How to export dataframe column wise to separate csv files? And how to append the columns from different dataframe to the separated csv files

You can use df.to_csv() to export the relevant dataframes

import os
import pandas as pd

filepath = os.getcwd()
df = pd.concat([df1, df2], axis=0).sort_index()
cols = ['frequency', 'resistance', 'phase']
for col in cols:
    df[[col]].to_csv(filepath + '\\' + col + '.csv')

print(df)
    accel_name numb frequency resistance phase
idx                                           
3            K    0       -33          1  4030
4            K    1       -16          2  4028
5            K    2       -18         12  4036
6            K    3       -14         -3  4054
7            P    0      -452       4089   329
8            P    1      -428       4082   427
9            P    2      -382       4078   518
10           P    3      -363       4052   545
11           P    4      -347       4064   508
12           K    4        -2         17  4048
13           K    5       -18         12  4048
14           P    5      -373       4068   409

Output: frequency.csv, phase.csv, resistance.csv

df.to_csv is the simple way to export pandas Dataframes to csvs. To combine the columns, I have first extracted them into phase and phase2 dataframes, and then used pd.concat to combine (concatenate) them. Note that this is probably not the fastest way to do this, but it is an easy to understand set of steps.

import pandas as pd
phase = df1['phase']
phase2 = df2['phase']
phases = pd.concat([phase, phase2])
phasees.to_csv('phase_data.csv')

IIUC, use a loop on the concatenated dataframes, slice columns using set operations:

cols = {'frequency', 'resistance', 'phase'}
df = pd.concat([df1, df2])
for c in cols:
    df.drop(columns=cols.difference([c])).to_csv(f'{c}.csv')

example, frequency.csv:

,accel_name,numb,frequency
3,K,0,-33
4,K,1,-16
5,K,2,-18
6,K,3,-14
12,K,4,-2
13,K,5,-18
7,P,0,-452
8,P,1,-428
9,P,2,-382
10,P,3,-363
11,P,4,-347
14,P,5,-373