In case you want to normalize all of the nulls with python's None.
df.fillna(np.nan).replace([np.nan], [None])
The first fillna will replace all of (None, NAT, np.nan, etc) with Numpy's NaN, then replace Numpy's NaN with python's None.
Top answer 1 of 6
110
In case you want to normalize all of the nulls with python's None.
df.fillna(np.nan).replace([np.nan], [None])
The first fillna will replace all of (None, NAT, np.nan, etc) with Numpy's NaN, then replace Numpy's NaN with python's None.
2 of 6
23
Setup
Consider the sample dataframe df
df = pd.DataFrame(dict(A=[1, None], B=[None, 2], C=[None, 'D']))
df
A B C
0 1.0 NaN None
1 NaN 2.0 D
I can confirm the error
df.fillna(dict(A=1, B=None, C=4))
ValueError: must specify a fill method or value
This happens because pandas is cycling through keys in the dictionary and executing a fillna for each relevant column. If you look at the signature of the pd.Series.fillna method
Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
You'll see the default value is None. So we can replicate this error with
df.A.fillna(None)
Or equivalently
df.A.fillna()
I'll add that I'm not terribly surprised considering that you are attempting to fill a null value with a null value.
What you need is a work around
Solution
Use pd.DataFrame.fillna over columns that you want to fill with non-null values. Then follow that up with a pd.DataFrame.replace on the specific columns you want to swap one null value with another.
df.fillna(dict(A=1, C=2)).replace(dict(B={np.nan: None}))
A B C
0 1.0 None 2
1 1.0 2 D
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 3.0.2 documentation
DataFrame.fillna(value, *, axis=None, inplace=False, limit=None)[source]# Fill NA/NaN values with value. Parameters: valuescalar, dict, Series, or DataFrame · Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame).
BUG: fillna() doesn't work with `value=None` without method specified, but method is deprecated
Do we want to allow None as the possible value in fillna()? This could be a docs issue or a code issue. ... commit : fd3f571 python : 3.10.13.final.0 python-bits : 64 OS : Linux OS-release : 4.4.0-19041-Microsoft Version : #3996-Microsoft Thu Jan 18 16:36:00 PST 2024 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 · pandas ... More on github.com
6
March 4, 2024Can't fillna with None in a dataframe
I was looking to replace all np.nan values in a dataframe with None, I was trying to do this using fillna, but it seems like this is not supported (through fillna, though you can use where): In [1]: import pandas as pd i In [2]: import n... More on github.com
2
August 20, 2015Issue replacing 'None' with df.fillna()
It's possible that it's actually holding the string "None" rather than the Python value of None. Try: df.replace("None","",inplace=True) More on reddit.com
4
2
July 10, 2022How to fillna with None in a pandas dataframe?
Unlike a list, a dataframe has to have all elements be the same type. You can't insert None into a dataframe of floats, because None is not a float. However, nan is a float. More on reddit.com
11
1
May 2, 2018Videos
Reddit
reddit.com › r/learnpython › how to fillna with none in a pandas dataframe?
r/learnpython on Reddit: How to fillna with None in a pandas dataframe?
May 2, 2018 -
Pretty straightforward, I have a dataframe that has columns with different mixtures of np.nan and None as the "null" value for that column. I want to get them all to be "None", but
df.fillna(None) df.replace(np.nan, None) df.fillna(values=None)
all don't work. Anyone run into this issue before?
Also why the bloody fucking hell does
np.nan==np.nan
evaluate to false? (Yes I know its by definition, but some bloke thought it was a good idea to write the definition that way.) Is that actually useful for anything? In this particular case, it makes me want to use None instead.
Top answer 1 of 2
1
Unlike a list, a dataframe has to have all elements be the same type. You can't insert None into a dataframe of floats, because None is not a float. However, nan is a float.
2 of 2
1
I agree with u/novel_yet_trivial , but if you really want to disregard our advice and do it anyway I believe this will work. df.where(False, None)
GeeksforGeeks
geeksforgeeks.org › python › python-pandas-dataframe-fillna-to-replace-null-values-in-dataframe
Pandas DataFrame.fillna() | Python - GeeksforGeeks
February 23, 2026 - This example replaces missing values in the College column with a fixed text "No College" using fillna(). This is useful when you want to assign a default value to missing entries. ... import pandas as pd nba = pd.read_csv("nba.csv") nba["College"].fillna("No College", inplace = True) print(nba)
GitHub
github.com › pandas-dev › pandas › issues › 57723
BUG: fillna() doesn't work with `value=None` without method specified, but method is deprecated · Issue #57723 · pandas-dev/pandas
March 4, 2024 - The method parameter is deprecated in version 2.1. But the docs for Series.fillna() indicate that a value of None is valid.
Author Dr-Irv
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas.dataframe.fillna() – explained by examples
pandas.DataFrame.fillna() - Explained by Examples - Spark By {Examples}
June 26, 2025 - pandas.DataFrame.fillna() method is used to fill column (one or multiple columns) containing NA/NaN/None with 0, empty, blank, or any specified values etc. NaN is considered a missing value.
GeeksforGeeks
geeksforgeeks.org › python-pandas-dataframe-fillna-to-replace-null-values-in-dataframe
Python | Pandas DataFrame.fillna() to replace Null values in dataframe - GeeksforGeeks
August 21, 2024 - Just like the pandas dropna() method manages and remove Null values from a data frame, fillna() manages and let the user replace NaN values with some value of their own. Syntax: DataFrame.fillna(value=None, method=None, axis=None, inplace=False, ...
Pandas
pandas.pydata.org › docs › dev › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 3.0.0rc2+8.g2b571cac91 documentation
DataFrame.fillna(value, *, axis=None, inplace=False, limit=None)[source]# Fill NA/NaN values with value. Parameters: valuescalar, dict, Series, or DataFrame · Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame).
Pandas
pandas.pydata.org › docs › user_guide › missing_data.html
Working with missing data — pandas documentation - PyData |
In [89]: data = {"np": [1.0, np.nan, np.nan, 2], "arrow": pd.array([1.0, pd.NA, pd.NA, 2], dtype="float64[pyarrow]")} In [90]: df = pd.DataFrame(data) In [91]: df Out[91]: np arrow 0 1.0 1.0 1 NaN <NA> 2 NaN <NA> 3 2.0 2.0 In [92]: df.fillna(0) Out[92]: np arrow 0 1.0 1.0 1 0.0 0.0 2 0.0 0.0 3 2.0 2.0 · When the data has object dtype, you can control what type of NA values are present. In [93]: df = pd.DataFrame({"a": [pd.NA, np.nan, None]}, dtype=object) In [94]: df Out[94]: a 0 <NA> 1 NaN 2 None In [95]: df.fillna(None) Out[95]: a 0 None 1 None 2 None In [96]: df.fillna(np.nan) Out[96]: a 0 NaN 1 NaN 2 NaN In [97]: df.fillna(pd.NA) Out[97]: a 0 <NA> 1 <NA> 2 <NA> However when the dtype is not object, these will all be replaced with the proper NA value for the dtype.
Pandas
pandas.pydata.org › pandas-docs › version › 1.5 › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 1.5.3 documentation
DataFrame.fillna(value=None, *, method=None, axis=None, inplace=False, limit=None, downcast=None)[source]#
Pandas
pandas.pydata.org › pandas-docs › version › 2.0 › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 2.0.3 documentation
DataFrame.fillna(value=None, *, method=None, axis=None, inplace=False, limit=None, downcast=None)[source]#
Saturn Cloud
saturncloud.io › blog › how-to-replace-none-with-nan-in-pandas-dataframe
How to Replace None with NaN in Pandas DataFrame | Saturn Cloud Blog
January 16, 2024 - The simplest way to replace None with NaN in a Pandas DataFrame is to use the fillna() method. The fillna() method replaces missing values with a specified value.
W3Schools
w3schools.com › python › pandas › ref_df_fillna.asp
Pandas DataFrame fillna() Method
dataframe.fillna(value, method, axis, inplace, limit, downcast) The axis, method, inplace, limit, downcast parameters are keyword arguments. A DataFrame with the result, or None if the inplace parameter is set to True.
Statology
statology.org › home › pandas: how to replace nan with none
Pandas: How to Replace NaN with None
December 1, 2022 - This tutorial explains how to replace NaN values with a None value in a pandas DataFrame, including an example.
Pandas
pandas.pydata.org › pandas-docs › version › 1.0.1 › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 1.0.1 documentation
DataFrame.fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None) → Union[ForwardRef('DataFrame'), NoneType][source]¶
pandas
pandas.pydata.org › pandas-docs › dev › reference › api › pandas.DataFrame.fillna.html
pandas.DataFrame.fillna — pandas 3.0.0rc1+79.g6ad2bb0510 documentation
DataFrame.fillna(value, *, axis=None, inplace=False, limit=None)[source]# Fill NA/NaN values with value. Parameters: valuescalar, dict, Series, or DataFrame · Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame).
Pandas
pandas.pydata.org › pandas-docs › version › 0.17.0 › generated › pandas.Series.fillna.html
pandas.Series.fillna — pandas 0.17.0 documentation
Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)¶ · Fill NA/NaN values using the specified method · See also · reindex, asfreq · index · modules | next | previous | pandas 0.17.0 documentation » · API Reference » ·
GitHub
github.com › pandas-dev › pandas › issues › 10871
Can't fillna with None in a dataframe · Issue #10871 · pandas-dev/pandas
August 20, 2015 - I was looking to replace all np.nan values in a dataframe with None, I was trying to do this using fillna, but it seems like this is not supported (through fillna, though you can use where): In [1]: import pandas as pd i In [2]: import numpy as np In [3]: df = pd.DataFrame([[np.nan, 1], [1, np.nan]]) In [4]: df.fillna(value=None) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-4-f459dbd09698> in <module>() ----> 1 df.fillna(value=None) /Users/yoshikivazquezbaeza/.virtualenvs/ipy/lib/python2.7/site-packages/
Author ElDeveloper
Reddit
reddit.com › r/learnpython › issue replacing 'none' with df.fillna()
r/learnpython on Reddit: Issue replacing 'None' with df.fillna()
July 10, 2022 -
Hey All,
I am using the below code to export Jira issues. Not all of my issues have all the custom fields I am trying to export so they end up being 'None'. I would like to replace the 'None' with an empty string. I am trying to use pandas df.fillna but it's not doing what I thought it would. In spite of using fillna, my output is unchanged and still contains 'None' values.
What am I doing wrong?
Thanks!
Code:
# Search all issues, and time execution
jira_issues = jira.search_issues(jql,maxResults=50)
# Converge JSON to Pandas DataFrame
for issue in jira_issues:
try:
issue_fields = pd.DataFrame({
'id': [issue.id],
'Due date': str(issue.fields.duedate),
'Actual Start Date': str(issue.fields.customfield_10055),
'Actual Completion Date': str(issue.fields.customfield_10057)
})
except AttributeError:
pass
issues = pd.concat([issues,issue_fields])
issues.fillna("",inplace=True)
print(issues)Output:
0 79906 None None None 0 79904 2022-07-07 2022-05-18 2022-05-25 0 79903 2022-04-13 2022-04-22 2022-04-22 0 79902 2022-06-15 2022-06-04 2022-06-04 0 79901 None None None 0 79900 None 2022-06-14 None 0 79899 2022-05-06 2022-05-02 2022-05-06 0 79897 None None None 0 79896 None None None
Checking column types using dtypes:
id object Due date object Actual Start Date object Actual Completion Date object dtype: object
w3resource
w3resource.com › pandas › series › series-fillna.php
Pandas Series: fillna() function - w3resource
Last update on September 15 2022 12:55:26 (UTC/GMT +8 hours) The fillna() function is used to fill NA/NaN values using the specified method. Syntax: Series.fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, ...