| Name | dfquick JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Library to create custom dataframe quickly |
| upload_time | 2024-03-30 14:49:05 |
| maintainer | None |
| docs_url | None |
| author | Marcel Tino |
| requires_python | None |
| license | None |
| keywords |
dataframe
quick
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[English](README.md) | [Español](./docs/README.es.md) | [Français](./docs/README.fr.md) | [Deutsch](./docs/README.de.md) | [中文](./docs/README.zh.md) | [Türkçe](./docs/README.tr.md) | [日本語](./docs/README.ja.md) | [한국어](./docs/README.ko.md)
# DFQUICK
A library to create quick custom dataframe. You can create integer columns, category columns and Data Columns easilys
Developed by Marcel Tino (c) 2024
## Examples of How To Use the library
You can use this to alter according to your requirements
```
##syntax
int_column(column name,starting value, ending value, count of rows)
cat_column(column name, Values in a list, count of rows, Probablities of each occurence (Optional))
random_dates(column name,starting date, ending date, count of rows
```
```python
import pandas as pd
from dfquick import int_column
from dfquick import cat_column
from dfquick import random_dates
data=int_column("column1", 1, 500, 500)
data=cat_column("Column2",['A','B','C','D'],500,['0.25','0.5','0.1','0.15'])
data=random_dates("Dates",'2020-05-10','2022-05-10',500)
```
Note: We can create the dataframe using the name data only. You can alter the name later
+ Share retail_dictionary on these social media platforms if you like it!
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