I'd like to do a cross-examination. Please tell me how to do it.

import numpy as np
import pandas aspd
df_maize=pd.read_csv("PSD online data maize.csv", thousands=',')]
df_maize.corr()

I was able to calculate the correlation coefficient, but there was an error in the following test

import pandas as pd
import matplotlib.pyplot asplt
import seaborn as sns
from scipy.stats import spearmanr
bs=df_maize ['Beginning Stocks'].values
es=df_maize ['Ending Stocks'].values
p_value=df_maize.corr[0]
TypeError Traceback (most recent call last)
<ipython-input-11-9b62ddc60036>in<module>
      1bs = df_maize ['Beginning Stocks'].values
      2es=df_maize ['Ending Stocks'].values
---->3p_value=df_maize.corr[0]

TypeError: 'method' object is not subscriptable

python pandas numpy scipy

2023-02-06 21:25

1 Answers

If you want p-value, you should use scipy.stats.pearsonr().

import pandas as pd
from scipy.stats import pearsonr

df = pd.DataFrame({'A':range(5),
                   'B': [x**2 for x in range (5)],
                   'C': [x**3 for x in range (5) ]})

p_value=df.corr(method=lambdax, y:pearsonr(x,y)[1])
print(df)
print(p_value)

dataframe

p-value

2023-02-06 21:59

If you have any answers or tips

Popular Tags

python x 4647

android x 1593

java x 1494

javascript x 1427

c x 927

c++ x 878

ruby-on-rails x 696

php x 692

python3 x 685

html x 656