pandas_summary
An extension to pandas dataframes describe function.
The module contains DataFrameSummary object that extend describe() with:
- properties
- dfs.columns_stats: counts, uniques, missing, missing_perc, and type per column
- dsf.columns_types: a count of the types of columns
- dfs[column]: more in depth summary of the column
- function
- summary(): extends the
describe()function with the values withcolumns_stats
- summary(): extends the
Installation
The module can be easily installed with pip:
> pip install pandas-summary
This module depends on numpy and pandas. Optionally you can get also some nice visualisations if you have matplotlib installed.
Tests
To run the tests, execute the command python setup.py test
Usage
The module contains one class:
DataFrameSummary
The DataFrameSummary expect a pandas DataFrame to summarise.
from pandas_summary import DataFrameSummary
dfs = DataFrameSummary(df)
getting the columns types
dfs.columns_types
numeric 9
bool 3
categorical 2
unique 1
date 1
constant 1
dtype: int64
getting the columns stats
dfs.columns_stats
A B C D E
counts 5802 5794 5781 5781 4617
uniques 5802 3 5771 128 121
missing 0 8 21 21 1185
missing_perc 0% 0.14% 0.36% 0.36% 20.42%
types unique categorical numeric numeric numeric
getting a single column summary, e.g. numerical column
# we can also access the column using numbers A[1]
dfs['A']
std 0.2827146
max 1.072792
min 0
variance 0.07992753
mean 0.5548516
5% 0.1603367
25% 0.3199776
50% 0.4968588
75% 0.8274732
95% 1.011255
iqr 0.5074956
kurtosis -1.208469
skewness 0.2679559
sum 3207.597
mad 0.2459508
cv 0.5095319
zeros_num 11
zeros_perc 0,1%
deviating_of_mean 21
deviating_of_mean_perc 0.36%
deviating_of_median 21
deviating_of_median_perc 0.36%
top_correlations {u'D': 0.702240243124, u'E': -0.663}
counts 5781
uniques 5771
missing 21
missing_perc 0.36%
types numeric
Name: A, dtype: object
Future development
Summary analysis between columns, i.e. dfs[[1, 2]]
2 Nov 27, 2021
3 Dec 23, 2022
4 Aug 02, 2022
1 Jan 19, 2022
75 Dec 06, 2022
81 Dec 26, 2022
4 Jun 05, 2022
2 Feb 14, 2022
330 Dec 30, 2022
3 Jan 31, 2022
6 Aug 10, 2021
104 Nov 27, 2022
1 Jul 27, 2021
1.5k Jan 03, 2023
3 Apr 13, 2022
1 Jan 17, 2022
3.3k Jan 04, 2023
114 Dec 23, 2022
6 Jun 07, 2022
3.8k Jan 05, 2023