ValueError: 'object too deep for desired array'

Question

I have a ValueError: 'object too deep for desired array' in a Python program. I have this error while using numpy.digitize.
I think it's how I use Pandas DataFrames:
To keep it simple (because this is done through an external library), I have a list in my program but the library needs a DataFrame so I do something like this:

ts = range(1000)
df = pandas.DataFrame(ts)
res = numpy.digitize(df.values, bins)

But then it seems like df.values is an array of lists instead of an array of floats. I mean:

array([[   0],
   [   1],
   [   2],
   ..., 
   [997],
   [998],
   [999]], dtype=int64)

Help please, I spent too much time on this.

I think if you upgrade to the latest version of numpy(1.11), you could pass an array of any shape to np.digitize — Nickil Maveli
– Nickil Maveli, Commented Sep 13, 2016 at 16:13
Does this answer your question? What does "ValueError: object too deep for desired array" mean and how to fix it? — mkrieger1
– mkrieger1, Commented Feb 2, 2022 at 13:03

Kartik · Accepted Answer · 2016-09-13 15:49:48Z

4

Try this:

numpy.digitize(df.iloc[:, 0], bins)

You are trying to get the values from a whole DataFrame. That is why you get the 2D array. Each row in the array is a row of the DataFrame.

answered Sep 13, 2016 at 15:49

Kartik

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2 Comments

othymomo Over a year ago

This worked but I actually don't understand it. Can I create the DataFrame so that I get a 1D array (maybe transpose the initial time series)?

Kartik Over a year ago

Create a Series instead then (s = pandas.Series(ts)). Series are always 1D. It depends on your use case. If you get a DataFrame, you need to index it such that you pass only a column to digitize. If your data is 1D, you can use Series from the outset.

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