NaN values in Pandas are not being filled by the interpolate function when it's applied to a full dataframe

So, i'm a beginner at the Pandas Python and noticed the interpolate function is pretty interesting, but i have one problem when using the line:

result = df.interpolate(method='linear')

I found out that even though it did filled a lot of the NaN's in 'df', the four first NaN's of Ambient_temp and the first NaN on the Intake_temp column are not filled the way i wanted. Any hints on how to get this working? The interpolation worked very well with every other column besides those two.

Image of said dataframe

Example:

amb_temp = [np.nan, np.nan, 32, 32]
in_temp = [ 29, 27, 23, 22]
volts = [np.nan, 13, 11, 11]

dict = {'ambient_temperature': amb_temp, 'temperature_inside': in_temp, 'volts': volts} 

df = pd.DataFrame(dict)

(it's not exactly the same dataframe, but encapsulates the same problem. I got this one based off and example on 'geeksforgeeks' and used numpy.nan to simulate the absence of data.)

This is it:

import numpy as np
import pandas as  pd
amb_temp = [np.nan, np.nan, 32, 32]
in_temp = [ 29, 27, 23, 22]
volts = [np.nan, 13, 11, 11]

dict1 = {'ambient_temperature': amb_temp, 'temperature_inside': in_temp, 'volts': volts} 
keys = list(dict1.keys())

for k in keys:
    data_array = np.array(dict1[k])
    print("1   {}".format(data_array))
    not_nan = ~np.isnan(data_array)
    indices = np.arange(len(data_array))
    dict1[k] = np.interp(indices, indices[not_nan], data_array[not_nan])
print(dict1)