filter values in a dataframe column based on null values in a different column python dataframe - dataframe

Question

I've been stuck on this for a bit so hopefully someone has better guidance.
I currently have a dataframe that looks something like this(only way more rows):
|"released_date"| "status" |
+-------------+--------+
| 12/12/20 |released|
+-------------+--------+
| 10/01/20 | NaN |
+-------------+--------+
| NaN | NaN |
+-------------+--------+
| NaN. |released|
+-------------+--------+
I wanted to do df['status'].fillna('released' if df.released_date.notnull())
aka, fill any Nan value in the status column of df with "released" as long as df.released_date is't a null value.
I keep getting various error messages when I do this though in different variations, first for the code above is a syntax error, which I imagine is because notnull() returns a boolean array?
I feel like there is a simple answer for this and I somehow am not seeing it. I haven't found any questions like this where I'm trying to organize something based on the null values in a dataframe, which leads me to wonder if my methodology isn't ideal in the first place? How can I filter values in a dataframe column based on null values in a different column without using isnull() or notnull() if those only return boolean arrays anyways? using == Null doesn't seem to work either...

Answer 1

Try:
idx = df[(df['status'].isnull()) & (~df['released_date'].isnull())].index
df.loc[idx,'status'] = 'released'
First get the index of all rows with 'status' equals null and 'released_date' notequals null. Then use df.loc to update the status column.
Prints:
released_date status
0 12/12/20 released
1 10/01/20 released
2 NaN NaN
3 NaN released