Below is a sample of a pandas dataframe, a single column with 1000’s of rows.
I need second/third columns putting data in rows 1 and 2, 4 and 5 etc in the second/third column
0
0 \\path_1\file1.csv
1 23082022 DS
2 520i2146002
3 \\path_2\file2.csv
4 230822 NS
5 520i2146002
4 \\path_3\file3.csv
5 24082022 DS
6 520i2146002
Desired Output
0 1 2
0 \\path_1\file1.csv 23082022 DS 520i2146002
1 \\path_2\file2.csv 230822 NS 520i2146002
2 \\path_3\file3.csv 24082022 DS 520i2146002
Can only manage to pull out the odds with:
df = pd.DataFrame({0: df[0].iloc[::2].values, 'value': df[0].iloc[1::2].values})
Suggestions?
tks
>Solution :
Make three subsets by taking every third value – starting at 0, 1, and 2. Reset each subset’s index, and concat them all together.
df = pd.concat([df.loc[::3].reset_index(drop=True),
df.loc[1::3].reset_index(drop=True),
df.loc[2::3].reset_index(drop=True)], axis=1, ignore_index=True)
print(df)
Output:
0 1 2
0 \\path_1\file1.csv 23082022 DS 520i2146002
1 \\path_2\file2.csv 230822 NS 520i2146002
2 \\path_3\file3.csv 24082022 DS 520i2146002
Slightly more concise:
df = pd.concat([df.loc[i::3].reset_index(drop=True) for i in range(3)],
axis=1, ignore_index=True)