如何将.strip().split() function 应用于 Pandas dataframe 中的整个列

Question

Example of Dataframe My Pandas dataframe has a column EvaRange which is captured in the following way.

<1000 mm
1000-1200mm
1200-1400mm
>1400mm

Desired Output I want to perform some Machine Learning on the dataframe so I need to convert this into a single numerical value.

So far I have managed to do this for a single row in the dataframe but I want to apply it to the entire column.

Code Example

a = df["EvaRange"][0].strip().split('mm')[0].split('-')
b = (float(a[0])+float(a[1]))/2
b

This manages to return an averaged value between the two ranges where 2 numbers are available.

Request Please could someone assist me with generalizing this so that I can apply it to the entire column and accomodate for the "<" and ">" values.

Answer 1

I would recommend extracting numbers and then averaging them:

df["EvaRange"].str.extract(r"(\d+)\D*(\d+)?").astype(float).mean(axis=1)
#0    1000.0
#1    1100.0
#2    1300.0
#3    1400.0

Here, the regular expression r"(\d+)\D*(\d+)?" asks for one or more digits (a number), optionally followed by some non-digits, optionally followed by some more digits (another number).

Answer 2

I would suggest using str.extractall to get all the numbers, then get the mean on the first level:

df.EvaRange.str.extractall(r"(\d+)").astype(float).mean(level=0)

         0
0   1000.0
1   1100.0
2   1300.0
3   1400.0

Building on your idea of strip and split:

(df.EvaRange
 .str.strip("<> mm")
 .str.split("-")
 .explode()
 .astype(float)
 .mean(level=0)
 )

0    1000.0
1    1100.0
2    1300.0
3    1400.0
Name: EvaRange, dtype: float64