Python numpy array operations

Question

I have some hard times learning Python array handling with numpy.

I have a .csv file which contains in one column unsigned integer data which represents binary values from an analog digital converter. I would like to convert this unsigned integer values in 12 bit binary representation using Python inside a jupyter notebook.

I tried several ways of implementing it, but I still fail...

here is my code:

import pandas as pd
df = pd.read_csv('my_adc_values.csv', delimiter ='\s+', header=None, usecols=[19])
decimalValues = df.values
print(decimalValues.shape)

so far so good... I have all my adc data column values in the decimalValues numpy array.

Now, I would like to iterate through the array and convert the integers in the array to a binary representation:

import numpy as np

# destination array of shape of source array
binaryValues = np.zeros(decimalValues.shape)

for i in range(len(decimalValues)):
    print(decimalValues[i])
    binaryValues[i]=(bin(decimalValues[i]))    

print(binaryValues)

With this code I get the error message

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-10-890444040b2e> in <module>()
      6 for i in range(len(decimalValues)):
      7     print(decimalValues[i])
----> 8     binaryValues[i]=(bin(decimalValues[i]))
      9 
     10 print(binaryValues)

TypeError: only integer scalar arrays can be converted to a scalar index

I tried several different solutions, but none of them worked. It seems as if I have a massive misunderstanding of numpy arrays.

I'm looking for a tip on how to solve my described problem. I found some threads, describing the the mentioned error message. I suspected, it had something to do with the shape of the source/destination arrays. therefore, I initialized the destination array with the same shape as the source. It did not help...

Thank you, Maik

Answer 1

Numpy is primarily for working with numeric data, it doesn't give you much benefit when you're working with strings. Numpy can convert integers to decimal or hexadecimal strings, using the numpy.char.mod function, which utilises the old % string interpolation operator. Unfortunately, that doesn't support binary output. We can create a Numpy vectorized function that uses the standard Python format function to do the conversion. This is better than bin, since you don't get the leading '0b', and you can specify the minimum length.

import numpy as np

# Make some fake numeric data
nums = (1 << np.arange(1, 10)) - 1
print(nums)

# Convert to 12 bit binary strings
func = np.vectorize(lambda n: format(n, '012b'))
bins = func(nums)
print(bins)

output

[  1   3   7  15  31  63 127 255 511]
['000000000001' '000000000011' '000000000111' '000000001111' '000000011111'
 '000000111111' '000001111111' '000011111111' '000111111111']

Alternatively, do the conversion using plain Python. You can convert the result back to a Numpy array, if you really need that. This code uses the str.format method, rather than the format function used by the previous version.

bins = list(map('{:012b}'.format, nums))

Answer 2

What is causing the error in your case is that you are trying to apply a bin function on a slice, whereas it can only be applied on a single value. You might need an extra for loop to iterate over column values. Try changing your code in this way:

for i in range(len(decimalValues)):
    for j in range(decimalValues.shape[1]):     
        print(decimalValues[i])
        binaryValues[i, j]=(bin(decimalValues[i, j]))  
print(binaryValues)

Let me know if it works!