WebAug 20, 2024 · How to fix ValueError: cannot convert float NaN to integer? Method 1 – Drop rows that have NaN values using the dropna () method Method 2 – Replace NaN values using fillna () method Method 3 … WebSep 5, 2024 · 1 Answer Sorted by: 1 Try this: dt = dt.dropna () dt ['Spam'] = dt ['type'].map ( {'Spam' : 1, 'ham' : 0}).astype ('int64') or this: dt ['type'] = dt ['type'].replace (np.inf, np.nan) dt = dt.dropna () dt ['Spam'] = dt ['type'].map ( {'Spam' : 1, 'ham' : 0}).astype ('int64') Share Improve this answer Follow edited Sep 5, 2024 at 16:03
How to Fix: ValueError: cannot convert float NaN to integer
WebSep 27, 2024 · Somehow they are checking for types and forcing a conversion to int even if there isn't an integer field in your feature layer. I did find a work-around. The layer has a method for sdf of which I wasn't aware. Instead of: agol_df = pd.DataFrame.spatial.from_layer (fLayer) Use: agol_df = fLayer.query ().sdf This works … WebThe stacktrace says the error is thrown at the dropna line There is columns of other dtypes, but the only column in use here is value, where is successfully downcast to a np.float32 prior to creating the relative history. df ['value'] = df ['value'].astype (np.float32) dewberry body shop perfume oil
ValueError: Cannot convert non-finite values (NA or inf) to integer …
WebNov 16, 2024 · You can convert it to a nullable int type (choose from one of Int16, Int32, or Int64) with, s2 = s.astype ('Int32') # note the 'I' is uppercase s2 0 1 1 2 2 NaN 3 4 dtype: Int32 s2.dtype # Int32Dtype () Your column needs to have whole numbers for the cast to happen. Anything else will raise a TypeError: WebMar 18, 2024 · ValueError: Cannot convert non-finite values (NA or inf) to integer However, the following works: for col in df.columns: df[col] = df[col].dropna() The following dtypes are in the df: ... Cannot convert non-finite values (NA or inf) to integer. Hot Network Questions WebDec 24, 2024 · ValueError: Cannot convert non-finite values (NA or inf) to integer. Because the NaN values are not possible to convert the dataframe. So in order to fix this issue, we have to remove NaN values. Method 1: Drop rows with NaN values. Here we are going to remove NaN values from the dataframe column by using dropna() function. This … church of st gregory clarks green pa