Extract non- empty values from the regex array output in python
Question: I have a column of type numpy.ndarray which looks like:

    
    
             col
        ['','','5','']
        ['','8']
        ['6','','']
        ['7']
        []
        ['5']
    

I want the ouput like this :

    
    
             col
              5
              8
              6
              7
              0
              5
    

How can I do this in python.Any help is highly appreciated.


Answer: To convert the data to numeric values you could use:

    
    
    import numpy as np
    import pandas as pd
    data = list(map(np.array, [ ['','','5',''], ['','8'], ['6','',''], ['7'], [], ['5']]))
    df = pd.DataFrame({'col': data})
    df['col'] = pd.to_numeric(df['col'].str.join('')).fillna(0).astype(int)
    print(df)
    

yields

    
    
       col
    0    5
    1    8
    2    6
    3    7
    4    0
    5    5
    

* * *

To convert the data to strings use:

    
    
    df['col'] = df['col'].str.join('').replace('', '0')
    

The result looks the same, but the dtype of the column is `object` since the
values are strings.

* * *

If there is more than one number in some rows and you wish to pick the
largest, then you'll have to loop through each item in each row, convert each
string to a numeric value and take the max:

    
    
    import numpy as np
    import pandas as pd
    data = list(map(np.array, [ ['','','5','6'], ['','8'], ['6','',''], ['7'], [], ['5']]))
    df = pd.DataFrame({'col': data})
    df['col'] = [max([int(xi) if xi else 0 for xi in x] or [0]) for x in df['col']]
    print(df)
    

yields

    
    
       col
    0    6   # <-- note  ['','','5','6'] was converted to 6
    1    8
    2    6
    3    7
    4    0
    5    5
    

* * *

For versions of pandas prior to 0.17, you could use `df.convert_objects`
instead:

    
    
    import numpy as np
    import pandas as pd
    data = list(map(np.array, [ ['','','5',''], ['','8'], ['6','',''], ['7'], [], ['5']]))
    df = pd.DataFrame({'col': data})
    df['col'] = df['col'].str.join('').replace('', '0')
    df = df.convert_objects(convert_numeric=True)
    

