Numpy: ValueError: cannot convert float NaN to integer (Python)

Question

I want to insert NaN at specific locations in A . However, there is an error. I attach the expected output.

import numpy as np
from numpy import NaN

A = np.array([10, 20, 30, 40, 50, 60, 70])
C=[2,4]

A=np.insert(A,C,NaN,axis=0)
print("A =",[A])

The error is

<module>
    A=np.insert(A,C,NaN,axis=0)

  File "<__array_function__ internals>", line 5, in insert

  File "C:\Users\USER\anaconda3\lib\site-packages\numpy\lib\function_base.py", line 4678, in insert
    new[tuple(slobj)] = values

ValueError: cannot convert float NaN to integer

The expected output is

[array([10, 20,  NaN, 30, 40,  NaN, 50, 60, 70])]

Answer 1

Designate a type for your array of float32 (or float16 , float64 , etc. as appropriate)

import numpy as np

A = np.array([10, 20, 30, 40, 50, 60, 70], dtype=np.float32)
C=[2,4]

A=np.insert(A,C,np.NaN,axis=0)
print("A =",[A])

A = [array([10., 20., nan, 30., 40., nan, 50., 60., 70.], dtype=float32)]

Answer 2

The way to fix this error is to deal with the NaN values before attempting to convert the column from a float to an integer.

you can follow the steps as follows

We can use the following code to first identify the rows that contain NaN values:

 #print rows in DataFrame that contain NaN in 'rebounds' column print(df[df['rebounds'].isnull()]) points assists rebounds 1 12 7 NaN 5 23 9 NaN

We can then either drop the rows with NaN values or replace the NaN values with some other value before converting the column from a float to an integer:

Method 1: Drop Rows with NaN Values

 #drop all rows with NaN values df = df.dropna() #convert 'rebounds' column from float to integer df['rebounds'] = df['rebounds'].astype(int) #view updated DataFrame df points assists rebounds 0 25 5 11 2 15 7 10 3 14 9 6 4 19 12 5 6 25 9 9 7 29 4 12 #view class of 'rebounds' column df['rebounds'].dtype dtype('int64')

Method 2: Replace NaN Values

 #replace all NaN values with zeros df['rebounds'] = df['rebounds'].fillna(0) #convert 'rebounds' column from float to integer df['rebounds'] = df['rebounds'].astype(int) #view updated DataFrame df points assists rebounds 0 25 5 11 1 12 7 0 2 15 7 10 3 14 9 6 4 19 12 5 5 23 9 0 6 25 9 9 7 29 4 12 #view class of 'rebounds' column df['rebounds'].dtype dtype('int64')

good luck