Removing zeros from row column in pandas dataframe
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 remove all zeros:
a) that are in front of other numbers and letters (for example, in case 00000000000010SG
I want to remove this part 000000000000
and keep 10SG
).
b) In cases like 20000000000992LG
, I want to remove this part 0000000000
and combine 2
with 992LG
.
str.stprip ('0') only solves the a) part as I tested.
But what is the correct solution for both cases?
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I would recommend something similar to Ed's answer, but using a regex to make sure not all 0s are replaced and eliminate the need to hard-code the number 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 line of 0s is replaced.
Thanks to @jezrael for pointing out a small bug in my answer.
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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 the fixed number with a 0
space, it is not dynamic but for a given dataset this is the easiest task if you can't explain the pattern better
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