问如何在dataframe上设置一个可变数目的条件？
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Stack Overflow用户

提问于 2019-12-26 10:00:13

回答 3查看 157关注 0票数 1

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。

python

pandas

iterable-unpacking

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回答 3

Stack Overflow用户

回答已采纳

发布于 2019-12-26 10:24:51

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      1

票数 3

Stack Overflow用户

发布于 2019-12-26 10:36:25

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。

票数 2

Stack Overflow用户

发布于 2019-12-26 10:37:22

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