Split column on condition in dataframe - python-3.x

Question

The data frame I am working on has a column named "Phone" and I want to split in on / or , in a way such that I get the data frame as shown below in separate columns. For example, the first row is 0674-2537100/101 and I want to split it on "/" into two columns having values as 0674-2537100 and 0674-2537101.
Input:
+-------------------------------+
| Phone |
+-------------------------------+
| 0674-2537100/101 |
| 0674-2725627 |
| 0671 – 2647509 |
| 2392229 |
| 2586198/2583361 |
| 0663-2542855/2405168 |
| 0674 – 2563832/0674-2590796 |
| 0671-6520579/3200479 |
+-------------------------------+
Output:
+-----------------------------------+
| Phone | Phone1 |
+-----------------------------------+
| 0674-2537100 | 0674-2537101 |
| 0674-2725627 | |
| 0671 – 2647509 | |
| 2392229 | |
| 2586198 | 2583361 |
| 0663-2542855 | 0663-2405168 |
| 0674 – 2563832 | 0674-2590796 |
| 0671-6520579 | 0671-3200479 |
+-----------------------------------+
Here I came up with a solution where I can take out the length of strings on both sides of the separator(/). Take out their difference. Copy the substring from the first column from character position [:difference-1] to the second column.
So far my progress is,
df['Phone'] = df['Phone'].str.replace(' ', '')
df['Phone'] = df['Phone'].str.replace('–', '-')
df[['Phone','Phone1']] = df['Phone'].str.split("/",expand=True)
df["Phone1"].fillna(value=np.nan, inplace=True)
m2 = (df["Phone1"].str.len() < 12) & (df["Phone"].str.len() > 7)
m3 = df["Phone"].str.len() - df["Phonenew"].str.len()
df.loc[m2, "Phone1"] = df["Phone"].str[:m3-1] + df["Phonenew"]
It gives an error and the column has only nan values after I run this. PLease help me out here.

Answer 1

Considering you're only going to have 2 '/' in the 'Phone' column. Here's what you can do:
'''
This fucntion takes in rows of a dataframe as an input and returns row with appropriate values.
'''
def split_phone_number(row):
split_str=row['Phone'].split('/')
# Considering that you're only going to have 2 or lesser values, update
# the passed row's columns with appropriate values.
if len(split_str)>1:
row['Phone']=split_str[0]
row['Phone1']=split_str[1]
else:
row['Phone']=split_str[0]
row['Phone1']=''
# Return the updated row.
return row
# Making a dummy dataframe.
d={'Phone':['0674-2537100/101','0674-257349','0671-257349','257349','257349/100','101/100','5688343/438934']}
dataFrame= pd.DataFrame(data=d)
# Considering you're only going to have one extra column. adding that column to dataframe.
dataFrame=dataFrame.assign(Phone1=['' for i in range(dataFrame.shape[0])])
# applying the split_phone_number function to dataframe.
dataFrame=dataFrame.apply(split_phone_number,axis=1)
# Prinitng dataframe.
print(dataFrame)
Input:
+---------------------+
| Phone |
+---------------------+
| 0 0674-2537100/101 |
| 1 0674-257349 |
| 2 0671-257349 |
| 3 257349 |
| 4 257349/100 |
| 5 101/100 |
| 6 5688343/438934 |
+---------------------+
Output:
+----------------------------+
| Phone Phone1 |
+----------------------------+
| 0 0674-2537100 101 |
| 1 0674-257349 |
| 2 0671-257349 |
| 3 257349 |
| 4 257349 100 |
| 5 101 100 |
| 6 5688343 438934 |
+----------------------------+
For further reading:
dataframe.apply()
Hope this helps. Cheers!