In Pandas, I am trying to apply this lambda function, using .apply() and I am getting this error: 'ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()'.
What am I doing wrong?
(beers[:10] - beers.mean()).apply(lambda x: 'low' if x < 0 else 'high')
beers is a series.
Thanks!
beers is not a Series else you code work well. It's certainly a DataFrame and probably a DataFrame with only one column.
Demo:
>>> beers = pd.Series(np.random.randint(1, 10, 20))
>>> type(beers)
pandas.core.series.Series
>>> (beers[:10] - beers.mean()).apply(lambda x: 'low' if x < 0 else 'high')
0 high
1 low
2 high
3 low
4 high
5 high
6 low
7 high
8 high
9 low
dtype: object
Now if beers is a DataFrame:
>>> beers = beers.to_frame()
>>> type(beers)
pandas.core.frame.DataFrame
>>> (beers[:10] - beers.mean()).apply(lambda x: 'low' if x < 0 else 'high')
...
ValueError: The truth value of a Series is ambiguous.
Use a.empty, a.bool(), a.item(), a.any() or a.all().
In the case where beers has only one column, you can use squeeze:
>>> (beers[:10] - beers.mean()).squeeze().apply(lambda x: 'low' if x < 0 else 'high')
0 high
1 low
2 high
3 low
4 high
5 high
6 low
7 high
8 high
9 low
Name: 0, dtype: object
I think what you are trying to do is this if beers is a series if it is a df you will need column name as well
beers_mean = beers.mean()
beers[:10].apply(lambda x: 'low' if (x-beers_mean)<0 else 'high'
it cant resultve the beers[:10] - beers.mean() into a new series that why you got an exception
if beers is a DF and you want to do this on a single column in the table then just picked it first
beer_col = beers[['col_name']]
beers_mean = beer_col .mean()
beer_col[:10].apply(lambda x: 'low' if (x-beers_mean)<0 else 'high'
