I have a dataframe of strings that looks like this:
In [58]: d['upin'].head()
Out[58]:
0 'H8409
1 'H8409
2 .31961
3 .31961
4 000000
Name: upin, dtype: object
I want to drop the rows that have only zeros, ie the last row in this example. I haven't found a nice regexp way of doing this, and I'm not sure what else to try.
This should work if I understood your problem correctly:
df = pd.DataFrame()
df['pin'] = ["00000","F4923","'222R","0","00001"]
ndf = df[~(df['pin'].str.contains('^0+$'))]
You can use pd.to_numeric:
df.loc[pd.to_numeric(df.pin,errors='coerce').ne(0)]
Out[278]:
pin
1 F4923
2 '222R
4 00001
You can use str.contains
df = df[df.upin.str.contains('[^0]')]
You get
upin
0 'H8409
1 'H8409
2 .31961
3 .31961
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.