I have a field that normally has a date value, and I would like to check if it's empty.
I've tried this:
np.isnan(row['date'] == True
However, this causes an error. This I used for string field. Is it different for each data type?
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What is `row` a pandas dataframe or are you iterating over a frame? – It_is_Chris Sep 29 '21 at 13:17
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1You can just use `np.isnan(row['date'])`. There is no need for the `== True` – prnvbn Sep 29 '21 at 13:18
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I am iterating over a frame. I am using this in a function. So i want to check if a date row is empty to give it a different value. – Datawoman Sep 29 '21 at 13:31
2 Answers
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Use boolean indexing, do not iterate
# set up a sample frame
df = pd.DataFrame(pd.date_range('2021-01-01', '2021-01-15'), columns=['Date'])
# change some values to null
df.iloc[::4] = np.nan
# boolean indexing to find all null values
df_null = df[df['Date'].isna()]
Date
0 NaT
4 NaT
8 NaT
12 NaT
It_is_Chris
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Thanks! I get now the null values. How do I use it in an if statement? If I use it than I get the error: The truth value of a dataframe is ambiguous. I need to learn a lot. – Datawoman Sep 29 '21 at 14:54
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@Datawoman you do not need to use an if statement if you want to assign values to nulls. If you want to assign values based on a condition use numpy.select, numpy.where or df.where. If you want to assign a value to all nulls you can also use `df['Date'].fillna()` – It_is_Chris Sep 29 '21 at 15:33
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NAN stands for Not A Number, which is not appropriate for dates. It seems that checking for null is a better approach.
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