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.

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)