Convert Pandas column containing NaNs to dtype `int`

Question

I read data from a .csv file to a Pandas dataframe as below. For one of the columns, namely id, I want to specify the column type as int. The probl

Answer 1

It is now possible to create a pandas column containing NaNs as dtype int, since it is now officially added on pandas 0.24.0

pandas 0.24.x release notes Quote: "Pandas has gained the ability to hold integer dtypes with missing values

Answer 2

import pandas as pd

df= pd.read_csv("data.csv")
df['id'] = pd.to_numeric(df['id'])

Answer 3

My use case is munging data prior to loading into a DB table:

df[col] = df[col].fillna(-1)
df[col] = df[col].astype(int)
df[col] = df[col].astype(str)
df[col] = df[col].replace('-1', np.nan)

Remove NaNs, convert to int, convert to str and then reinsert NANs.

It's not pretty but it gets the job done!

Answer 4

use pd.to_numeric()

df["DateColumn"] = pd.to_numeric(df["DateColumn"])

simple and clean

Answer 5

Try this:

df[['id']] = df[['id']].astype(pd.Int64Dtype())

If you print it's dtypes, you will get id Int64 instead of normal one int64