如何在dataframe上设置一个可变数目的条件？
如何在dataframe上设置一个可变数目的条件？
提问于 2019-12-26 18:00:13
from itertools import product
import pandas as pd
animals = ["dogs", "cats"]
eyes = ['brown', 'blue', 'green']
height = ['short', 'average', 'tall']
a = [animals, eyes, height]
df = pd.DataFrame(list(product(*a)), columns=["animals", "eyes", "height"])
df['value'] = 1输出：
animals eyes height value
0 dogs brown short 1
1 dogs brown average 1
2 dogs brown tall 1
3 dogs blue short 1
4 dogs blue average 1
5 dogs blue tall 1
6 dogs green short 1问题:如何创建一个函数，使一个或多个行中的零“值”给定一个或多个条件？
示例：
# This would change all the 1s into 0s for all dogs with blue eyes.
zero_out(df, [("animals", "dogs"), ("eyes", "blue")])
# This would change all the 1s into 0s for all tall animals.
zero_out(df, [("height", "tall")])到目前为止，我的尝试是:我尝试用*解包来完成这个任务，但是没有成功，因为我不知道如何使用未打包的变量设置多个条件。如果我硬编码条件的数量，很容易设置多个条件.df[(condition1) & (condition2) & (condition3)] = 0
此外，也许这超出了问题的范围，我如何使用*解包装(或者不对if语句中的条件数进行硬编码)来设置给定常规if语句的可变条件数？
例如。
if a > 0 and b > 4
#Or...
if a > 0 and b > 4 and c < 2谢谢你的帮助。
回答 3
Stack Overflow用户
回答已采纳
发布于 2019-12-26 18:24:51
如果我正确地理解了您，您正在寻找.query()方法：
import pandas as pd
from itertools import product
animals = ["dogs", "cats"]
eyes = ['brown', 'blue', 'green']
height = ['short', 'average', 'tall']
a = [animals, eyes, height]
df = pd.DataFrame(list(product(*a)), columns=["animals", "eyes", "height"])
df['value'] = 1
def zero_out(df, lst):
q = ' & '.join( '{} == "{}"'.format(col, val) for col, val in lst )
df.loc[df.query(q).index, 'value'] = 0
zero_out(df, [("height", "tall")])
print(df)指纹：
animals eyes height value
0 dogs brown short 1
1 dogs brown average 1
2 dogs brown tall 0
3 dogs blue short 1
4 dogs blue average 1
5 dogs blue tall 0
6 dogs green short 1
7 dogs green average 1
8 dogs green tall 0
9 cats brown short 1
10 cats brown average 1
11 cats brown tall 0
12 cats blue short 1
13 cats blue average 1
14 cats blue tall 0
15 cats green short 1
16 cats green average 1
17 cats green tall 0或zero_out(df, [("animals", "dogs"), ("eyes", "blue")])
animals eyes height value
0 dogs brown short 1
1 dogs brown average 1
2 dogs brown tall 1
3 dogs blue short 0
4 dogs blue average 0
5 dogs blue tall 0
6 dogs green short 1
7 dogs green average 1
8 dogs green tall 1
9 cats brown short 1
10 cats brown average 1
11 cats brown tall 1
12 cats blue short 1
13 cats blue average 1
14 cats blue tall 1
15 cats green short 1
16 cats green average 1
17 cats green tall 1Stack Overflow用户
发布于 2019-12-26 18:36:25
def zero_out(df, list_of_filters, out_column='value'):
conds = np.ones(df.shape[0], dtype=bool)
for col_name, val in list_of_filters:
cond = df[col_name].eq(val)
conds &= cond
df.loc[conds, out_column] = 0
return df你也可以用这个。它比Andrej的方法更通用，因为它不假定过滤器值是字符串。
Stack Overflow用户
发布于 2019-12-26 18:37:22
你可以试试：
def zero_out(df, *args):
df_temp = df.copy()
for arg in args:
df_temp = df_temp[df_temp[arg[0]] == arg[1]].copy()
df.iloc[df_temp.index, -1] = 0
return df
zero_out(df, ("animals", "dogs"), ("eyes", "blue"))结果：
animals eyes height value
0 dogs brown short 0
1 dogs brown average 0
2 dogs brown tall 0
3 dogs blue short 0
4 dogs blue average 0
5 dogs blue tall 0
6 dogs green short 0
7 dogs green average 0
8 dogs green tall 0
9 cats brown short 1
10 cats brown average 1
11 cats brown tall 1
12 cats blue short 0
13 cats blue average 0
14 cats blue tall 0
15 cats green short 1
16 cats green average 1
17 cats green tall 1页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/59491847
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