我正在尝试在熊猫中的几列中应用地图以反映数据何时无效。当 df['Count'] 列中的数据无效时,我想将我的 df['值']、df['下限置信区间']、df['上置信区间'] 和 df['分母'] 列设置为 -1。
这是数据帧的示例:
Count Value Lower Confidence Interval Upper Confidence Interval Denominator
121743 54.15758428 53.95153779 54.36348867 224794
280 91.80327869 88.18009411 94.38654088 305
430 56.95364238 53.39535553 60.44152684 755
970 70.54545455 68.0815009 72.89492873 1375
nan
70 28.57142857 23.27957213 34.52488678 245
125 62.5 55.6143037 68.91456314 200
目前,我正在尝试:
set_minus_1s = {np.nan: -1, '*': -1, -1: -1}
然后:
df[['Value', 'Count', 'Lower Confidence Interval', 'Upper Confidence Interval', 'Denominator']] = df['Count'].map(set_minus_1s)
并收到错误:
ValueError: Must have equal len keys and value when setting with an iterable
有没有办法链接列引用以对映射进行一次调用,而不是为每个列使用单独的行来调用set_minus_1s
字典作为映射?
我认为您可以使用where
或mask
并替换应用后未isnull
的所有行 map
:
val = df['Count'].map(set_minus_1s)
print (val)
0 NaN
1 NaN
2 NaN
3 NaN
4 -1.0
5 NaN
6 NaN
Name: Count, dtype: float64
cols =['Value','Count','Lower Confidence Interval','Upper Confidence Interval','Denominator']
df[cols] = df[cols].where(val.isnull(), val, axis=0)
print (df)
Count Value Lower Confidence Interval Upper Confidence Interval
0 121743.0 54.157584 53.951538 54.363489
1 280.0 91.803279 88.180094 94.386541
2 430.0 56.953642 53.395356 60.441527
3 970.0 70.545455 68.081501 72.894929
4 -1.0 -1.000000 -1.000000 -1.000000
5 70.0 28.571429 23.279572 34.524887
6 125.0 62.500000 55.614304 68.914563
Denominator
0 224794.0
1 305.0
2 755.0
3 1375.0
4 -1.0
5 245.0
6 200.0
cols = ['Value', 'Count', 'Lower Confidence Interval', 'Upper Confidence Interval', 'Denominator']
df[cols] = df[cols].mask(val.notnull(), val, axis=0)
print (df)
Count Value Lower Confidence Interval Upper Confidence Interval
0 121743.0 54.157584 53.951538 54.363489
1 280.0 91.803279 88.180094 94.386541
2 430.0 56.953642 53.395356 60.441527
3 970.0 70.545455 68.081501 72.894929
4 -1.0 -1.000000 -1.000000 -1.000000
5 70.0 28.571429 23.279572 34.524887
6 125.0 62.500000 55.614304 68.914563
Denominator
0 224794.0
1 305.0
2 755.0
3 1375.0
4 -1.0
5 245.0
6 200.0