Question

我有以下熊猫数据框。

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

执行它时，得到下表。

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

现在，我想为每个场馆类别分配数值。

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

因此，我尝试使用此代码分配标记。我想在单独的列中显示标记。

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)

一旦执行，它会给我以下警告。我可以知道如何解决这个问题吗？

/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__':

Answer 1

为所有可能的Venue Category创建字典，然后使用Series.map，如果返回了字典关键字中不存在的列值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

可以使用np.select解决方案，但我认为过于复杂：

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

Pandas Dataframe：根据列值分配整数值

1 个答案: