从两列计算并创建百分比列

我有一个 df (Apple_farm）并且需要根据两列中找到的值计算百分比（Good_apples and Total_apples），然后将结果值添加到 Apple_farm 中名为“Perc_Good”的新列中。

我努力了：

Apple_farm['Perc_Good'] = (Apple_farm['Good_apples'] / Apple_farm['Total_apples']) *100

但这会导致此错误：

类型错误：/ 不支持的操作数类型：“str”和“str”

Doing

Print Apple_farm['Good_apples'] and Print Apple_farm['Total_apples']

生成一个包含数值的列表，但是将它们分开似乎会导致它们转换为字符串？

我还尝试定义一个新函数：

def percentage(amount, total):
    percent = amount/total*100
    return percent

但不确定如何使用它。

任何帮助将不胜感激，因为我对 Python 和 pandas 相当陌生！

我认为你需要转换string列至float or int，因为他们的type is string（但看起来像数字）：

Apple_farm['Good_apples'] = Apple_farm['Good_apples'].astype(float)
Apple_farm['Total_apples'] = Apple_farm['Total_apples'].astype(float)

Apple_farm['Good_apples'] = Apple_farm['Good_apples'].astype(int)
Apple_farm['Total_apples'] = Apple_farm['Total_apples'].astype(int)

Sample:

import pandas as pd

Good_apples = ["10", "20", "3", "7", "9"]
Total_apples = ["20", "80", "30", "70", "90"]
d = {"Good_apples": Good_apples, "Total_apples": Total_apples}
Apple_farm = pd.DataFrame(d)
print Apple_farm 
  Good_apples Total_apples
0          10           20
1          20           80
2           3           30
3           7           70
4           9           90

print Apple_farm.dtypes
Good_apples     object
Total_apples    object
dtype: object

print Apple_farm.at[0,'Good_apples']
10

print type(Apple_farm.at[0,'Good_apples'])
<type 'str'>

Apple_farm['Good_apples'] = Apple_farm['Good_apples'].astype(int)
Apple_farm['Total_apples'] = Apple_farm['Total_apples'].astype(int)

print Apple_farm.dtypes
Good_apples     int32
Total_apples    int32
dtype: object

print Apple_farm.at[0,'Good_apples']
10

print type(Apple_farm.at[0,'Good_apples'])
<type 'numpy.int32'>

Apple_farm['Perc_Good'] = (Apple_farm['Good_apples'] / Apple_farm['Total_apples']) *100

print Apple_farm
   Good_apples  Total_apples  Perc_Good
0           10            20       50.0
1           20            80       25.0
2            3            30       10.0
3            7            70       10.0
4            9            90       10.0