Numpy isnan() fails on an array of floats (from pandas dataframe apply)

Question

I have an array of floats (some normal numbers, some nans) that is coming out of an apply on a pandas dataframe.

For some reason, numpy.isnan is failing on this array, however as shown below, each element is a float, numpy.isnan runs correctly on each element, the type of the variable is definitely a numpy array.

What’s going on?!

set([type(x) for x in tester])
Out[59]: {float}

tester
Out[60]: 
array([-0.7000000000000001, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan], dtype=object)

set([type(x) for x in tester])
Out[61]: {float}

np.isnan(tester)
Traceback (most recent call last):

File "<ipython-input-62-e3638605b43c>", line 1, in <module>
np.isnan(tester)

TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

set([np.isnan(x) for x in tester])
Out[65]: {False, True}

type(tester)
Out[66]: numpy.ndarray

Asked By: tim654321

||

Source

Answer 1

np.isnan can be applied to NumPy arrays of native dtype (such as np.float64):

In [99]: np.isnan(np.array([np.nan, 0], dtype=np.float64))
Out[99]: array([ True, False], dtype=bool)

but raises TypeError when applied to object arrays:

In [96]: np.isnan(np.array([np.nan, 0], dtype=object))
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Since you have Pandas, you could use pd.isnull instead — it can accept NumPy arrays of object or native dtypes:

In [97]: pd.isnull(np.array([np.nan, 0], dtype=float))
Out[97]: array([ True, False], dtype=bool)

In [98]: pd.isnull(np.array([np.nan, 0], dtype=object))
Out[98]: array([ True, False], dtype=bool)

Note that None is also considered a null value in object arrays.

Answered By: unutbu

Answer 2

On top of @unutbu answer, you could coerce pandas numpy object array to native (float64) type, something along the line

import pandas as pd
pd.to_numeric(df['tester'], errors='coerce')

Specify errors=’coerce’ to force strings that can’t be parsed to a numeric value to become NaN. Column type would be dtype: float64, and then isnan check should work

Answered By: Severin Pappadeux

Answer 3

A great substitute for np.isnan() and pd.isnull() is

for i in range(0,a.shape[0]):
    if(a[i]!=a[i]):
       //do something here
       //a[i] is nan

since only nan is not equal to itself.

Answered By: Statham

Answer 4

Make sure you import csv file using Pandas

import pandas as pd

condition = pd.isnull(data[i][j])

Answered By: Dariswan Janweri P.

Answer 5

Just answer this for a reminder of myself. It took me a whole day to solve.
After digging deep into the code, I found that in _encodepy.py:

if values.dtype.kind in 'UO':
    # correct branch
else
    # wrong branch, if in this branch whatever data you give it will produce the error
    if np.isnan(known_values).any(): # here is problematic line

so the solution is very simple, just astype your data with np.object

Answered By: Crazy Mulberry

Numpy isnan() fails on an array of floats (from pandas dataframe apply)

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