How to get rid of multilevel index after using pivot table pandas?

You need remove only index name, use rename_axis (new in pandas 0.18.0):

print (reshaped_df)sale_product_id  1    8    52   312  315sale_user_id                            1                  1    1    1    5    1print (reshaped_df.index.name)sale_user_idprint (reshaped_df.rename_axis(None))sale_product_id  1    8    52   312  3151                  1    1    1    5    1

Another solution working in pandas below 0.18.0:

reshaped_df.index.name = Noneprint (reshaped_df)sale_product_id  1    8    52   312  3151                  1    1    1    5    1

If need remove columns name also:

print (reshaped_df.columns.name)sale_product_idprint (reshaped_df.rename_axis(None).rename_axis(None, axis=1))   1    8    52   312  3151    1    1    1    5    1

Another solution:

reshaped_df.columns.name = Nonereshaped_df.index.name = Noneprint (reshaped_df)   1    8    52   312  3151    1    1    1    5    1

EDIT by comment:

You need reset_index with parameter drop=True:

reshaped_df = reshaped_df.reset_index(drop=True)print (reshaped_df)sale_product_id  1    8    52   312  3150                  1    1    1    5    1#if need reset index nad remove column namereshaped_df = reshaped_df.reset_index(drop=True).rename_axis(None, axis=1)print (reshaped_df)   1    8    52   312  3150    1    1    1    5    1

Of if need remove only column name:

reshaped_df = reshaped_df.rename_axis(None, axis=1)print (reshaped_df)              1    8    52   312  315sale_user_id                         1               1    1    1    5    1

Edit1:

So if need create new column from index and remove columns names:

reshaped_df =  reshaped_df.rename_axis(None, axis=1).reset_index() print (reshaped_df)   sale_user_id  1  8  52  312  3150             1  1  1   1    5    1

Make a DataFrame

import randomd = {'Country': ['Afghanistan','Albania','Algeria','Andorra','Angola']*2,      'Year': [2005]*5 + [2006]*5, 'Value': random.sample(range(1,20),10)}df = pd.DataFrame(data=d)

df:

                Country         Year   Value    1               Afghanistan     2005    62               Albania         2005    133               Algeria         2005    104               Andorra         2005    115               Angola          2005    56               Afghanistan     2006    37               Albania         2006    28               Algeria         2006    79               Andorra         2006    310              Angola          2006    6

Pivot

table = df.pivot(index='Country',columns='Year',values='Value')

Table:

Year    Country         2005    20060       Afghanistan     16      91       Albania         17      192       Algeria         11      73       Andorra         5       124       Angola          6       18

I want 'Year' to be 'index':

clean_tbl = table.rename_axis(None, axis=1).reset_index(drop=True)

clean_tbl:

    Country         2005    20060   Afghanistan     16      91   Albania         17      192   Algeria         11      73   Andorra         5       124   Angola          6       18

Done!

The way it works for me is

df_cross=pd.DataFrame(pd.crosstab(df[c1], df[c2]).to_dict()).reset_index()