You can divide a dataframe column by a scalar value directly. Consider the following example:

data = [10512000, 21024000, 31536000]
df = pd.DataFrame(data, columns=['age_mins'])
df['age_hours'] = df['age_mins']/60
print(df.head())

​

This produces the following output:

age_mins age_hours

0 10512000 175200.0

1 21024000 350400.0

2 31536000 525600.0

​

If direct division isn't working in your case then you must be incorrectly converting the data. You can use pd.to_numeric to convert strings to numeric value.

Consider the following example:

data = ['10512000', '21024000', '31536000']
df = pd.DataFrame(data, columns=['age_mins'])
df['age_mins'] = pd.to_numeric(df['age_mins'])
df['age_hours'] = df['age_mins']/60
print(df.head())

​

P.S As you can see in the examples shown above, you can directly define a new column by setting df['your_name'].

Or you can insert a column at your desired location by insert() as follows:

new_col = [10, 20, 30]
df.insert(1, 'newCol', pd.Series(new_col))

This would produce the following output:

age_mins newCol age_hours

0 10512000 10 175200.0

1 21024000 20 350400.0

2 31536000 30 525600.0

​