Pandas Dataframe : Assigning integer values based on the column value

Question

I have the following pandas dataframe.

df = pd.DataFrame({'Neighborhood': ['Marble Hill', 'Chelsea', 'Sutton Place'],
                   'Venue Category': ['Hospital', 'Bridge', 'School']})

When I execute it, I get the following table.

 Neighborhood Venue Category
0 Marble Hill Hospital
1 Chelsea Bridge
2 Sutton Place School

Now, I want to assign numerical values for each Venue Category.

Hospital - 5 marks
School - 4 marks
Bridge - 2 marks

So I tried to assign marks using this code. I want to display the marks in a separate column.

def df2(df):

    if (df['Venue Category'] == 'Hospital'):
        return 5
    elif (df['Venue Category'] == 'School'):
        return 4
    elif (df['Venue Category'] != 'Hospital' or df['Venue Category'] != 'School'):
        return np.nan
df['Value'] = df.apply(df2, axis = 1)

Once executed, it gives me the following warning. May I know how to fix this please?

/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  if __name__ == '__main__':

stackoverflow.com/questions/39109045/…

Alexander
– Alexander

2019-11-24 06:07:55 +00:00
Commented Nov 24, 2019 at 6:07 — Alexander
– Alexander, Commented Nov 24, 2019 at 6:07

jezrael · Accepted Answer · 2019-11-24 06:18:44Z

Create dictionary for all possible Venue Category and then use Series.map, if some value from column not exist in keys of dictionary is returned NaN:

df = pd.DataFrame({'Neighborhood': ['Marble Hill', 'Chelsea', 'Sutton Place', 'aaa'],
                   'Venue Category': ['Hospital', 'Bridge', 'School', 'a']})

print (df)
   Neighborhood Venue Category
0   Marble Hill       Hospital
1       Chelsea         Bridge
2  Sutton Place         School
3           aaa              a

d = {'Hospital':5, 'School':4, 'Bridge':2}
df['Value'] = df['Venue Category'].map(d)
print (df)
   Neighborhood Venue Category  Value
0   Marble Hill       Hospital    5.0
1       Chelsea         Bridge    2.0
2  Sutton Place         School    4.0
3           aaa              a    NaN

Solution with np.select is possible, but in my opinion overcomplicated:

conditions = [df['Venue Category'] == 'Hospital',
              df['Venue Category'] == 'School',
              df['Venue Category'] == 'Bridge']
choices = [5,4,3]
df['Value'] = np.select(conditions, choices, default=np.nan)

print (df)
   Neighborhood Venue Category  Value
0   Marble Hill       Hospital    5.0
1       Chelsea         Bridge    3.0
2  Sutton Place         School    4.0
3           aaa              a    NaN

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