I have a column with the values changing from 0 to 600 and I want to group that values from 0 to 9.2 by 0.4 increments and 1 group between 9.2 and 600 values as outlier.I tried the following code ;
bin_labels = ['0-0.4', '0.4-0.8', '0.8-1.2', '1.2-1.6',
'1.6-2.0', '2.0-2.4','2.4-2.8', '2.8-3.2',
'3.2-3.6', '3.6-4.0','4.0-4.4', '4.4-4.8',
'4.8-5.2', '5.2-5.6','5.6-6.0', '6.0-6.4',
'6.4-6.8', '6.8-7.2','7.2-7.6', '7.6-8.0',
'8.0-8.4', '8.4-8.8','8.8-9.2']
bins = np.linspace(0.0,9.2,24)
df['A_group'] = pd.cut(df['A'], bins = bins, labels = bin_labels, include_lowest = True)
After that I want to fill the values between 9.2 and 600 with '9.2-more' label value using following code ;
df['A_group'] = df['A_group'].fillna('9.2-more')
But it says following error ;
Cannot setitem on a Categorical with a new category, set the categories first
CodePudding user response:
You can append float("inf")
to the bins
and include "9.2-more" in the bin_labels
:
bin_labels = [ '0-0.4', '0.4-0.8', '0.8-1.2', '1.2-1.6',
'1.6-2.0', '2.0-2.4', '2.4-2.8', '2.8-3.2',
'3.2-3.6', '3.6-4.0', '4.0-4.4', '4.4-4.8',
'4.8-5.2', '5.2-5.6', '5.6-6.0', '6.0-6.4',
'6.4-6.8', '6.8-7.2', '7.2-7.6', '7.6-8.0',
'8.0-8.4', '8.4-8.8', '8.8-9.2', "9.20-more"]
bins = np.append(np.linspace(0.0, 9.2, 24), float("inf"))
df["A_group"] = pd.cut(df['A'], bins = bins, labels = bin_labels, include_lowest = True)