Pair columns in a dataframe-CodePudding

I have one dataframe contains a timestamp column and n number of value columns. I need to prepare two column dataframes with a timestamp column and a value column. That is total n number of dataframes. for example,

------------------------
|timestamp | A | B | C |
------------------------
| 1        |0.1|1.2|3.0|
| 2        |0.3|0.2|0.0|

Outputs

---------------
|timestamp| A |
---------------
|1        |0.1|
|2        |0.3|

---------------
|timestamp| B |
---------------
|1        |1.2|
|2        |0.2|

---------------
|timestamp| C |
---------------
|1        |3.0|
|2        |0.0|

I tried,

for i in range(len(df.columns)):
    df1= df[['timestamp',df.columns]]
    df1.to_csv('file.csv')

But got type error.

CodePudding user response：

Try:

for col in df.columns[1:]:
    df[['timestamp', col]].to_csv(f'file_{col}.csv', index=False)

Output:

# file_A.csv
timestamp,A
1,0.1
2,0.3

# file_B.csv
timestamp,B
1,1.2
2,0.2

# file_C.csv
timestamp,C
1,3.0
2,0.0

CodePudding user response：

Just try this code:

dfs= []
for i in range(1, df.columns.shape[0] ):
    new = pd.DataFrame(df.iloc[:, [0,i]])
    dfs.append(new) #store them in variables
    new.to_csv('file{}.csv'.format(i 1)) #  save them to file

dfs[0] results in your first desired dataframe, etc.

dfs[0] and file1.csv includes:

timestamp   A
0   1   0.1
1   1   0.1

dfs[1] and file2.csv includes:

timestamp   B
0   1   1.2
1   1   1.2

and

dfs[2] and file3.csv includes:

timestamp   C
0   1   3.0
1   1   3.0