[英]Elegant and efficient way to replace values in multiple columns using pandas
I have a dataframe like as shown below我有一个 dataframe 如下图所示
f = pd.DataFrame({'person_id': [101,101,101,201,201,201,203],
'test_id':[123,123,124,321,321,321,456],
'los_24':[0.3,0.7,0.6,1.01,2,1,2],
'los_48':[1,0.2,0.4,0.7,11,2,3],
'in_24':[21,24,0.3,2.3,0.8,23,1.001],
'in_48':[11.3,202.0,0.2,0.3,41.0,47,2],
'test':['A','B','C','D','E','F','G']})
I would like to replace all values less than 1 with value 1 under columns like los_24,los_48,in_24,in_48
我想all values less than 1 with value 1 under columns like los_24,los_48,in_24,in_48
I tried the below我尝试了以下
f['los_24'] = np.where((f.los_24 < 1.0),1,f.los_24)
f['los_48'] = np.where((f.los_48 < 1.0),1,f.los_48)
f['in_24'] = np.where((f.in_24 < 1.0),1,f.in_24)
f['in_48'] = np.where((f.in_48 < 1.0),1,f.in_48)
But you can see am writing the same line of code multiple times with different column names.但是您可以看到我用不同的列名多次编写同一行代码。
In real data, I have more than 10 columns to replace values.在实际数据中,我有超过 10 列来替换值。 So, Is there any other efficient and elegant way to write this?那么,有没有其他有效和优雅的方式来写这个?
I expect my output to be like as shown below我希望我的 output 如下所示
cols = ["los_24", "los_48", "in_24", "in_48"]
f[cols] = f[cols].clip(lower=1)
to get要得到
person_id test_id los_24 los_48 in_24 in_48 test
0 101 123 1.00 1.0 21.000 11.3 A
1 101 123 1.00 1.0 24.000 202.0 B
2 101 124 1.00 1.0 1.000 1.0 C
3 201 321 1.01 1.0 2.300 1.0 D
4 201 321 2.00 11.0 1.000 41.0 E
5 201 321 1.00 2.0 23.000 47.0 F
6 203 456 2.00 3.0 1.001 2.0 G
You can select all columns for processing in list and only once call function numpy.where
with selected columns:您可以 select 列表中的所有列进行处理,并且只调用一次 function numpy.where
与选定的列:
cols = ['los_24','los_48','in_24','in_48']
f[cols] = np.where((f[cols] < 1.0),1,f[cols])
Or with DataFrame.mask
:或使用DataFrame.mask
:
f[cols] = f[cols].mask((f[cols] < 1.0),1)
person_id test_id los_24 los_48 in_24 in_48 test
0 101 123 1.00 1.0 21.000 11.3 A
1 101 123 1.00 1.0 24.000 202.0 B
2 101 124 1.00 1.0 1.000 1.0 C
3 201 321 1.01 1.0 2.300 1.0 D
4 201 321 2.00 11.0 1.000 41.0 E
5 201 321 1.00 2.0 23.000 47.0 F
6 203 456 2.00 3.0 1.001 2.0 G
Wow, there are so many ways to skin the cat.. You could also use the lambda
function:哇,给猫剥皮的方法有很多。你也可以使用lambda
function:
cols = ['los_24','los_48','in_24','in_48']
for col in cols:
f[col] = f[col].apply(lambda x: 1 if x<1 else x)
Same output:-)相同的 output :-)
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