从pandas dataframe中的字符串列中删除零

Question

I have a column in my dataframe,where the values are something like this:

col1:
    00000000000012VG
    00000000000014SG
    00000000000014VG
    00000000000010SG
    20000000000933LG
    20000000000951LG
    20000000000957LG
    20000000000963LG
    20000000000909LG
    20000000000992LG

I want to delete all zeros:

a)that are in front of other numbers and letters(For example in case of 00000000000010SG I want to delete this part 000000000000 and keep 10SG ).

b) In cases like 20000000000992LG I want to delete this part 0000000000 and unite 2 with 992LG .

str.stprip('0') solves only part a), as I checked.

But what is the right solution for both cases?

Answer 1

I would recommend something similar to Ed's answer, but using regex to ensure that not all 0s are replaced, and the eliminate the need to hardcode the number of 0s.

In [2426]: df.col1.str.replace(r'[0]{2,}', '', 1)
Out[2426]: 
0      12VG
1      14SG
2      14VG
3      10SG
4    2933LG
5    2951LG
6    2957LG
7    2963LG
8    2909LG
9    2992LG
Name: col1, dtype: object

Only the first string of 0s is replaced.

Thanks to @jezrael for pointing out a small bug in my answer.

Answer 2

You can just do

In[9]:
df['col1'] = df['col1'].str.replace('000000000000','')
df['col1'] = df['col1'].str.replace('0000000000','')
df

Out[9]: 
         col1
0        12VG
1        14SG
2        14VG
3        10SG
4      2933LG
5      2951LG
6      2957LG
7      2963LG
8      2909LG
9      2992LG

This will replace a fixed number of 0 s with a blank space, this isn't dynamic but for your given dataset this is the simplest thing to do unless you can explains better the pattern