How to export dataframe column wise to separate csv files? And how to append the columns from different dataframe to the separated csv files
Question:
df1 is this:
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
12 K 4 -2 17 4048
13 K 5 -18 12 4048
df2 is this:
accel_name numb frequency resistance phase
idx
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
14 P 5 -373 4068 409
output :
- append df2 columns to df1
- output frequency.csv, resistance.csv, phase.csv
Answers:
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
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
df1 is this:
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
12 K 4 -2 17 4048
13 K 5 -18 12 4048
df2 is this:
accel_name numb frequency resistance phase
idx
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
14 P 5 -373 4068 409
output :
- append df2 columns to df1
- output frequency.csv, resistance.csv, phase.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
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