lambda function on list in dataframe column error

Question

I have a list of numbers inside of a pandas dataframe and i am trying to use a lambda function + list comprehension to remove values from these lists.

col 1 col2

a [-1, 2, 10, 600, -10]

b [-0, -5, -6, -200, -30]

c .....

etc.

df.col2.apply(lambda x: [i for i in x if i>= 0]) #just trying to remove negative values

numbers are always ascending and can be all negative, all positive or a mix. lists are about 200 items long all integers.

I get this error:

TypeError: 'numpy.float64' object is not iterable

Edit: when i do it this way it works [i for i in df[col2][#] if i >= 0] i guess i could run this through a for loop.. seems slow though

Edit2: looking at it with fresh eyes. turns out that the column isn't entirely made up of lists there are a few float values spread throughout (duh). Something weird was going on with the merge, once i corrected that the code above worked as expected. Thanks for the help!

The error says what your problem is. Try making the lambda a variable, convert your column into a list, then pass it through. — A random coder
– A random coder, Commented Dec 3, 2020 at 20:49
the error makes no sense to me. lambda x is a list why would there be a type error? if i do: ``` [i for i in df[col2][#] if i >= 0] ``` this works — Reison
– Reison, Commented Dec 3, 2020 at 20:52
Is every item in column 2 a list of numbers or is there any instance in which its a single float instead of a list? — Whip
– Whip, Commented Dec 3, 2020 at 22:11

Amir saleem · Accepted Answer · 2020-12-03 22:14:29Z

1

Because x in your lambda is a float number, and you cant loop over float :p. if you need to do so. you can

In [2]: np.random.seed(4)
   ...: df = pd.DataFrame(np.random.randint(-5,5, 7)).rename(columns={0:"col2"})
   ...: df.col2 = df.col2.astype(float)
   ...: df
Out[2]: 
   col2
0   2.0
1   0.0
2  -4.0
3   3.0
4   2.0
5   3.0
6  -3.0

In [3]: df.col2.apply(lambda x: x if x > 0 else None).dropna()
Out[3]: 
0    2.0
3    3.0
4    2.0
5    3.0
Name: col2, dtype: float64

answered Dec 3, 2020 at 22:14

Amir saleem

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2 Comments

Mad Physicist Over a year ago

Why not just df.col2[df.col2 >= 0]?

Amir saleem Over a year ago

Because df.col2[df.col2 >= 0] return only >= 0 values so you can't append resulting column to existing df, , on the other hand df.col2.apply(lambda x: x if x > 0 else None) return the same length so you can append it to existing df.

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