Every time you call append, Pandas returns a copy of the original dataframe plus your new row. This is called quadratic copy, and it is an O(N^2) operation that will quickly become very slow (especially since you have lots of data). In your case, I would recommend using lists, appending to them, and then calling … Read more
MySQL is different from most DBMSs’ use of + or || for concatenation. It uses the CONCAT function: SELECT CONCAT(first_name, ‘ ‘, last_name) AS Name FROM test.student There’s also the CONCAT_WS (Concatenate With Separator) function, which is a special form of CONCAT(): SELECT CONCAT_WS(‘ ‘, first_name, last_name) from test.student That said, if you want to … Read more
Use merge, which is an inner join by default: pd.merge(df1, df2, left_index=True, right_index=True) Or join, which is a left join by default: df1.join(df2) Or concat), which is an outer join by default: pd.concat([df1, df2], axis=1) Samples: df1 = pd.DataFrame({‘a’:range(6), ‘b’:[5,3,6,9,2,4]}, index=list(‘abcdef’)) print (df1) a b a 0 5 b 1 3 c 2 6 d … Read more
select id, group_concat(`Name` separator ‘,’) as `ColumnName` from ( select id, concat(`Name`, ‘:’, group_concat(`Value` separator ‘,’)) as `Name` from mytbl group by id, `Name` ) tbl group by id; You can see it implemented here : Sql Fiddle Demo. Exactly what you need. Update Splitting in two steps. First we get a table having all … Read more
You can use GROUP_CONCAT: SELECT person_id, GROUP_CONCAT(hobbies SEPARATOR ‘, ‘) FROM peoples_hobbies GROUP BY person_id; As Ludwig stated in his comment, you can add the DISTINCT operator to avoid duplicates: SELECT person_id, GROUP_CONCAT(DISTINCT hobbies SEPARATOR ‘, ‘) FROM peoples_hobbies GROUP BY person_id; As Jan stated in their comment, you can also sort the values before … Read more
foo=”Hello” foo=”${foo} World” echo “${foo}” > Hello World In general to concatenate two variables you can just write them one after another: a=”Hello” b=’World’ c=”${a} ${b}” echo “${c}” > Hello World