Check whether a DataFrame or ndrray contains digits

Question

It is few hours I am stuck with this: I have a DataFrame containing a list of email addresses, from those email addresses I want to check whether in the mail is contained or not a number I.E. [email protected], if yes I want this number to be appended to an array:

I have tried both with a DataFrame, and also a ndarray woth numpy, but it does not work. This is what i am trying to do:

mail_addresses = pd.DataFrame(customers_df.iloc[:,0].values)
mail_addresses = mail_addresses.dropna(axis = 0, how= 'all')
mail_addresses_toArray = mail_addresses.values

for i in mail_addresses:
dates =[]
if any(i.isdigit()) == True:
    dates.append(i)
    print(dates)

I think my problem is that I don't know how I can convert all the elements in this array to string so that the method isdigit() would work and iterate through all the elements inside (825 mail addresses).

When running the code above this is the error i get:

AttributeError: 'numpy.int64' object has no attribute 'isdigit'

Meanwhile, if i try with the numpy array (mail_addresses_toArray) this is the error:

AttributeError: 'numpy.ndarray' object has no attribute 'isdigit'

Answer 1

Use extract if each mail contains only one number or findall if there is possible multiple ones:

customers_df = pd.DataFrame({'A':['[email protected]','[email protected]',
                                  '[email protected]','[email protected]'],
                   'B':[4,5,4,5],
                   'C':[7,8,9,4]})

print (customers_df)
                        A  B  C
0  [email protected]  4  7
1          [email protected]  5  8
2             [email protected]  4  9
3           [email protected]  5  4

L = customers_df.iloc[:,0].str.extract('(\d+)', expand=False).dropna().astype(int).tolist()
print (L)
[123, 123, 23]

L = np.concatenate(customers_df.iloc[:,0].str.findall('(\d+)')).astype(int).tolist()
print (L)
[123, 123, 23, 55]

Answer 2

Here is one way.

import pandas as pd

df = pd.DataFrame({'A': ['[email protected]', '[email protected]',
                         '[email protected]', None]})

s = df['A'].dropna()

t = s.map(lambda x: ''.join([i for i in x if i.isdigit()]).strip())
res = t.loc[t != ''].map(int).tolist()

# [123, 43]