library and call the `parse` function with the date string and the format of the date string. The function will return a standardized RFC 3339 timestamp.

```python
from datepy import datepy

date_string = "2021-01-01 12:00:00"
rfc3339_date = datepy.convert_to_rfc3339(date_string)
print(rfc3339_date)
```

### Additional Formats

Please suggest a request and I can add the code/feature or format!
grphpkg 0.0.4
Graph package
Keywords: python,data structure,graph,visualization

# grphpkg
**grphpkg** is a simple graph library that has DFS and BFS implemented that you can create your own operation.

## Graph
```python
from grphpkg import Graph
matrix_representation = [
    [0, 1, 0, 1, 0, 0, 0, 0, 1],
    [1, 0, 1, 0, 0, 1, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 1, 0, 0],
    [1, 0, 0, 0, 1, 0, 0, 1, 0],
    [0, 0, 0, 1, 0, 1, 0, 0, 0],
    [0, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 0, 1, 0, 0, 1, 0, 1, 0],
    [0, 0, 0, 1, 0, 0, 1, 0, 1],
    [1, 0, 0, 0, 0, 0, 0, 1, 0]
]

g = Graph(matrix_representation)                    # Initialize weighted graph with a matrix
g.print_graph()                                     # Print the graph
g.dfs(0, lambda x: print(str(x)+" ", end=""))       # Run DFS or BFS with custom callback
g.draw_graph()                                      # Graph visualization
```
> Create your callback function and pass it in dfs or bfs to execute your operation
## Weighted Graph
from grphpkg import Graph
```python
matrix = [
    [0, 2, 3, float('inf')],
    [2, 0, 1, 4],
    [3, 1, 0, 5],
    [float('inf'), 4, 5, 0]
]

weighted_graph = WeightedGraph(matrix)              # Initialize weighted graph with a matrix
weighted_graph.print_graph()                        # Print the graph
mst_edges, mst_cost = weighted_graph.prim_mst(0)    # Get minimun spanning and the edges
weighted_graph.draw_graph()                         # Graph visualization

```
## Graph visualization
Call draw graph function
```
g.draw_graph()
```
<img src="./documents/graph.png" width="60%"/>
<img src="./documents/wgraph.png" width="60%"/>


## Installing
```
pip install grphpkg
```

## Documentation
infp 0.0.3
Add, Multiply, Mod, Divide Large number
Keywords: python,data structure,math,big number,infinite precision

# Infinite Precision
Adding big big number!!

# Usage

```python
from infp import BigNumber

num1 = BigNumber("1648976548.65498")
num2 = BigNumber("-4564984945.654")
sum = num1 + num2       # -2916008396.99902
print(sum)      
num1 = BigNumber("10")
num2 = BigNumber("10")
sub = num1 - num2
print(sub)              # 0
ans = BigNumber("132") / BigNumber("132")
print(ans)              # 1

```

# Unit Test

* Install pytest if haven't

```
pip install pytest
```

* Run test command

```
pytest
```

# Run the main program example
```
python main.py
```
BT-SQLAlchemy-DB-ORM-Template 1.0.0
A Semi-Automatic DB-ORM Tool Template That Allows You To Make Your Migration Operations Very Fast With SQLAlchemy
Requires Python: >=3.7

# BrainyTech-SQLAlchemy-DB-ORM-Tool-Template


```
1. python Manage.py db init

2. python Manage.py db migrate

3. python Manage.py db upgrade

```
datacompass 0.4
EDA in 4 lines of Code
Requires Python: >=3.6

# DataCompass

DataCompass is a Python package designed to enhance data analysis with pandas DataFrames. It provides a set of tools to quickly inspect and understand the structure and contents of DataFrames, aiding in efficient data exploration and preprocessing.

## Features

- **Describe Columns**: Analyze and list numerical and categorical columns in a DataFrame.
- **Display Missing Information**: Calculate and display the count and percentage of missing values in each column.
- **Display Basic Information**: Show basic details like the number of columns, rows, and the first few observations.
- **Display Unique Values**: Enumerate unique values or the count of unique values in each column.

## Installation

To install DataCompass, simply use pip:

```bash
pip install datacompass
```
```bash
import pandas as pd
from datacompass import describe_columns, display_missing_info, display_basic_info, display_unique_values

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, None, 4], 'B': ['a', 'b', 'b', 'c']})

# Describe Columns
describe_columns(df)

# Display Missing Information
display_missing_info(df)

# Display Basic Information
display_basic_info(df)

# Display Unique Values
display_unique_values(df)
```
```bash
### Describe Columns Output

Number of Numerical Columns: 1
['A']
-------------------------------------------------------------------------------------
Number of Categorical Columns: 1
['B']

### Display Missing Information Output

|   Missing Count | Missing Percentage |
|----------------:|-------------------:|
| A              |                  1 |             25.0 |
| B              |                  0 |              0.0 |

### Display Basic Information Output

Number of Columns: 2
Number of Rows: 4

First 6 Observations of Our Data:
  A    B
1.0  a
2.0  b
NaN  b
4.0  c

### Display Unique Values Output

A contains: 1.0, 2.0, nan, 4.0
B contains: a, b, c

```


## Requirements

- pandas

## Contribution

Contributions to DataCompass are welcome! Please feel free to submit a pull request or open an issue on the [GitHub repository](https://github.com/koushik2299/datacompass).

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Author

**[Sai Koushik Gandikota](https://www.linkedin.com/in/gandikota-sai-koushik/)**