Normally when a dataframe undergoes a reset_index() the new column is assigned the name index or level_i depending on the level.
Is it possible to assign the new column a name?
04 Answers
You can call rename on the returned df from reset_index:
In [145]: # create a df df = pd.DataFrame(np.random.randn(5,3)) df Out[145]: 0 1 2 0 -2.845811 -0.182439 -0.526785 1 -0.112547 0.661461 0.558452 2 0.587060 -1.232262 -0.997973 3 -1.009378 -0.062442 0.125875 4 -1.129376 3.282447 -0.403731 Set the index name
In [146]: df.index = df.index.set_names(['foo']) df Out[146]: 0 1 2 foo 0 -2.845811 -0.182439 -0.526785 1 -0.112547 0.661461 0.558452 2 0.587060 -1.232262 -0.997973 3 -1.009378 -0.062442 0.125875 4 -1.129376 3.282447 -0.403731 call reset_index and chain with rename:
In [147]: df.reset_index().rename(columns={df.index.name:'bar'}) Out[147]: bar 0 1 2 0 0 -2.845811 -0.182439 -0.526785 1 1 -0.112547 0.661461 0.558452 2 2 0.587060 -1.232262 -0.997973 3 3 -1.009378 -0.062442 0.125875 4 4 -1.129376 3.282447 -0.403731 Thanks to @ayhan
alternatively you can use rename_axis to rename the index prior to reset_index:
In [149]: df.rename_axis('bar').reset_index() Out[149]: bar 0 1 2 0 0 -2.845811 -0.182439 -0.526785 1 1 -0.112547 0.661461 0.558452 2 2 0.587060 -1.232262 -0.997973 3 3 -1.009378 -0.062442 0.125875 4 4 -1.129376 3.282447 -0.403731 or just overwrite the index name directly first:
df.index.name = 'bar' and then call reset_index
You could do this (Jan of 2020):
df = df.reset_index().rename(columns={'index': 'bar'}) print(df) bar 0 1 2 0 0 -2.845811 -0.182439 -0.526785 1 1 -0.112547 0.661461 0.558452 2 2 0.587060 -1.232262 -0.997973 3 3 -1.009378 -0.062442 0.125875 4 4 -1.129376 3.282447 -0.403731 For a Series you can specify the name directly. E.g.:
>>> df.groupby('s1').size().reset_index(name='new_name') s1 new_name 0 b 1 1 r 1 2 s 1 If you're using reset_index() to go from a Series to a DataFrame you can name the column like this
my_series.rename('Example').reset_index() 
