¿Cómo se seleccionan filas de un DataFrame en función de los valores de las columnas?

How do you select rows from a DataFrame based on column values?

There are several ways to select rows from a Pandas dataframe:

1. Boolean indexing ( df[df[‘col’] == value ] )
2. Positional indexing ( df. iloc[…] )
3. Label indexing ( df. xs(…) )
4. df. query(…) API.

Selecting a Row or Column To select a row in a table, move the cursor to the left of the row until it turns into a white arrow pointing up and to the right, as shown in the following image. To select multiple rows this way, drag the mouse down over the other rows once you’ve selected one row.

Anti-join
Anti-join between two tables returns rows from the first table where no matches are found in the second table. It is opposite of a semi-join. An anti-join returns one copy of each row in the first table for which no match is found.

You can also click anywhere in the table, and then press CTRL+A to select the table data in the entire table, or you can click the top-left most cell in the table, and then press CTRL+SHIFT+END. Press CTRL+A twice to select the entire table, including the table headers.

SELECT *, (SELECT v.Field1 (dynamic column name) FROM DynamicFieldsValue v WHERE CUSTOMERID =1 ) VALUE FROM [dbo]. [DynamicFieldsMaster] DM

The simplest solution would be a correlated sub select: select A.* from table_A A where A.id in ( select B.id from table_B B where B.tag = ‘chair’ ) Alternatively you could join the tables and filter the rows you want: select A.* from table_A A inner join table_B B on A.id = B.id where B.tag = ‘chair’.

Now, I wanted to insert a calculated field that outputs the ratio of the sum of the values of each name for ALL years to the sum of all values. Basically the percentage of all values attributed to A and B, respectively, like:

I’d really appreciate some help with an SQL query across tables. I realise this sort of thing is asked constantly, but I can’t find a similar enough question to make sense of the answers. Also, id is a unique in table_a, and not in table_b.