熊猫 - 替换值而不保留旧的不匹配项

Question

import pandas as pd
test_csv = """
time,val1,what_new_val1_should_be
2004-07-21 09:00:00,apple,1
2004-07-21 10:00:00,N,
2004-07-21 11:00:00,pear,2
2004-07-21 12:00:00,apple,1
2004-07-21 13:00:00,bread,3
2004-07-21 13:00:00,pear,2
2004-07-21 13:00:00,,
2004-07-21 13:00:00,,
"""
from io import StringIO
test_csv = StringIO(test_csv)
df = pd.read_csv(test_csv)

def coded_val(df):
"""
Create a new column "new_val1" that has an integer responding to the wor din val1
:param df: dataframe. A pandas dataframe with column val1 where the values are food items or N for none or lank for none
:return: daraframe. A pandas dataframe with a new column "new_val1"
"""
replacement_dict = {
'apple': 1,
'pear': 2,
'bread': 3
}
df['new_val1'] = df['val1'].replace(to_replace=replacement_dict, inplace=False)
return df

df = coded_val(df=df)
print(df)

如何让我的函数生成我想要的值？

time   val1  what_new_val1_should_be new_val1
0  2004-07-21 09:00:00  apple                      1.0        1
1  2004-07-21 10:00:00      N                      NaN        N
2  2004-07-21 11:00:00   pear                      2.0        2
3  2004-07-21 12:00:00  apple                      1.0        1
4  2004-07-21 13:00:00  bread                      3.0        3
5  2004-07-21 13:00:00   pear                      2.0        2
6  2004-07-21 13:00:00    NaN                      NaN      NaN
7  2004-07-21 13:00:00    NaN                      NaN      NaN

Answer 1

更改为map

df['val1'].map(replacement_dict)
Out[44]: 
0    1.0
1    NaN
2    2.0
3    1.0
4    3.0
5    2.0
6    NaN
7    NaN
Name: val1, dtype: float64