在熊猫系列中将NaN转换为int

Question

I have missing values in a column of a series, so the command dataframe.colname.astype("int64") yields an error.

Any workarounds?

Answer 1

The datatype or dtype of a pd.Series has very little impact on the actual way it is used.

You can have a pd.Series with integers, and set the dtype to be object . You can still do the same things with the pd.Series .

However, if you manually set dtypes of pd.Series , pandas will start to cast the entries inside the pd.Series . In my experience, this only leads to confusion.

Do not try to use dtypes as field types in relational databases. They are not the same thing.

If you want to have integes and NaN s/ None s mixed in a pd.Series , just set the dtype to object .

Settings the dtype to float will let you have float representations of int s and NaN s mixed. But remember that float s are prone to be unexact in their representation

One common pitfall with dtypes which I should mention is the pd.merge operation, which will silently refuse to join when the keys used has different dtypes , for example int vs object even if the object only contains int s.

Other workarounds

1. You can use the Series.fillna method to fill your NaN values with something unlikely. 0 or -1 .
2. Copy the NaN s to a new column df['was_nan'] = pd.isnull(df['floatcol']) , then use the Series.fillna method . This way you do not lose any information.
3. When calling the Series.astype() method, give it the keyword argument raise_on_error=False , and just use the current dtype if it fails. Because dtypes do not matter that much.

TLDR;

Don't focus on having the 'right dtype', dtypes are strange. Focus on what you want the column to actually do. dtype=object is fine.