CODEWITHSUNDEEP
pythonpandasobject
yash agarwal
yash agarwal

Reputation: 95

Converting object type column to float type converts all to Nan?

While converting my object type columns to Float type, all my values are turned to Nan.

I checked the dtype of columns post conversion which shows all are float type. Hence succesful in that regard. But Why does it produces Nan values.

This is the code I'm using. Both versions of code below are producing same results.

data.M0=pd.to_numeric(data.M0, errors='coerce')
data['M1'] = data['M1'].apply(pd.to_numeric, downcast='float', errors='coerce')

Below is the pic of output dataframe.

Upvotes: 1

Views: 831

Answers (2)

U13-Forward
U13-Forward

Reputation: 71560

pd.to_numeric has an errors argument which can be ignore, raise and coerce.

coerce sets to NaN if the values aren't numeric-like values.

As mentioned in the [docs]9https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_numeric.html).

If ‘coerce’, then invalid parsing will be set as NaN.

Upvotes: 0

SeaBean
SeaBean

Reputation: 23217

pd.to_numeric with parameter errors='coerce' returns NaN with invalid parsing for entries that can't be converted to float values:

Official document states that:

errors{‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’

If ‘raise’, then invalid parsing will raise an exception.

If ‘coerce’, then invalid parsing will be set as NaN.

If ‘ignore’, then invalid parsing will return the input.

Probably, your elements in the dataframe are non-numeric and can't be converted.

Upvotes: 3

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