How to use loc from pandas?

Question

I have this code to replace ages from numeric data to categorical data. I'm trying to do it that way, but it's not working. Can anybody help me?

for df in treino_teste:
    df.loc[df['Age'] <= 13, 'Age'] = 0,
    df.loc[(df['Age'] > 13) & (df['Age'] <= 18), 'Age'] = 1,
    df.loc[(df['Age'] > 18) & (df['Age'] <= 25), 'Age'] = 2,
    df.loc[(df['Age'] > 25) & (df['Age'] <= 35), 'Age'] = 3,
    df.loc[(df['Age'] > 35) & (df['Age'] <= 60), 'Age'] = 4,
    df.loc[df['Age'] > 60, 'Age'] = 5

Error:

df['Age'] = pd.cut(df['Age'], [0, 13, 18, 25, 35, 60,130], labels=[0,1,2,3,4,5]),refer pandas cut — Shijith
– Shijith, Commented Feb 28, 2021 at 16:02

Rob Raymond · Accepted Answer · 2021-02-28 16:12:35Z

1

there is capability for categorising continuous data
for purpose of example I've assign the bin to a new column. I could have assigned it back to Age
for ease of reading results I have sorted, this is not needed

df = pd.DataFrame({"Age":np.random.randint(1,65,10)}).sort_values(["Age"])

bins = [0,13,18,25,35,60,100]
df.assign(AgeB=pd.cut(df.Age, bins=bins, labels=[i for i,v in enumerate(bins[:-1])]))

	Age	AgeB
5	12	0
3	13	0
8	18	1
7	25	2
9	25	2
1	27	3
2	30	3
4	57	4
0	59	4
6	64	5

answered Feb 28, 2021 at 16:12

Rob Raymond

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Comments

LevB · Accepted Answer · 2021-02-28 16:28:17Z

1

You can use numpy.digitize()

bins = [0,13,18,25,35,60,100]
df['AgeC'] =numpy.digitize(df['Age'],bins)

answered Feb 28, 2021 at 16:28

LevB

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