Remove row values containing non-numeric values in pandas dataframe

Asked Aug 05 '17 at 17:04

Active Aug 05 '17 at 17:04

Viewed 166 times

I have a dataframe and I want to remove rows containing non-numeric values and also convert float to int

# Dataframe
productid
7819210
9600146.0
031351AA2
3255214
0018392MM
1785506
6924986
1915444
6007828.0

# cuurent approach
df.productid = df[df.productid.apply(lambda x: str(x).isnumeric())].astype(int) # It also remove floating point values because they contain decimal which is non-numeric

I also made a method which does this

def filternumeric(x):
    if (re.search('\d+', str(x))): pass
    else: return x

df.productid = df[df.productid.apply(filternumeric)].astype(int)

But it is also not working correctly. Any better suggestions ?

asked Aug 05 '17 at 17:04

muazfaiz

4,611
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It contains df.applymap which I think works on the whole dataframe but I want to work with only one column – muazfaiz Aug 05 '17 at 17:08
2

Please see the linked answers on that link as well. These question has been answered multiple times here. – Zero Aug 05 '17 at 17:10
1

Try `df[pd.to_numeric(df.productid, errors='coerce').notnull()]` – cs95 Aug 05 '17 at 17:10
And, https://stackoverflow.com/q/33961028 – Zero Aug 05 '17 at 17:11

Remove row values containing non-numeric values in pandas dataframe

0 Answers0