CODEWITHSUNDEEP
pythonpandas
chetan sharma
chetan sharma

Reputation: 49

Changing column value in groups in pandas

I need to change values of a column in 2 groups like country column has several value but I need US and Non -US in Pandas Dataframe. Please suggest how to achieve this in python dataframe.

I tried below code but no luck

1.

if df['Country'] != 'United-States':
     df['Country'] = 'Non-US'

2.

df.loc[df['Country'] != 'United-States', 'Country'] = 'Non-US'

Upvotes: 2

Views: 310

Answers (4)

Brad Solomon
Brad Solomon

Reputation: 40878

In addition to the NumPy version in @jezrael's answer, pandas also has its own Series.where() function:

>>> df = pd.DataFrame({'Country': ['United-States', 'Canada', 'Slovakia']})

>>> df.Country.where(df.Country == 'United-States', 'Non-US')
0    United-States
1           Non-US
2           Non-US

The where method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is True the element is used; otherwise the corresponding element from the DataFrame other is used.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

Upvotes: 0

jezrael
jezrael

Reputation: 862431

You need:

df = pd.DataFrame({'Country': ['United-States', 'Canada', 'Slovakia']})
print(df)
         Country
0  United-States
1         Canada
2       Slovakia

df['Country'] = np.where(df['Country'] == 'United-States', 'US', 'Non-US')

Or:

df['Country'] = np.where(df['Country'] != 'United-States', 'Non-US', 'US')

Another solution:

df['Country'] = df['Country'].map({'United-States':'US'}).fillna('Non-US')

print (df)
  Country
0      US
1  Non-US
2  Non-US

Upvotes: 5

Sociopath
Sociopath

Reputation: 13401

You can use apply function as below: 

df['country']=df['country'].apply(lambda x: "Non-US" if x != 'United-States' else "United-States")

Upvotes: 0

user5722540
user5722540

Reputation: 600

Try the following:

US = df[df['Country']=='United-States']
Other = df[df['Country']!='United-States']

This will surely help

Upvotes: 1

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