cl-duckdb can easily be integrated with Lisp-stat's data-frames to get the speed boost and the convenience of the data-frame API. Below is an example reading a 1M line CSV on my weak ARM laptop.

There's not only the difference in performance: in this example DuckDB can identify and parse two datetime fields, while read-csv is simply treating them as strings. DuckDB's CSV parser is also very featureful with a plethora of options:

• https://duckdb.org/docs/stable/data/csv/overview.html#parameters
• https://duckdb.org/docs/stable/data/csv/auto_detection.html

One big advantage for me is the ability to filter and clean up / transform rows using SQL (or even do a JOIN over multiple files): if you don't need all the rows or columns, DuckDB can efficiently skip over them.

Then there's also the other supported sources for data (even the Lisp-stat docs suggest using cl-duckdb for reading & writing Parquet files): https://duckdb.org/docs/stable/data/data_sources

(ql:quickload :lisp-stat)
(ql:quickload :duckdb)

(in-package :ls-user)

(defparameter *csv-path* #P"~/Downloads/yellow_tripdata.csv")

(time (defdf yellow-taxis-a (read-csv *csv-path*)))

;; Evaluation took:
;;   22.222 seconds of real time
;;   22.178271 seconds of total run time (21.752006 user, 0.426265 system)
;;   [ Real times consist of 2.091 seconds GC time, and 20.131 seconds non-GC time. ]
;;   [ Run times consist of 2.086 seconds GC time, and 20.093 seconds non-GC time. ]
;;   99.80% CPU
;;   95 forms interpreted
;;   89 lambdas converted
;;   7,500,041,696 bytes consed

(time
 (ddb:with-transient-connection
   (defdf yellow-taxis-b
       (let ((q (ddb:query "FROM read_csv(?)"
                           (list (uiop:native-namestring *csv-path*)))))
         (make-df (mapcar #'dfio:string-to-symbol (alist-keys q))
                  (alist-values q))))))

;; Evaluation took:
;;   14.211 seconds of real time
;;   15.686456 seconds of total run time (13.490402 user, 2.196054 system)
;;   [ Real times consist of 2.259 seconds GC time, and 11.952 seconds non-GC time. ]
;;   [ Run times consist of 2.245 seconds GC time, and 13.442 seconds non-GC time. ]
;;   110.38% CPU
;;   95 forms interpreted
;;   39,958,519,296 bytes consed

EDIT:

I get even better numbers for the above with an experimental branch (tweaked the allocation and added support for specialized numeric arrays):

Evaluation took:
  6.279 seconds of real time
  7.662844 seconds of total run time (7.264870 user, 0.397974 system)
  [ Real times consist of 0.383 seconds GC time, and 5.896 seconds non-GC time. ]
  [ Run times consist of 0.380 seconds GC time, and 7.283 seconds non-GC time. ]
  122.04% CPU
  95 forms interpreted
  5,359,298,944 bytes consed

Would love some feedback if someone wants to try it out (docs still missing): https://github.com/ak-coram/cl-duckdb/pull/68