Converting DataFrame to dictionary with header as key and column as array with values

Question

I have a dataframe as follows:

I want a dictionary like this:

{A: [1,2,3,4], B:[6,5,4,2], C:[1,7,9,2]}

I have tried using the normal df.to_dict() and it is no where close. If I use the transposed dataframe, hence df.T.to_dict() it gets close, but I have something like this:

{0: {A: 1, B: 6, C:1} , ... , 4:{A: 4, B: 2, C: 2 } }

The questions in stack overflow are limited to the dictionary having one value per key, not an array.

It would be very valuable for me to use to_dict() and avoid any for loop, since the database I am using is quite big and I want the computational complexity to be as low as possible.

@piRSquared It worked, you should put it as an official answer. — Antonio López Ruiz
– Antonio López Ruiz, Commented Jun 25, 2019 at 21:25
By the way, would it be possible to preserve it as a numpy array and not just a list? — Antonio López Ruiz
– Antonio López Ruiz, Commented Jun 25, 2019 at 21:27

piRSquared · Accepted Answer · 2019-06-25 21:27:15Z

5

`orient='list'`

df.to_dict(orient='list')

Or: the method actually just checks the first character

df.to_dict('l')

If you want to preserve the numpy array

{k: v.to_numpy() for k, v in df.items()}

answered Jun 25, 2019 at 21:27

piRSquared

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2 Comments

Antonio López Ruiz Over a year ago

Would the computational cost increase a lot if you preserve it as a numpy array?

piRSquared Over a year ago

Not really. That should be pretty snappy.

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`orient='list'`