semvec/pypi-packages huggingface dataset:
Report-of-Monaco-2018-Racing-Volodymyr-Perehuda 0.0.1
A report generation package for results of Monaco 2018 Racing
Requires Python: >=3.11.4

# Formula 1 Monaco 2018 Racing Report Generator

This Python package provides a tool for generating reports based on the start and end data of the best lap for each racer of Formula 1 - Monaco 2018 Racing during the first stage of the qualification (Q1).

## Installation

To use the package, you need to install it first. You can do this using `pip`:

```UPDATE LATER
```

## Usage

The package provides a command-line interface with several options.

### Generating a Report

You can generate a report for the first 20 minutes of the Monaco 2018 Racing by providing the path to the folder containing the data files. The `--asc` or `--desc` flag allows you to specify the sorting order (default is ascending).

```bash (update later)
report.py --files <folder_path> [--asc | --desc]
```

### Driver Statistics

To get statistics about a specific driver, use the `--driver` option along with the folder path.

```bash
report.py --files <folder_path> --driver "Sebastian Vettel"
```

## Package Structure

The package consists of the following components:

- **`Record` Class:** Represents a racer record with start and end times.
- **`FileNotFound` Exception:** Raised when a specified file is not found.
- **`ReportInvalidOrder` Exception:** Raised for an invalid sorting order in the report.
- **`build_report` Function:** Compiles a report summarizing valid and invalid records.
- **`print_report` Function:** Prints a formatted report based on ordered results.
- **`cli` Function:** Parses command-line arguments and orchestrates the report generation process.

## Example

```python
import report.py
#(update later)
# Command-line interface
args = formula1_report.Record.cli()

# Build the report
good_records, bad_records = formula1_report.Record.build_report(args.files, args.sort)

# Filter records if a specific driver is provided
if args.driver:
    good_records = [rec for rec in good_records if rec.driver == args.driver]
    bad_records = [rec for rec in bad_records if rec.driver == args.driver]

# Print the report
print(report.Record.print_report(good_records, bad_records))
```

## License

This package is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
db-env 0.4.2
Creating relational databases with SQLite but easier. More information follows.

**Version 0.4**

--- What can db_env do? --- 
db_env aims to make it easier and more straight forward to create relational database environments within Python. db_env uses SQLite and Pandas as dependencies. Currently db_env holds the following functions:
- create_connect_db --> connecting to newly created database
- create_table_sql --> connecting to database and creating tables within
- insert_data_sql --> connecting to database and inserting data
- select_data_sql --> connecting and fetching data from chosen tables. It automatically creates a new Pandas dataframe for further analysis.


--- important Info: --- 
- this library needs SQLite3 and Pandas in addition. As dependencies all packages will be installed automatically.
- SQL-Usage: db_env uses SQLite and therefore SQL-statements for creating, calling and manipulating data. 
- it is encouraged to import your writen SQL-strings from another py-file as a variable (e.g. ENUM). However, you can directly insert your SQL-statement as a string into the function.

--- Example SQL-statements for existing functions: --- 
--> create_table_sql = 
"DROP TABLE IF EXISTS ORDERS; CREATE TABLE ORDERS(ID INTEGER, NAME VARCHAR(30));"

--> insert_data_sql = 
"INSERT INTO ORDERS (ID, NAME) VALUES (1, 'Frank');" 
or 
(f'''INSERT INTO ORDERS (ID, NAME) VALUES 
                         (5, 'John'),
                         (6, 'Tobias'),
                         (7, 'Lisa');''', "orders.db")

--> select_data_sql = 
"SELECT * FROM ORDERS;"


--- Goal for upcoming versions: --- 
- Functions which give user the option to write his own SQL-statements into an extra Python file and call writen SQL-strings as variables into your working environment.
- Functions to collect and insert data from called API.


-------------- CHANGES in VERSION 0.4 --------------
2024-02-25

- installed dependencies (Pandas)
- created function to select data from db and create dataframe
- created function to insert data into database manually

----------------------------------------------------
impconverter1.0 0.1.0
A package for converting imperial lengths and weights

# Impyrial 

A package for converting between imperial unit lengths and weights.

This package was created for the [DataCamp] (https://www.datacamp.com) course "Developing Python Packages".

### Features

- Convert lengths between miles, yards, feet and inches.
- Convert weights between hundredweight, stone, pounds and ounces.

### Usage

```
import impyrial

# Convert 500 miles to feet
impyrial.length.convert_unit(500, from_unit='yd', to_unit='ft')  # returns 1500.0

# Convert 100 ounces to pounds
impyrial.weight.convert_unit(100, from_unit='oz', to_unit='lb')  # returns 6.25
```