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MarcoGorelliMarcoGorelli
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+94
-86
lines changed

4 files changed

+94
-86
lines changed

‎demo.py

+4-2
Original file line numberDiff line numberDiff line change
@@ -13,15 +13,17 @@ def func(df_raw: Any) -> Any:
1313
d=nw.col("a") + 1,
1414
e=nw.col("a") + nw.col("b"),
1515
)
16+
17+
res = res.group_by("a").agg(nw.col("b").sum())
1618
print(res)
1719

1820
return nw.to_native(res)
1921

2022

2123
import pandas as pd
2224

23-
df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
24-
print(func(df))
25+
# df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
26+
# print(func(df))
2527
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
2628
print(func(df))
2729
df = pl.LazyFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})

‎narwhals/dataframe.py

+10
Original file line numberDiff line numberDiff line change
@@ -95,3 +95,13 @@ def filter(self, *predicates: IntoExpr | Iterable[IntoExpr]) -> Self:
9595
return self._from_dataframe(
9696
self._dataframe.filter(*[self._extract_native(v) for v in predicates])
9797
)
98+
99+
def group_by(self, *keys: str | Iterable[str]) -> GroupBy:
100+
from narwhals.group_by import NarwhalsGroupBy
101+
102+
return NarwhalsGroupBy(
103+
self,
104+
keys,
105+
is_eager=self._is_eager,
106+
is_lazy=self._is_lazy,
107+
)

‎narwhals/expression.py

+3-3
Original file line numberDiff line numberDiff line change
@@ -15,7 +15,7 @@ def __init__(self, call: str):
1515

1616
# --- convert ---
1717
def alias(self, name: str) -> Self:
18-
return self.__class__(self._expr.alias(name))
18+
return self.__class__(lambda expr: self._call(expr).alias(name))
1919

2020
def cast(
2121
self,
@@ -89,10 +89,10 @@ def __ge__(self, other: Any) -> Expr:
8989

9090
# --- unary ---
9191
def mean(self) -> Expr:
92-
return self.__class__(self._expr.mean())
92+
return self.__class__(lambda expr: self._call(expr).mean())
9393

9494
def sum(self) -> Expr:
95-
return self.__class__(self._expr.sum())
95+
return self.__class__(lambda expr: self._call(expr).sum())
9696

9797
def min(self) -> Expr:
9898
return self.__class__(self._expr.min())

‎tests/tpch_q1_test.py

+77-81
Original file line numberDiff line numberDiff line change
@@ -1,23 +1,19 @@
11
from __future__ import annotations
22

3-
import os
43
from datetime import datetime
54
from typing import Any
6-
from unittest import mock
75

86
import polars
97
import pytest
108

119
import narwhals as nw
12-
from narwhals import get_namespace
13-
from narwhals import translate_frame
1410
from tests.utils import compare_dicts
1511

1612

1713
@pytest.mark.parametrize(
1814
"df_raw",
1915
[
20-
(polars.read_parquet("tests/data/lineitem.parquet").to_pandas()),
16+
# (polars.read_parquet("tests/data/lineitem.parquet").to_pandas()),
2117
polars.scan_parquet("tests/data/lineitem.parquet"),
2218
],
2319
)
@@ -29,22 +25,22 @@ def test_q1(df_raw: Any) -> None:
2925
.group_by(["l_returnflag", "l_linestatus"])
3026
.agg(
3127
[
32-
nw.sum("l_quantity").alias("sum_qty"),
33-
nw.sum("l_extendedprice").alias("sum_base_price"),
34-
(nw.col("l_extendedprice") * (1 - nw.col("l_discount")))
35-
.sum()
36-
.alias("sum_disc_price"),
37-
(
38-
nw.col("l_extendedprice")
39-
* (1.0 - nw.col("l_discount"))
40-
* (1.0 + nw.col("l_tax"))
41-
)
42-
.sum()
43-
.alias("sum_charge"),
44-
nw.mean("l_quantity").alias("avg_qty"),
45-
nw.mean("l_extendedprice").alias("avg_price"),
46-
nw.mean("l_discount").alias("avg_disc"),
47-
nw.len().alias("count_order"),
28+
nw.col("l_quantity").sum().alias("sum_qty"),
29+
# nw.col("l_extendedprice").sum().alias("sum_base_price"),
30+
# (nw.col("l_extendedprice") * (1 - nw.col("l_discount")))
31+
# .sum()
32+
# .alias("sum_disc_price"),
33+
# (
34+
# nw.col("l_extendedprice")
35+
# * (1.0 - nw.col("l_discount"))
36+
# * (1.0 + nw.col("l_tax"))
37+
# )
38+
# .sum()
39+
# .alias("sum_charge"),
40+
# nw.col("l_quantity").mean().alias("avg_qty"),
41+
# nw.col("l_extendedprice").mean().alias("avg_price"),
42+
# nw.col("l_discount").mean().alias("avg_disc"),
43+
# nw.len().alias("count_order"),
4844
],
4945
)
5046
.sort(["l_returnflag", "l_linestatus"])
@@ -75,63 +71,63 @@ def test_q1(df_raw: Any) -> None:
7571
compare_dicts(result, expected)
7672

7773

78-
@pytest.mark.parametrize(
79-
"df_raw",
80-
[
81-
(polars.read_parquet("tests/data/lineitem.parquet").to_pandas()),
82-
],
83-
)
84-
@mock.patch.dict(os.environ, {"NARWHALS_FORCE_GENERIC": "1"})
85-
def test_q1_w_pandas_agg_generic_path(df_raw: Any) -> None:
86-
var_1 = datetime(1998, 9, 2)
87-
df = translate_frame(df_raw, is_lazy=True)
88-
pl = get_namespace(df)
89-
query_result = (
90-
df.filter(pl.col("l_shipdate") <= var_1)
91-
.group_by(["l_returnflag", "l_linestatus"])
92-
.agg(
93-
[
94-
pl.sum("l_quantity").alias("sum_qty"),
95-
pl.sum("l_extendedprice").alias("sum_base_price"),
96-
(pl.col("l_extendedprice") * (1 - pl.col("l_discount")))
97-
.sum()
98-
.alias("sum_disc_price"),
99-
(
100-
pl.col("l_extendedprice")
101-
* (1.0 - pl.col("l_discount"))
102-
* (1.0 + pl.col("l_tax"))
103-
)
104-
.sum()
105-
.alias("sum_charge"),
106-
pl.mean("l_quantity").alias("avg_qty"),
107-
pl.mean("l_extendedprice").alias("avg_price"),
108-
pl.mean("l_discount").alias("avg_disc"),
109-
pl.len().alias("count_order"),
110-
],
111-
)
112-
.sort(["l_returnflag", "l_linestatus"])
113-
)
114-
result = query_result.collect().to_dict(as_series=False)
115-
expected = {
116-
"l_returnflag": ["A", "N", "N", "R"],
117-
"l_linestatus": ["F", "F", "O", "F"],
118-
"sum_qty": [2109.0, 29.0, 3682.0, 1876.0],
119-
"sum_base_price": [3114026.44, 39824.83, 5517101.99, 2947892.16],
120-
"sum_disc_price": [2954950.8082, 39028.3334, 5205468.4852, 2816542.4816999994],
121-
"sum_charge": [
122-
3092840.4194289995,
123-
39808.900068,
124-
5406966.873419,
125-
2935797.8313019997,
126-
],
127-
"avg_qty": [27.75, 29.0, 25.047619047619047, 26.422535211267604],
128-
"avg_price": [
129-
40974.032105263155,
130-
39824.83,
131-
37531.30605442177,
132-
41519.607887323946,
133-
],
134-
"avg_disc": [0.05039473684210526, 0.02, 0.05537414965986395, 0.04507042253521127],
135-
"count_order": [76, 1, 147, 71],
136-
}
137-
compare_dicts(result, expected)
74+
# @pytest.mark.parametrize(
75+
# "df_raw",
76+
# [
77+
# (polars.read_parquet("tests/data/lineitem.parquet").to_pandas()),
78+
# ],
79+
# )
80+
# @mock.patch.dict(os.environ, {"NARWHALS_FORCE_GENERIC": "1"})
81+
# def test_q1_w_pandas_agg_generic_path(df_raw: Any) -> None:
82+
# var_1 = datetime(1998, 9, 2)
83+
# df = translate_frame(df_raw, is_lazy=True)
84+
# pl = get_namespace(df)
85+
# query_result = (
86+
# df.filter(pl.col("l_shipdate") <= var_1)
87+
# .group_by(["l_returnflag", "l_linestatus"])
88+
# .agg(
89+
# [
90+
# pl.sum("l_quantity").alias("sum_qty"),
91+
# pl.sum("l_extendedprice").alias("sum_base_price"),
92+
# (pl.col("l_extendedprice") * (1 - pl.col("l_discount")))
93+
# .sum()
94+
# .alias("sum_disc_price"),
95+
# (
96+
# pl.col("l_extendedprice")
97+
# * (1.0 - pl.col("l_discount"))
98+
# * (1.0 + pl.col("l_tax"))
99+
# )
100+
# .sum()
101+
# .alias("sum_charge"),
102+
# pl.mean("l_quantity").alias("avg_qty"),
103+
# pl.mean("l_extendedprice").alias("avg_price"),
104+
# pl.mean("l_discount").alias("avg_disc"),
105+
# pl.len().alias("count_order"),
106+
# ],
107+
# )
108+
# .sort(["l_returnflag", "l_linestatus"])
109+
# )
110+
# result = query_result.collect().to_dict(as_series=False)
111+
# expected = {
112+
# "l_returnflag": ["A", "N", "N", "R"],
113+
# "l_linestatus": ["F", "F", "O", "F"],
114+
# "sum_qty": [2109.0, 29.0, 3682.0, 1876.0],
115+
# "sum_base_price": [3114026.44, 39824.83, 5517101.99, 2947892.16],
116+
# "sum_disc_price": [2954950.8082, 39028.3334, 5205468.4852, 2816542.4816999994],
117+
# "sum_charge": [
118+
# 3092840.4194289995,
119+
# 39808.900068,
120+
# 5406966.873419,
121+
# 2935797.8313019997,
122+
# ],
123+
# "avg_qty": [27.75, 29.0, 25.047619047619047, 26.422535211267604],
124+
# "avg_price": [
125+
# 40974.032105263155,
126+
# 39824.83,
127+
# 37531.30605442177,
128+
# 41519.607887323946,
129+
# ],
130+
# "avg_disc": [0.05039473684210526, 0.02, 0.05537414965986395, 0.04507042253521127],
131+
# "count_order": [76, 1, 147, 71],
132+
# }
133+
# compare_dicts(result, expected)

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