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

Question

I had following data frame (the real data frame is much more larger than this one ) :

sale_user_id    sale_product_id count
1                 1              1
1                 8              1
1                 52             1
1                 312            5
1                 315            1

Then reshaped it to move the values in sale_product_id as column headers using the following code:

reshaped_df=id_product_count.pivot(index='sale_user_id',columns='sale_product_id',values='count')

and the resulting data frame is:

sale_product_id -1057   1   2   3   4   5   6   8   9   10  ... 98  980 981 982 983 984 985 986 987 99
sale_user_id                                                                                    
1                NaN    1.0 NaN NaN NaN NaN NaN 1.0 NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3                NaN    1.0 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4                NaN    NaN 1.0 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

as you can see we have a multililevel index , what i need is to have sale_user_is in the first column without multilevel indexing:

i take the following approach :

reshaped_df.reset_index()

the the result would be like this i still have the sale_product_id column , but i do not need it anymore:

sale_product_id sale_user_id    -1057   1   2   3   4   5   6   8   9   ... 98  980 981 982 983 984 985 986 987 99
0                          1    NaN 1.0 NaN NaN NaN NaN NaN 1.0 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1                          3    NaN 1.0 NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2                          4    NaN NaN 1.0 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN

i can subset this data frame to get rid of sale_product_id but i don’t think it would be efficient.I am looking for an efficient way to get rid of multilevel indexing while reshaping the original data frame

Asked By: chessosapiens

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Source

Answer 1

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  315
sale_user_id                            
1                  1    1    1    5    1

print (reshaped_df.index.name)
sale_user_id

print (reshaped_df.rename_axis(None))
sale_product_id  1    8    52   312  315
1                  1    1    1    5    1

Another solution working in pandas below 0.18.0:

reshaped_df.index.name = None
print (reshaped_df)

sale_product_id  1    8    52   312  315
1                  1    1    1    5    1

If need remove columns name also:

print (reshaped_df.columns.name)
sale_product_id

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

Another solution:

reshaped_df.columns.name = None
reshaped_df.index.name = None
print (reshaped_df)
   1    8    52   312  315
1    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  315
0                  1    1    1    5    1

#if need reset index nad remove column name
reshaped_df = reshaped_df.reset_index(drop=True).rename_axis(None, axis=1)
print (reshaped_df)
   1    8    52   312  315
0    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  315
sale_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  315
0             1  1  1   1    5    1

Answered By: jezrael

Answer 2

The way it works for me is

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

Answered By: Yury Wallet

Answer 3

Make a DataFrame

import random

d = {'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    6
2               Albania         2005    13
3               Algeria         2005    10
4               Andorra         2005    11
5               Angola          2005    5
6               Afghanistan     2006    3
7               Albania         2006    2
8               Algeria         2006    7
9               Andorra         2006    3
10              Angola          2006    6

Pivot

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

Table:

Year    Country         2005    2006
0       Afghanistan     16      9
1       Albania         17      19
2       Algeria         11      7
3       Andorra         5       12
4       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    2006
0   Afghanistan     16      9
1   Albania         17      19
2   Algeria         11      7
3   Andorra         5       12
4   Angola          6       18

Done!

Answered By: ChrisDanger

Answer 4

We need to reset_index() to reset the index columns back into the dataframe, then rename_axis() to rename the index to None and the columns to their axis=1 (column headers) values.

reshaped_df = reshaped_df.reset_index().rename_axis(None, axis=1)

Answered By: embulldogs99

Answer 5

Pivot from long to wide format using pivot:

import pandas
df = pandas.DataFrame({
    "lev1": [1, 1, 1, 2, 2, 2],
    "lev2": [1, 1, 2, 1, 1, 2],
    "lev3": [1, 2, 1, 2, 1, 2],
    "lev4": [1, 2, 3, 4, 5, 6],
    "values": [0, 1, 2, 3, 4, 5]})
df_wide = df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
df_wide

# lev2    1         2
# lev3    1    2    1    2
# lev1
# 1     0.0  1.0  2.0  NaN
# 2     4.0  3.0  NaN  5.0

Rename the (sometimes confusing) axis names

df_wide.rename_axis(columns=[None, None])

#         1         2
#         1    2    1    2
# lev1
# 1     0.0  1.0  2.0  NaN
# 2     4.0  3.0  NaN  5.0

Answered By: Paul Rougieux

Answer 6

You can also use a to_flat_index method of MultiIndex object to convert it into a list of tuples, which you can then concatenate with list comprehension and use it to overwrite the .columns attribute of your dataframe.

# create a dataframe
df = pd.DataFrame({"a": [1, 2, 3, 1], "b": ["x", "x", "y", "y"], "c": [0.1, 0.2, 0.1, 0.2]})


    a   b   c
0   1   x   0.1
1   2   x   0.2
2   3   y   0.1
3   1   y   0.2

# pivot the dataframe
df_pivoted = df.pivot(index="a", columns="b")

    c
b   x   y
a       
1   0.1 0.2
2   0.2 NaN
3   NaN 0.1

Now let’s overwrite the .columns attribute and .reset_index():

df_pivoted.columns = ["_".join(tup) for tup in df_pivoted.columns.to_flat_index()]
df_pivoted.reset_index()

    a   c_x c_y
0   1   0.1 0.2
1   2   0.2 NaN
2   3   NaN 0.1

Answered By: Tomasz Bartkowiak

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

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