I have a column of a pandas dataframe that I got from a database query with blank cells. The blank cells become "None" and I want to check if each of the rows is None:
In [325]: yes_records_sample['name']
Out[325]:
41055 John J Murphy Professional Building
25260 None
41757 Armand Bayou Nature Center
31397 None
33104 Hubert Humphrey Building
16891 Williams Hall
29618 None
3770 Covenant House
39618 None
1342 Bhathal Student Services Building
20506 None
My understanding per the documentation is that I can check if each row is null with isnull()
command http://pandas.pydata.org/pandas-docs/dev/missing_data.html#values-considered-missing
That function, however, is not working for me:
In [332]: isnull(yes_records_sample['name'])
I get the following error:
NameError Traceback (most recent call last)
<ipython-input-332-55873906e7e6> in <module>()
----> 1 isnull(yes_records_sample['name'])
NameError: name 'isnull' is not defined
I also saw that someone just replaced the "None" strings, but neither of these variations on that approach worked for me: Rename "None" value in Pandas
yes_records_sample['name'].replace('None', "--no value--")
yes_records_sample['name'].replace(None, "--no value--")
I was ultimately able to use the fillna
function and fill each of those rows with an empty string yes_records_sample.fillna('')
as a workaround and then I could check yes_records_sample['name']==''
But I am profoundly confused by how 'None' works and what it means. Is there a way to easily just check if a cell in a dataframe is 'None'?
In order to check null values in Pandas DataFrame, we use isnull() function this function return dataframe of Boolean values which are True for NaN values.
None is also considered a missing value In pandas, None is also treated as a missing value. None is a built-in constant in Python. For numeric columns, None is converted to nan when a DataFrame or Series containing None is created, or None is assigned to an element.
You can filter out rows with NAN value from pandas DataFrame column string, float, datetime e.t.c by using DataFrame. dropna() and DataFrame. notnull() methods. Python doesn't support Null hence any missing data is represented as None or NaN.
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