[Python] 100 knocks on data science (structured data processing) 015 Explanation

Youtube Video commentary is also available.

problem

P-015: From the customer data frame (df_customer), extract all the data whose status code (status_cd) starts with the letters A to F and ends with the numbers 1 to 9, and display only 10 items.

answer

`code`


df_customer.query("status_cd.str.contains('^[A-F].*[1-9]$')", engine='python').head(10)

output

	customer_id	customer_name	gender_cd	gender	birth_day	age	postal_cd	address	application_store_cd	application_date	status_cd
12	CS011215000048	Saya Ashida	1	Female	1992-02-01	27	223-0062	Hiyoshihoncho, Kohoku Ward, Yokohama City, Kanagawa Prefecture**********	S14011	20150228	C-20100421-9
68	CS022513000105	Kimiko Shimamura	1	Female	1962-03-12	57	249-0002	Yamanone, Zushi City, Kanagawa Prefecture**********	S14022	20150320	A-20091115-7
71	CS001515000096	Yoko Mizuno	9	unknown	1960-11-29	58	144-0053	Kamatahoncho, Ota-ku, Tokyo**********	S13001	20150614	A-20100724-7
122	CS013615000053	Nishiwaki Kii	1	Female	1953-10-18	65	261-0026	Makuharinishi, Mihama Ward, Chiba City, Chiba Prefecture**********	S12013	20150128	B-20100329-6
144	CS020412000161	Kaoru Komiya	1	Female	1974-05-21	44	174-0042	Higashisakashita, Itabashi-ku, Tokyo**********	S13020	20150822	B-20081021-3
178	CS001215000097	Asami Takenaka	1	Female	1990-07-25	28	146-0095	Tamagawa, Ota-ku, Tokyo**********	S13001	20170315	A-20100211-2
252	CS035212000007	Erika Uchimura	1	Female	1990-12-04	28	152-0023	Yakumo, Meguro-ku, Tokyo**********	S13035	20151013	B-20101018-6
259	CS002515000386	Ko Noda	1	Female	1963-05-30	55	185-0013	Nishikoigakubo, Kokubunji-shi, Tokyo**********	S13002	20160410	C-20100127-8
293	CS001615000372	Inagaki Suzuka	1	Female	1956-10-29	62	144-0035	Minamikamata, Ota-ku, Tokyo**********	S13001	20170403	A-20100104-1
297	CS032512000121	Tomoyo Matsui	1	Female	1962-09-04	56	210-0011	Fujimi, Kawasaki Ward, Kawasaki City, Kanagawa Prefecture**********	S13032	20150727	A-20100103-5

Commentary

** ・ It is a method to check the first data that meets the conditions in Pandas DataFrame / Series. -Use when you want to check the information that meets the conditions. -'Contains ()' is a function that determines whether the specified character string is included. If it is included, it returns True, and if it is not included, it returns False. -However,'.query ('--- .str.contains ())' is specified on condition that the specified character string is included. ** ** ** ・ In this case, set'status_cd.str' to replace status_cd with a character string, and continue with'.contains ('^ [AF]. * [1-9] ')' "Specifies status_cd with" 1-9 "at the beginning and" 1-9 "at the end. ** ** ** ・'.' Is a regular expression that represents one character, and'*' is a regular expression that represents that the previous character is repeated 0 or more times. A regular expression is a "method of representing multiple strings with a single symbol". ** ** **'. *'Indicates that 0 or more characters are inserted in between. ** ** **-A regular expression that indicates that'^'is the first character and'' is the last character. ** **

** * For regular expressions, this article will be helpful. ** ** https://qiita.com/hiroyuki_mrp/items/29e87bf5fe46de62983c

** ·'regex = True' was needed when working with regular expressions. In the example solution,'^'' $''-'' ʻ*' is treated as a regular expression. Currently, it is treated as a regular expression without writing it, so there is no problem even if it is not written. ** ** ** ・ For'engine ='python'', you can select'python' or'numexpr' for engine which is the argument of query, but when using str, specify'python'. If you do not give it, an error will occur. ** **