CODEWITHSUNDEEP
pythonpandas
manu
manu

Reputation: 1453

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

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?

Upvotes: 4

Views: 284

Answers (3)

satomacoto
satomacoto

Reputation: 11973

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

Upvotes: 5

Alexander
Alexander

Reputation: 109726

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

Upvotes: 1

DSM
DSM

Reputation: 353499

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

Upvotes: 1

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