Create column based on percentage of recurring customers

So I'm fairly new to Python but I've managed to answer my own question. Can't say this is the best, easiest, fastest way but it surely helped.

First of all I made a new dataframe which is an exact copy of the original dataframe, but only with 'True' values of the column 'recurring_customer'. I did that by using the following code:

df_recurring_customers = df.loc[df['recurring_customer'] == True]

It gave me the following dataframe:

df_recurring_customers.head()
    {
        "date_created" ["2019-11-25", "2019-11-28", "2019-12-02", "2019-12-09", "2019-12-11"]
        "customer_id": ["577", "6457", "577", "6647", "840"],
        "total": ["33891.12", "81.98", "9937.68", "1166.28", "2969.60"],
        "recurring_customer": ["True", "True", "True", "True", "True"],
    }
)

Then I resampled the values using:

df_recurring_customers_monthly_sum = df_recurring_customers.resample('1M').sum()

I then dropped the 'number' and 'customer_id' column, which had no value. The next step was to join the two dataframes 'df_monthly' and 'df_recurring_customers_monthly_sum' using:

df_total = df_recurring_customers_monthly_sum.join(df_monthly)

This gave me:

| date_created | total      | recurring_customer_total |
| ------------ | ---------- | ------------------------ |
|  2019-11-30  | 644272.02  |         33973.10         |
|  2019-12-31  | 612205.99  |         15775.29         |
|  2020-01-31  | 887761.60  |         61612.27         |
|  2020-02-29  | 910724.75  |         125315.31        |
|  2020-03-31  | 1174662.59 |         125315.31        |
|  2020-04-30  | 1399332.26 |         248277.97        |

Then I wanted to know the percentage so

df_total['total_recurring_customer_percentage'] = (df_total['recurring_customer_total'] / df_total['total']) * 100

Which gave me:

| date_created | total      | recurring_customer_total | recurring_customer_total_percentage | 
| ------------ | ---------- | ------------------------ | ----------------------------------- |
|  2019-11-30  | 644272.02  |         33973.10         |        5.273099
|  2019-12-31  | 612205.99  |         15775.29         |        2.576794
|  2020-01-31  | 887761.60  |         61612.27         |        6.940182
|  2020-02-29  | 910724.75  |         125315.31        |        13.759954
|  2020-03-31  | 1174662.59 |         125315.31        |        13.967221
|  2020-04-30  | 1399332.26 |         248277.97        |        17.742603