Turning a column of strings into a column of integers in Pandas

Question

I'm trying to turn a column of strings into integer identifiers...and I cannot find an elegant way of doing this in pandas (or python). In the following example, I transform "A", which is a column/variable of strings into numbers through a mapping, but it looks like a dirty hack to me

import pandas as pd                                                                             
import numpy as np

df = pd.DataFrame({'A': ['homer_simpson', 'mean_street', 'homer_simpson', 'bla_bla'], 'B': 4})

unique = df['A'].unique()
mapping = dict(zip(unique, np.arange(len(unique))))

new_df = df.replace({'A': mapping})

Is there a better, more direct, way of achieving this?

satomacoto · Accepted Answer · 2015-12-17 08:49:59Z

5

How about using factorize?

>>> labels, uniques = df.A.factorize()
>>> df.A = labels
>>> df
   A  B
0  0  4
1  1  4
2  0  4
3  2  4

http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.factorize.html

answered Dec 17, 2015 at 8:49

satomacoto

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1 Comment

Alexander Over a year ago

Well done. Never heard of factorize.

%timeit labels, uniques = df.A.factorize() 10000 loops, best of 3: 89 µs per loop  %timeit df.A.map({val: n for n, val in enumerate(df['A'].unique())}) 1000 loops, best of 3: 363 µs per loop

DSM · Accepted Answer · 2015-12-17 08:21:05Z

1

Assuming you don't care much about what the integers are, simply that there's a consistent mapping, you could (1) use the Categorical codes or (2) rank the values:

>>> df["A_categ"] = pd.Categorical(df.A).codes
>>> df["A_rank"] = df["A"].rank("dense").astype(int)
>>> df
               A  B  A_categ  A_rank
0  homer_simpson  4        1       2
1    mean_street  4        2       3
2  homer_simpson  4        1       2
3        bla_bla  4        0       1

answered Dec 17, 2015 at 8:21

DSM

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Comments

Alexander · Accepted Answer · 2015-12-17 08:32:42Z

1

A simple map on a transposed dictionary should get you what you want. All the values in the dictionary are unique, so transposing it won't result in duplicate keys.

df['A'] = df.A.map({val: n for n, val in enumerate(df['A'].unique())})

>>> df
   A  B
0  0  4
1  1  4
2  0  4
3  2  4

edited Dec 17, 2015 at 8:32

answered Dec 17, 2015 at 8:24

Alexander

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