CODEWITHSUNDEEP
pandas
reservoirinvest
reservoirinvest

Reputation: 1777

Updating a pandas column with a dictionary lookup

Have a dataframe, df:

import pandas as pd
import numpy as np

i = ['dog', 'cat', 'rabbit', 'elephant'] * 3

df = pd.DataFrame(np.random.randn(12, 2), index=i, columns=list('AB'))

...and a lookup dict for column B:

b_dict = {'elephant': 2.0, 'dog': 5.0}

How can column B of df be replaced for elephant and dog rows?

df['B'].update(b_dict) gives:

AttributeError: 'dict' object has no attribute 'reindex_like'

Upvotes: 3

Views: 2259

Answers (3)

BENY
BENY

Reputation: 323376

Convert to pd.Series and update will work 

df['B'].update(pd.Series(b_dict))
df
Out[185]: 
                 A         B
dog      -1.340695  5.000000
cat      -0.196993 -0.021518
rabbit   -0.274504 -0.260294
elephant -0.170860  2.000000
dog      -0.432042  5.000000
cat       0.868669  0.204100
rabbit    0.435023 -1.968735
elephant -0.668397  2.000000
dog       0.706603  5.000000
cat       0.158067  0.675130
rabbit    0.429419  0.374914
elephant  1.559330  2.000000

Upvotes: 3

anky
anky

Reputation: 75140

Use np.where() to replace only where condition matches and retain the rest:

df['B']=np.where(df.index.isin(b_dict.keys()),df.index.map(b_dict),df.B)

Upvotes: 3

Alex
Alex

Reputation: 7075

You could use np.select:

cond = [(df.index == x) for x in b_dict.keys()]
vals = b_dict.values()

df['B'] = np.select(cond, vals)

Out[13]:         A    B
dog       0.523732  5.0
cat      -0.923483  0.0
rabbit    0.339749  0.0
elephant  1.204516  2.0
dog       1.799287  5.0
cat       0.214085  0.0
rabbit   -1.685739  0.0
elephant -0.475113  2.0
dog       1.381671  5.0
cat       1.343616  0.0
rabbit    0.627273  0.0
elephant  0.630255  2.0

Upvotes: 1

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