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Replace spaces in a numpy array

The third column in my numpy array is Age. In this column, about 75% of the entries are valid and 25% are empty. Column 2 is gender-specific and uses some manipulation. I calculated the average age of men in my dataset to be 30. The average age of women in my dataset is 28.

I want to replace all the empty Age values ​​for men with 30, and for all white values ​​for women - 28.

However, I cannot do this. Does anyone have a suggestion or know what I'm doing wrong?

Here is my code:

# my entire data set is stored in a numpy array defined as x

ismale = x[::,1]=='male'
maleAgeBlank = x[ismale][::,2]==''
x[ismale][maleAgeBlank][::,2] = 30

For some reason, when I finished with the code above, I type xto display the data set, and spaces still exist, although I set them to 30. Please note that I cannot do x[maleAgeBlank]this because some of the list will include female data, since female data are not yet excluded.

Is there a way to get what I want? For some reason, if I do x[ismale][::,1] = 1(setting the column with "male" to 1), this works, but it x[ismale][maleAgeBlank][::,2] = 30doesn't work.

array pattern:

#output from typing x
array([['3', '1', '22', ..., '0', '7.25', '2'],
   ['1', '0', '38', ..., '0', '71.2833', '0'],
   ['3', '0', '26', ..., '0', '7.925', '2'],
   ..., 
   ['3', '0', '', ..., '2', '23.45', '2'],
   ['1', '1', '26', ..., '0', '30', '0'],
   ['3', '1', '32', ..., '0', '7.75', '1']], 
  dtype='<U82')

#output from typing x[0]

array(['3', '1', '22', '1', '0', '7.25', '2'], 
  dtype='<U82')

Note that I changed column 2 as 0 for female and 1 for men already at the output above

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3 answers

How about this:

my_data =  np.array([['3', '1', '22', '0', '7.25', '2'],
                     ['1', '0', '38', '0', '71.2833', '0'],
                     ['3', '0', '26', '0', '7.925', '2'],
                     ['3', '0', '', '2', '23.45', '2'],
                     ['1', '1', '26', '0', '30', '0'],
                     ['3', '1', '32', '0', '7.75', '1']], 
                     dtype='<U82')

ismale = my_data[:,1] == '0'
missing_age = my_data[:, 2] == ''
maleAgeBlank = missing_age & ismale
my_data[maleAgeBlank, 2] = '30'

Result:

>>> my_data
array([[u'3', u'1', u'22', u'0', u'7.25', u'2'],
       [u'1', u'0', u'38', u'0', u'71.2833', u'0'],
       [u'3', u'0', u'26', u'0', u'7.925', u'2'],
       [u'3', u'0', u'30', u'2', u'23.45', u'2'], 
       [u'1', u'1', u'26', u'0', u'30', u'0'],
       [u'3', u'1', u'32', u'0', u'7.75', u'1']], 
      dtype='<U82')
+1

where:

arr = array([['3', '1', '22', '1', '0', '7.25', '2'], 
            ['3', '', '22', '1', '0', '7.25', '2']], 
           dtype='<U82')

blank = np.where(arr=='')

arr[blank] = 20

array([[u'3', u'1', u'22', u'1', u'0', u'7.25', u'2'],
       [u'3', u'20', u'22', u'1', u'0', u'7.25', u'2']], 
      dtype='<U82')

, :

male = np.where(arr[:, 1]=='') # where 1 is the column
arr[male] = 30

female = np.where(arr[:, 2]=='') # where 2 is the column
arr[female] = 28
+1

. , .

for row in range(len(x)):
    if row[2] == '':
        if row[1] == 1:
            row[2] == 30
        else:
            row[2] == 28
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