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I am using pandas read_sql_query to read data from a MySQL database table into a pandas dataframe. Some columns in this table have all NULL values. For those columns the pandas dataframe contains None in every row. For all other columns the dataframe contains NaN where there was a NULL value. Can anyone explain why None is returned for the all NULL columns? And how do I make sure I have all NaNs, hopefully without doing manual conversions?
I should add that two of the columns causing this problem are float and the third is of type double,

EDIT

Here is an example. The columns pef and fer contain all NULLS in the database.

from sqlalchemy import create_engine
import pandas as pd
import math

querystr = "SELECT * FROM dbname.mytable"
engine = create_engine('mysql+pymysql://username:password@localhost/' + "dbname")
df = pd.read_sql_query(querystr, engine)
df.head()

 sys dias pef fer
0 NaN NaN None None
1 159.0 92.666 None None
2 NaN NaN None None
3 NaN NaN None None
4 102.0 63.333 None None

In the MySQL database these columns are defined as:

Columns: 
 sys float 
 dias float 
 pef float 
 fer float

I would expect the columns pef and fer to contain NaN in each row, not None.

The problem is an open issue and is explained here: here: https://github.com/pandas-dev/pandas/issues/14314

read_sql_query just gets result sets back, without any column type
information. If you use the read_sql_table functions, there it uses
the column type information through SQLAlchemy.

It seems that read_sql_query only checks the first 3 values returned in a column to determine the type of the column. So if the first 3 values are NULL it cannot determine the type of the column and so returns None.

So a partial workaround is to use read_sql_table. I changed my code to use read_sql_table and it returns NaN values as expected even for the all NULL columns. But in my real application I really need to use read_sql_query. So I am now replacing any None values with NaN as soon as the results are returned:

df.replace([None], np.nan, inplace=True)