def test_limit_df(self, duckdb_cursor):
conn = duckdb.connect()
conn.execute("create table t (a integer)")
conn.execute("insert into t values (1),(4)")
test_df = pd.DataFrame.from_dict({"i":[1, 2, 3, 4]})
rel = duckdb.limit(test_df,1, connection=conn)
assert rel.query('t_2','select * from t inner join t_2 on (a = i)').fetchall()[0] == (1,1)
# multi-relation operators are also supported, e.g union
print(rel.union(rel))
# join rel with itself on i
rel2 = conn.from_df(test_df)
print(rel.join(rel2, 'i'))
# for explicit join conditions the relations can be named using alias()
print(rel.set_alias('a').join(rel.set_alias('b'), 'a.i=b.i'))
# there are also shorthand methods to directly create a relation and apply an operator from pandas data frame objects
print(duckdb.filter(test_df, 'i > 1'))
print(duckdb.project(test_df, 'i +1'))
print(duckdb.order(test_df, 'j'))
print(duckdb.limit(test_df, 2))
print(duckdb.aggregate(test_df, "sum(i)"))
print(duckdb.distinct(test_df))
# when chaining only the first call needs to include the data frame parameter
print(duckdb.filter(test_df, 'i > 1').project('i + 1').order('j').limit(2))
# turn the relation into something else again
# compute the query result from the relation
res = rel.execute()
print(res)
# res is a query result, you can call fetchdf() or fetchnumpy() or fetchone() on it
print(res.fetchone())
print(res.fetchall())
def test_limit_df(self, duckdb_cursor):
df_in = pd.DataFrame({
'numbers': [1, 2, 3, 4, 5],
})
limit_df = duckdb.limit(df_in, 2)
assert len(limit_df.execute().fetchall()) == 2