[英]string replace in pandas
I have a pandas data frame , that has some regression equations, with bias terms at the end of each equation.我有一个 Pandas 数据框,它有一些回归方程,每个方程末尾都有偏差项。 (+250 , -150, +450, +250 )
(+250, -150, +450, +250 )
df: df:
a b
0 [TC100]+250 [TC200]-150
1 [FC100]+450 [FC200]+250
I would like to replace the bias terms [specifically , whatever comes after the last occurrence of the character ]
in each equation] .我想[具体而言,字符最后一次出现后,一切以代替偏置条件
]
每个方程中。 The replacement string should be based on the corresponding column name.替换字符串应基于相应的列名。 Desired output as below
所需的输出如下
output:输出:
a b
0 [TC100]+a1 [TC200]+b1
1 [FC100]+a2 [FC200]+b2
I tried using rsplit
, df.replace
, Series.str.extract
but no luck.我尝试使用
rsplit
, df.replace
, Series.str.extract
但没有运气。 I would appreciate very much any help .我将不胜感激任何帮助。
Using split
and just re-construct your str for each cell使用
split
并为每个单元格重新构建你的 str
s1=df.apply(lambda x : x.str.split(']',expand=True)[0])
df.astype(bool)
a b
0 True True
1 True True
s2=df.astype(bool)
s=s1+']+'+s2*s2.columns+(s2.T*(np.arange(len(df))+1).astype(str)).T
s
a b
0 [TC100]+a1 [TC200]+b1
1 [FC100]+a2 [FC200]+b2
Or use apply
in one-line (very long tho):或者在一行中使用
apply
(很长):
>>> df.apply(lambda x: x.str.split(']',expand=True)[0]+']+'+df.columns[df.isin([x[0]]).any()].item()+str(df[df.columns[df.isin([x[0]]).any()].item()].tolist().index(x[0])+1),axis=1)
a b
0 [TC100]+a1 [TC200]+a1
1 [FC100]+a2 [FC200]+a2
>>>
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