2. How to Install¶
2.1. Install with pip¶
You can install the PyAudit from [PyPI](https://pypi.org/project/PyAudit):
pip install PyAudit
2.2. Install from Repo¶
2.2.1. Clone the Repository¶
git clone https://github.com/runawayhorse001/PyAudit.git
2.2.2. Install¶
cd PyAudit
pip install -r requirements.txt
python setup.py install
2.2.3. Uninstall¶
pip uninstall statspy
2.2.4. Test¶
cd PyAudit/test
python test.py
test.py
from PyAudit.basics import missing_rate, zero_rate, dtypes_class
from PyAudit.basics import feature_variance, freq_items_df, feature_len
from PyAudit.basics import corr_matrix,numeric_summary, category_summary
import pandas as pd
import os, sys
output = os.path.abspath(os.path.join(sys.path[0])) + '/output'
print(output)
d = {'A': [1, 0, None, 3],
'B': [1, 0, 0, 0],
'C': ['a', None, 'c', 'd']}
# create DataFrame
df = pd.DataFrame(d)
print(missing_rate(df))
print(zero_rate(df))
print(feature_variance(df))
print(df)
print(feature_len(df))
print(numeric_summary(df, output))
print(category_summary(df, output))
print(corr_matrix(df, output))
d ={
'num': list('1223334444'),
'cat': list('wxxyyyzzzz')
}
df = pd.DataFrame(d)
df = df.astype({"num": int, "cat": object})
print(freq_items_df(df, top_n=4))
# read df
df = pd.read_csv('Heart.csv', dtype={'Sex': bool})
print(df.head(5))
(num_fields, cat_fields, bool_fields, data_types, type_class) = dtypes_class(df)
print(num_fields)
print(cat_fields)
print(bool_fields)
print(data_types)
print(type_class)
print(missing_rate(df))
print(zero_rate(df))
print(freq_items_df(df, top_n=4))
print(feature_len(df))
print(numeric_summary(df, output))
print(category_summary(df, output))
print(corr_matrix(df, output))
Results:
feature missing_rate
0 A 0.25
1 B 0.00
2 C 0.25
feature zero_rate
0 A 0.333333
1 B 0.750000
2 C 0.000000
feature feature_variance
0 A 1.0
1 B 0.5
2 C 1.0
Age Sex ChestPain RestBP Chol ... Oldpeak Slope Ca Thal AHD
0 63 True typical 145 233 ... 2.3 3 0.0 fixed No
1 67 True asymptomatic 160 286 ... 1.5 2 3.0 normal Yes
2 67 True asymptomatic 120 229 ... 2.6 2 2.0 reversable Yes
3 37 True nonanginal 130 250 ... 3.5 3 0.0 normal No
4 41 False nontypical 130 204 ... 1.4 1 0.0 normal No
[5 rows x 14 columns]
['Age', 'RestBP', 'Chol', 'Fbs', 'RestECG', 'MaxHR', 'ExAng', 'Oldpeak', 'Slope', 'Ca']
['ChestPain', 'Thal', 'AHD']
['Sex']
feature dtypes
0 Age int64
1 Sex bool
2 ChestPain object
3 RestBP int64
4 Chol int64
5 Fbs int64
6 RestECG int64
7 MaxHR int64
8 ExAng int64
9 Oldpeak float64
10 Slope int64
11 Ca float64
12 Thal object
13 AHD object
feature dtypes class
0 Age int64 numeric
1 Sex bool bool
2 ChestPain object category
3 RestBP int64 numeric
4 Chol int64 numeric
5 Fbs int64 numeric
6 RestECG int64 numeric
7 MaxHR int64 numeric
8 ExAng int64 numeric
9 Oldpeak float64 numeric
10 Slope int64 numeric
11 Ca float64 numeric
12 Thal object category
13 AHD object category
feature missing_rate
0 Age 0.000000
1 Sex 0.000000
2 ChestPain 0.000000
3 RestBP 0.000000
4 Chol 0.000000
5 Fbs 0.000000
6 RestECG 0.000000
7 MaxHR 0.000000
8 ExAng 0.000000
9 Oldpeak 0.000000
10 Slope 0.000000
11 Ca 0.013201
12 Thal 0.006601
13 AHD 0.000000
Process finished with exit code 0