CODEWITHSUNDEEP
pythonpython-3.xpandas
Qubix
Qubix

Reputation: 4353

Remove pandas dataframe row if one column's element is non-numeric

I have been searching for an answer for this simple thing for an hour now. I have a pandas dataframe that looks like this:

   title      |   price

"some text 1"     124.5
"some text 2"     543.2
"some text 3"     156.2
"some text 4"     "Price N/A"
"some text 5"     216.7

I want to remove the rows that don't contain an actual float price. I tried the suggestion from this answer:

raw_data[raw_data.price.apply(lambda x: x.isnumeric())]

and I get:

AttributeError: 'float' object has no attribute 'isnumeric'

Nothing seems to work for such a simple operation. I tried pretty much all the answers I found on stack and elsewhere.

Upvotes: 2

Views: 2523

Answers (2)

aj7amigo
aj7amigo

Reputation: 378

You can use the 'to_numeric' operation of pandas which will throw up an error when a string value is met. This error will be handled by 'coerce' which forces the content to null. We then use the 'notnull' operation to filter this particular row.

df[pd.to_numeric(df['price'], errors='coerce').notnull()]

Upvotes: 5

Urh
Urh

Reputation: 187

You get an error, since you are applying (lambda x: x.isnumeric()) on a float.

float.isnumeric() doesn't exist, but you can use isinstance(x, t), which returns True if x is of a type t.

raw_data[raw_data['price'].apply(lambda x : isinstance(x, float))]

Upvotes: 1

Related Questions