How to split DataFrame by column

Question

I have a Pandas DataFrame and it has 11 columns, but I want to split the DataFrame from column 1, not column 0, to column 10. I can implement it by more complex method, not the method provided by Pandas itself. How can I do it by using the DataFrame method supported by Pandas ?

The data is 595 rows by 11 columns and I want to get :

>>> import numpy as np
>>> import pandas as pd
>>> train_data = pd.DataFrame(my_data, columns=my_columns)
>>> train_data
       stockid           prich_m1        prich_m3         prich_m6  \
1    000002.SZ      1.55755700445  0.861009772647    5.42726384781   
2    000009.SZ  3.00223270244e-07    4.8010096027    4.46164511978       
..         ...                ...             ...              ...    
..         ...                ...             ...              ...          
594  603699.SH    0.0491892903353  0.934596516371  0.0196757161342   
595  603993.SH      0.83105321611  0.771692272102    2.02816558693   

                rsi            mkt_cap    held_by_ins        found_own  \
1    0.650879566982     153108876954.0  42.6353598479    14.9550575226   
2    0.462085308057      19492802690.5  25.8866394448    5.31468116104   
..         ...                ...             ...              ...       
..         ...                ...             ...              ...       
..         ...                ...             ...              ...       
594  0.482105263158      14580000000.0  3.98638909091    3.41028242424   
595  0.559681697613      44416492093.8  90.0529786043    1.56995953686   

    debt_to_equity         eps_rate       share_out  
1           4.4521   0.292628452536   11021632763.0  
2           2.8257   -50.6773540796    1489669062.0  
..             ...              ...             ...    
..             ...              ...             ...  
594         0.6798    1.48454654486      82500000.0  
595         0.8822    10.3388360485    3279577050.0  

[595 rows x 11 columns]

And I want to drop the stockid column and get the rest data.

Answer 1

Try

new_df = df.iloc[:, 1:]

there's also new_df = df.ix[:, 1:] . This should eliminate stock_id . If you want to drop this column then use new_df = df.drop('stock_id', axis=1) .

You could also use column names with .ix[] or .loc[] if you prefer. Has been asked & answered here before. See relevant docs .