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将数据放入范围

conditional dataframe pandas python

Ahamed Moosa · 技术社区 · 6 年前

我有一个像下面这样的数据框架,我想创建4列来计算精度分布

Company Error_Rate
A       9
B      10
c      20
GK     17
GK     18
GK     30
GK     35
GK     25
GK     32
GK     40
GK     50
MB     60
MB     70
MB     70

我想要一张这样的桌子

Company Error_Rate  Above 90%   80% - 90%   65% - 80%   Below 65%
A              9    1           0           0           0
B             10    1           0           0           0
c             20    0           1           0           0
GK            17    0           1           0           0
GK            18    0           1           0           0
GK            30    0           0           1           0
GK            35    0           0           1           0
GK            40    0           0           0           1

我试过了

df['Above 90%'] = np.where(df['Error_Rate']<=10,1,0)
df['80% - 90%'] = np.where(df['Error_Rate'] <= 20,(np.where(df['Error_Rate'] > 10, 1, 0)),0)
df['65% - 80%'] = np.where(df['Error_Rate'] <= 35,(np.where(df['Error_Rate'] > 20, 1, 0)),0)
df['Below 65%'] = np.where(df['Error_Rate']>35,1,0)

它没有给我期望的结果。我哪里出错了吗?

2 回复 | 直到 6 年前

cs95 abhishek58g 6 年前

如果你必须写4 np.where 计算列的条件,你做的不对。我认为考虑一种不同的方法是明智的。

一个简洁的选择包括 pd.cut + pd.get_dummies .

bins = [0, 65, 80, 90, 100]
labels = ['Below 65%', '65% - 80%', '80% - 90%', 'Above 90%']

pd.concat([
    df, pd.get_dummies(pd.cut(100 - df.Error_Rate, bins=bins, labels=labels, right=True))
   ], axis=1
)

   Company  Error_Rate  Below 65%  65% - 80%  80% - 90%  Above 90%
0        A           9          0          0          0          1
1        B          10          0          0          0          1
2        c          20          0          0          1          0
3       GK          17          0          0          1          0
4       GK          18          0          0          1          0
5       GK          30          0          1          0          0
6       GK          35          0          1          0          0
7       GK          25          0          1          0          0
8       GK          32          0          1          0          0
9       GK          40          1          0          0          0
10      GK          50          1          0          0          0
11      MB          60          1          0          0          0
12      MB          70          1          0          0          0
13      MB          70          1          0          0          0

jezrael 6 年前

用途:

df['Above 90%'] = np.where(df['Error_Rate']<=10,1,0)
df['80% - 90%'] = np.where((df['Error_Rate'] <= 20) & (df['Error_Rate'] > 10),1,0)
df['65% - 80%'] = np.where((df['Error_Rate'] <= 35) & (df['Error_Rate'] > 20),1,0)
df['Below 65%'] = np.where(df['Error_Rate']>35,1,0)

print (df)
   Company  Error_Rate  Above 90%  80% - 90%  65% - 80%  Below 65%
0        A           9          1          0          0          0
1        B          10          1          0          0          0
2        c          20          0          1          0          0
3       GK          17          0          1          0          0
4       GK          18          0          1          0          0
5       GK          30          0          0          1          0
6       GK          35          0          0          1          0
7       GK          25          0          0          1          0
8       GK          32          0          0          1          0
9       GK          40          0          0          0          1
10      GK          50          0          0          0          1
11      MB          60          0          0          0          1
12      MB          70          0          0          0          1
13      MB          70          0          0          0          1