A python package built with pandas 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.
Project description
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
pip install xplore
Import the package into your code
from xplore.data import xplore
Assign the read/open command to the file path or URL of your structured dataset to a variable name
data = < Read in your dataset file here >
Explore your dataset using the xplore() method
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
------------------------------------
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:
<bound method NDFrame.describe of 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
... ... ... ... ... ... ... ... ... ...
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
[57793 rows x 16 columns]>
------------------------------------
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:
<bound method DataFrame.info of 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
... ... ... ... ... ... ... ... ... ...
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
[57793 rows x 16 columns]>
------------------------------------
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]:
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:
Release history Release notifications | RSS feed
Download files
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Source Distribution
xplore-0.0.1.tar.gz
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