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chore: Support Python 3.10 and bump pandas 1.4 and pyarrow 6 (#21002) 

* Bump pandas 1.4 and pyarrow 6

* Use engine="pyarrow" for pd.read_csv()

* Refactoring

* Refactoring

* Refactoring

* Use bytes in pd.read_json()

* Fix test_contribution

* Fix pandas issue when 'arrays' are empty but 'names' contain values

* fix: ValueError: For argument "ascending" expected type bool, received type NoneType.

* Remove engine="pyarrow" and convert bytes to string

* make copy of selected df to fix regression

* Simplify pd.read_json() and pd.read_csv() for example data

Co-authored-by: Ville Brofeldt <ville.brofeldt@apple.com>
This commit is contained in:
EugeneTorap 2022-08-17 16:36:36 +03:00 committed by GitHub
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commit 76d6a9af91
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20 changed files with 55 additions and 60 deletions
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2
CONTRIBUTING.md
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@ -420,7 +420,7 @@ Commits to `master` trigger a rebuild and redeploy of the documentation site. Su
Make sure your machine meets the [OS dependencies](https://superset.apache.org/docs/installation/installing-superset-from-scratch#os-dependencies) before following these steps.
You also need to install MySQL or [MariaDB](https://mariadb.com/downloads).
Ensure that you are using Python version 3.8 or 3.9, then proceed with:
Ensure that you are using Python version 3.8, 3.9 or 3.10, then proceed with:
```bash
# Create a virtual environment and activate it (recommended)

6
Makefile
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@ -15,8 +15,8 @@
# limitations under the License.
#
# Python version installed; we need 3.8-3.9
PYTHON=`command -v python3.9 || command -v python3.8`
# Python version installed; we need 3.8-3.10
PYTHON=`command -v python3.10 || command -v python3.9 || command -v python3.8`
.PHONY: install superset venv pre-commit
@ -70,7 +70,7 @@ update-js:
venv:
# Create a virtual environment and activate it (recommended)
if ! [ -x "${PYTHON}" ]; then echo "You need Python 3.8 or 3.9 installed"; exit 1; fi
if ! [ -x "${PYTHON}" ]; then echo "You need Python 3.8, 3.9 or 3.10 installed"; exit 1; fi
test -d venv || ${PYTHON} -m venv venv # setup a python3 virtualenv
. venv/bin/activate

1
UPDATING.md
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@ -26,6 +26,7 @@ assists people when migrating to a new version.
- [20606](https://github.com/apache/superset/pull/20606): When user clicks on chart title or "Edit chart" button in Dashboard page, Explore opens in the same tab. Clicking while holding cmd/ctrl opens Explore in a new tab. To bring back the old behaviour (always opening Explore in a new tab), flip feature flag `DASHBOARD_EDIT_CHART_IN_NEW_TAB` to `True`.
- [20799](https://github.com/apache/superset/pull/20799): Presto and Trino engine will now display tracking URL for running queries in SQL Lab. If for some reason you don't want to show the tracking URL (for example, when your data warehouse hasn't enable access for to Presto or Trino UI), update `TRACKING_URL_TRANSFORMER` in `config.py` to return `None`.
- [21002](https://github.com/apache/superset/pull/21002): Support Python 3.10 and bump pandas 1.4 and pyarrow 6.
### Breaking Changes

4
requirements/base.txt
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@ -185,7 +185,7 @@ packaging==21.3
# via
# bleach
# deprecation
pandas==1.3.4
pandas==1.4.3
# via apache-superset
parsedatetime==2.6
# via apache-superset
@ -197,7 +197,7 @@ prison==0.2.1
# via flask-appbuilder
prompt-toolkit==3.0.28
# via click-repl
pyarrow==5.0.0
pyarrow==6.0.1
# via apache-superset
pycparser==2.20
# via cffi

5
setup.py
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@ -100,7 +100,7 @@ setup(
"markdown>=3.0",
"msgpack>=1.0.0, <1.1",
"numpy==1.22.1",
"pandas>=1.3.0, <1.4",
"pandas>=1.4.3, <1.5",
"parsedatetime",
"pgsanity",
"polyline",
@ -108,7 +108,7 @@ setup(
"python-dateutil",
"python-dotenv",
"python-geohash",
"pyarrow>=5.0.0, <6.0",
"pyarrow>=6.0.1, <7",
"pyyaml>=5.4",
"PyJWT>=2.4.0, <3.0",
"redis",
@ -183,5 +183,6 @@ setup(
classifiers=[
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
],
)

6
superset/examples/bart_lines.py
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@ -23,7 +23,7 @@ from sqlalchemy import inspect, String, Text
from superset import db
from ..utils.database import get_example_database
from .helpers import get_example_data, get_table_connector_registry
from .helpers import get_example_url, get_table_connector_registry
def load_bart_lines(only_metadata: bool = False, force: bool = False) -> None:
@ -34,8 +34,8 @@ def load_bart_lines(only_metadata: bool = False, force: bool = False) -> None:
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
content = get_example_data("bart-lines.json.gz")
df = pd.read_json(content, encoding="latin-1")
url = get_example_url("bart-lines.json.gz")
df = pd.read_json(url, encoding="latin-1", compression="gzip")
df["path_json"] = df.path.map(json.dumps)
df["polyline"] = df.path.map(polyline.encode)
del df["path"]

5
superset/examples/birth_names.py
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@ -33,7 +33,7 @@ from superset.utils.core import DatasourceType
from ..utils.database import get_example_database
from .helpers import (
get_example_data,
get_example_url,
get_slice_json,
get_table_connector_registry,
merge_slice,
@ -66,7 +66,8 @@ def gen_filter(
def load_data(tbl_name: str, database: Database, sample: bool = False) -> None:
pdf = pd.read_json(get_example_data("birth_names2.json.gz"))
url = get_example_url("birth_names2.json.gz")
pdf = pd.read_json(url, compression="gzip")
# TODO(bkyryliuk): move load examples data into the pytest fixture
if database.backend == "presto":
pdf.ds = pd.to_datetime(pdf.ds, unit="ms")

8
superset/examples/country_map.py
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@ -27,7 +27,7 @@ from superset.models.slice import Slice
from superset.utils.core import DatasourceType
from .helpers import (
get_example_data,
get_example_url,
get_slice_json,
get_table_connector_registry,
merge_slice,
@ -44,10 +44,8 @@ def load_country_map_data(only_metadata: bool = False, force: bool = False) -> N
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
csv_bytes = get_example_data(
"birth_france_data_for_country_map.csv", is_gzip=False, make_bytes=True
)
data = pd.read_csv(csv_bytes, encoding="utf-8")
url = get_example_url("birth_france_data_for_country_map.csv")
data = pd.read_csv(url, encoding="utf-8")
data["dttm"] = datetime.datetime.now().date()
data.to_sql(
tbl_name,

6
superset/examples/energy.py
View File

@ -28,7 +28,7 @@ from superset.models.slice import Slice
from superset.utils.core import DatasourceType
from .helpers import (
get_example_data,
get_example_url,
get_table_connector_registry,
merge_slice,
misc_dash_slices,
@ -46,8 +46,8 @@ def load_energy(
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("energy.json.gz")
pdf = pd.read_json(data)
url = get_example_url("energy.json.gz")
pdf = pd.read_json(url, compression="gzip")
pdf = pdf.head(100) if sample else pdf
pdf.to_sql(
tbl_name,

10
superset/examples/flights.py
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@ -20,7 +20,7 @@ from sqlalchemy import DateTime, inspect
import superset.utils.database as database_utils
from superset import db
from .helpers import get_example_data, get_table_connector_registry
from .helpers import get_example_url, get_table_connector_registry
def load_flights(only_metadata: bool = False, force: bool = False) -> None:
@ -32,12 +32,12 @@ def load_flights(only_metadata: bool = False, force: bool = False) -> None:
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("flight_data.csv.gz", make_bytes=True)
pdf = pd.read_csv(data, encoding="latin-1")
flight_data_url = get_example_url("flight_data.csv.gz")
pdf = pd.read_csv(flight_data_url, encoding="latin-1", compression="gzip")
# Loading airports info to join and get lat/long
airports_bytes = get_example_data("airports.csv.gz", make_bytes=True)
airports = pd.read_csv(airports_bytes, encoding="latin-1")
airports_url = get_example_url("airports.csv.gz")
airports = pd.read_csv(airports_url, encoding="latin-1", compression="gzip")
airports = airports.set_index("IATA_CODE")
pdf[ # pylint: disable=unsupported-assignment-operation,useless-suppression

16
superset/examples/helpers.py
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@ -17,10 +17,7 @@
"""Loads datasets, dashboards and slices in a new superset instance"""
import json
import os
import zlib
from io import BytesIO
from typing import Any, Dict, List, Set
from urllib import request
from superset import app, db
from superset.connectors.sqla.models import SqlaTable
@ -73,14 +70,5 @@ def get_slice_json(defaults: Dict[Any, Any], **kwargs: Any) -> str:
return json.dumps(defaults_copy, indent=4, sort_keys=True)
def get_example_data(
filepath: str, is_gzip: bool = True, make_bytes: bool = False
) -> BytesIO:
content = request.urlopen( # pylint: disable=consider-using-with
f"{BASE_URL}{filepath}?raw=true"
).read()
if is_gzip:
content = zlib.decompress(content, zlib.MAX_WBITS | 16)
if make_bytes:
content = BytesIO(content)
return content
def get_example_url(filepath: str) -> str:
return f"{BASE_URL}{filepath}?raw=true"

6
superset/examples/long_lat.py
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@ -27,7 +27,7 @@ from superset.models.slice import Slice
from superset.utils.core import DatasourceType
from .helpers import (
get_example_data,
get_example_url,
get_slice_json,
get_table_connector_registry,
merge_slice,
@ -44,8 +44,8 @@ def load_long_lat_data(only_metadata: bool = False, force: bool = False) -> None
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("san_francisco.csv.gz", make_bytes=True)
pdf = pd.read_csv(data, encoding="utf-8")
url = get_example_url("san_francisco.csv.gz")
pdf = pd.read_csv(url, encoding="utf-8", compression="gzip")
start = datetime.datetime.now().replace(
hour=0, minute=0, second=0, microsecond=0
)

6
superset/examples/multiformat_time_series.py
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@ -25,7 +25,7 @@ from superset.utils.core import DatasourceType
from ..utils.database import get_example_database
from .helpers import (
get_example_data,
get_example_url,
get_slice_json,
get_table_connector_registry,
merge_slice,
@ -44,8 +44,8 @@ def load_multiformat_time_series( # pylint: disable=too-many-locals
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("multiformat_time_series.json.gz")
pdf = pd.read_json(data)
url = get_example_url("multiformat_time_series.json.gz")
pdf = pd.read_json(url, compression="gzip")
# TODO(bkyryliuk): move load examples data into the pytest fixture
if database.backend == "presto":
pdf.ds = pd.to_datetime(pdf.ds, unit="s")

6
superset/examples/paris.py
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@ -22,7 +22,7 @@ from sqlalchemy import inspect, String, Text
import superset.utils.database as database_utils
from superset import db
from .helpers import get_example_data, get_table_connector_registry
from .helpers import get_example_url, get_table_connector_registry
def load_paris_iris_geojson(only_metadata: bool = False, force: bool = False) -> None:
@ -33,8 +33,8 @@ def load_paris_iris_geojson(only_metadata: bool = False, force: bool = False) ->
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("paris_iris.json.gz")
df = pd.read_json(data)
url = get_example_url("paris_iris.json.gz")
df = pd.read_json(url, compression="gzip")
df["features"] = df.features.map(json.dumps)
df.to_sql(

6
superset/examples/random_time_series.py
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@ -24,7 +24,7 @@ from superset.models.slice import Slice
from superset.utils.core import DatasourceType
from .helpers import (
get_example_data,
get_example_url,
get_slice_json,
get_table_connector_registry,
merge_slice,
@ -42,8 +42,8 @@ def load_random_time_series_data(
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("random_time_series.json.gz")
pdf = pd.read_json(data)
url = get_example_url("random_time_series.json.gz")
pdf = pd.read_json(url, compression="gzip")
if database.backend == "presto":
pdf.ds = pd.to_datetime(pdf.ds, unit="s")
pdf.ds = pdf.ds.dt.strftime("%Y-%m-%d %H:%M%:%S")

6
superset/examples/sf_population_polygons.py
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@ -22,7 +22,7 @@ from sqlalchemy import BigInteger, Float, inspect, Text
import superset.utils.database as database_utils
from superset import db
from .helpers import get_example_data, get_table_connector_registry
from .helpers import get_example_url, get_table_connector_registry
def load_sf_population_polygons(
@ -35,8 +35,8 @@ def load_sf_population_polygons(
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("sf_population.json.gz")
df = pd.read_json(data)
url = get_example_url("sf_population.json.gz")
df = pd.read_json(url, compression="gzip")
df["contour"] = df.contour.map(json.dumps)
df.to_sql(

6
superset/examples/world_bank.py
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@ -33,7 +33,7 @@ from superset.utils.core import DatasourceType
from ..connectors.base.models import BaseDatasource
from .helpers import (
get_example_data,
get_example_url,
get_examples_folder,
get_slice_json,
get_table_connector_registry,
@ -56,8 +56,8 @@ def load_world_bank_health_n_pop( # pylint: disable=too-many-locals, too-many-s
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
data = get_example_data("countries.json.gz")
pdf = pd.read_json(data)
url = get_example_url("countries.json.gz")
pdf = pd.read_json(url, compression="gzip")
pdf.columns = [col.replace(".", "_") for col in pdf.columns]
if database.backend == "presto":
pdf.year = pd.to_datetime(pdf.year)

3
superset/result_set.py
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@ -161,6 +161,9 @@ class SupersetResultSet:
except Exception as ex: # pylint: disable=broad-except
logger.exception(ex)
if not pa_data:
column_names = []
self.table = pa.Table.from_arrays(pa_data, names=column_names)
self._type_dict: Dict[str, Any] = {}
try:

3
superset/utils/pandas_postprocessing/contribution.py
View File

@ -49,6 +49,9 @@ def contribution(
"""
contribution_df = df.copy()
numeric_df = contribution_df.select_dtypes(include=["number", Decimal])
# TODO: copy needed due to following regression in 1.4, remove if not needed:
# https://github.com/pandas-dev/pandas/issues/48090
numeric_df = numeric_df.copy()
numeric_df.fillna(0, inplace=True)
# verify column selections
if columns:

4
superset/viz.py
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@ -2172,14 +2172,14 @@ class FilterBoxViz(BaseViz):
if df is not None and not df.empty:
if metric:
df = df.sort_values(
utils.get_metric_name(metric), ascending=flt.get("asc")
utils.get_metric_name(metric), ascending=flt.get("asc", False)
)
data[col] = [
{"id": row[0], "text": row[0], "metric": row[1]}
for row in df.itertuples(index=False)
]
else:
df = df.sort_values(col, ascending=flt.get("asc"))
df = df.sort_values(col, ascending=flt.get("asc", False))
data[col] = [
{"id": row[0], "text": row[0]}
for row in df.itertuples(index=False)