CODEWITHSUNDEEP
pythonpython-3.xpandasdataframe
MTALY
MTALY

Reputation: 1772

Conditional loop through dataframe rows with last non NAN values

I am new to python :)

I have the following dataframe, some columns are without header:

enter image description here

I am trying to do pretty basic condition for each name: like if dinner print "Good Evening".

What I did so far is trying to find the last non NAN values for each row:

df.ffill(axis=1).iloc[:, -1]

Result:

0       dinner
1        lunch
2       dinner
3        lunch
4    breakfast
5        lunch
6        lunch
7       dinner
Name: Unnamed: 11, dtype: object

Then trying to carry out the condition using for:

for index, column in df.iterrows():
        if df.ffill(axis=1).iloc[:, -1] == "dinner":
            print ("Good evening",column['Name'])

I got this error:

The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I then tried:

for i in df.ffill(axis=1).iloc[:, -1]:
    if i == "dinner":
        print ("Good evening",column['Name'])

But the condition does not work as expected!

Good evening Sara
Good evening Sara
Good evening Sara

Thanks for help

Upvotes: 0

Views: 171

Answers (1)

Henry Ecker
Henry Ecker

Reputation: 35626

last_valid_index is also an option:

m = df.apply(lambda s: s[s.last_valid_index()], axis=1).eq('dinner')

for name in df.loc[m, 'Name']:
    print('Good evening', name)

Good evening Sara
Good evening Remon
Good evening Alber

Or create a new series:

m = df.apply(lambda s: s[s.last_valid_index()], axis=1).eq('dinner')

print('Good evening ' + df.loc[m, 'Name'])

0     Good evening Sara
2    Good evening Remon
3    Good evening Alber
Name: Name, dtype: object

Upvotes: 1

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