panda read_csv() converting imaginary to real

Question

After calling a file using pandas by this two lines:

import pandas as pd
import numpy as np
df = pd.read_csv('PN_lateral_n_eff.txt', header=None)
df.columns = ["effective_index"]

here is my output:

                         effective_index
0  2.568393573877396+1.139080496494329e-006i
1  2.568398351899841+1.129979376397734e-006i
2  2.568401556986464+1.123872317134941e-006i

after that, i can not use the numpy to convert it into a real number. Because, panda dtype was object. I tried this:

np.real(df, dtype = float)

TypeError: real() got an unexpected keyword argument 'dtype'

Any way to do that?

Possible duplicate of [How to read complex numbers from file with NumPy?](https://stackoverflow.com/questions/16659818/how-to-read-complex-numbers-from-file-with-numpy) — rafaelc, Apr 11 '18 at 21:57
`np.real` docs say it only takes one argument. Why did you specify a `dtype`? — hpaulj, Apr 11 '18 at 22:06
I tried with one only by slicing the array. It didnt work unfortunately — Khalilrazu, Apr 11 '18 at 22:10
And that 'complex' number, is that really complex or is it a string? `(2+3.12j)` is the normal Python display format. — hpaulj, Apr 11 '18 at 22:28
I guess the complex number is a string while panda is reading that from txt — Khalilrazu, Apr 11 '18 at 23:53

DYZ · Answer 1 · 2018-04-11T22:30:27.417

Looks like astype(complex) works with Numpy arrays of strings, but not with Pandas Series of objects:

cmplx = df['effective_index'].str.replace('i','j')\ # Go engineering
                             .values\               # Go NumPy
                             .astype('str')\        # Go string
                             .astype(np.complex)    # Go complex
#array([ 2.56839357 +1.13908050e-06j,  2.56839835 +1.12997938e-06j,
#        2.56840156 +1.12387232e-06j])
df['effective_index'] = cmplx # Go Pandas again

panda read_csv() converting imaginary to real

1 Answers1