Add single quotes around items in string & integer columns in a Python Pandas Dataframe

Question

I needed to add single quotes around each item for 2 different columns in a pandas dataframe. One column is has integer values, the other has string values. Then I wanted to put the items with single quotes around them into a new column.

I tried multiple suggestions on stackoverflow using a for loop with numpy's savetxt method. (I don't need to use numpy) I tried Regex. Couldn't get it to exactly work.

import pandas as pd
import numpy as np
data = {"id": [101, 102, 103, 104, 105],
        "person": ['Ty', 'Al', 'Lou', 'Tao', 'Mick']}
df = pd.DataFrame(data)
id_in_quotes=[] #Wanted to put the new items with single quotes into an empty list and put into a new column
person_in_quotes=[] #Wanted to put the new items with single quotes into an empty list and put into a new column
for x in df: #DOES NOT WORK
   np.savetxt('text.txt',x, fmt='%r') #DOES NOT WORK
   x.append(id_in_quotes)#DOES NOT WORK

In the end, wanted to see 4 columns: id, person, id_with_quotes, person_with_quotes. Columns id and person remain the same. Columns id_with_quotes, person_with_quotes are id and person with each item wrapped in single quotes.

Answer 1

You could achieve this using DataFrame.applymap and DataFrame.merge like this:

df_new = (df.merge(
            df.astype(str).applymap(lambda x: "'" + x + "'"),
            left_index=True, right_index=True,
            suffixes=('', '_with_quotes')))

print(df_new)

    id person id_with_quotes person_with_quotes
0  101     Ty          '101'               'Ty'
1  102     Al          '102'               'Al'
2  103    Lou          '103'              'Lou'
3  104    Tao          '104'              'Tao'
4  105   Mick          '105'             'Mick'

Answer 2

You could do something like this if I'm reading your question right. Basically going through each column and adding quotation marks to the start and end of each item in the column. Convert to str both cases just to be safe.

import pandas as pd

data = {"id": [101, 102, 103, 104, 105],
    "person": ['Ty', 'Al', 'Lou', 'Tao', 'Mick']}
df = pd.DataFrame(data)

df['id_w_quotes'] = df['id'].apply(lambda x: "'" + str(x) + "'")
df['person_w_quotes'] = df['person'].apply(lambda x: "'" + str(x) + "'")

df.head()

Which gives this output

    id  person id_w_quotes  person_w_quotes
0   101 Ty      '101'           'Ty'
1   102 Al      '102'           'Al'
2   103 Lou     '103'           'Lou'
3   104 Tao     '104'           'Tao'
4   105 Mick    '105'           'Mick'