https://github.com/buabaj/xplore
A python package built for data scientist/analysts, AI/ML engineers for exploring features of a dataset in minimal number of lines of code for quick analysis before data wrangling and feature extraction.
https://github.com/buabaj/xplore
artificial-intelligence data-preprocessing data-science data-wrangling machine-learning
Last synced: 9 months ago
JSON representation
A python package built for data scientist/analysts, AI/ML engineers for exploring features of a dataset in minimal number of lines of code for quick analysis before data wrangling and feature extraction.
- Host: GitHub
- URL: https://github.com/buabaj/xplore
- Owner: buabaj
- License: mit
- Created: 2020-08-12T18:51:09.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-04-29T14:21:52.000Z (over 4 years ago)
- Last Synced: 2025-04-04T23:29:17.216Z (9 months ago)
- Topics: artificial-intelligence, data-preprocessing, data-science, data-wrangling, machine-learning
- Language: Python
- Homepage: https://pypi.org/project/xplore/
- Size: 1.74 MB
- Stars: 21
- Watchers: 6
- Forks: 11
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# xplore [](https://pepy.tech/project/xplore)
---
xplore is a python package built with Pandas for data scientist or analysts, AI/ML engineers or researchers for exploring features of a dataset in one line of code for quick analysis before data wrangling and feature extraction. You can also choose to generate a more detailed report on the exploration of your dataset upon request.
---
## Getting started
### Install the package
```bash
pip install xplore
```
### Import the package into your code
```python
from xplore.data import xplore
```
### Assign the read/open command to the file path or URL of your structured dataset to a variable name
```python
data = < Read in your dataset file here >
```
### Explore your dataset using the xplore() method
```python
xplore(data)
```
---
### Testing xplore
Navigate to the test.py file after installing the package and run the code in that file to see and understand how xplore works.
---
## Sample Output
```python
------------------------------------
The fist 5 entries of your dataset are:
rank country_full country_abrv total_points ... three_year_ago_avg three_year_ago_weighted confederation rank_date
0 1 Germany GER 0.0 ... 0.0 0.0 UEFA 1993-08-08
1 2 Italy ITA 0.0 ... 0.0 0.0 UEFA 1993-08-08
2 3 Switzerland SUI 0.0 ... 0.0 0.0 UEFA 1993-08-08
3 4 Sweden SWE 0.0 ... 0.0 0.0 UEFA 1993-08-08
4 5 Argentina ARG 0.0 ... 0.0 0.0 CONMEBOL 1993-08-08
[5 rows x 16 columns]
------------------------------------
The last 5 entries of your dataset are:
rank country_full country_abrv total_points ... three_year_ago_avg three_year_ago_weighted confederation rank_date
57788 206 Anguilla AIA 0.0 ... 0.0 0.0 CONCACAF 2018-06-07
57789 206 Bahamas BAH 0.0 ... 0.0 0.0 CONCACAF 2018-06-07
57790 206 Eritrea ERI 0.0 ... 0.0 0.0 CAF 2018-06-07
57791 206 Somalia SOM 0.0 ... 0.0 0.0 CAF 2018-06-07
57792 206 Tonga TGA 0.0 ... 0.0 0.0 OFC 2018-06-07
[5 rows x 16 columns]
------------------------------------
Stats on your dataset:
------------------------------------
The Value types of each column are:
rank int64
country_full object
country_abrv object
total_points float64
previous_points int64
rank_change int64
cur_year_avg float64
cur_year_avg_weighted float64
last_year_avg float64
last_year_avg_weighted float64
two_year_ago_avg float64
two_year_ago_weighted float64
three_year_ago_avg float64
three_year_ago_weighted float64
confederation object
rank_date object
dtype: object
------------------------------------
Info on your Dataset:
------------------------------------
The shape of your dataset in the order of rows and columns is:
(57793, 16)
------------------------------------
The features of your dataset are:
Index(['rank', 'country_full', 'country_abrv', 'total_points',
'previous_points', 'rank_change', 'cur_year_avg',
'cur_year_avg_weighted', 'last_year_avg', 'last_year_avg_weighted',
'two_year_ago_avg', 'two_year_ago_weighted', 'three_year_ago_avg',
'three_year_ago_weighted', 'confederation', 'rank_date'],
dtype='object')
------------------------------------
The total number of null values from individual columns of your data set are:
rank 0
country_full 0
country_abrv 0
total_points 0
previous_points 0
rank_change 0
cur_year_avg 0
cur_year_avg_weighted 0
last_year_avg 0
last_year_avg_weighted 0
two_year_ago_avg 0
two_year_ago_weighted 0
three_year_ago_avg 0
three_year_ago_weighted 0
confederation 0
rank_date 0
dtype: int64
------------------------------------
The number of rows in your dataset are:
57793
------------------------------------
The values in your dataset are:
[[1 'Germany' 'GER' ... 0.0 'UEFA' '1993-08-08']
[2 'Italy' 'ITA' ... 0.0 'UEFA' '1993-08-08']
[3 'Switzerland' 'SUI' ... 0.0 'UEFA' '1993-08-08']
...
[206 'Eritrea' 'ERI' ... 0.0 'CAF' '2018-06-07']
[206 'Somalia' 'SOM' ... 0.0 'CAF' '2018-06-07']
[206 'Tonga' 'TGA' ... 0.0 'OFC' '2018-06-07']]
------------------------------------
Do you want to generate a detailed report on the exploration of your dataset?
[y/n]: y
Generating report...
Summarize dataset: 100%|████████████████████████████████████████████████████████████████████████████| 30/30 [03:34<00:00, 7.14s/it, Completed]
Generate report structure: 100%|█████████████████████████████████████████████████████████████████████████████████| 1/1 [00:31<00:00, 31.42s/it]
Render HTML: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:12<00:00, 12.07s/it]
Export report to file: 100%|█████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 8.00it/s]
Your Report has been generated and saved as 'output.html'
```
---
## Contributing to xplore
Fork and clone this repo if you have any contributions you want to make.
Push your commits to a new branch and send a PR when done.
I'll review your code and merge your PR as soon as possible.
## Maintainers:
[Jerry Buaba](https://www.linkedin.com/in/buabaj/) |
[Labaran Mohammed](https://linkedin.com/in/adam-labaran-111358181) |
[Benjamin Acquaah](https://linkedin.com/in/benjamin-acquaah-9294aa14b)