CODEWITHSUNDEEP
python-3.xpandasif-statement
singularity2047
singularity2047

Reputation: 1071

If else condition doesn't work on column with None value

I have an output table for my python script which, depending on the month can have a single record with none value or multiple records with proper values. My code converts the salary amount to $ and , format. But if there is a None in the Salary column this won't work. How can I convert this salary column conditionally? I have provided sample data and what I tried so far.

sample data

d1 = {'name': ['john', 'tom'], 'Address': ['NY', 'CA'], 'Salary' : ['5000', '6000']}
df1 = pd.DataFrame(data = d1)

my code

if df1['Salary'] is None:
    pass
else:
    df1['Salary'] = df1['Salary'].astype(int)
    df1['Salary'] = df1.apply(lambda x: "${:,}".format(x['Salary']), axis=1)

This is the output I want

    Address Salary  name
0   NY      $5,000  john
1   CA      $6,000  tom

But if the salary column has None in it then this code doesn't work.

d2 = {'name': ['john', 'tom'], 'Address': ['NY', 'CA'], 'Salary' : ['5000', None]}
df2 = pd.DataFrame(data = d2)

I get the following error:

TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

I understand the error, a column can not be simultaneously integer and also have None in it, let alone the formatted value. How can I get the output I am looking for even if there is None value in my column ?

Upvotes: 1

Views: 73

Answers (1)

Jondiedoop
Jondiedoop

Reputation: 3353

Only apply the function to the value in the column that are not None:

d2 = {'name': ['john', 'tom'], 'Address': ['NY', 'CA'], 'Salary' : ['5000', None]}
df2 = pd.DataFrame(data = d2)
df2.loc[df2['Salary'].notnull(), 'Salary'] = (df2.loc[df2['Salary'].notnull(), 'Salary']
                                            .apply(lambda x: "${:,}".format(int(x))))

#   name Address  Salary
# 0  john      NY  $5,000
# 1   tom      CA    None

Upvotes: 2

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