Pandas select columns ordered at the beginning and the rest remain unchanged

For example, I have dataframe with many columns, with the number of columns not clear, e.g.. between 10 and 20.

The column name in the follows:

RecordID, price, company, date, feature1, return, some_inf, feature2, feature3, ...

Sample data:

column_names = ["RecordID", "price", "company", "date", "feature1", "return", "some_inf", "feature2", "feature3"]
values = [1, 9.99, "ABC", 20230101, 888, 0.666, "happy_everyday", "helloworld", "test"]
df = pd.DataFrame(values).T
df.columns = column_names

Among all these columns, I would like to pick out some columns (if they exist) and put them at the beginning, and the rest columns follows with order unchanged. For example, if I want to select date, volume, price, return

Then the output (with re-ordered columns) will be

date, price, return, RecordID, company, feature1, some_inf, feature2, feature3, ...

The volume column does not exist in the original dataframe, so that it should also not be in the final output. I.e. The output dataframe should have the first several column in the selection list (if they also are in the original dataframe), then followed by columns not in this list, with orders unchanged.

Any fast way to implement this?

Use Index.intersection for all columns for begining with Index.append by columns from Index.difference:

cols = ['date', 'volume', 'price', 'return']
new = (pd.Index(cols).intersection(df.columns, sort=False)
         .append(df.columns.difference(cols, sort=False)))
df = df[new]
print (df)
       date price return RecordID company feature1        some_inf  \
0  20230101  9.99  0.666        1     ABC      888  happy_everyday   

     feature2 feature3  
0  helloworld     test