corr_pair()
Description
Conducts Pearson (default method), Spearman rank, or Kendall’s Tau-b correlation analysis using pair wise deletion. Returns the relevant information and results in 1 DataFrame for easy exporting.
DataFrame 1 contains the variables being compared in the index, followed by the corresponding r value, p-value, and N for the groups being compared.
Parameters
Input
corr_pair(dataframe, method= “pearson”)
Examples
Loading Packages and Data
import researchpy, numpy, pandas
numpy.random.seed(12345)
df = pandas.DataFrame(numpy.random.randint(10, size= (100, 4)),
columns= ['mental_score', 'physical_score', 'emotional_score',
'happiness_index'])
Pearson r
# Can pass the entire DataFrame or multiple Series
researchpy.correlation.corr_pair(df)
r value | p-value | N | |
---|---|---|---|
mental_score & physical_score | 0.0557 | 0.5823 | 100 |
mental_score & emotional_score | -0.0237 | 0.8153 | 100 |
mental_score & happiness_index | 0.1360 | 0.1773 | 100 |
physical_score & emotional_score | 0.0580 | 0.5663 | 100 |
physical_score & happiness_index | -0.1366 | 0.1754 | 100 |
emotional_score & happiness_index | -0.0632 | 0.5323 | 100 |
# Demonstrating how the output looks if there are different Ns for groups
df['happiness_index'][0:30] = numpy.nan
researchpy.correlation.corr_pair(df)
r value | p-value | N | |
---|---|---|---|
mental_score & physical_score | 0.0557 | 0.5823 | 100 |
mental_score & emotional_score | -0.0237 | 0.8153 | 100 |
mental_score & happiness_index | 0.0933 | 0.4423 | 70 |
physical_score & emotional_score | 0.0580 | 0.5663 | 100 |
physical_score & happiness_index | -0.0268 | 0.8254 | 70 |
emotional_score & happiness_index | -0.0873 | 0.4726 | 70 |