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.
pip install xplorefrom xplore.data import xploredata = < Read in your dataset file here >xplore(data)Navigate to the test.py file after installing the package and run the code in that file to see and understand how xplore works.
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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]
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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]
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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]>
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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
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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]>
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The shape of your dataset in the order of rows and columns is:
(57793, 16)
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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')
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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
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The number of rows in your dataset are:
57793
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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']]
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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'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.