如何将每个列名和 append 迭代到 python 中的表或数据框

Question

I want to make a customize correlation table, after a long script this is my final data look like:

ID        a      ...  z                                           
1         76.0   ...  1.033004
2         257.0  ...  0.629641
3         99.0   ...  0.672139
4         52.0   ...  1.007708
5         129.0  ...  1.080922

And this is the script I made to my customize correlation table for a vs all variable

pg.pairwise_corr(df_pvt, columns=[['a'], list(df_pvt.columns)], method='pearson')[['X','Y','r']].append
pg.pairwise_corr(df_pvt, columns=[['b'], list(df_pvt.columns)], method='pearson')[['X','Y','r']].append ......... #until variable z

How can I loop or select specific column number without typing variable name 'a' until 'z'?

I am new in python. Thank you in advance

Answer 1

import pandas as pd

data = pd.DataFrame({"ID": [1,2,3,4,5], "a": [76.0, 257.0, 99.0,52.0,129.0], "z": [1.033004, 0.629641, 0.672139,1.007708, 1.080922]}, columns=["ID", "a", "z"])

true_columns = []

for index, row in data.iterrows():
    true_col_list = [col for col in data.columns[1] if row[col]]
    true_columns.append(",".join(true_col_list))

data["Columns"] = true_columns

print(data)

Answer 2

As I'm not allowed to comment yet, I think this is what you're looking for. Correct me if I'm wrong.

for letter in 'abcdefghijklmnopqrstuvwxyz':
    pg.pairwise_corr(df_pvt, columns=[letter], list(df_pvt.columns)], method='pearson')[['X','Y','r']].append ....

edit: And I think, if you want to be cool and pythonic (I've seen this in many answers) you could not write out the alphabet yourself but import it with

import string
alphabet = string.ascii_lowercase # this equals 'abcdefghijklmnopqrstuvwxyz'

# then iterate over the alphabet
for letter in alphabet:
    # do your stuff here something something[letter] something :P