Replace boolean values with integers in numPy array

Question

I am working on determining correlation for a set of data containing boolean values. The ideal situation would be to replace all instances of booleans with 1's and 0's. How can I most efficiently parse through my numPy array and replace these values? Bellow is what I have to work with and the output...

def findCorrelation(csvFileName):
    data = pd.read_csv(csvFileName)
    data = data.values
    df = pd.DataFrame(data=data)
    npList = np.asarray(df)
    print npList
    print df.corr()

Output:

   [[320 True]
     [400 False]
     [350 True]
     [360 True]
     [340 True]
     [340 True]
     [425 False]
     [380 False]
     [365 True]]
    Empty DataFrame
    Columns: []
    Index: []
    Success

Process finished with exit code 0

If your array is arr, then what you need is arr.astype(int). — DYZ
– DYZ, Commented May 31, 2017 at 2:14

0xA2C2A · Accepted Answer · 2017-05-31 02:16:41Z

5

The function you're looking for is astype (documentation).

Example:

import numpy as np

a = np.asarray([[320, True], [400, False], [350, True], [360, True], [340, True], [340, True], [425, False], [380, False], [365, True]]).astype(int)

print (a)

Output:

[[320   1]
 [400   0]
 [350   1]
 [360   1]
 [340   1]
 [340   1]
 [425   0]
 [380   0]
 [365   1]]

answered May 31, 2017 at 2:16

0xA2C2A

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