https://github.com/dhchenx/correlation-kit
A toolkit for estimating the correlation between variables
https://github.com/dhchenx/correlation-kit
binary-variable correlation-analysis kendalltau multi-category pearson spearman
Last synced: 3 months ago
JSON representation
A toolkit for estimating the correlation between variables
- Host: GitHub
- URL: https://github.com/dhchenx/correlation-kit
- Owner: dhchenx
- License: mit
- Created: 2021-12-05T19:21:21.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-05T19:23:23.000Z (over 3 years ago)
- Last Synced: 2025-03-01T11:46:58.529Z (4 months ago)
- Topics: binary-variable, correlation-analysis, kendalltau, multi-category, pearson, spearman
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Correlation Kit
A toolkit for estimating the correlation values between variables
## Installation
```pip
pip install correlation-kit
```
## Correlation between two continual variables
```python
import pandas as pd
from correlation_kit.ck_wrapper import CorrelationKit
# set a dataframe or read from a csv file
d = {'x': [1, 2, 3.5, 4], 'y': [3, 4, 4.5, 6]}
df = pd.DataFrame(data=d)
# set x label and y label for correlation
x = "x"
y = "y"
# calc
def get_correlation(x, y, corr_type):
stat = 0
p = 0
if corr_type == "pearson":
stat, p = CorrelationKit(df).get_pearson(x, y)
elif corr_type == "spearman":
stat, p = CorrelationKit(df).get_spearman(x, y)
elif corr_type == "kendalltau":
stat, p = CorrelationKit(df).get_kendalltau(x, y)
return stat, p
# print results
print("pearson = ", get_correlation(x, y, "pearson"))
print("spearman = ", get_correlation(x, y, "spearman"))
print("kendalltau = ", get_correlation(x, y, "kendalltau"))
```
## Estimate correlation between binary and continual variables
```python
import pandas as pd
from correlation_kit.ck_wrapper import CorrelationKit
# set a dataframe or read from a csv file
d = {'x': ['large', 'large', 'small', 'small'], 'y': ['hot', 'hot', 'cold', 'cold'],'z':[0,1,2.5,3]}
df = pd.DataFrame(data=d)
# set x label and y label for correlation, which is suitable for binary variables
r_p,r_s,r_k=CorrelationKit(df).get_corr_between_category_and_continual('x','large','z') # large=1; otherewise 0
# results
print('pearson: ',r_p)
print('speraman: ',r_s)
print('kendalltau: ',r_k)
```
## Estimate F value between multiple-category variable and continual variables
```python
import pandas as pd
from ck_wrapper import CorrelationKit
# set a dataframe or read from a csv file
d = {'x': ['large', 'large', 'middle','small', 'small'], 'y': ['hot', 'hot','warm', 'cold', 'cold'],'z':[0,1,2,2.5,3]}
df = pd.DataFrame(data=d)
# set x label and y label for correlation, which is suitable for multiple-category variables
F,p=CorrelationKit(df).get_f_oneway('x',['large','middle','small'],'z')
# results
print('F: ',F)
print('p: ',p)
```
## License
The `Correlation-Kit` project is provided by [Donghua Chen](https://github.com/dhchenx).