Pandas Dataframe 编码向量来自 Sqlite3 中的多对多连接表

Question

So I have three tables (A, A_to_B, B), one of which is a join table for many->many relationships... I need to create a dataframe that contains an array of flags for each id in B (whether or not a transaction in the join table exists), for each row in A... Very hard to explain, but below are the example tables...

A_to_B             A                  B
+------+------+    +------+------+    +------+------+
| id_a | id_b |    | id   | val  |    | id   | val  |
+------+------+    +------+------+    +------+------+
|  1   |  2   |    |  1   | foo  |    |  1   | foob |
+------+------+    +------+------+    +------+------+
|  1   |  3   |    |  2   | bar  |    |  2   | barb |
+------+------+    +------+------+    +------+------+
|  2   |  3   |    |  3   | baz  |    |  3   | bazb |
+------+------+    +------+------+    +------+------+

And I want to end up with a dataframe that looks like this

     1     2     3
_______________________
 1   0     1     1     # id 1 from table A contains entries for ids 2/3 in B
 2   0     0     1     # id 2 from table A contains entry for id 3 in B
 3   0     0     0     # id 3 contains no transactions in the join table

Hopefully that makes sense. Also, keep in mind that this has to be an efficient sqlite query as I am dealing with potentially tens of thousands of rows from each table.

I have each table loaded into a separate dataframe, as follows (but of course that is by no means a constraint on the solution to this).

import pandas as pd
import sqlite3

conn = sqlite3.connect('database.sqlite3')
cur = conn.cursor()

df_A = pd.read_sql_query('SELECT * FROM A', conn)
df_B = pd.read_sql_query('SELECT * FROM B', conn)
df_A_to_B = pd.read_sql_query('SELECT * FROM A_to_B', conn)

Answer 1

# input
df = pd.DataFrame({'A':[1,1,2], 'B':[2,3,3]})
dfa = pd.DataFrame({'A':[1,2,3], 'tt':['f','b','z']})
dfb = pd.DataFrame({'B':[1,2,3], 'tt':['fb','bb','zb']})

# output
a = pd.Categorical(df['A'], categories=dfa['A'])
b = pd.Categorical(df['B'], categories=dfb['B'])
pd.crosstab(a, b, dropna=False, rownames=['A'], colnames=['B'])