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apache / spark / 387a251a682a596ba4156b7d12e6025762ebac85 / . / python / pyspark / pandas / tests / test_dataframe.py
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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from datetime import datetime
from distutils.version import LooseVersion
import inspect
import sys
import unittest
from io import StringIO
import numpy as np
import pandas as pd
from pandas.tseries.offsets import DateOffset
from pyspark import StorageLevel
from pyspark.ml.linalg import SparseVector
from pyspark.sql.types import StructType
from pyspark import pandas as ps
from pyspark.pandas.config import option_context
from pyspark.pandas.exceptions import PandasNotImplementedError
from pyspark.pandas.frame import CachedDataFrame
from pyspark.pandas.missing.frame import _MissingPandasLikeDataFrame
from pyspark.pandas.typedef.typehints import (
extension_dtypes,
extension_dtypes_available,
extension_float_dtypes_available,
extension_object_dtypes_available,
)
from pyspark.testing.pandasutils import (
have_tabulate,
PandasOnSparkTestCase,
SPARK_CONF_ARROW_ENABLED,
tabulate_requirement_message,
)
from pyspark.testing.sqlutils import SQLTestUtils
from pyspark.pandas.utils import name_like_string
class DataFrameTest(PandasOnSparkTestCase, SQLTestUtils):
@property
def pdf(self):
return pd.DataFrame(
{"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]},
index=np.random.rand(9),
)
@property
def psdf(self):
return ps.from_pandas(self.pdf)
@property
def df_pair(self):
pdf = self.pdf
psdf = ps.from_pandas(pdf)
return pdf, psdf
def test_dataframe(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf["a"] + 1, pdf["a"] + 1)
self.assert_eq(psdf.columns, pd.Index(["a", "b"]))
self.assert_eq(psdf[psdf["b"] > 2], pdf[pdf["b"] > 2])
self.assert_eq(-psdf[psdf["b"] > 2], -pdf[pdf["b"] > 2])
self.assert_eq(psdf[["a", "b"]], pdf[["a", "b"]])
self.assert_eq(psdf.a, pdf.a)
self.assert_eq(psdf.b.mean(), pdf.b.mean())
self.assert_eq(psdf.b.var(), pdf.b.var())
self.assert_eq(psdf.b.std(), pdf.b.std())
pdf, psdf = self.df_pair
self.assert_eq(psdf[["a", "b"]], pdf[["a", "b"]])
self.assertEqual(psdf.a.notnull().rename("x").name, "x")
# check ps.DataFrame(ps.Series)
pser = pd.Series([1, 2, 3], name="x", index=np.random.rand(3))
psser = ps.from_pandas(pser)
self.assert_eq(pd.DataFrame(pser), ps.DataFrame(psser))
# check psdf[pd.Index]
pdf, psdf = self.df_pair
column_mask = pdf.columns.isin(["a", "b"])
index_cols = pdf.columns[column_mask]
self.assert_eq(psdf[index_cols], pdf[index_cols])
def _check_extension(self, psdf, pdf):
if LooseVersion("1.1") <= LooseVersion(pd.__version__) < LooseVersion("1.2.2"):
self.assert_eq(psdf, pdf, check_exact=False)
for dtype in psdf.dtypes:
self.assertTrue(isinstance(dtype, extension_dtypes))
else:
self.assert_eq(psdf, pdf)
@unittest.skipIf(not extension_dtypes_available, "pandas extension dtypes are not available")
def test_extension_dtypes(self):
pdf = pd.DataFrame(
{
"a": pd.Series([1, 2, None, 4], dtype="Int8"),
"b": pd.Series([1, None, None, 4], dtype="Int16"),
"c": pd.Series([1, 2, None, None], dtype="Int32"),
"d": pd.Series([None, 2, None, 4], dtype="Int64"),
}
)
psdf = ps.from_pandas(pdf)
self._check_extension(psdf, pdf)
self._check_extension(psdf + psdf, pdf + pdf)
@unittest.skipIf(not extension_dtypes_available, "pandas extension dtypes are not available")
def test_astype_extension_dtypes(self):
pdf = pd.DataFrame(
{
"a": [1, 2, None, 4],
"b": [1, None, None, 4],
"c": [1, 2, None, None],
"d": [None, 2, None, 4],
}
)
psdf = ps.from_pandas(pdf)
astype = {"a": "Int8", "b": "Int16", "c": "Int32", "d": "Int64"}
self._check_extension(psdf.astype(astype), pdf.astype(astype))
@unittest.skipIf(
not extension_object_dtypes_available, "pandas extension object dtypes are not available"
)
def test_extension_object_dtypes(self):
pdf = pd.DataFrame(
{
"a": pd.Series(["a", "b", None, "c"], dtype="string"),
"b": pd.Series([True, None, False, True], dtype="boolean"),
}
)
psdf = ps.from_pandas(pdf)
self._check_extension(psdf, pdf)
@unittest.skipIf(
not extension_object_dtypes_available, "pandas extension object dtypes are not available"
)
def test_astype_extension_object_dtypes(self):
pdf = pd.DataFrame({"a": ["a", "b", None, "c"], "b": [True, None, False, True]})
psdf = ps.from_pandas(pdf)
astype = {"a": "string", "b": "boolean"}
self._check_extension(psdf.astype(astype), pdf.astype(astype))
@unittest.skipIf(
not extension_float_dtypes_available, "pandas extension float dtypes are not available"
)
def test_extension_float_dtypes(self):
pdf = pd.DataFrame(
{
"a": pd.Series([1.0, 2.0, None, 4.0], dtype="Float32"),
"b": pd.Series([1.0, None, 3.0, 4.0], dtype="Float64"),
}
)
psdf = ps.from_pandas(pdf)
self._check_extension(psdf, pdf)
self._check_extension(psdf + 1, pdf + 1)
self._check_extension(psdf + psdf, pdf + pdf)
@unittest.skipIf(
not extension_float_dtypes_available, "pandas extension float dtypes are not available"
)
def test_astype_extension_float_dtypes(self):
pdf = pd.DataFrame({"a": [1.0, 2.0, None, 4.0], "b": [1.0, None, 3.0, 4.0]})
psdf = ps.from_pandas(pdf)
astype = {"a": "Float32", "b": "Float64"}
self._check_extension(psdf.astype(astype), pdf.astype(astype))
def test_insert(self):
#
# Basic DataFrame
#
pdf = pd.DataFrame([1, 2, 3])
psdf = ps.from_pandas(pdf)
psdf.insert(1, "b", 10)
pdf.insert(1, "b", 10)
self.assert_eq(psdf.sort_index(), pdf.sort_index(), almost=True)
psdf.insert(2, "c", 0.1)
pdf.insert(2, "c", 0.1)
self.assert_eq(psdf.sort_index(), pdf.sort_index(), almost=True)
psdf.insert(3, "d", psdf.b + 1)
pdf.insert(3, "d", pdf.b + 1)
self.assert_eq(psdf.sort_index(), pdf.sort_index(), almost=True)
psser = ps.Series([4, 5, 6])
self.assertRaises(ValueError, lambda: psdf.insert(0, "y", psser))
self.assertRaisesRegex(
ValueError, "cannot insert b, already exists", lambda: psdf.insert(1, "b", 10)
)
self.assertRaisesRegex(
TypeError,
'"column" should be a scalar value or tuple that contains scalar values',
lambda: psdf.insert(0, list("abc"), psser),
)
self.assertRaises(ValueError, lambda: psdf.insert(0, "e", [7, 8, 9, 10]))
self.assertRaises(ValueError, lambda: psdf.insert(0, "f", ps.Series([7, 8])))
self.assertRaises(AssertionError, lambda: psdf.insert(100, "y", psser))
self.assertRaises(AssertionError, lambda: psdf.insert(1, "y", psser, allow_duplicates=True))
#
# DataFrame with MultiIndex as columns
#
pdf = pd.DataFrame({("x", "a", "b"): [1, 2, 3]})
psdf = ps.from_pandas(pdf)
psdf.insert(1, "b", 10)
pdf.insert(1, "b", 10)
self.assert_eq(psdf.sort_index(), pdf.sort_index(), almost=True)
psdf.insert(2, "c", 0.1)
pdf.insert(2, "c", 0.1)
self.assert_eq(psdf.sort_index(), pdf.sort_index(), almost=True)
psdf.insert(3, "d", psdf.b + 1)
pdf.insert(3, "d", pdf.b + 1)
self.assert_eq(psdf.sort_index(), pdf.sort_index(), almost=True)
self.assertRaisesRegex(
ValueError, "cannot insert d, already exists", lambda: psdf.insert(4, "d", 11)
)
self.assertRaisesRegex(
ValueError,
'"column" must have length equal to number of column levels.',
lambda: psdf.insert(4, ("e",), 11),
)
def test_inplace(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
pdf["a"] = pdf["a"] + 10
psdf["a"] = psdf["a"] + 10
self.assert_eq(psdf, pdf)
self.assert_eq(psser, pser)
def test_assign_list(self):
pdf, psdf = self.df_pair
pser = pdf.a
psser = psdf.a
pdf["x"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
psdf["x"] = [10, 20, 30, 40, 50, 60, 70, 80, 90]
self.assert_eq(psdf.sort_index(), pdf.sort_index())
self.assert_eq(psser, pser)
with self.assertRaisesRegex(ValueError, "Length of values does not match length of index"):
psdf["z"] = [10, 20, 30, 40, 50, 60, 70, 80]
def test_dataframe_multiindex_columns(self):
pdf = pd.DataFrame(
{
("x", "a", "1"): [1, 2, 3],
("x", "b", "2"): [4, 5, 6],
("y.z", "c.d", "3"): [7, 8, 9],
("x", "b", "4"): [10, 11, 12],
},
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
self.assert_eq(psdf["x"], pdf["x"])
self.assert_eq(psdf["y.z"], pdf["y.z"])
self.assert_eq(psdf["x"]["b"], pdf["x"]["b"])
self.assert_eq(psdf["x"]["b"]["2"], pdf["x"]["b"]["2"])
self.assert_eq(psdf.x, pdf.x)
self.assert_eq(psdf.x.b, pdf.x.b)
self.assert_eq(psdf.x.b["2"], pdf.x.b["2"])
self.assertRaises(KeyError, lambda: psdf["z"])
self.assertRaises(AttributeError, lambda: psdf.z)
self.assert_eq(psdf[("x",)], pdf[("x",)])
self.assert_eq(psdf[("x", "a")], pdf[("x", "a")])
self.assert_eq(psdf[("x", "a", "1")], pdf[("x", "a", "1")])
def test_dataframe_column_level_name(self):
column = pd.Index(["A", "B", "C"], name="X")
pdf = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=column, index=np.random.rand(2))
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
self.assert_eq(psdf.columns.names, pdf.columns.names)
self.assert_eq(psdf.to_pandas().columns.names, pdf.columns.names)
def test_dataframe_multiindex_names_level(self):
columns = pd.MultiIndex.from_tuples(
[("X", "A", "Z"), ("X", "B", "Z"), ("Y", "C", "Z"), ("Y", "D", "Z")],
names=["lvl_1", "lvl_2", "lv_3"],
)
pdf = pd.DataFrame(
[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20]],
columns=columns,
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.columns.names, pdf.columns.names)
self.assert_eq(psdf.to_pandas().columns.names, pdf.columns.names)
psdf1 = ps.from_pandas(pdf)
self.assert_eq(psdf1.columns.names, pdf.columns.names)
self.assertRaises(
AssertionError,
lambda: ps.DataFrame(psdf1._internal.copy(column_label_names=("level",))),
)
self.assert_eq(psdf["X"], pdf["X"])
self.assert_eq(psdf["X"].columns.names, pdf["X"].columns.names)
self.assert_eq(psdf["X"].to_pandas().columns.names, pdf["X"].columns.names)
self.assert_eq(psdf["X"]["A"], pdf["X"]["A"])
self.assert_eq(psdf["X"]["A"].columns.names, pdf["X"]["A"].columns.names)
self.assert_eq(psdf["X"]["A"].to_pandas().columns.names, pdf["X"]["A"].columns.names)
self.assert_eq(psdf[("X", "A")], pdf[("X", "A")])
self.assert_eq(psdf[("X", "A")].columns.names, pdf[("X", "A")].columns.names)
self.assert_eq(psdf[("X", "A")].to_pandas().columns.names, pdf[("X", "A")].columns.names)
self.assert_eq(psdf[("X", "A", "Z")], pdf[("X", "A", "Z")])
def test_itertuples(self):
pdf = pd.DataFrame({"num_legs": [4, 2], "num_wings": [0, 2]}, index=["dog", "hawk"])
psdf = ps.from_pandas(pdf)
for ptuple, ktuple in zip(
pdf.itertuples(index=False, name="Animal"), psdf.itertuples(index=False, name="Animal")
):
self.assert_eq(ptuple, ktuple)
for ptuple, ktuple in zip(pdf.itertuples(name=None), psdf.itertuples(name=None)):
self.assert_eq(ptuple, ktuple)
pdf.index = pd.MultiIndex.from_arrays(
[[1, 2], ["black", "brown"]], names=("count", "color")
)
psdf = ps.from_pandas(pdf)
for ptuple, ktuple in zip(pdf.itertuples(name="Animal"), psdf.itertuples(name="Animal")):
self.assert_eq(ptuple, ktuple)
pdf.columns = pd.MultiIndex.from_arrays(
[["CA", "WA"], ["age", "children"]], names=("origin", "info")
)
psdf = ps.from_pandas(pdf)
for ptuple, ktuple in zip(pdf.itertuples(name="Animal"), psdf.itertuples(name="Animal")):
self.assert_eq(ptuple, ktuple)
pdf = pd.DataFrame([1, 2, 3])
psdf = ps.from_pandas(pdf)
for ptuple, ktuple in zip(
(pdf + 1).itertuples(name="num"), (psdf + 1).itertuples(name="num")
):
self.assert_eq(ptuple, ktuple)
# DataFrames with a large number of columns (>254)
pdf = pd.DataFrame(np.random.random((1, 255)))
psdf = ps.from_pandas(pdf)
for ptuple, ktuple in zip(pdf.itertuples(name="num"), psdf.itertuples(name="num")):
self.assert_eq(ptuple, ktuple)
def test_iterrows(self):
pdf = pd.DataFrame(
{
("x", "a", "1"): [1, 2, 3],
("x", "b", "2"): [4, 5, 6],
("y.z", "c.d", "3"): [7, 8, 9],
("x", "b", "4"): [10, 11, 12],
},
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
for (pdf_k, pdf_v), (psdf_k, psdf_v) in zip(pdf.iterrows(), psdf.iterrows()):
self.assert_eq(pdf_k, psdf_k)
self.assert_eq(pdf_v, psdf_v)
def test_reset_index(self):
pdf = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=np.random.rand(3))
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.reset_index(), pdf.reset_index())
self.assert_eq(psdf.reset_index().index, pdf.reset_index().index)
self.assert_eq(psdf.reset_index(drop=True), pdf.reset_index(drop=True))
pdf.index.name = "a"
psdf.index.name = "a"
with self.assertRaisesRegex(ValueError, "cannot insert a, already exists"):
psdf.reset_index()
self.assert_eq(psdf.reset_index(drop=True), pdf.reset_index(drop=True))
# inplace
pser = pdf.a
psser = psdf.a
pdf.reset_index(drop=True, inplace=True)
psdf.reset_index(drop=True, inplace=True)
self.assert_eq(psdf, pdf)
self.assert_eq(psser, pser)
def test_reset_index_with_default_index_types(self):
pdf = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=np.random.rand(3))
psdf = ps.from_pandas(pdf)
with ps.option_context("compute.default_index_type", "sequence"):
self.assert_eq(psdf.reset_index(), pdf.reset_index())
with ps.option_context("compute.default_index_type", "distributed-sequence"):
self.assert_eq(psdf.reset_index(), pdf.reset_index())
with ps.option_context("compute.default_index_type", "distributed"):
# the index is different.
self.assert_eq(psdf.reset_index().to_pandas().reset_index(drop=True), pdf.reset_index())
def test_reset_index_with_multiindex_columns(self):
index = pd.MultiIndex.from_tuples(
[("bird", "falcon"), ("bird", "parrot"), ("mammal", "lion"), ("mammal", "monkey")],
names=["class", "name"],
)
columns = pd.MultiIndex.from_tuples([("speed", "max"), ("species", "type")])
pdf = pd.DataFrame(
[(389.0, "fly"), (24.0, "fly"), (80.5, "run"), (np.nan, "jump")],
index=index,
columns=columns,
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
self.assert_eq(psdf.reset_index(), pdf.reset_index())
self.assert_eq(psdf.reset_index(level="class"), pdf.reset_index(level="class"))
self.assert_eq(
psdf.reset_index(level="class", col_level=1),
pdf.reset_index(level="class", col_level=1),
)
self.assert_eq(
psdf.reset_index(level="class", col_level=1, col_fill="species"),
pdf.reset_index(level="class", col_level=1, col_fill="species"),
)
self.assert_eq(
psdf.reset_index(level="class", col_level=1, col_fill="genus"),
pdf.reset_index(level="class", col_level=1, col_fill="genus"),
)
with self.assertRaisesRegex(IndexError, "Index has only 2 levels, not 3"):
psdf.reset_index(col_level=2)
pdf.index.names = [("x", "class"), ("y", "name")]
psdf.index.names = [("x", "class"), ("y", "name")]
self.assert_eq(psdf.reset_index(), pdf.reset_index())
with self.assertRaisesRegex(ValueError, "Item must have length equal to number of levels."):
psdf.reset_index(col_level=1)
def test_index_to_frame_reset_index(self):
def check(psdf, pdf):
self.assert_eq(psdf.reset_index(), pdf.reset_index())
self.assert_eq(psdf.reset_index(drop=True), pdf.reset_index(drop=True))
pdf.reset_index(drop=True, inplace=True)
psdf.reset_index(drop=True, inplace=True)
self.assert_eq(psdf, pdf)
pdf, psdf = self.df_pair
check(psdf.index.to_frame(), pdf.index.to_frame())
check(psdf.index.to_frame(index=False), pdf.index.to_frame(index=False))
if LooseVersion(pd.__version__) >= LooseVersion("0.24"):
# The `name` argument is added in pandas 0.24.
check(psdf.index.to_frame(name="a"), pdf.index.to_frame(name="a"))
check(
psdf.index.to_frame(index=False, name="a"),
pdf.index.to_frame(index=False, name="a"),
)
check(psdf.index.to_frame(name=("x", "a")), pdf.index.to_frame(name=("x", "a")))
check(
psdf.index.to_frame(index=False, name=("x", "a")),
pdf.index.to_frame(index=False, name=("x", "a")),
)
def test_multiindex_column_access(self):
columns = pd.MultiIndex.from_tuples(
[
("a", "", "", "b"),
("c", "", "d", ""),
("e", "", "f", ""),
("e", "g", "", ""),
("", "", "", "h"),
("i", "", "", ""),
]
)
pdf = pd.DataFrame(
[
(1, "a", "x", 10, 100, 1000),
(2, "b", "y", 20, 200, 2000),
(3, "c", "z", 30, 300, 3000),
],
columns=columns,
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
self.assert_eq(psdf["a"], pdf["a"])
self.assert_eq(psdf["a"]["b"], pdf["a"]["b"])
self.assert_eq(psdf["c"], pdf["c"])
self.assert_eq(psdf["c"]["d"], pdf["c"]["d"])
self.assert_eq(psdf["e"], pdf["e"])
self.assert_eq(psdf["e"][""]["f"], pdf["e"][""]["f"])
self.assert_eq(psdf["e"]["g"], pdf["e"]["g"])
self.assert_eq(psdf[""], pdf[""])
self.assert_eq(psdf[""]["h"], pdf[""]["h"])
self.assert_eq(psdf["i"], pdf["i"])
self.assert_eq(psdf[["a", "e"]], pdf[["a", "e"]])
self.assert_eq(psdf[["e", "a"]], pdf[["e", "a"]])
self.assert_eq(psdf[("a",)], pdf[("a",)])
self.assert_eq(psdf[("e", "g")], pdf[("e", "g")])
# self.assert_eq(psdf[("i",)], pdf[("i",)])
self.assert_eq(psdf[("i", "")], pdf[("i", "")])
self.assertRaises(KeyError, lambda: psdf[("a", "b")])
def test_repr_cache_invalidation(self):
# If there is any cache, inplace operations should invalidate it.
df = ps.range(10)
df.__repr__()
df["a"] = df["id"]
self.assertEqual(df.__repr__(), df.to_pandas().__repr__())
def test_repr_html_cache_invalidation(self):
# If there is any cache, inplace operations should invalidate it.
df = ps.range(10)
df._repr_html_()
df["a"] = df["id"]
self.assertEqual(df._repr_html_(), df.to_pandas()._repr_html_())
def test_empty_dataframe(self):
pdf = pd.DataFrame({"a": pd.Series([], dtype="i1"), "b": pd.Series([], dtype="str")})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
def test_all_null_dataframe(self):
pdf = pd.DataFrame(
{
"a": [None, None, None, "a"],
"b": [None, None, None, 1],
"c": [None, None, None] + list(np.arange(1, 2).astype("i1")),
"d": [None, None, None, 1.0],
"e": [None, None, None, True],
"f": [None, None, None] + list(pd.date_range("20130101", periods=1)),
},
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.iloc[:-1], pdf.iloc[:-1])
with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
self.assert_eq(psdf.iloc[:-1], pdf.iloc[:-1])
pdf = pd.DataFrame(
{
"a": pd.Series([None, None, None], dtype="float64"),
"b": pd.Series([None, None, None], dtype="str"),
},
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
def test_nullable_object(self):
pdf = pd.DataFrame(
{
"a": list("abc") + [np.nan, None],
"b": list(range(1, 4)) + [np.nan, None],
"c": list(np.arange(3, 6).astype("i1")) + [np.nan, None],
"d": list(np.arange(4.0, 7.0, dtype="float64")) + [np.nan, None],
"e": [True, False, True, np.nan, None],
"f": list(pd.date_range("20130101", periods=3)) + [np.nan, None],
},
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
with self.sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
def test_assign(self):
pdf, psdf = self.df_pair
psdf["w"] = 1.0
pdf["w"] = 1.0
self.assert_eq(psdf, pdf)
psdf.w = 10.0
pdf.w = 10.0
self.assert_eq(psdf, pdf)
psdf[1] = 1.0
pdf[1] = 1.0
self.assert_eq(psdf, pdf)
psdf = psdf.assign(a=psdf["a"] * 2)
pdf = pdf.assign(a=pdf["a"] * 2)
self.assert_eq(psdf, pdf)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "w"), ("y", "v")])
pdf.columns = columns
psdf.columns = columns
psdf[("a", "c")] = "def"
pdf[("a", "c")] = "def"
self.assert_eq(psdf, pdf)
psdf = psdf.assign(Z="ZZ")
pdf = pdf.assign(Z="ZZ")
self.assert_eq(psdf, pdf)
psdf["x"] = "ghi"
pdf["x"] = "ghi"
self.assert_eq(psdf, pdf)
def test_head(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf.head(2), pdf.head(2))
self.assert_eq(psdf.head(3), pdf.head(3))
self.assert_eq(psdf.head(0), pdf.head(0))
self.assert_eq(psdf.head(-3), pdf.head(-3))
self.assert_eq(psdf.head(-10), pdf.head(-10))
def test_attributes(self):
psdf = self.psdf
self.assertIn("a", dir(psdf))
self.assertNotIn("foo", dir(psdf))
self.assertRaises(AttributeError, lambda: psdf.foo)
psdf = ps.DataFrame({"a b c": [1, 2, 3]})
self.assertNotIn("a b c", dir(psdf))
psdf = ps.DataFrame({"a": [1, 2], 5: [1, 2]})
self.assertIn("a", dir(psdf))
self.assertNotIn(5, dir(psdf))
def test_column_names(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf.columns, pdf.columns)
self.assert_eq(psdf[["b", "a"]].columns, pdf[["b", "a"]].columns)
self.assert_eq(psdf["a"].name, pdf["a"].name)
self.assert_eq((psdf["a"] + 1).name, (pdf["a"] + 1).name)
self.assert_eq((psdf.a + psdf.b).name, (pdf.a + pdf.b).name)
self.assert_eq((psdf.a + psdf.b.rename("a")).name, (pdf.a + pdf.b.rename("a")).name)
self.assert_eq((psdf.a + psdf.b.rename()).name, (pdf.a + pdf.b.rename()).name)
self.assert_eq((psdf.a.rename() + psdf.b).name, (pdf.a.rename() + pdf.b).name)
self.assert_eq(
(psdf.a.rename() + psdf.b.rename()).name, (pdf.a.rename() + pdf.b.rename()).name
)
def test_rename_columns(self):
pdf = pd.DataFrame(
{"a": [1, 2, 3, 4, 5, 6, 7], "b": [7, 6, 5, 4, 3, 2, 1]}, index=np.random.rand(7)
)
psdf = ps.from_pandas(pdf)
psdf.columns = ["x", "y"]
pdf.columns = ["x", "y"]
self.assert_eq(psdf.columns, pd.Index(["x", "y"]))
self.assert_eq(psdf, pdf)
self.assert_eq(psdf._internal.data_spark_column_names, ["x", "y"])
self.assert_eq(psdf.to_spark().columns, ["x", "y"])
self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "x", "y"])
columns = pdf.columns
columns.name = "lvl_1"
psdf.columns = columns
self.assert_eq(psdf.columns.names, ["lvl_1"])
self.assert_eq(psdf, pdf)
msg = "Length mismatch: Expected axis has 2 elements, new values have 4 elements"
with self.assertRaisesRegex(ValueError, msg):
psdf.columns = [1, 2, 3, 4]
# Multi-index columns
pdf = pd.DataFrame(
{("A", "0"): [1, 2, 2, 3], ("B", "1"): [1, 2, 3, 4]}, index=np.random.rand(4)
)
psdf = ps.from_pandas(pdf)
columns = pdf.columns
self.assert_eq(psdf.columns, columns)
self.assert_eq(psdf, pdf)
pdf.columns = ["x", "y"]
psdf.columns = ["x", "y"]
self.assert_eq(psdf.columns, pd.Index(["x", "y"]))
self.assert_eq(psdf, pdf)
self.assert_eq(psdf._internal.data_spark_column_names, ["x", "y"])
self.assert_eq(psdf.to_spark().columns, ["x", "y"])
self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "x", "y"])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.columns, columns)
self.assert_eq(psdf, pdf)
self.assert_eq(psdf._internal.data_spark_column_names, ["(A, 0)", "(B, 1)"])
self.assert_eq(psdf.to_spark().columns, ["(A, 0)", "(B, 1)"])
self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "(A, 0)", "(B, 1)"])
columns.names = ["lvl_1", "lvl_2"]
psdf.columns = columns
self.assert_eq(psdf.columns.names, ["lvl_1", "lvl_2"])
self.assert_eq(psdf, pdf)
self.assert_eq(psdf._internal.data_spark_column_names, ["(A, 0)", "(B, 1)"])
self.assert_eq(psdf.to_spark().columns, ["(A, 0)", "(B, 1)"])
self.assert_eq(psdf.to_spark(index_col="index").columns, ["index", "(A, 0)", "(B, 1)"])
def test_rename_dataframe(self):
pdf1 = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
psdf1 = ps.from_pandas(pdf1)
self.assert_eq(
psdf1.rename(columns={"A": "a", "B": "b"}), pdf1.rename(columns={"A": "a", "B": "b"})
)
result_psdf = psdf1.rename(index={1: 10, 2: 20})
result_pdf = pdf1.rename(index={1: 10, 2: 20})
self.assert_eq(result_psdf, result_pdf)
# inplace
pser = result_pdf.A
psser = result_psdf.A
result_psdf.rename(index={10: 100, 20: 200}, inplace=True)
result_pdf.rename(index={10: 100, 20: 200}, inplace=True)
self.assert_eq(result_psdf, result_pdf)
self.assert_eq(psser, pser)
def str_lower(s) -> str:
return str.lower(s)
self.assert_eq(
psdf1.rename(str_lower, axis="columns"), pdf1.rename(str_lower, axis="columns")
)
def mul10(x) -> int:
return x * 10
self.assert_eq(psdf1.rename(mul10, axis="index"), pdf1.rename(mul10, axis="index"))
self.assert_eq(
psdf1.rename(columns=str_lower, index={1: 10, 2: 20}),
pdf1.rename(columns=str_lower, index={1: 10, 2: 20}),
)
idx = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Y", "D")])
pdf2 = pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=idx)
psdf2 = ps.from_pandas(pdf2)
self.assert_eq(psdf2.rename(columns=str_lower), pdf2.rename(columns=str_lower))
self.assert_eq(
psdf2.rename(columns=str_lower, level=0), pdf2.rename(columns=str_lower, level=0)
)
self.assert_eq(
psdf2.rename(columns=str_lower, level=1), pdf2.rename(columns=str_lower, level=1)
)
pdf3 = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], index=idx, columns=list("ab"))
psdf3 = ps.from_pandas(pdf3)
self.assert_eq(psdf3.rename(index=str_lower), pdf3.rename(index=str_lower))
self.assert_eq(
psdf3.rename(index=str_lower, level=0), pdf3.rename(index=str_lower, level=0)
)
self.assert_eq(
psdf3.rename(index=str_lower, level=1), pdf3.rename(index=str_lower, level=1)
)
pdf4 = pdf2 + 1
psdf4 = psdf2 + 1
self.assert_eq(psdf4.rename(columns=str_lower), pdf4.rename(columns=str_lower))
pdf5 = pdf3 + 1
psdf5 = psdf3 + 1
self.assert_eq(psdf5.rename(index=str_lower), pdf5.rename(index=str_lower))
def test_rename_axis(self):
index = pd.Index(["A", "B", "C"], name="index")
columns = pd.Index(["numbers", "values"], name="cols")
pdf = pd.DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], index=index, columns=columns)
psdf = ps.from_pandas(pdf)
for axis in [0, "index"]:
self.assert_eq(
pdf.rename_axis("index2", axis=axis).sort_index(),
psdf.rename_axis("index2", axis=axis).sort_index(),
)
self.assert_eq(
pdf.rename_axis(["index2"], axis=axis).sort_index(),
psdf.rename_axis(["index2"], axis=axis).sort_index(),
)
for axis in [1, "columns"]:
self.assert_eq(
pdf.rename_axis("cols2", axis=axis).sort_index(),
psdf.rename_axis("cols2", axis=axis).sort_index(),
)
self.assert_eq(
pdf.rename_axis(["cols2"], axis=axis).sort_index(),
psdf.rename_axis(["cols2"], axis=axis).sort_index(),
)
pdf2 = pdf.copy()
psdf2 = psdf.copy()
pdf2.rename_axis("index2", axis="index", inplace=True)
psdf2.rename_axis("index2", axis="index", inplace=True)
self.assert_eq(pdf2.sort_index(), psdf2.sort_index())
self.assertRaises(ValueError, lambda: psdf.rename_axis(["index2", "index3"], axis=0))
self.assertRaises(ValueError, lambda: psdf.rename_axis(["cols2", "cols3"], axis=1))
self.assertRaises(TypeError, lambda: psdf.rename_axis(mapper=["index2"], index=["index3"]))
# index/columns parameters and dict_like/functions mappers introduced in pandas 0.24.0
if LooseVersion(pd.__version__) >= LooseVersion("0.24.0"):
self.assert_eq(
pdf.rename_axis(index={"index": "index2"}, columns={"cols": "cols2"}).sort_index(),
psdf.rename_axis(index={"index": "index2"}, columns={"cols": "cols2"}).sort_index(),
)
self.assert_eq(
pdf.rename_axis(
index={"missing": "index2"}, columns={"missing": "cols2"}
).sort_index(),
psdf.rename_axis(
index={"missing": "index2"}, columns={"missing": "cols2"}
).sort_index(),
)
self.assert_eq(
pdf.rename_axis(index=str.upper, columns=str.upper).sort_index(),
psdf.rename_axis(index=str.upper, columns=str.upper).sort_index(),
)
else:
expected = pdf
expected.index.name = "index2"
expected.columns.name = "cols2"
result = psdf.rename_axis(
index={"index": "index2"}, columns={"cols": "cols2"}
).sort_index()
self.assert_eq(expected, result)
expected.index.name = "index"
expected.columns.name = "cols"
result = psdf.rename_axis(
index={"missing": "index2"}, columns={"missing": "cols2"}
).sort_index()
self.assert_eq(expected, result)
expected.index.name = "INDEX"
expected.columns.name = "COLS"
result = psdf.rename_axis(index=str.upper, columns=str.upper).sort_index()
self.assert_eq(expected, result)
index = pd.MultiIndex.from_tuples(
[("A", "B"), ("C", "D"), ("E", "F")], names=["index1", "index2"]
)
columns = pd.MultiIndex.from_tuples(
[("numbers", "first"), ("values", "second")], names=["cols1", "cols2"]
)
pdf = pd.DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], index=index, columns=columns)
psdf = ps.from_pandas(pdf)
for axis in [0, "index"]:
self.assert_eq(
pdf.rename_axis(["index3", "index4"], axis=axis).sort_index(),
psdf.rename_axis(["index3", "index4"], axis=axis).sort_index(),
)
for axis in [1, "columns"]:
self.assert_eq(
pdf.rename_axis(["cols3", "cols4"], axis=axis).sort_index(),
psdf.rename_axis(["cols3", "cols4"], axis=axis).sort_index(),
)
self.assertRaises(
ValueError, lambda: psdf.rename_axis(["index3", "index4", "index5"], axis=0)
)
self.assertRaises(ValueError, lambda: psdf.rename_axis(["cols3", "cols4", "cols5"], axis=1))
# index/columns parameters and dict_like/functions mappers introduced in pandas 0.24.0
if LooseVersion(pd.__version__) >= LooseVersion("0.24.0"):
self.assert_eq(
pdf.rename_axis(
index={"index1": "index3"}, columns={"cols1": "cols3"}
).sort_index(),
psdf.rename_axis(
index={"index1": "index3"}, columns={"cols1": "cols3"}
).sort_index(),
)
self.assert_eq(
pdf.rename_axis(
index={"missing": "index3"}, columns={"missing": "cols3"}
).sort_index(),
psdf.rename_axis(
index={"missing": "index3"}, columns={"missing": "cols3"}
).sort_index(),
)
self.assert_eq(
pdf.rename_axis(
index={"index1": "index3", "index2": "index4"},
columns={"cols1": "cols3", "cols2": "cols4"},
).sort_index(),
psdf.rename_axis(
index={"index1": "index3", "index2": "index4"},
columns={"cols1": "cols3", "cols2": "cols4"},
).sort_index(),
)
self.assert_eq(
pdf.rename_axis(index=str.upper, columns=str.upper).sort_index(),
psdf.rename_axis(index=str.upper, columns=str.upper).sort_index(),
)
else:
expected = pdf
expected.index.names = ["index3", "index2"]
expected.columns.names = ["cols3", "cols2"]
result = psdf.rename_axis(
index={"index1": "index3"}, columns={"cols1": "cols3"}
).sort_index()
self.assert_eq(expected, result)
expected.index.names = ["index1", "index2"]
expected.columns.names = ["cols1", "cols2"]
result = psdf.rename_axis(
index={"missing": "index2"}, columns={"missing": "cols2"}
).sort_index()
self.assert_eq(expected, result)
expected.index.names = ["index3", "index4"]
expected.columns.names = ["cols3", "cols4"]
result = psdf.rename_axis(
index={"index1": "index3", "index2": "index4"},
columns={"cols1": "cols3", "cols2": "cols4"},
).sort_index()
self.assert_eq(expected, result)
expected.index.names = ["INDEX1", "INDEX2"]
expected.columns.names = ["COLS1", "COLS2"]
result = psdf.rename_axis(index=str.upper, columns=str.upper).sort_index()
self.assert_eq(expected, result)
def test_dot_in_column_name(self):
self.assert_eq(
ps.DataFrame(ps.range(1)._internal.spark_frame.selectExpr("1L as `a.b`"))["a.b"],
ps.Series([1], name="a.b"),
)
def test_aggregate(self):
pdf = pd.DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9], [np.nan, np.nan, np.nan]], columns=["A", "B", "C"]
)
psdf = ps.from_pandas(pdf)
self.assert_eq(
psdf.agg(["sum", "min"])[["A", "B", "C"]].sort_index(), # TODO?: fix column order
pdf.agg(["sum", "min"])[["A", "B", "C"]].sort_index(),
)
self.assert_eq(
psdf.agg({"A": ["sum", "min"], "B": ["min", "max"]})[["A", "B"]].sort_index(),
pdf.agg({"A": ["sum", "min"], "B": ["min", "max"]})[["A", "B"]].sort_index(),
)
self.assertRaises(KeyError, lambda: psdf.agg({"A": ["sum", "min"], "X": ["min", "max"]}))
# multi-index columns
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.agg(["sum", "min"])[[("X", "A"), ("X", "B"), ("Y", "C")]].sort_index(),
pdf.agg(["sum", "min"])[[("X", "A"), ("X", "B"), ("Y", "C")]].sort_index(),
)
self.assert_eq(
psdf.agg({("X", "A"): ["sum", "min"], ("X", "B"): ["min", "max"]})[
[("X", "A"), ("X", "B")]
].sort_index(),
pdf.agg({("X", "A"): ["sum", "min"], ("X", "B"): ["min", "max"]})[
[("X", "A"), ("X", "B")]
].sort_index(),
)
self.assertRaises(TypeError, lambda: psdf.agg({"X": ["sum", "min"], "Y": ["min", "max"]}))
# non-string names
pdf = pd.DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9], [np.nan, np.nan, np.nan]], columns=[10, 20, 30]
)
psdf = ps.from_pandas(pdf)
self.assert_eq(
psdf.agg(["sum", "min"])[[10, 20, 30]].sort_index(),
pdf.agg(["sum", "min"])[[10, 20, 30]].sort_index(),
)
self.assert_eq(
psdf.agg({10: ["sum", "min"], 20: ["min", "max"]})[[10, 20]].sort_index(),
pdf.agg({10: ["sum", "min"], 20: ["min", "max"]})[[10, 20]].sort_index(),
)
columns = pd.MultiIndex.from_tuples([("X", 10), ("X", 20), ("Y", 30)])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.agg(["sum", "min"])[[("X", 10), ("X", 20), ("Y", 30)]].sort_index(),
pdf.agg(["sum", "min"])[[("X", 10), ("X", 20), ("Y", 30)]].sort_index(),
)
self.assert_eq(
psdf.agg({("X", 10): ["sum", "min"], ("X", 20): ["min", "max"]})[
[("X", 10), ("X", 20)]
].sort_index(),
pdf.agg({("X", 10): ["sum", "min"], ("X", 20): ["min", "max"]})[
[("X", 10), ("X", 20)]
].sort_index(),
)
pdf = pd.DataFrame(
[datetime(2019, 2, 2, 0, 0, 0, 0), datetime(2019, 2, 3, 0, 0, 0, 0)],
columns=["timestamp"],
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.timestamp.min(), pdf.timestamp.min())
self.assert_eq(psdf.timestamp.max(), pdf.timestamp.max())
def test_droplevel(self):
pdf = (
pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
.set_index([0, 1])
.rename_axis(["a", "b"])
)
pdf.columns = pd.MultiIndex.from_tuples(
[("c", "e"), ("d", "f")], names=["level_1", "level_2"]
)
psdf = ps.from_pandas(pdf)
self.assertRaises(ValueError, lambda: psdf.droplevel(["a", "b"]))
self.assertRaises(ValueError, lambda: psdf.droplevel([1, 1, 1, 1, 1]))
self.assertRaises(IndexError, lambda: psdf.droplevel(2))
self.assertRaises(IndexError, lambda: psdf.droplevel(-3))
self.assertRaises(KeyError, lambda: psdf.droplevel({"a"}))
self.assertRaises(KeyError, lambda: psdf.droplevel({"a": 1}))
self.assertRaises(ValueError, lambda: psdf.droplevel(["level_1", "level_2"], axis=1))
self.assertRaises(IndexError, lambda: psdf.droplevel(2, axis=1))
self.assertRaises(IndexError, lambda: psdf.droplevel(-3, axis=1))
self.assertRaises(KeyError, lambda: psdf.droplevel({"level_1"}, axis=1))
self.assertRaises(KeyError, lambda: psdf.droplevel({"level_1": 1}, axis=1))
# droplevel is new in pandas 0.24.0
if LooseVersion(pd.__version__) >= LooseVersion("0.24.0"):
self.assert_eq(pdf.droplevel("a"), psdf.droplevel("a"))
self.assert_eq(pdf.droplevel(["a"]), psdf.droplevel(["a"]))
self.assert_eq(pdf.droplevel(("a",)), psdf.droplevel(("a",)))
self.assert_eq(pdf.droplevel(0), psdf.droplevel(0))
self.assert_eq(pdf.droplevel(-1), psdf.droplevel(-1))
self.assert_eq(pdf.droplevel("level_1", axis=1), psdf.droplevel("level_1", axis=1))
self.assert_eq(pdf.droplevel(["level_1"], axis=1), psdf.droplevel(["level_1"], axis=1))
self.assert_eq(
pdf.droplevel(("level_1",), axis=1), psdf.droplevel(("level_1",), axis=1)
)
self.assert_eq(pdf.droplevel(0, axis=1), psdf.droplevel(0, axis=1))
self.assert_eq(pdf.droplevel(-1, axis=1), psdf.droplevel(-1, axis=1))
else:
expected = pdf.copy()
expected.index = expected.index.droplevel("a")
self.assert_eq(expected, psdf.droplevel("a"))
self.assert_eq(expected, psdf.droplevel(["a"]))
self.assert_eq(expected, psdf.droplevel(("a",)))
self.assert_eq(expected, psdf.droplevel(0))
expected = pdf.copy()
expected.index = expected.index.droplevel(-1)
self.assert_eq(expected, psdf.droplevel(-1))
expected = pdf.copy()
expected.columns = expected.columns.droplevel("level_1")
self.assert_eq(expected, psdf.droplevel("level_1", axis=1))
self.assert_eq(expected, psdf.droplevel(["level_1"], axis=1))
self.assert_eq(expected, psdf.droplevel(("level_1",), axis=1))
self.assert_eq(expected, psdf.droplevel(0, axis=1))
expected = pdf.copy()
expected.columns = expected.columns.droplevel(-1)
self.assert_eq(expected, psdf.droplevel(-1, axis=1))
# Tupled names
pdf.columns.names = [("level", 1), ("level", 2)]
pdf.index.names = [("a", 10), ("x", 20)]
psdf = ps.from_pandas(pdf)
self.assertRaises(KeyError, lambda: psdf.droplevel("a"))
self.assertRaises(KeyError, lambda: psdf.droplevel(("a", 10)))
# droplevel is new in pandas 0.24.0
if LooseVersion(pd.__version__) >= LooseVersion("0.24.0"):
self.assert_eq(pdf.droplevel([("a", 10)]), psdf.droplevel([("a", 10)]))
self.assert_eq(
pdf.droplevel([("level", 1)], axis=1), psdf.droplevel([("level", 1)], axis=1)
)
else:
expected = pdf.copy()
expected.index = expected.index.droplevel([("a", 10)])
self.assert_eq(expected, psdf.droplevel([("a", 10)]))
expected = pdf.copy()
expected.columns = expected.columns.droplevel([("level", 1)])
self.assert_eq(expected, psdf.droplevel([("level", 1)], axis=1))
# non-string names
pdf = (
pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
.set_index([0, 1])
.rename_axis([10.0, 20.0])
)
pdf.columns = pd.MultiIndex.from_tuples([("c", "e"), ("d", "f")], names=[100.0, 200.0])
psdf = ps.from_pandas(pdf)
# droplevel is new in pandas 0.24.0
if LooseVersion(pd.__version__) >= LooseVersion("0.24.0"):
self.assert_eq(pdf.droplevel(10.0), psdf.droplevel(10.0))
self.assert_eq(pdf.droplevel([10.0]), psdf.droplevel([10.0]))
self.assert_eq(pdf.droplevel((10.0,)), psdf.droplevel((10.0,)))
self.assert_eq(pdf.droplevel(0), psdf.droplevel(0))
self.assert_eq(pdf.droplevel(-1), psdf.droplevel(-1))
self.assert_eq(pdf.droplevel(100.0, axis=1), psdf.droplevel(100.0, axis=1))
self.assert_eq(pdf.droplevel(0, axis=1), psdf.droplevel(0, axis=1))
else:
expected = pdf.copy()
expected.index = expected.index.droplevel(10.0)
self.assert_eq(expected, psdf.droplevel(10.0))
self.assert_eq(expected, psdf.droplevel([10.0]))
self.assert_eq(expected, psdf.droplevel((10.0,)))
self.assert_eq(expected, psdf.droplevel(0))
expected = pdf.copy()
expected.index = expected.index.droplevel(-1)
self.assert_eq(expected, psdf.droplevel(-1))
expected = pdf.copy()
expected.columns = expected.columns.droplevel(100.0)
self.assert_eq(expected, psdf.droplevel(100.0, axis=1))
self.assert_eq(expected, psdf.droplevel(0, axis=1))
def test_drop(self):
pdf = pd.DataFrame({"x": [1, 2], "y": [3, 4], "z": [5, 6]}, index=np.random.rand(2))
psdf = ps.from_pandas(pdf)
# Assert 'labels' or 'columns' parameter is set
expected_error_message = "Need to specify at least one of 'labels' or 'columns'"
with self.assertRaisesRegex(ValueError, expected_error_message):
psdf.drop()
# Assert axis cannot be 0
with self.assertRaisesRegex(NotImplementedError, "Drop currently only works for axis=1"):
psdf.drop("x", axis=0)
# Assert using a str for 'labels' works
self.assert_eq(psdf.drop("x", axis=1), pdf.drop("x", axis=1))
# Assert axis is 1 by default
self.assert_eq(psdf.drop("x"), pdf.drop("x", axis=1))
# Assert using a list for 'labels' works
self.assert_eq(psdf.drop(["y", "z"], axis=1), pdf.drop(["y", "z"], axis=1))
# Assert using 'columns' instead of 'labels' produces the same results
self.assert_eq(psdf.drop(columns="x"), pdf.drop(columns="x"))
self.assert_eq(psdf.drop(columns=["y", "z"]), pdf.drop(columns=["y", "z"]))
# Assert 'labels' being used when both 'labels' and 'columns' are specified
# TODO: should throw an error?
expected_output = pd.DataFrame({"y": [3, 4], "z": [5, 6]}, index=psdf.index.to_pandas())
self.assert_eq(psdf.drop(labels=["x"], columns=["y"]), expected_output)
columns = pd.MultiIndex.from_tuples([(1, "x"), (1, "y"), (2, "z")])
pdf.columns = columns
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.drop(columns=1), pdf.drop(columns=1))
self.assert_eq(psdf.drop(columns=(1, "x")), pdf.drop(columns=(1, "x")))
self.assert_eq(psdf.drop(columns=[(1, "x"), 2]), pdf.drop(columns=[(1, "x"), 2]))
self.assertRaises(KeyError, lambda: psdf.drop(columns=3))
self.assertRaises(KeyError, lambda: psdf.drop(columns=(1, "z")))
# non-string names
pdf = pd.DataFrame({10: [1, 2], 20: [3, 4], 30: [5, 6]}, index=np.random.rand(2))
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.drop(10), pdf.drop(10, axis=1))
self.assert_eq(psdf.drop([20, 30]), pdf.drop([20, 30], axis=1))
def _test_dropna(self, pdf, axis):
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.dropna(axis=axis), pdf.dropna(axis=axis))
self.assert_eq(psdf.dropna(axis=axis, how="all"), pdf.dropna(axis=axis, how="all"))
self.assert_eq(psdf.dropna(axis=axis, subset=["x"]), pdf.dropna(axis=axis, subset=["x"]))
self.assert_eq(psdf.dropna(axis=axis, subset="x"), pdf.dropna(axis=axis, subset=["x"]))
self.assert_eq(
psdf.dropna(axis=axis, subset=["y", "z"]), pdf.dropna(axis=axis, subset=["y", "z"])
)
self.assert_eq(
psdf.dropna(axis=axis, subset=["y", "z"], how="all"),
pdf.dropna(axis=axis, subset=["y", "z"], how="all"),
)
self.assert_eq(psdf.dropna(axis=axis, thresh=2), pdf.dropna(axis=axis, thresh=2))
self.assert_eq(
psdf.dropna(axis=axis, thresh=1, subset=["y", "z"]),
pdf.dropna(axis=axis, thresh=1, subset=["y", "z"]),
)
pdf2 = pdf.copy()
psdf2 = psdf.copy()
pser = pdf2[pdf2.columns[0]]
psser = psdf2[psdf2.columns[0]]
pdf2.dropna(inplace=True)
psdf2.dropna(inplace=True)
self.assert_eq(psdf2, pdf2)
self.assert_eq(psser, pser)
# multi-index
columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")])
if axis == 0:
pdf.columns = columns
else:
pdf.index = columns
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.dropna(axis=axis), pdf.dropna(axis=axis))
self.assert_eq(psdf.dropna(axis=axis, how="all"), pdf.dropna(axis=axis, how="all"))
self.assert_eq(
psdf.dropna(axis=axis, subset=[("a", "x")]), pdf.dropna(axis=axis, subset=[("a", "x")])
)
self.assert_eq(
psdf.dropna(axis=axis, subset=("a", "x")), pdf.dropna(axis=axis, subset=[("a", "x")])
)
self.assert_eq(
psdf.dropna(axis=axis, subset=[("a", "y"), ("b", "z")]),
pdf.dropna(axis=axis, subset=[("a", "y"), ("b", "z")]),
)
self.assert_eq(
psdf.dropna(axis=axis, subset=[("a", "y"), ("b", "z")], how="all"),
pdf.dropna(axis=axis, subset=[("a", "y"), ("b", "z")], how="all"),
)
self.assert_eq(psdf.dropna(axis=axis, thresh=2), pdf.dropna(axis=axis, thresh=2))
self.assert_eq(
psdf.dropna(axis=axis, thresh=1, subset=[("a", "y"), ("b", "z")]),
pdf.dropna(axis=axis, thresh=1, subset=[("a", "y"), ("b", "z")]),
)
def test_dropna_axis_index(self):
pdf = pd.DataFrame(
{
"x": [np.nan, 2, 3, 4, np.nan, 6],
"y": [1, 2, np.nan, 4, np.nan, np.nan],
"z": [1, 2, 3, 4, np.nan, np.nan],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
self._test_dropna(pdf, axis=0)
# empty
pdf = pd.DataFrame(index=np.random.rand(6))
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.dropna(), pdf.dropna())
self.assert_eq(psdf.dropna(how="all"), pdf.dropna(how="all"))
self.assert_eq(psdf.dropna(thresh=0), pdf.dropna(thresh=0))
self.assert_eq(psdf.dropna(thresh=1), pdf.dropna(thresh=1))
with self.assertRaisesRegex(ValueError, "No axis named foo"):
psdf.dropna(axis="foo")
self.assertRaises(KeyError, lambda: psdf.dropna(subset="1"))
with self.assertRaisesRegex(ValueError, "invalid how option: 1"):
psdf.dropna(how=1)
with self.assertRaisesRegex(TypeError, "must specify how or thresh"):
psdf.dropna(how=None)
def test_dropna_axis_column(self):
pdf = pd.DataFrame(
{
"x": [np.nan, 2, 3, 4, np.nan, 6],
"y": [1, 2, np.nan, 4, np.nan, np.nan],
"z": [1, 2, 3, 4, np.nan, np.nan],
},
index=[str(r) for r in np.random.rand(6)],
).T
self._test_dropna(pdf, axis=1)
# empty
pdf = pd.DataFrame({"x": [], "y": [], "z": []})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.dropna(axis=1), pdf.dropna(axis=1))
self.assert_eq(psdf.dropna(axis=1, how="all"), pdf.dropna(axis=1, how="all"))
self.assert_eq(psdf.dropna(axis=1, thresh=0), pdf.dropna(axis=1, thresh=0))
self.assert_eq(psdf.dropna(axis=1, thresh=1), pdf.dropna(axis=1, thresh=1))
def test_dtype(self):
pdf = pd.DataFrame(
{
"a": list("abc"),
"b": list(range(1, 4)),
"c": np.arange(3, 6).astype("i1"),
"d": np.arange(4.0, 7.0, dtype="float64"),
"e": [True, False, True],
"f": pd.date_range("20130101", periods=3),
},
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
self.assertTrue((psdf.dtypes == pdf.dtypes).all())
# multi-index columns
columns = pd.MultiIndex.from_tuples(zip(list("xxxyyz"), list("abcdef")))
pdf.columns = columns
psdf.columns = columns
self.assertTrue((psdf.dtypes == pdf.dtypes).all())
def test_fillna(self):
pdf = pd.DataFrame(
{
"x": [np.nan, 2, 3, 4, np.nan, 6],
"y": [1, 2, np.nan, 4, np.nan, np.nan],
"z": [1, 2, 3, 4, np.nan, np.nan],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
self.assert_eq(psdf.fillna(-1), pdf.fillna(-1))
self.assert_eq(
psdf.fillna({"x": -1, "y": -2, "z": -5}), pdf.fillna({"x": -1, "y": -2, "z": -5})
)
self.assert_eq(pdf.fillna(method="ffill"), psdf.fillna(method="ffill"))
self.assert_eq(pdf.fillna(method="ffill", limit=2), psdf.fillna(method="ffill", limit=2))
self.assert_eq(pdf.fillna(method="bfill"), psdf.fillna(method="bfill"))
self.assert_eq(pdf.fillna(method="bfill", limit=2), psdf.fillna(method="bfill", limit=2))
pdf = pdf.set_index(["x", "y"])
psdf = ps.from_pandas(pdf)
# check multi index
self.assert_eq(psdf.fillna(-1), pdf.fillna(-1))
self.assert_eq(pdf.fillna(method="bfill"), psdf.fillna(method="bfill"))
self.assert_eq(pdf.fillna(method="ffill"), psdf.fillna(method="ffill"))
pser = pdf.z
psser = psdf.z
pdf.fillna({"x": -1, "y": -2, "z": -5}, inplace=True)
psdf.fillna({"x": -1, "y": -2, "z": -5}, inplace=True)
self.assert_eq(psdf, pdf)
self.assert_eq(psser, pser)
s_nan = pd.Series([-1, -2, -5], index=["x", "y", "z"], dtype=int)
self.assert_eq(psdf.fillna(s_nan), pdf.fillna(s_nan))
with self.assertRaisesRegex(NotImplementedError, "fillna currently only"):
psdf.fillna(-1, axis=1)
with self.assertRaisesRegex(NotImplementedError, "fillna currently only"):
psdf.fillna(-1, axis="columns")
with self.assertRaisesRegex(ValueError, "limit parameter for value is not support now"):
psdf.fillna(-1, limit=1)
with self.assertRaisesRegex(TypeError, "Unsupported.*DataFrame"):
psdf.fillna(pd.DataFrame({"x": [-1], "y": [-1], "z": [-1]}))
with self.assertRaisesRegex(TypeError, "Unsupported.*int64"):
psdf.fillna({"x": np.int64(-6), "y": np.int64(-4), "z": -5})
with self.assertRaisesRegex(ValueError, "Expecting 'pad', 'ffill', 'backfill' or 'bfill'."):
psdf.fillna(method="xxx")
with self.assertRaisesRegex(
ValueError, "Must specify a fillna 'value' or 'method' parameter."
):
psdf.fillna()
# multi-index columns
pdf = pd.DataFrame(
{
("x", "a"): [np.nan, 2, 3, 4, np.nan, 6],
("x", "b"): [1, 2, np.nan, 4, np.nan, np.nan],
("y", "c"): [1, 2, 3, 4, np.nan, np.nan],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.fillna(-1), pdf.fillna(-1))
self.assert_eq(
psdf.fillna({("x", "a"): -1, ("x", "b"): -2, ("y", "c"): -5}),
pdf.fillna({("x", "a"): -1, ("x", "b"): -2, ("y", "c"): -5}),
)
self.assert_eq(pdf.fillna(method="ffill"), psdf.fillna(method="ffill"))
self.assert_eq(pdf.fillna(method="ffill", limit=2), psdf.fillna(method="ffill", limit=2))
self.assert_eq(pdf.fillna(method="bfill"), psdf.fillna(method="bfill"))
self.assert_eq(pdf.fillna(method="bfill", limit=2), psdf.fillna(method="bfill", limit=2))
self.assert_eq(psdf.fillna({"x": -1}), pdf.fillna({"x": -1}))
if sys.version_info >= (3, 6):
# flaky in Python 3.5.
self.assert_eq(
psdf.fillna({"x": -1, ("x", "b"): -2}), pdf.fillna({"x": -1, ("x", "b"): -2})
)
self.assert_eq(
psdf.fillna({("x", "b"): -2, "x": -1}), pdf.fillna({("x", "b"): -2, "x": -1})
)
# check multi index
pdf = pdf.set_index([("x", "a"), ("x", "b")])
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.fillna(-1), pdf.fillna(-1))
self.assert_eq(
psdf.fillna({("x", "a"): -1, ("x", "b"): -2, ("y", "c"): -5}),
pdf.fillna({("x", "a"): -1, ("x", "b"): -2, ("y", "c"): -5}),
)
def test_isnull(self):
pdf = pd.DataFrame(
{"x": [1, 2, 3, 4, None, 6], "y": list("abdabd")}, index=np.random.rand(6)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.notnull(), pdf.notnull())
self.assert_eq(psdf.isnull(), pdf.isnull())
def test_to_datetime(self):
pdf = pd.DataFrame(
{"year": [2015, 2016], "month": [2, 3], "day": [4, 5]}, index=np.random.rand(2)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pd.to_datetime(pdf), ps.to_datetime(psdf))
def test_nunique(self):
pdf = pd.DataFrame({"A": [1, 2, 3], "B": [np.nan, 3, np.nan]}, index=np.random.rand(3))
psdf = ps.from_pandas(pdf)
# Assert NaNs are dropped by default
self.assert_eq(psdf.nunique(), pdf.nunique())
# Assert including NaN values
self.assert_eq(psdf.nunique(dropna=False), pdf.nunique(dropna=False))
# Assert approximate counts
self.assert_eq(
ps.DataFrame({"A": range(100)}).nunique(approx=True),
pd.Series([103], index=["A"]),
)
self.assert_eq(
ps.DataFrame({"A": range(100)}).nunique(approx=True, rsd=0.01),
pd.Series([100], index=["A"]),
)
# Assert unsupported axis value yet
msg = 'axis should be either 0 or "index" currently.'
with self.assertRaisesRegex(NotImplementedError, msg):
psdf.nunique(axis=1)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("X", "A"), ("Y", "B")], names=["1", "2"])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.nunique(), pdf.nunique())
self.assert_eq(psdf.nunique(dropna=False), pdf.nunique(dropna=False))
def test_sort_values(self):
pdf = pd.DataFrame(
{"a": [1, 2, 3, 4, 5, None, 7], "b": [7, 6, 5, 4, 3, 2, 1]}, index=np.random.rand(7)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.sort_values("b"), pdf.sort_values("b"))
self.assert_eq(psdf.sort_values(["b", "a"]), pdf.sort_values(["b", "a"]))
self.assert_eq(
psdf.sort_values(["b", "a"], ascending=[False, True]),
pdf.sort_values(["b", "a"], ascending=[False, True]),
)
self.assertRaises(ValueError, lambda: psdf.sort_values(["b", "a"], ascending=[False]))
self.assert_eq(
psdf.sort_values(["b", "a"], na_position="first"),
pdf.sort_values(["b", "a"], na_position="first"),
)
self.assertRaises(ValueError, lambda: psdf.sort_values(["b", "a"], na_position="invalid"))
pserA = pdf.a
psserA = psdf.a
self.assert_eq(psdf.sort_values("b", inplace=True), pdf.sort_values("b", inplace=True))
self.assert_eq(psdf, pdf)
self.assert_eq(psserA, pserA)
# multi-index columns
pdf = pd.DataFrame(
{("X", 10): [1, 2, 3, 4, 5, None, 7], ("X", 20): [7, 6, 5, 4, 3, 2, 1]},
index=np.random.rand(7),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.sort_values(("X", 20)), pdf.sort_values(("X", 20)))
self.assert_eq(
psdf.sort_values([("X", 20), ("X", 10)]), pdf.sort_values([("X", 20), ("X", 10)])
)
self.assertRaisesRegex(
ValueError,
"For a multi-index, the label must be a tuple with elements",
lambda: psdf.sort_values(["X"]),
)
# non-string names
pdf = pd.DataFrame(
{10: [1, 2, 3, 4, 5, None, 7], 20: [7, 6, 5, 4, 3, 2, 1]}, index=np.random.rand(7)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.sort_values(20), pdf.sort_values(20))
self.assert_eq(psdf.sort_values([20, 10]), pdf.sort_values([20, 10]))
def test_sort_index(self):
pdf = pd.DataFrame(
{"A": [2, 1, np.nan], "B": [np.nan, 0, np.nan]}, index=["b", "a", np.nan]
)
psdf = ps.from_pandas(pdf)
# Assert invalid parameters
self.assertRaises(NotImplementedError, lambda: psdf.sort_index(axis=1))
self.assertRaises(NotImplementedError, lambda: psdf.sort_index(kind="mergesort"))
self.assertRaises(ValueError, lambda: psdf.sort_index(na_position="invalid"))
# Assert default behavior without parameters
self.assert_eq(psdf.sort_index(), pdf.sort_index())
# Assert sorting descending
self.assert_eq(psdf.sort_index(ascending=False), pdf.sort_index(ascending=False))
# Assert sorting NA indices first
self.assert_eq(psdf.sort_index(na_position="first"), pdf.sort_index(na_position="first"))
# Assert sorting inplace
pserA = pdf.A
psserA = psdf.A
self.assertEqual(psdf.sort_index(inplace=True), pdf.sort_index(inplace=True))
self.assert_eq(psdf, pdf)
self.assert_eq(psserA, pserA)
# Assert multi-indices
pdf = pd.DataFrame(
{"A": range(4), "B": range(4)[::-1]}, index=[["b", "b", "a", "a"], [1, 0, 1, 0]]
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.sort_index(), pdf.sort_index())
self.assert_eq(psdf.sort_index(level=[1, 0]), pdf.sort_index(level=[1, 0]))
self.assert_eq(psdf.reset_index().sort_index(), pdf.reset_index().sort_index())
# Assert with multi-index columns
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.sort_index(), pdf.sort_index())
def test_swaplevel(self):
# MultiIndex with two levels
arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"]]
pidx = pd.MultiIndex.from_arrays(arrays, names=("number", "color"))
pdf = pd.DataFrame({"x1": ["a", "b", "c", "d"], "x2": ["a", "b", "c", "d"]}, index=pidx)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.swaplevel(), psdf.swaplevel())
self.assert_eq(pdf.swaplevel(0, 1), psdf.swaplevel(0, 1))
self.assert_eq(pdf.swaplevel(1, 1), psdf.swaplevel(1, 1))
self.assert_eq(pdf.swaplevel("number", "color"), psdf.swaplevel("number", "color"))
# MultiIndex with more than two levels
arrays = [[1, 1, 2, 2], ["red", "blue", "red", "blue"], ["l", "m", "s", "xs"]]
pidx = pd.MultiIndex.from_arrays(arrays, names=("number", "color", "size"))
pdf = pd.DataFrame({"x1": ["a", "b", "c", "d"], "x2": ["a", "b", "c", "d"]}, index=pidx)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.swaplevel(), psdf.swaplevel())
self.assert_eq(pdf.swaplevel(0, 1), psdf.swaplevel(0, 1))
self.assert_eq(pdf.swaplevel(0, 2), psdf.swaplevel(0, 2))
self.assert_eq(pdf.swaplevel(1, 2), psdf.swaplevel(1, 2))
self.assert_eq(pdf.swaplevel(1, 1), psdf.swaplevel(1, 1))
self.assert_eq(pdf.swaplevel(-1, -2), psdf.swaplevel(-1, -2))
self.assert_eq(pdf.swaplevel("number", "color"), psdf.swaplevel("number", "color"))
self.assert_eq(pdf.swaplevel("number", "size"), psdf.swaplevel("number", "size"))
self.assert_eq(pdf.swaplevel("color", "size"), psdf.swaplevel("color", "size"))
self.assert_eq(
pdf.swaplevel("color", "size", axis="index"),
psdf.swaplevel("color", "size", axis="index"),
)
self.assert_eq(
pdf.swaplevel("color", "size", axis=0), psdf.swaplevel("color", "size", axis=0)
)
pdf = pd.DataFrame(
{
"x1": ["a", "b", "c", "d"],
"x2": ["a", "b", "c", "d"],
"x3": ["a", "b", "c", "d"],
"x4": ["a", "b", "c", "d"],
}
)
pidx = pd.MultiIndex.from_arrays(arrays, names=("number", "color", "size"))
pdf.columns = pidx
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.swaplevel(axis=1), psdf.swaplevel(axis=1))
self.assert_eq(pdf.swaplevel(0, 1, axis=1), psdf.swaplevel(0, 1, axis=1))
self.assert_eq(pdf.swaplevel(0, 2, axis=1), psdf.swaplevel(0, 2, axis=1))
self.assert_eq(pdf.swaplevel(1, 2, axis=1), psdf.swaplevel(1, 2, axis=1))
self.assert_eq(pdf.swaplevel(1, 1, axis=1), psdf.swaplevel(1, 1, axis=1))
self.assert_eq(pdf.swaplevel(-1, -2, axis=1), psdf.swaplevel(-1, -2, axis=1))
self.assert_eq(
pdf.swaplevel("number", "color", axis=1), psdf.swaplevel("number", "color", axis=1)
)
self.assert_eq(
pdf.swaplevel("number", "size", axis=1), psdf.swaplevel("number", "size", axis=1)
)
self.assert_eq(
pdf.swaplevel("color", "size", axis=1), psdf.swaplevel("color", "size", axis=1)
)
self.assert_eq(
pdf.swaplevel("color", "size", axis="columns"),
psdf.swaplevel("color", "size", axis="columns"),
)
# Error conditions
self.assertRaises(AssertionError, lambda: ps.DataFrame([1, 2]).swaplevel())
self.assertRaises(IndexError, lambda: psdf.swaplevel(0, 9, axis=1))
self.assertRaises(KeyError, lambda: psdf.swaplevel("not_number", "color", axis=1))
self.assertRaises(ValueError, lambda: psdf.swaplevel(axis=2))
def test_swapaxes(self):
pdf = pd.DataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=["x", "y", "z"], columns=["a", "b", "c"]
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.swapaxes(0, 1), pdf.swapaxes(0, 1))
self.assert_eq(psdf.swapaxes(1, 0), pdf.swapaxes(1, 0))
self.assert_eq(psdf.swapaxes("index", "columns"), pdf.swapaxes("index", "columns"))
self.assert_eq(psdf.swapaxes("columns", "index"), pdf.swapaxes("columns", "index"))
self.assert_eq((psdf + 1).swapaxes(0, 1), (pdf + 1).swapaxes(0, 1))
self.assertRaises(AssertionError, lambda: psdf.swapaxes(0, 1, copy=False))
self.assertRaises(ValueError, lambda: psdf.swapaxes(0, -1))
def test_nlargest(self):
pdf = pd.DataFrame(
{"a": [1, 2, 3, 4, 5, None, 7], "b": [7, 6, 5, 4, 3, 2, 1]}, index=np.random.rand(7)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.nlargest(n=5, columns="a"), pdf.nlargest(5, columns="a"))
self.assert_eq(psdf.nlargest(n=5, columns=["a", "b"]), pdf.nlargest(5, columns=["a", "b"]))
def test_nsmallest(self):
pdf = pd.DataFrame(
{"a": [1, 2, 3, 4, 5, None, 7], "b": [7, 6, 5, 4, 3, 2, 1]}, index=np.random.rand(7)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.nsmallest(n=5, columns="a"), pdf.nsmallest(5, columns="a"))
self.assert_eq(
psdf.nsmallest(n=5, columns=["a", "b"]), pdf.nsmallest(5, columns=["a", "b"])
)
def test_xs(self):
d = {
"num_legs": [4, 4, 2, 2],
"num_wings": [0, 0, 2, 2],
"class": ["mammal", "mammal", "mammal", "bird"],
"animal": ["cat", "dog", "bat", "penguin"],
"locomotion": ["walks", "walks", "flies", "walks"],
}
pdf = pd.DataFrame(data=d)
pdf = pdf.set_index(["class", "animal", "locomotion"])
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.xs("mammal"), pdf.xs("mammal"))
self.assert_eq(psdf.xs(("mammal",)), pdf.xs(("mammal",)))
self.assert_eq(psdf.xs(("mammal", "dog", "walks")), pdf.xs(("mammal", "dog", "walks")))
self.assert_eq(
ps.concat([psdf, psdf]).xs(("mammal", "dog", "walks")),
pd.concat([pdf, pdf]).xs(("mammal", "dog", "walks")),
)
self.assert_eq(psdf.xs("cat", level=1), pdf.xs("cat", level=1))
self.assert_eq(psdf.xs("flies", level=2), pdf.xs("flies", level=2))
self.assert_eq(psdf.xs("mammal", level=-3), pdf.xs("mammal", level=-3))
msg = 'axis should be either 0 or "index" currently.'
with self.assertRaisesRegex(NotImplementedError, msg):
psdf.xs("num_wings", axis=1)
with self.assertRaises(KeyError):
psdf.xs(("mammal", "dog", "walk"))
msg = r"'Key length \(4\) exceeds index depth \(3\)'"
with self.assertRaisesRegex(KeyError, msg):
psdf.xs(("mammal", "dog", "walks", "foo"))
self.assertRaises(IndexError, lambda: psdf.xs("foo", level=-4))
self.assertRaises(IndexError, lambda: psdf.xs("foo", level=3))
self.assertRaises(KeyError, lambda: psdf.xs(("dog", "walks"), level=1))
# non-string names
pdf = pd.DataFrame(data=d)
pdf = pdf.set_index(["class", "animal", "num_legs", "num_wings"])
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.xs(("mammal", "dog", 4)), pdf.xs(("mammal", "dog", 4)))
self.assert_eq(psdf.xs(2, level=2), pdf.xs(2, level=2))
self.assert_eq((psdf + "a").xs(("mammal", "dog", 4)), (pdf + "a").xs(("mammal", "dog", 4)))
self.assert_eq((psdf + "a").xs(2, level=2), (pdf + "a").xs(2, level=2))
def test_missing(self):
psdf = self.psdf
missing_functions = inspect.getmembers(_MissingPandasLikeDataFrame, inspect.isfunction)
unsupported_functions = [
name for (name, type_) in missing_functions if type_.__name__ == "unsupported_function"
]
for name in unsupported_functions:
with self.assertRaisesRegex(
PandasNotImplementedError,
"method.*DataFrame.*{}.*not implemented( yet\\.|\\. .+)".format(name),
):
getattr(psdf, name)()
deprecated_functions = [
name for (name, type_) in missing_functions if type_.__name__ == "deprecated_function"
]
for name in deprecated_functions:
with self.assertRaisesRegex(
PandasNotImplementedError, "method.*DataFrame.*{}.*is deprecated".format(name)
):
getattr(psdf, name)()
missing_properties = inspect.getmembers(
_MissingPandasLikeDataFrame, lambda o: isinstance(o, property)
)
unsupported_properties = [
name
for (name, type_) in missing_properties
if type_.fget.__name__ == "unsupported_property"
]
for name in unsupported_properties:
with self.assertRaisesRegex(
PandasNotImplementedError,
"property.*DataFrame.*{}.*not implemented( yet\\.|\\. .+)".format(name),
):
getattr(psdf, name)
deprecated_properties = [
name
for (name, type_) in missing_properties
if type_.fget.__name__ == "deprecated_property"
]
for name in deprecated_properties:
with self.assertRaisesRegex(
PandasNotImplementedError, "property.*DataFrame.*{}.*is deprecated".format(name)
):
getattr(psdf, name)
def test_to_numpy(self):
pdf = pd.DataFrame(
{
"a": [4, 2, 3, 4, 8, 6],
"b": [1, 2, 9, 4, 2, 4],
"c": ["one", "three", "six", "seven", "one", "5"],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.to_numpy(), pdf.values)
def test_to_pandas(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf.to_pandas(), pdf)
def test_isin(self):
pdf = pd.DataFrame(
{
"a": [4, 2, 3, 4, 8, 6],
"b": [1, 2, 9, 4, 2, 4],
"c": ["one", "three", "six", "seven", "one", "5"],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.isin([4, "six"]), pdf.isin([4, "six"]))
# Seems like pandas has a bug when passing `np.array` as parameter
self.assert_eq(psdf.isin(np.array([4, "six"])), pdf.isin([4, "six"]))
self.assert_eq(
psdf.isin({"a": [2, 8], "c": ["three", "one"]}),
pdf.isin({"a": [2, 8], "c": ["three", "one"]}),
)
self.assert_eq(
psdf.isin({"a": np.array([2, 8]), "c": ["three", "one"]}),
pdf.isin({"a": np.array([2, 8]), "c": ["three", "one"]}),
)
msg = "'DataFrame' object has no attribute {'e'}"
with self.assertRaisesRegex(AttributeError, msg):
psdf.isin({"e": [5, 7], "a": [1, 6]})
msg = "DataFrame and Series are not supported"
with self.assertRaisesRegex(NotImplementedError, msg):
psdf.isin(pdf)
msg = "Values should be iterable, Series, DataFrame or dict."
with self.assertRaisesRegex(TypeError, msg):
psdf.isin(1)
def test_merge(self):
left_pdf = pd.DataFrame(
{
"lkey": ["foo", "bar", "baz", "foo", "bar", "l"],
"value": [1, 2, 3, 5, 6, 7],
"x": list("abcdef"),
},
columns=["lkey", "value", "x"],
)
right_pdf = pd.DataFrame(
{
"rkey": ["baz", "foo", "bar", "baz", "foo", "r"],
"value": [4, 5, 6, 7, 8, 9],
"y": list("efghij"),
},
columns=["rkey", "value", "y"],
)
right_ps = pd.Series(list("defghi"), name="x", index=[5, 6, 7, 8, 9, 10])
left_psdf = ps.from_pandas(left_pdf)
right_psdf = ps.from_pandas(right_pdf)
right_psser = ps.from_pandas(right_ps)
def check(op, right_psdf=right_psdf, right_pdf=right_pdf):
k_res = op(left_psdf, right_psdf)
k_res = k_res.to_pandas()
k_res = k_res.sort_values(by=list(k_res.columns))
k_res = k_res.reset_index(drop=True)
p_res = op(left_pdf, right_pdf)
p_res = p_res.sort_values(by=list(p_res.columns))
p_res = p_res.reset_index(drop=True)
self.assert_eq(k_res, p_res)
check(lambda left, right: left.merge(right))
check(lambda left, right: left.merge(right, on="value"))
check(lambda left, right: left.merge(right, left_on="lkey", right_on="rkey"))
check(lambda left, right: left.set_index("lkey").merge(right.set_index("rkey")))
check(
lambda left, right: left.set_index("lkey").merge(
right, left_index=True, right_on="rkey"
)
)
check(
lambda left, right: left.merge(
right.set_index("rkey"), left_on="lkey", right_index=True
)
)
check(
lambda left, right: left.set_index("lkey").merge(
right.set_index("rkey"), left_index=True, right_index=True
)
)
# MultiIndex
check(
lambda left, right: left.merge(
right, left_on=["lkey", "value"], right_on=["rkey", "value"]
)
)
check(
lambda left, right: left.set_index(["lkey", "value"]).merge(
right, left_index=True, right_on=["rkey", "value"]
)
)
check(
lambda left, right: left.merge(
right.set_index(["rkey", "value"]), left_on=["lkey", "value"], right_index=True
)
)
# TODO: when both left_index=True and right_index=True with multi-index
# check(lambda left, right: left.set_index(['lkey', 'value']).merge(
# right.set_index(['rkey', 'value']), left_index=True, right_index=True))
# join types
for how in ["inner", "left", "right", "outer"]:
check(lambda left, right: left.merge(right, on="value", how=how))
check(lambda left, right: left.merge(right, left_on="lkey", right_on="rkey", how=how))
# suffix
check(
lambda left, right: left.merge(
right, left_on="lkey", right_on="rkey", suffixes=["_left", "_right"]
)
)
# Test Series on the right
# pd.DataFrame.merge with Series is implemented since version 0.24.0
if LooseVersion(pd.__version__) >= LooseVersion("0.24.0"):
check(lambda left, right: left.merge(right), right_psser, right_ps)
check(
lambda left, right: left.merge(right, left_on="x", right_on="x"),
right_psser,
right_ps,
)
check(
lambda left, right: left.set_index("x").merge(right, left_index=True, right_on="x"),
right_psser,
right_ps,
)
# Test join types with Series
for how in ["inner", "left", "right", "outer"]:
check(lambda left, right: left.merge(right, how=how), right_psser, right_ps)
check(
lambda left, right: left.merge(right, left_on="x", right_on="x", how=how),
right_psser,
right_ps,
)
# suffix with Series
check(
lambda left, right: left.merge(
right,
suffixes=["_left", "_right"],
how="outer",
left_index=True,
right_index=True,
),
right_psser,
right_ps,
)
# multi-index columns
left_columns = pd.MultiIndex.from_tuples([(10, "lkey"), (10, "value"), (20, "x")])
left_pdf.columns = left_columns
left_psdf.columns = left_columns
right_columns = pd.MultiIndex.from_tuples([(10, "rkey"), (10, "value"), (30, "y")])
right_pdf.columns = right_columns
right_psdf.columns = right_columns
check(lambda left, right: left.merge(right))
check(lambda left, right: left.merge(right, on=[(10, "value")]))
check(
lambda left, right: (left.set_index((10, "lkey")).merge(right.set_index((10, "rkey"))))
)
check(
lambda left, right: (
left.set_index((10, "lkey")).merge(
right.set_index((10, "rkey")), left_index=True, right_index=True
)
)
)
# TODO: when both left_index=True and right_index=True with multi-index columns
# check(lambda left, right: left.merge(right,
# left_on=[('a', 'lkey')], right_on=[('a', 'rkey')]))
# check(lambda left, right: (left.set_index(('a', 'lkey'))
# .merge(right, left_index=True, right_on=[('a', 'rkey')])))
# non-string names
left_pdf.columns = [10, 100, 1000]
left_psdf.columns = [10, 100, 1000]
right_pdf.columns = [20, 100, 2000]
right_psdf.columns = [20, 100, 2000]
check(lambda left, right: left.merge(right))
check(lambda left, right: left.merge(right, on=[100]))
check(lambda left, right: (left.set_index(10).merge(right.set_index(20))))
check(
lambda left, right: (
left.set_index(10).merge(right.set_index(20), left_index=True, right_index=True)
)
)
def test_merge_same_anchor(self):
pdf = pd.DataFrame(
{
"lkey": ["foo", "bar", "baz", "foo", "bar", "l"],
"rkey": ["baz", "foo", "bar", "baz", "foo", "r"],
"value": [1, 1, 3, 5, 6, 7],
"x": list("abcdef"),
"y": list("efghij"),
},
columns=["lkey", "rkey", "value", "x", "y"],
)
psdf = ps.from_pandas(pdf)
left_pdf = pdf[["lkey", "value", "x"]]
right_pdf = pdf[["rkey", "value", "y"]]
left_psdf = psdf[["lkey", "value", "x"]]
right_psdf = psdf[["rkey", "value", "y"]]
def check(op, right_psdf=right_psdf, right_pdf=right_pdf):
k_res = op(left_psdf, right_psdf)
k_res = k_res.to_pandas()
k_res = k_res.sort_values(by=list(k_res.columns))
k_res = k_res.reset_index(drop=True)
p_res = op(left_pdf, right_pdf)
p_res = p_res.sort_values(by=list(p_res.columns))
p_res = p_res.reset_index(drop=True)
self.assert_eq(k_res, p_res)
check(lambda left, right: left.merge(right))
check(lambda left, right: left.merge(right, on="value"))
check(lambda left, right: left.merge(right, left_on="lkey", right_on="rkey"))
check(lambda left, right: left.set_index("lkey").merge(right.set_index("rkey")))
check(
lambda left, right: left.set_index("lkey").merge(
right, left_index=True, right_on="rkey"
)
)
check(
lambda left, right: left.merge(
right.set_index("rkey"), left_on="lkey", right_index=True
)
)
check(
lambda left, right: left.set_index("lkey").merge(
right.set_index("rkey"), left_index=True, right_index=True
)
)
def test_merge_retains_indices(self):
left_pdf = pd.DataFrame({"A": [0, 1]})
right_pdf = pd.DataFrame({"B": [1, 2]}, index=[1, 2])
left_psdf = ps.from_pandas(left_pdf)
right_psdf = ps.from_pandas(right_pdf)
self.assert_eq(
left_psdf.merge(right_psdf, left_index=True, right_index=True),
left_pdf.merge(right_pdf, left_index=True, right_index=True),
)
self.assert_eq(
left_psdf.merge(right_psdf, left_on="A", right_index=True),
left_pdf.merge(right_pdf, left_on="A", right_index=True),
)
self.assert_eq(
left_psdf.merge(right_psdf, left_index=True, right_on="B"),
left_pdf.merge(right_pdf, left_index=True, right_on="B"),
)
self.assert_eq(
left_psdf.merge(right_psdf, left_on="A", right_on="B"),
left_pdf.merge(right_pdf, left_on="A", right_on="B"),
)
def test_merge_how_parameter(self):
left_pdf = pd.DataFrame({"A": [1, 2]})
right_pdf = pd.DataFrame({"B": ["x", "y"]}, index=[1, 2])
left_psdf = ps.from_pandas(left_pdf)
right_psdf = ps.from_pandas(right_pdf)
psdf = left_psdf.merge(right_psdf, left_index=True, right_index=True)
pdf = left_pdf.merge(right_pdf, left_index=True, right_index=True)
self.assert_eq(
psdf.sort_values(by=list(psdf.columns)).reset_index(drop=True),
pdf.sort_values(by=list(pdf.columns)).reset_index(drop=True),
)
psdf = left_psdf.merge(right_psdf, left_index=True, right_index=True, how="left")
pdf = left_pdf.merge(right_pdf, left_index=True, right_index=True, how="left")
self.assert_eq(
psdf.sort_values(by=list(psdf.columns)).reset_index(drop=True),
pdf.sort_values(by=list(pdf.columns)).reset_index(drop=True),
)
psdf = left_psdf.merge(right_psdf, left_index=True, right_index=True, how="right")
pdf = left_pdf.merge(right_pdf, left_index=True, right_index=True, how="right")
self.assert_eq(
psdf.sort_values(by=list(psdf.columns)).reset_index(drop=True),
pdf.sort_values(by=list(pdf.columns)).reset_index(drop=True),
)
psdf = left_psdf.merge(right_psdf, left_index=True, right_index=True, how="outer")
pdf = left_pdf.merge(right_pdf, left_index=True, right_index=True, how="outer")
self.assert_eq(
psdf.sort_values(by=list(psdf.columns)).reset_index(drop=True),
pdf.sort_values(by=list(pdf.columns)).reset_index(drop=True),
)
def test_merge_raises(self):
left = ps.DataFrame(
{"value": [1, 2, 3, 5, 6], "x": list("abcde")},
columns=["value", "x"],
index=["foo", "bar", "baz", "foo", "bar"],
)
right = ps.DataFrame(
{"value": [4, 5, 6, 7, 8], "y": list("fghij")},
columns=["value", "y"],
index=["baz", "foo", "bar", "baz", "foo"],
)
with self.assertRaisesRegex(ValueError, "No common columns to perform merge on"):
left[["x"]].merge(right[["y"]])
with self.assertRaisesRegex(ValueError, "not a combination of both"):
left.merge(right, on="value", left_on="x")
with self.assertRaisesRegex(ValueError, "Must pass right_on or right_index=True"):
left.merge(right, left_on="x")
with self.assertRaisesRegex(ValueError, "Must pass right_on or right_index=True"):
left.merge(right, left_index=True)
with self.assertRaisesRegex(ValueError, "Must pass left_on or left_index=True"):
left.merge(right, right_on="y")
with self.assertRaisesRegex(ValueError, "Must pass left_on or left_index=True"):
left.merge(right, right_index=True)
with self.assertRaisesRegex(
ValueError, "len\\(left_keys\\) must equal len\\(right_keys\\)"
):
left.merge(right, left_on="value", right_on=["value", "y"])
with self.assertRaisesRegex(
ValueError, "len\\(left_keys\\) must equal len\\(right_keys\\)"
):
left.merge(right, left_on=["value", "x"], right_on="value")
with self.assertRaisesRegex(ValueError, "['inner', 'left', 'right', 'full', 'outer']"):
left.merge(right, left_index=True, right_index=True, how="foo")
with self.assertRaisesRegex(KeyError, "id"):
left.merge(right, on="id")
def test_append(self):
pdf = pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"))
psdf = ps.from_pandas(pdf)
other_pdf = pd.DataFrame([[3, 4], [5, 6]], columns=list("BC"), index=[2, 3])
other_psdf = ps.from_pandas(other_pdf)
self.assert_eq(psdf.append(psdf), pdf.append(pdf))
self.assert_eq(psdf.append(psdf, ignore_index=True), pdf.append(pdf, ignore_index=True))
# Assert DataFrames with non-matching columns
self.assert_eq(psdf.append(other_psdf), pdf.append(other_pdf))
# Assert appending a Series fails
msg = "DataFrames.append() does not support appending Series to DataFrames"
with self.assertRaises(TypeError, msg=msg):
psdf.append(psdf["A"])
# Assert using the sort parameter raises an exception
msg = "The 'sort' parameter is currently not supported"
with self.assertRaises(NotImplementedError, msg=msg):
psdf.append(psdf, sort=True)
# Assert using 'verify_integrity' only raises an exception for overlapping indices
self.assert_eq(
psdf.append(other_psdf, verify_integrity=True),
pdf.append(other_pdf, verify_integrity=True),
)
msg = "Indices have overlapping values"
with self.assertRaises(ValueError, msg=msg):
psdf.append(psdf, verify_integrity=True)
# Skip integrity verification when ignore_index=True
self.assert_eq(
psdf.append(psdf, ignore_index=True, verify_integrity=True),
pdf.append(pdf, ignore_index=True, verify_integrity=True),
)
# Assert appending multi-index DataFrames
multi_index_pdf = pd.DataFrame([[1, 2], [3, 4]], columns=list("AB"), index=[[2, 3], [4, 5]])
multi_index_psdf = ps.from_pandas(multi_index_pdf)
other_multi_index_pdf = pd.DataFrame(
[[5, 6], [7, 8]], columns=list("AB"), index=[[2, 3], [6, 7]]
)
other_multi_index_psdf = ps.from_pandas(other_multi_index_pdf)
self.assert_eq(
multi_index_psdf.append(multi_index_psdf), multi_index_pdf.append(multi_index_pdf)
)
# Assert DataFrames with non-matching columns
self.assert_eq(
multi_index_psdf.append(other_multi_index_psdf),
multi_index_pdf.append(other_multi_index_pdf),
)
# Assert using 'verify_integrity' only raises an exception for overlapping indices
self.assert_eq(
multi_index_psdf.append(other_multi_index_psdf, verify_integrity=True),
multi_index_pdf.append(other_multi_index_pdf, verify_integrity=True),
)
with self.assertRaises(ValueError, msg=msg):
multi_index_psdf.append(multi_index_psdf, verify_integrity=True)
# Skip integrity verification when ignore_index=True
self.assert_eq(
multi_index_psdf.append(multi_index_psdf, ignore_index=True, verify_integrity=True),
multi_index_pdf.append(multi_index_pdf, ignore_index=True, verify_integrity=True),
)
# Assert trying to append DataFrames with different index levels
msg = "Both DataFrames have to have the same number of index levels"
with self.assertRaises(ValueError, msg=msg):
psdf.append(multi_index_psdf)
# Skip index level check when ignore_index=True
self.assert_eq(
psdf.append(multi_index_psdf, ignore_index=True),
pdf.append(multi_index_pdf, ignore_index=True),
)
columns = pd.MultiIndex.from_tuples([("A", "X"), ("A", "Y")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.append(psdf), pdf.append(pdf))
def test_clip(self):
pdf = pd.DataFrame(
{"A": [0, 2, 4], "B": [4, 2, 0], "X": [-1, 10, 0]}, index=np.random.rand(3)
)
psdf = ps.from_pandas(pdf)
# Assert list-like values are not accepted for 'lower' and 'upper'
msg = "List-like value are not supported for 'lower' and 'upper' at the moment"
with self.assertRaises(TypeError, msg=msg):
psdf.clip(lower=[1])
with self.assertRaises(TypeError, msg=msg):
psdf.clip(upper=[1])
# Assert no lower or upper
self.assert_eq(psdf.clip(), pdf.clip())
# Assert lower only
self.assert_eq(psdf.clip(1), pdf.clip(1))
# Assert upper only
self.assert_eq(psdf.clip(upper=3), pdf.clip(upper=3))
# Assert lower and upper
self.assert_eq(psdf.clip(1, 3), pdf.clip(1, 3))
pdf["clip"] = pdf.A.clip(lower=1, upper=3)
psdf["clip"] = psdf.A.clip(lower=1, upper=3)
self.assert_eq(psdf, pdf)
# Assert behavior on string values
str_psdf = ps.DataFrame({"A": ["a", "b", "c"]}, index=np.random.rand(3))
self.assert_eq(str_psdf.clip(1, 3), str_psdf)
def test_binary_operators(self):
pdf = pd.DataFrame(
{"A": [0, 2, 4], "B": [4, 2, 0], "X": [-1, 10, 0]}, index=np.random.rand(3)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf + psdf.copy(), pdf + pdf.copy())
self.assertRaisesRegex(
ValueError,
"it comes from a different dataframe",
lambda: ps.range(10).add(ps.range(10)),
)
self.assertRaisesRegex(
TypeError,
"add with a sequence is currently not supported",
lambda: ps.range(10).add(ps.range(10).id),
)
def test_binary_operator_add(self):
# Positive
pdf = pd.DataFrame({"a": ["x"], "b": ["y"], "c": [1], "d": [2]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf["a"] + psdf["b"], pdf["a"] + pdf["b"])
self.assert_eq(psdf["c"] + psdf["d"], pdf["c"] + pdf["d"])
# Negative
ks_err_msg = "Addition can not be applied to given types"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] + psdf["c"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["c"] + psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["c"] + "literal")
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" + psdf["c"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 + psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] + 1)
def test_binary_operator_sub(self):
# Positive
pdf = pd.DataFrame({"a": [2], "b": [1]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf["a"] - psdf["b"], pdf["a"] - pdf["b"])
# Negative
psdf = ps.DataFrame({"a": ["x"], "b": [1]})
ks_err_msg = "Subtraction can not be applied to given types"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] - psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] - "literal")
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" - psdf["b"])
ks_err_msg = "Subtraction can not be applied to strings"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] - psdf["b"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 - psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] - 1)
psdf = ps.DataFrame({"a": ["x"], "b": ["y"]})
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] - psdf["b"])
def test_binary_operator_truediv(self):
# Positive
pdf = pd.DataFrame({"a": [3], "b": [2]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf["a"] / psdf["b"], pdf["a"] / pdf["b"])
# Negative
psdf = ps.DataFrame({"a": ["x"], "b": [1]})
ks_err_msg = "True division can not be applied to given types"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] / psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] / "literal")
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" / psdf["b"])
ks_err_msg = "True division can not be applied to strings"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] / psdf["b"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 / psdf["a"])
def test_binary_operator_floordiv(self):
psdf = ps.DataFrame({"a": ["x"], "b": [1]})
ks_err_msg = "Floor division can not be applied to strings"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] // psdf["b"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 // psdf["a"])
ks_err_msg = "Floor division can not be applied to given types"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] // psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] // "literal")
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" // psdf["b"])
def test_binary_operator_mod(self):
# Positive
pdf = pd.DataFrame({"a": [3], "b": [2]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf["a"] % psdf["b"], pdf["a"] % pdf["b"])
# Negative
psdf = ps.DataFrame({"a": ["x"], "b": [1]})
ks_err_msg = "Modulo can not be applied to given types"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] % psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] % "literal")
ks_err_msg = "Modulo can not be applied to strings"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] % psdf["b"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 1 % psdf["a"])
def test_binary_operator_multiply(self):
# Positive
pdf = pd.DataFrame({"a": ["x", "y"], "b": [1, 2], "c": [3, 4]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf["b"] * psdf["c"], pdf["b"] * pdf["c"])
self.assert_eq(psdf["c"] * psdf["b"], pdf["c"] * pdf["b"])
self.assert_eq(psdf["a"] * psdf["b"], pdf["a"] * pdf["b"])
self.assert_eq(psdf["b"] * psdf["a"], pdf["b"] * pdf["a"])
self.assert_eq(psdf["a"] * 2, pdf["a"] * 2)
self.assert_eq(psdf["b"] * 2, pdf["b"] * 2)
self.assert_eq(2 * psdf["a"], 2 * pdf["a"])
self.assert_eq(2 * psdf["b"], 2 * pdf["b"])
# Negative
psdf = ps.DataFrame({"a": ["x"], "b": [2]})
ks_err_msg = "Multiplication can not be applied to given types"
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["b"] * "literal")
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" * psdf["b"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] * "literal")
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] * psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: psdf["a"] * 0.1)
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: 0.1 * psdf["a"])
self.assertRaisesRegex(TypeError, ks_err_msg, lambda: "literal" * psdf["a"])
def test_sample(self):
pdf = pd.DataFrame({"A": [0, 2, 4]})
psdf = ps.from_pandas(pdf)
# Make sure the tests run, but we can't check the result because they are non-deterministic.
psdf.sample(frac=0.1)
psdf.sample(frac=0.2, replace=True)
psdf.sample(frac=0.2, random_state=5)
psdf["A"].sample(frac=0.2)
psdf["A"].sample(frac=0.2, replace=True)
psdf["A"].sample(frac=0.2, random_state=5)
with self.assertRaises(ValueError):
psdf.sample()
with self.assertRaises(NotImplementedError):
psdf.sample(n=1)
def test_add_prefix(self):
pdf = pd.DataFrame({"A": [1, 2, 3, 4], "B": [3, 4, 5, 6]}, index=np.random.rand(4))
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.add_prefix("col_"), psdf.add_prefix("col_"))
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(pdf.add_prefix("col_"), psdf.add_prefix("col_"))
def test_add_suffix(self):
pdf = pd.DataFrame({"A": [1, 2, 3, 4], "B": [3, 4, 5, 6]}, index=np.random.rand(4))
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.add_suffix("first_series"), psdf.add_suffix("first_series"))
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(pdf.add_suffix("first_series"), psdf.add_suffix("first_series"))
def test_join(self):
# check basic function
pdf1 = pd.DataFrame(
{"key": ["K0", "K1", "K2", "K3"], "A": ["A0", "A1", "A2", "A3"]}, columns=["key", "A"]
)
pdf2 = pd.DataFrame(
{"key": ["K0", "K1", "K2"], "B": ["B0", "B1", "B2"]}, columns=["key", "B"]
)
psdf1 = ps.from_pandas(pdf1)
psdf2 = ps.from_pandas(pdf2)
join_pdf = pdf1.join(pdf2, lsuffix="_left", rsuffix="_right")
join_pdf.sort_values(by=list(join_pdf.columns), inplace=True)
join_psdf = psdf1.join(psdf2, lsuffix="_left", rsuffix="_right")
join_psdf.sort_values(by=list(join_psdf.columns), inplace=True)
self.assert_eq(join_pdf, join_psdf)
# join with duplicated columns in Series
with self.assertRaisesRegex(ValueError, "columns overlap but no suffix specified"):
ks1 = ps.Series(["A1", "A5"], index=[1, 2], name="A")
psdf1.join(ks1, how="outer")
# join with duplicated columns in DataFrame
with self.assertRaisesRegex(ValueError, "columns overlap but no suffix specified"):
psdf1.join(psdf2, how="outer")
# check `on` parameter
join_pdf = pdf1.join(pdf2.set_index("key"), on="key", lsuffix="_left", rsuffix="_right")
join_pdf.sort_values(by=list(join_pdf.columns), inplace=True)
join_psdf = psdf1.join(psdf2.set_index("key"), on="key", lsuffix="_left", rsuffix="_right")
join_psdf.sort_values(by=list(join_psdf.columns), inplace=True)
self.assert_eq(join_pdf.reset_index(drop=True), join_psdf.reset_index(drop=True))
join_pdf = pdf1.set_index("key").join(
pdf2.set_index("key"), on="key", lsuffix="_left", rsuffix="_right"
)
join_pdf.sort_values(by=list(join_pdf.columns), inplace=True)
join_psdf = psdf1.set_index("key").join(
psdf2.set_index("key"), on="key", lsuffix="_left", rsuffix="_right"
)
join_psdf.sort_values(by=list(join_psdf.columns), inplace=True)
self.assert_eq(join_pdf.reset_index(drop=True), join_psdf.reset_index(drop=True))
# multi-index columns
columns1 = pd.MultiIndex.from_tuples([("x", "key"), ("Y", "A")])
columns2 = pd.MultiIndex.from_tuples([("x", "key"), ("Y", "B")])
pdf1.columns = columns1
pdf2.columns = columns2
psdf1.columns = columns1
psdf2.columns = columns2
join_pdf = pdf1.join(pdf2, lsuffix="_left", rsuffix="_right")
join_pdf.sort_values(by=list(join_pdf.columns), inplace=True)
join_psdf = psdf1.join(psdf2, lsuffix="_left", rsuffix="_right")
join_psdf.sort_values(by=list(join_psdf.columns), inplace=True)
self.assert_eq(join_pdf, join_psdf)
# check `on` parameter
join_pdf = pdf1.join(
pdf2.set_index(("x", "key")), on=[("x", "key")], lsuffix="_left", rsuffix="_right"
)
join_pdf.sort_values(by=list(join_pdf.columns), inplace=True)
join_psdf = psdf1.join(
psdf2.set_index(("x", "key")), on=[("x", "key")], lsuffix="_left", rsuffix="_right"
)
join_psdf.sort_values(by=list(join_psdf.columns), inplace=True)
self.assert_eq(join_pdf.reset_index(drop=True), join_psdf.reset_index(drop=True))
join_pdf = pdf1.set_index(("x", "key")).join(
pdf2.set_index(("x", "key")), on=[("x", "key")], lsuffix="_left", rsuffix="_right"
)
join_pdf.sort_values(by=list(join_pdf.columns), inplace=True)
join_psdf = psdf1.set_index(("x", "key")).join(
psdf2.set_index(("x", "key")), on=[("x", "key")], lsuffix="_left", rsuffix="_right"
)
join_psdf.sort_values(by=list(join_psdf.columns), inplace=True)
self.assert_eq(join_pdf.reset_index(drop=True), join_psdf.reset_index(drop=True))
# multi-index
midx1 = pd.MultiIndex.from_tuples(
[("w", "a"), ("x", "b"), ("y", "c"), ("z", "d")], names=["index1", "index2"]
)
midx2 = pd.MultiIndex.from_tuples(
[("w", "a"), ("x", "b"), ("y", "c")], names=["index1", "index2"]
)
pdf1.index = midx1
pdf2.index = midx2
psdf1 = ps.from_pandas(pdf1)
psdf2 = ps.from_pandas(pdf2)
join_pdf = pdf1.join(pdf2, on=["index1", "index2"], rsuffix="_right")
join_pdf.sort_values(by=list(join_pdf.columns), inplace=True)
join_psdf = psdf1.join(psdf2, on=["index1", "index2"], rsuffix="_right")
join_psdf.sort_values(by=list(join_psdf.columns), inplace=True)
self.assert_eq(join_pdf, join_psdf)
with self.assertRaisesRegex(
ValueError, r'len\(left_on\) must equal the number of levels in the index of "right"'
):
psdf1.join(psdf2, on=["index1"], rsuffix="_right")
def test_replace(self):
pdf = pd.DataFrame(
{
"name": ["Ironman", "Captain America", "Thor", "Hulk"],
"weapon": ["Mark-45", "Shield", "Mjolnir", "Smash"],
},
index=np.random.rand(4),
)
psdf = ps.from_pandas(pdf)
with self.assertRaisesRegex(
NotImplementedError, "replace currently works only for method='pad"
):
psdf.replace(method="bfill")
with self.assertRaisesRegex(
NotImplementedError, "replace currently works only when limit=None"
):
psdf.replace(limit=10)
with self.assertRaisesRegex(
NotImplementedError, "replace currently doesn't supports regex"
):
psdf.replace(regex="")
with self.assertRaisesRegex(ValueError, "Length of to_replace and value must be same"):
psdf.replace(to_replace=["Ironman"], value=["Spiderman", "Doctor Strange"])
self.assert_eq(psdf.replace("Ironman", "Spiderman"), pdf.replace("Ironman", "Spiderman"))
self.assert_eq(
psdf.replace(["Ironman", "Captain America"], ["Rescue", "Hawkeye"]),
pdf.replace(["Ironman", "Captain America"], ["Rescue", "Hawkeye"]),
)
self.assert_eq(
psdf.replace(("Ironman", "Captain America"), ("Rescue", "Hawkeye")),
pdf.replace(("Ironman", "Captain America"), ("Rescue", "Hawkeye")),
)
# inplace
pser = pdf.name
psser = psdf.name
pdf.replace("Ironman", "Spiderman", inplace=True)
psdf.replace("Ironman", "Spiderman", inplace=True)
self.assert_eq(psdf, pdf)
self.assert_eq(psser, pser)
pdf = pd.DataFrame(
{"A": [0, 1, 2, 3, np.nan], "B": [5, 6, 7, 8, np.nan], "C": ["a", "b", "c", "d", None]},
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.replace([0, 1, 2, 3, 5, 6], 4), pdf.replace([0, 1, 2, 3, 5, 6], 4))
self.assert_eq(
psdf.replace([0, 1, 2, 3, 5, 6], [6, 5, 4, 3, 2, 1]),
pdf.replace([0, 1, 2, 3, 5, 6], [6, 5, 4, 3, 2, 1]),
)
self.assert_eq(psdf.replace({0: 10, 1: 100, 7: 200}), pdf.replace({0: 10, 1: 100, 7: 200}))
self.assert_eq(
psdf.replace({"A": [0, np.nan], "B": [5, np.nan]}, 100),
pdf.replace({"A": [0, np.nan], "B": [5, np.nan]}, 100),
)
self.assert_eq(
psdf.replace({"A": {0: 100, 4: 400, np.nan: 700}}),
pdf.replace({"A": {0: 100, 4: 400, np.nan: 700}}),
)
self.assert_eq(
psdf.replace({"X": {0: 100, 4: 400, np.nan: 700}}),
pdf.replace({"X": {0: 100, 4: 400, np.nan: 700}}),
)
self.assert_eq(psdf.replace({"C": ["a", None]}, "e"), pdf.replace({"C": ["a", None]}, "e"))
# multi-index columns
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.replace([0, 1, 2, 3, 5, 6], 4), pdf.replace([0, 1, 2, 3, 5, 6], 4))
self.assert_eq(
psdf.replace([0, 1, 2, 3, 5, 6], [6, 5, 4, 3, 2, 1]),
pdf.replace([0, 1, 2, 3, 5, 6], [6, 5, 4, 3, 2, 1]),
)
self.assert_eq(psdf.replace({0: 10, 1: 100, 7: 200}), pdf.replace({0: 10, 1: 100, 7: 200}))
self.assert_eq(
psdf.replace({("X", "A"): [0, np.nan], ("X", "B"): 5}, 100),
pdf.replace({("X", "A"): [0, np.nan], ("X", "B"): 5}, 100),
)
self.assert_eq(
psdf.replace({("X", "A"): {0: 100, 4: 400, np.nan: 700}}),
pdf.replace({("X", "A"): {0: 100, 4: 400, np.nan: 700}}),
)
self.assert_eq(
psdf.replace({("X", "B"): {0: 100, 4: 400, np.nan: 700}}),
pdf.replace({("X", "B"): {0: 100, 4: 400, np.nan: 700}}),
)
self.assert_eq(
psdf.replace({("Y", "C"): ["a", None]}, "e"),
pdf.replace({("Y", "C"): ["a", None]}, "e"),
)
def test_update(self):
# check base function
def get_data(left_columns=None, right_columns=None):
left_pdf = pd.DataFrame(
{"A": ["1", "2", "3", "4"], "B": ["100", "200", np.nan, np.nan]}, columns=["A", "B"]
)
right_pdf = pd.DataFrame(
{"B": ["x", np.nan, "y", np.nan], "C": ["100", "200", "300", "400"]},
columns=["B", "C"],
)
left_psdf = ps.DataFrame(
{"A": ["1", "2", "3", "4"], "B": ["100", "200", None, None]}, columns=["A", "B"]
)
right_psdf = ps.DataFrame(
{"B": ["x", None, "y", None], "C": ["100", "200", "300", "400"]}, columns=["B", "C"]
)
if left_columns is not None:
left_pdf.columns = left_columns
left_psdf.columns = left_columns
if right_columns is not None:
right_pdf.columns = right_columns
right_psdf.columns = right_columns
return left_psdf, left_pdf, right_psdf, right_pdf
left_psdf, left_pdf, right_psdf, right_pdf = get_data()
pser = left_pdf.B
psser = left_psdf.B
left_pdf.update(right_pdf)
left_psdf.update(right_psdf)
self.assert_eq(left_pdf.sort_values(by=["A", "B"]), left_psdf.sort_values(by=["A", "B"]))
self.assert_eq(psser.sort_index(), pser.sort_index())
left_psdf, left_pdf, right_psdf, right_pdf = get_data()
left_pdf.update(right_pdf, overwrite=False)
left_psdf.update(right_psdf, overwrite=False)
self.assert_eq(left_pdf.sort_values(by=["A", "B"]), left_psdf.sort_values(by=["A", "B"]))
with self.assertRaises(NotImplementedError):
left_psdf.update(right_psdf, join="right")
# multi-index columns
left_columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B")])
right_columns = pd.MultiIndex.from_tuples([("X", "B"), ("Y", "C")])
left_psdf, left_pdf, right_psdf, right_pdf = get_data(
left_columns=left_columns, right_columns=right_columns
)
left_pdf.update(right_pdf)
left_psdf.update(right_psdf)
self.assert_eq(
left_pdf.sort_values(by=[("X", "A"), ("X", "B")]),
left_psdf.sort_values(by=[("X", "A"), ("X", "B")]),
)
left_psdf, left_pdf, right_psdf, right_pdf = get_data(
left_columns=left_columns, right_columns=right_columns
)
left_pdf.update(right_pdf, overwrite=False)
left_psdf.update(right_psdf, overwrite=False)
self.assert_eq(
left_pdf.sort_values(by=[("X", "A"), ("X", "B")]),
left_psdf.sort_values(by=[("X", "A"), ("X", "B")]),
)
right_columns = pd.MultiIndex.from_tuples([("Y", "B"), ("Y", "C")])
left_psdf, left_pdf, right_psdf, right_pdf = get_data(
left_columns=left_columns, right_columns=right_columns
)
left_pdf.update(right_pdf)
left_psdf.update(right_psdf)
self.assert_eq(
left_pdf.sort_values(by=[("X", "A"), ("X", "B")]),
left_psdf.sort_values(by=[("X", "A"), ("X", "B")]),
)
def test_pivot_table_dtypes(self):
pdf = pd.DataFrame(
{
"a": [4, 2, 3, 4, 8, 6],
"b": [1, 2, 2, 4, 2, 4],
"e": [1, 2, 2, 4, 2, 4],
"c": [1, 2, 9, 4, 7, 4],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
# Skip columns comparison by reset_index
res_df = psdf.pivot_table(
index=["c"], columns="a", values=["b"], aggfunc={"b": "mean"}
).dtypes.reset_index(drop=True)
exp_df = pdf.pivot_table(
index=["c"], columns="a", values=["b"], aggfunc={"b": "mean"}
).dtypes.reset_index(drop=True)
self.assert_eq(res_df, exp_df)
# Results don't have the same column's name
# Todo: self.assert_eq(psdf.pivot_table(columns="a", values="b").dtypes,
# pdf.pivot_table(columns="a", values="b").dtypes)
# Todo: self.assert_eq(psdf.pivot_table(index=['c'], columns="a", values="b").dtypes,
# pdf.pivot_table(index=['c'], columns="a", values="b").dtypes)
# Todo: self.assert_eq(psdf.pivot_table(index=['e', 'c'], columns="a", values="b").dtypes,
# pdf.pivot_table(index=['e', 'c'], columns="a", values="b").dtypes)
# Todo: self.assert_eq(psdf.pivot_table(index=['e', 'c'],
# columns="a", values="b", fill_value=999).dtypes, pdf.pivot_table(index=['e', 'c'],
# columns="a", values="b", fill_value=999).dtypes)
def test_pivot_table(self):
pdf = pd.DataFrame(
{
"a": [4, 2, 3, 4, 8, 6],
"b": [1, 2, 2, 4, 2, 4],
"e": [10, 20, 20, 40, 20, 40],
"c": [1, 2, 9, 4, 7, 4],
"d": [-1, -2, -3, -4, -5, -6],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
# Checking if both DataFrames have the same results
self.assert_eq(
psdf.pivot_table(columns="a", values="b").sort_index(),
pdf.pivot_table(columns="a", values="b").sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(index=["c"], columns="a", values="b").sort_index(),
pdf.pivot_table(index=["c"], columns="a", values="b").sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(index=["c"], columns="a", values="b", aggfunc="sum").sort_index(),
pdf.pivot_table(index=["c"], columns="a", values="b", aggfunc="sum").sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(index=["c"], columns="a", values=["b"], aggfunc="sum").sort_index(),
pdf.pivot_table(index=["c"], columns="a", values=["b"], aggfunc="sum").sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=["c"], columns="a", values=["b", "e"], aggfunc="sum"
).sort_index(),
pdf.pivot_table(
index=["c"], columns="a", values=["b", "e"], aggfunc="sum"
).sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=["c"], columns="a", values=["b", "e", "d"], aggfunc="sum"
).sort_index(),
pdf.pivot_table(
index=["c"], columns="a", values=["b", "e", "d"], aggfunc="sum"
).sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=["c"], columns="a", values=["b", "e"], aggfunc={"b": "mean", "e": "sum"}
).sort_index(),
pdf.pivot_table(
index=["c"], columns="a", values=["b", "e"], aggfunc={"b": "mean", "e": "sum"}
).sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(index=["e", "c"], columns="a", values="b").sort_index(),
pdf.pivot_table(index=["e", "c"], columns="a", values="b").sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=["e", "c"], columns="a", values="b", fill_value=999
).sort_index(),
pdf.pivot_table(index=["e", "c"], columns="a", values="b", fill_value=999).sort_index(),
almost=True,
)
# multi-index columns
columns = pd.MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "e"), ("z", "c"), ("w", "d")]
)
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.pivot_table(columns=("x", "a"), values=("x", "b")).sort_index(),
pdf.pivot_table(columns=[("x", "a")], values=[("x", "b")]).sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=[("z", "c")], columns=("x", "a"), values=[("x", "b")]
).sort_index(),
pdf.pivot_table(
index=[("z", "c")], columns=[("x", "a")], values=[("x", "b")]
).sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=[("z", "c")], columns=("x", "a"), values=[("x", "b"), ("y", "e")]
).sort_index(),
pdf.pivot_table(
index=[("z", "c")], columns=[("x", "a")], values=[("x", "b"), ("y", "e")]
).sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=[("z", "c")], columns=("x", "a"), values=[("x", "b"), ("y", "e"), ("w", "d")]
).sort_index(),
pdf.pivot_table(
index=[("z", "c")],
columns=[("x", "a")],
values=[("x", "b"), ("y", "e"), ("w", "d")],
).sort_index(),
almost=True,
)
self.assert_eq(
psdf.pivot_table(
index=[("z", "c")],
columns=("x", "a"),
values=[("x", "b"), ("y", "e")],
aggfunc={("x", "b"): "mean", ("y", "e"): "sum"},
).sort_index(),
pdf.pivot_table(
index=[("z", "c")],
columns=[("x", "a")],
values=[("x", "b"), ("y", "e")],
aggfunc={("x", "b"): "mean", ("y", "e"): "sum"},
).sort_index(),
almost=True,
)
def test_pivot_table_and_index(self):
# https://github.com/databricks/koalas/issues/805
pdf = pd.DataFrame(
{
"A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
"C": [
"small",
"large",
"large",
"small",
"small",
"large",
"small",
"small",
"large",
],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
},
columns=["A", "B", "C", "D", "E"],
index=np.random.rand(9),
)
psdf = ps.from_pandas(pdf)
ptable = pdf.pivot_table(
values="D", index=["A", "B"], columns="C", aggfunc="sum", fill_value=0
).sort_index()
ktable = psdf.pivot_table(
values="D", index=["A", "B"], columns="C", aggfunc="sum", fill_value=0
).sort_index()
self.assert_eq(ktable, ptable)
self.assert_eq(ktable.index, ptable.index)
self.assert_eq(repr(ktable.index), repr(ptable.index))
def test_stack(self):
pdf_single_level_cols = pd.DataFrame(
[[0, 1], [2, 3]], index=["cat", "dog"], columns=["weight", "height"]
)
psdf_single_level_cols = ps.from_pandas(pdf_single_level_cols)
self.assert_eq(
psdf_single_level_cols.stack().sort_index(), pdf_single_level_cols.stack().sort_index()
)
multicol1 = pd.MultiIndex.from_tuples(
[("weight", "kg"), ("weight", "pounds")], names=["x", "y"]
)
pdf_multi_level_cols1 = pd.DataFrame(
[[1, 2], [2, 4]], index=["cat", "dog"], columns=multicol1
)
psdf_multi_level_cols1 = ps.from_pandas(pdf_multi_level_cols1)
self.assert_eq(
psdf_multi_level_cols1.stack().sort_index(), pdf_multi_level_cols1.stack().sort_index()
)
multicol2 = pd.MultiIndex.from_tuples([("weight", "kg"), ("height", "m")])
pdf_multi_level_cols2 = pd.DataFrame(
[[1.0, 2.0], [3.0, 4.0]], index=["cat", "dog"], columns=multicol2
)
psdf_multi_level_cols2 = ps.from_pandas(pdf_multi_level_cols2)
self.assert_eq(
psdf_multi_level_cols2.stack().sort_index(), pdf_multi_level_cols2.stack().sort_index()
)
pdf = pd.DataFrame(
{
("y", "c"): [True, True],
("x", "b"): [False, False],
("x", "c"): [True, False],
("y", "a"): [False, True],
}
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.stack().sort_index(), pdf.stack().sort_index())
self.assert_eq(psdf[[]].stack().sort_index(), pdf[[]].stack().sort_index(), almost=True)
def test_unstack(self):
pdf = pd.DataFrame(
np.random.randn(3, 3),
index=pd.MultiIndex.from_tuples([("rg1", "x"), ("rg1", "y"), ("rg2", "z")]),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.unstack().sort_index(), pdf.unstack().sort_index(), almost=True)
self.assert_eq(
psdf.unstack().unstack().sort_index(), pdf.unstack().unstack().sort_index(), almost=True
)
def test_pivot_errors(self):
psdf = ps.range(10)
with self.assertRaisesRegex(ValueError, "columns should be set"):
psdf.pivot(index="id")
with self.assertRaisesRegex(ValueError, "values should be set"):
psdf.pivot(index="id", columns="id")
def test_pivot_table_errors(self):
pdf = pd.DataFrame(
{
"a": [4, 2, 3, 4, 8, 6],
"b": [1, 2, 2, 4, 2, 4],
"e": [1, 2, 2, 4, 2, 4],
"c": [1, 2, 9, 4, 7, 4],
},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
self.assertRaises(KeyError, lambda: psdf.pivot_table(index=["c"], columns="a", values=5))
msg = "index should be a None or a list of columns."
with self.assertRaisesRegex(TypeError, msg):
psdf.pivot_table(index="c", columns="a", values="b")
msg = "pivot_table doesn't support aggfunc as dict and without index."
with self.assertRaisesRegex(NotImplementedError, msg):
psdf.pivot_table(columns="a", values=["b", "e"], aggfunc={"b": "mean", "e": "sum"})
msg = "columns should be one column name."
with self.assertRaisesRegex(TypeError, msg):
psdf.pivot_table(columns=["a"], values=["b"], aggfunc={"b": "mean", "e": "sum"})
msg = "Columns in aggfunc must be the same as values."
with self.assertRaisesRegex(ValueError, msg):
psdf.pivot_table(
index=["e", "c"], columns="a", values="b", aggfunc={"b": "mean", "e": "sum"}
)
msg = "values can't be a list without index."
with self.assertRaisesRegex(NotImplementedError, msg):
psdf.pivot_table(columns="a", values=["b", "e"])
msg = "Wrong columns A."
with self.assertRaisesRegex(ValueError, msg):
psdf.pivot_table(
index=["c"], columns="A", values=["b", "e"], aggfunc={"b": "mean", "e": "sum"}
)
psdf = ps.DataFrame(
{
"A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
"C": [
"small",
"large",
"large",
"small",
"small",
"large",
"small",
"small",
"large",
],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
},
columns=["A", "B", "C", "D", "E"],
index=np.random.rand(9),
)
msg = "values should be a numeric type."
with self.assertRaisesRegex(TypeError, msg):
psdf.pivot_table(
index=["C"], columns="A", values=["B", "E"], aggfunc={"B": "mean", "E": "sum"}
)
msg = "values should be a numeric type."
with self.assertRaisesRegex(TypeError, msg):
psdf.pivot_table(index=["C"], columns="A", values="B", aggfunc={"B": "mean"})
def test_transpose(self):
# TODO: what if with random index?
pdf1 = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}, columns=["col1", "col2"])
psdf1 = ps.from_pandas(pdf1)
pdf2 = pd.DataFrame(
data={"score": [9, 8], "kids": [0, 0], "age": [12, 22]},
columns=["score", "kids", "age"],
)
psdf2 = ps.from_pandas(pdf2)
self.assert_eq(pdf1.transpose().sort_index(), psdf1.transpose().sort_index())
self.assert_eq(pdf2.transpose().sort_index(), psdf2.transpose().sort_index())
with option_context("compute.max_rows", None):
self.assert_eq(pdf1.transpose().sort_index(), psdf1.transpose().sort_index())
self.assert_eq(pdf2.transpose().sort_index(), psdf2.transpose().sort_index())
pdf3 = pd.DataFrame(
{
("cg1", "a"): [1, 2, 3],
("cg1", "b"): [4, 5, 6],
("cg2", "c"): [7, 8, 9],
("cg3", "d"): [9, 9, 9],
},
index=pd.MultiIndex.from_tuples([("rg1", "x"), ("rg1", "y"), ("rg2", "z")]),
)
psdf3 = ps.from_pandas(pdf3)
self.assert_eq(pdf3.transpose().sort_index(), psdf3.transpose().sort_index())
with option_context("compute.max_rows", None):
self.assert_eq(pdf3.transpose().sort_index(), psdf3.transpose().sort_index())
def _test_cummin(self, pdf, psdf):
self.assert_eq(pdf.cummin(), psdf.cummin())
self.assert_eq(pdf.cummin(skipna=False), psdf.cummin(skipna=False))
self.assert_eq(pdf.cummin().sum(), psdf.cummin().sum())
def test_cummin(self):
pdf = pd.DataFrame(
[[2.0, 1.0], [5, None], [1.0, 0.0], [2.0, 4.0], [4.0, 9.0]],
columns=list("AB"),
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self._test_cummin(pdf, psdf)
def test_cummin_multiindex_columns(self):
arrays = [np.array(["A", "A", "B", "B"]), np.array(["one", "two", "one", "two"])]
pdf = pd.DataFrame(np.random.randn(3, 4), index=["A", "C", "B"], columns=arrays)
pdf.at["C", ("A", "two")] = None
psdf = ps.from_pandas(pdf)
self._test_cummin(pdf, psdf)
def _test_cummax(self, pdf, psdf):
self.assert_eq(pdf.cummax(), psdf.cummax())
self.assert_eq(pdf.cummax(skipna=False), psdf.cummax(skipna=False))
self.assert_eq(pdf.cummax().sum(), psdf.cummax().sum())
def test_cummax(self):
pdf = pd.DataFrame(
[[2.0, 1.0], [5, None], [1.0, 0.0], [2.0, 4.0], [4.0, 9.0]],
columns=list("AB"),
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self._test_cummax(pdf, psdf)
def test_cummax_multiindex_columns(self):
arrays = [np.array(["A", "A", "B", "B"]), np.array(["one", "two", "one", "two"])]
pdf = pd.DataFrame(np.random.randn(3, 4), index=["A", "C", "B"], columns=arrays)
pdf.at["C", ("A", "two")] = None
psdf = ps.from_pandas(pdf)
self._test_cummax(pdf, psdf)
def _test_cumsum(self, pdf, psdf):
self.assert_eq(pdf.cumsum(), psdf.cumsum())
self.assert_eq(pdf.cumsum(skipna=False), psdf.cumsum(skipna=False))
self.assert_eq(pdf.cumsum().sum(), psdf.cumsum().sum())
def test_cumsum(self):
pdf = pd.DataFrame(
[[2.0, 1.0], [5, None], [1.0, 0.0], [2.0, 4.0], [4.0, 9.0]],
columns=list("AB"),
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self._test_cumsum(pdf, psdf)
def test_cumsum_multiindex_columns(self):
arrays = [np.array(["A", "A", "B", "B"]), np.array(["one", "two", "one", "two"])]
pdf = pd.DataFrame(np.random.randn(3, 4), index=["A", "C", "B"], columns=arrays)
pdf.at["C", ("A", "two")] = None
psdf = ps.from_pandas(pdf)
self._test_cumsum(pdf, psdf)
def _test_cumprod(self, pdf, psdf):
self.assert_eq(pdf.cumprod(), psdf.cumprod(), almost=True)
self.assert_eq(pdf.cumprod(skipna=False), psdf.cumprod(skipna=False), almost=True)
self.assert_eq(pdf.cumprod().sum(), psdf.cumprod().sum(), almost=True)
def test_cumprod(self):
pdf = pd.DataFrame(
[[2.0, 1.0, 1], [5, None, 2], [1.0, -1.0, -3], [2.0, 0, 4], [4.0, 9.0, 5]],
columns=list("ABC"),
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self._test_cumprod(pdf, psdf)
def test_cumprod_multiindex_columns(self):
arrays = [np.array(["A", "A", "B", "B"]), np.array(["one", "two", "one", "two"])]
pdf = pd.DataFrame(np.random.rand(3, 4), index=["A", "C", "B"], columns=arrays)
pdf.at["C", ("A", "two")] = None
psdf = ps.from_pandas(pdf)
self._test_cumprod(pdf, psdf)
def test_drop_duplicates(self):
pdf = pd.DataFrame(
{"a": [1, 2, 2, 2, 3], "b": ["a", "a", "a", "c", "d"]}, index=np.random.rand(5)
)
psdf = ps.from_pandas(pdf)
# inplace is False
for keep in ["first", "last", False]:
with self.subTest(keep=keep):
self.assert_eq(
pdf.drop_duplicates(keep=keep).sort_index(),
psdf.drop_duplicates(keep=keep).sort_index(),
)
self.assert_eq(
pdf.drop_duplicates("a", keep=keep).sort_index(),
psdf.drop_duplicates("a", keep=keep).sort_index(),
)
self.assert_eq(
pdf.drop_duplicates(["a", "b"], keep=keep).sort_index(),
psdf.drop_duplicates(["a", "b"], keep=keep).sort_index(),
)
self.assert_eq(
pdf.set_index("a", append=True).drop_duplicates(keep=keep).sort_index(),
psdf.set_index("a", append=True).drop_duplicates(keep=keep).sort_index(),
)
self.assert_eq(
pdf.set_index("a", append=True).drop_duplicates("b", keep=keep).sort_index(),
psdf.set_index("a", append=True).drop_duplicates("b", keep=keep).sort_index(),
)
columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
pdf.columns = columns
psdf.columns = columns
# inplace is False
for keep in ["first", "last", False]:
with self.subTest("multi-index columns", keep=keep):
self.assert_eq(
pdf.drop_duplicates(keep=keep).sort_index(),
psdf.drop_duplicates(keep=keep).sort_index(),
)
self.assert_eq(
pdf.drop_duplicates(("x", "a"), keep=keep).sort_index(),
psdf.drop_duplicates(("x", "a"), keep=keep).sort_index(),
)
self.assert_eq(
pdf.drop_duplicates([("x", "a"), ("y", "b")], keep=keep).sort_index(),
psdf.drop_duplicates([("x", "a"), ("y", "b")], keep=keep).sort_index(),
)
# inplace is True
subset_list = [None, "a", ["a", "b"]]
for subset in subset_list:
pdf = pd.DataFrame(
{"a": [1, 2, 2, 2, 3], "b": ["a", "a", "a", "c", "d"]}, index=np.random.rand(5)
)
psdf = ps.from_pandas(pdf)
pser = pdf.a
psser = psdf.a
pdf.drop_duplicates(subset=subset, inplace=True)
psdf.drop_duplicates(subset=subset, inplace=True)
self.assert_eq(psdf.sort_index(), pdf.sort_index())
self.assert_eq(psser.sort_index(), pser.sort_index())
# multi-index columns, inplace is True
subset_list = [None, ("x", "a"), [("x", "a"), ("y", "b")]]
for subset in subset_list:
pdf = pd.DataFrame(
{"a": [1, 2, 2, 2, 3], "b": ["a", "a", "a", "c", "d"]}, index=np.random.rand(5)
)
psdf = ps.from_pandas(pdf)
columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
pdf.columns = columns
psdf.columns = columns
pser = pdf[("x", "a")]
psser = psdf[("x", "a")]
pdf.drop_duplicates(subset=subset, inplace=True)
psdf.drop_duplicates(subset=subset, inplace=True)
self.assert_eq(psdf.sort_index(), pdf.sort_index())
self.assert_eq(psser.sort_index(), pser.sort_index())
# non-string names
pdf = pd.DataFrame(
{10: [1, 2, 2, 2, 3], 20: ["a", "a", "a", "c", "d"]}, index=np.random.rand(5)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.drop_duplicates(10, keep=keep).sort_index(),
psdf.drop_duplicates(10, keep=keep).sort_index(),
)
self.assert_eq(
pdf.drop_duplicates([10, 20], keep=keep).sort_index(),
psdf.drop_duplicates([10, 20], keep=keep).sort_index(),
)
def test_reindex(self):
index = pd.Index(["A", "B", "C", "D", "E"])
columns = pd.Index(["numbers"])
pdf = pd.DataFrame([1.0, 2.0, 3.0, 4.0, None], index=index, columns=columns)
psdf = ps.from_pandas(pdf)
columns2 = pd.Index(["numbers", "2", "3"], name="cols2")
self.assert_eq(
pdf.reindex(columns=columns2).sort_index(),
psdf.reindex(columns=columns2).sort_index(),
)
columns = pd.Index(["numbers"], name="cols")
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
pdf.reindex(["A", "B", "C"], columns=["numbers", "2", "3"]).sort_index(),
psdf.reindex(["A", "B", "C"], columns=["numbers", "2", "3"]).sort_index(),
)
self.assert_eq(
pdf.reindex(["A", "B", "C"], index=["numbers", "2", "3"]).sort_index(),
psdf.reindex(["A", "B", "C"], index=["numbers", "2", "3"]).sort_index(),
)
self.assert_eq(
pdf.reindex(index=["A", "B"]).sort_index(), psdf.reindex(index=["A", "B"]).sort_index()
)
self.assert_eq(
pdf.reindex(index=["A", "B", "2", "3"]).sort_index(),
psdf.reindex(index=["A", "B", "2", "3"]).sort_index(),
)
self.assert_eq(
pdf.reindex(index=["A", "E", "2", "3"], fill_value=0).sort_index(),
psdf.reindex(index=["A", "E", "2", "3"], fill_value=0).sort_index(),
)
self.assert_eq(
pdf.reindex(columns=["numbers"]).sort_index(),
psdf.reindex(columns=["numbers"]).sort_index(),
)
# Using float as fill_value to avoid int64/32 clash
self.assert_eq(
pdf.reindex(columns=["numbers", "2", "3"], fill_value=0.0).sort_index(),
psdf.reindex(columns=["numbers", "2", "3"], fill_value=0.0).sort_index(),
)
columns2 = pd.Index(["numbers", "2", "3"])
self.assert_eq(
pdf.reindex(columns=columns2).sort_index(),
psdf.reindex(columns=columns2).sort_index(),
)
columns2 = pd.Index(["numbers", "2", "3"], name="cols2")
self.assert_eq(
pdf.reindex(columns=columns2).sort_index(),
psdf.reindex(columns=columns2).sort_index(),
)
# Reindexing single Index on single Index
pindex2 = pd.Index(["A", "C", "D", "E", "0"], name="index2")
kindex2 = ps.from_pandas(pindex2)
for fill_value in [None, 0]:
self.assert_eq(
pdf.reindex(index=pindex2, fill_value=fill_value).sort_index(),
psdf.reindex(index=kindex2, fill_value=fill_value).sort_index(),
)
pindex2 = pd.DataFrame({"index2": ["A", "C", "D", "E", "0"]}).set_index("index2").index
kindex2 = ps.from_pandas(pindex2)
for fill_value in [None, 0]:
self.assert_eq(
pdf.reindex(index=pindex2, fill_value=fill_value).sort_index(),
psdf.reindex(index=kindex2, fill_value=fill_value).sort_index(),
)
# Reindexing MultiIndex on single Index
pindex = pd.MultiIndex.from_tuples(
[("A", "B"), ("C", "D"), ("F", "G")], names=["name1", "name2"]
)
kindex = ps.from_pandas(pindex)
self.assert_eq(
pdf.reindex(index=pindex, fill_value=0.0).sort_index(),
psdf.reindex(index=kindex, fill_value=0.0).sort_index(),
)
self.assertRaises(TypeError, lambda: psdf.reindex(columns=["numbers", "2", "3"], axis=1))
self.assertRaises(TypeError, lambda: psdf.reindex(columns=["numbers", "2", "3"], axis=2))
self.assertRaises(TypeError, lambda: psdf.reindex(index=["A", "B", "C"], axis=1))
self.assertRaises(TypeError, lambda: psdf.reindex(index=123))
# Reindexing MultiIndex on MultiIndex
pdf = pd.DataFrame({"numbers": [1.0, 2.0, None]}, index=pindex)
psdf = ps.from_pandas(pdf)
pindex2 = pd.MultiIndex.from_tuples(
[("A", "G"), ("C", "D"), ("I", "J")], names=["name1", "name2"]
)
kindex2 = ps.from_pandas(pindex2)
for fill_value in [None, 0.0]:
self.assert_eq(
pdf.reindex(index=pindex2, fill_value=fill_value).sort_index(),
psdf.reindex(index=kindex2, fill_value=fill_value).sort_index(),
)
pindex2 = (
pd.DataFrame({"index_level_1": ["A", "C", "I"], "index_level_2": ["G", "D", "J"]})
.set_index(["index_level_1", "index_level_2"])
.index
)
kindex2 = ps.from_pandas(pindex2)
for fill_value in [None, 0.0]:
self.assert_eq(
pdf.reindex(index=pindex2, fill_value=fill_value).sort_index(),
psdf.reindex(index=kindex2, fill_value=fill_value).sort_index(),
)
columns = pd.MultiIndex.from_tuples([("X", "numbers")], names=["cols1", "cols2"])
pdf.columns = columns
psdf.columns = columns
# Reindexing MultiIndex index on MultiIndex columns and MultiIndex index
for fill_value in [None, 0.0]:
self.assert_eq(
pdf.reindex(index=pindex2, fill_value=fill_value).sort_index(),
psdf.reindex(index=kindex2, fill_value=fill_value).sort_index(),
)
index = pd.Index(["A", "B", "C", "D", "E"])
pdf = pd.DataFrame(data=[1.0, 2.0, 3.0, 4.0, None], index=index, columns=columns)
psdf = ps.from_pandas(pdf)
pindex2 = pd.Index(["A", "C", "D", "E", "0"], name="index2")
kindex2 = ps.from_pandas(pindex2)
# Reindexing single Index on MultiIndex columns and single Index
for fill_value in [None, 0.0]:
self.assert_eq(
pdf.reindex(index=pindex2, fill_value=fill_value).sort_index(),
psdf.reindex(index=kindex2, fill_value=fill_value).sort_index(),
)
for fill_value in [None, 0.0]:
self.assert_eq(
pdf.reindex(
columns=[("X", "numbers"), ("Y", "2"), ("Y", "3")], fill_value=fill_value
).sort_index(),
psdf.reindex(
columns=[("X", "numbers"), ("Y", "2"), ("Y", "3")], fill_value=fill_value
).sort_index(),
)
columns2 = pd.MultiIndex.from_tuples(
[("X", "numbers"), ("Y", "2"), ("Y", "3")], names=["cols3", "cols4"]
)
self.assert_eq(
pdf.reindex(columns=columns2).sort_index(),
psdf.reindex(columns=columns2).sort_index(),
)
self.assertRaises(TypeError, lambda: psdf.reindex(columns=["X"]))
self.assertRaises(ValueError, lambda: psdf.reindex(columns=[("X",)]))
def test_reindex_like(self):
data = [[1.0, 2.0], [3.0, None], [None, 4.0]]
index = pd.Index(["A", "B", "C"], name="index")
columns = pd.Index(["numbers", "values"], name="cols")
pdf = pd.DataFrame(data=data, index=index, columns=columns)
psdf = ps.from_pandas(pdf)
# Reindexing single Index on single Index
data2 = [[5.0, None], [6.0, 7.0], [8.0, None]]
index2 = pd.Index(["A", "C", "D"], name="index2")
columns2 = pd.Index(["numbers", "F"], name="cols2")
pdf2 = pd.DataFrame(data=data2, index=index2, columns=columns2)
psdf2 = ps.from_pandas(pdf2)
self.assert_eq(
pdf.reindex_like(pdf2).sort_index(),
psdf.reindex_like(psdf2).sort_index(),
)
pdf2 = pd.DataFrame({"index_level_1": ["A", "C", "I"]})
psdf2 = ps.from_pandas(pdf2)
self.assert_eq(
pdf.reindex_like(pdf2.set_index(["index_level_1"])).sort_index(),
psdf.reindex_like(psdf2.set_index(["index_level_1"])).sort_index(),
)
# Reindexing MultiIndex on single Index
index2 = pd.MultiIndex.from_tuples(
[("A", "G"), ("C", "D"), ("I", "J")], names=["name3", "name4"]
)
pdf2 = pd.DataFrame(data=data2, index=index2)
psdf2 = ps.from_pandas(pdf2)
self.assert_eq(
pdf.reindex_like(pdf2).sort_index(),
psdf.reindex_like(psdf2).sort_index(),
)
self.assertRaises(TypeError, lambda: psdf.reindex_like(index2))
self.assertRaises(AssertionError, lambda: psdf2.reindex_like(psdf))
# Reindexing MultiIndex on MultiIndex
columns2 = pd.MultiIndex.from_tuples(
[("numbers", "third"), ("values", "second")], names=["cols3", "cols4"]
)
pdf2.columns = columns2
psdf2.columns = columns2
columns = pd.MultiIndex.from_tuples(
[("numbers", "first"), ("values", "second")], names=["cols1", "cols2"]
)
index = pd.MultiIndex.from_tuples(
[("A", "B"), ("C", "D"), ("E", "F")], names=["name1", "name2"]
)
pdf = pd.DataFrame(data=data, index=index, columns=columns)
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.reindex_like(pdf2).sort_index(),
psdf.reindex_like(psdf2).sort_index(),
)
def test_melt(self):
pdf = pd.DataFrame(
{"A": [1, 3, 5], "B": [2, 4, 6], "C": [7, 8, 9]}, index=np.random.rand(3)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(
psdf.melt().sort_values(["variable", "value"]).reset_index(drop=True),
pdf.melt().sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars="A").sort_values(["variable", "value"]).reset_index(drop=True),
pdf.melt(id_vars="A").sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=["A", "B"]).sort_values(["variable", "value"]).reset_index(drop=True),
pdf.melt(id_vars=["A", "B"]).sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=("A", "B")).sort_values(["variable", "value"]).reset_index(drop=True),
pdf.melt(id_vars=("A", "B")).sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=["A"], value_vars=["C"])
.sort_values(["variable", "value"])
.reset_index(drop=True),
pdf.melt(id_vars=["A"], value_vars=["C"]).sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=["A"], value_vars=["B"], var_name="myVarname", value_name="myValname")
.sort_values(["myVarname", "myValname"])
.reset_index(drop=True),
pdf.melt(
id_vars=["A"], value_vars=["B"], var_name="myVarname", value_name="myValname"
).sort_values(["myVarname", "myValname"]),
)
self.assert_eq(
psdf.melt(value_vars=("A", "B"))
.sort_values(["variable", "value"])
.reset_index(drop=True),
pdf.melt(value_vars=("A", "B")).sort_values(["variable", "value"]),
)
self.assertRaises(KeyError, lambda: psdf.melt(id_vars="Z"))
self.assertRaises(KeyError, lambda: psdf.melt(value_vars="Z"))
# multi-index columns
if LooseVersion("0.24") <= LooseVersion(pd.__version__) < LooseVersion("1.0.0"):
# pandas >=0.24,<1.0 doesn't support mixed int/str columns in melt.
# see: https://github.com/pandas-dev/pandas/pull/29792
TEN = "10"
TWELVE = "20"
else:
TEN = 10.0
TWELVE = 20.0
columns = pd.MultiIndex.from_tuples([(TEN, "A"), (TEN, "B"), (TWELVE, "C")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.melt().sort_values(["variable_0", "variable_1", "value"]).reset_index(drop=True),
pdf.melt().sort_values(["variable_0", "variable_1", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=[(TEN, "A")])
.sort_values(["variable_0", "variable_1", "value"])
.reset_index(drop=True),
pdf.melt(id_vars=[(TEN, "A")])
.sort_values(["variable_0", "variable_1", "value"])
.rename(columns=name_like_string),
)
self.assert_eq(
psdf.melt(id_vars=[(TEN, "A")], value_vars=[(TWELVE, "C")])
.sort_values(["variable_0", "variable_1", "value"])
.reset_index(drop=True),
pdf.melt(id_vars=[(TEN, "A")], value_vars=[(TWELVE, "C")])
.sort_values(["variable_0", "variable_1", "value"])
.rename(columns=name_like_string),
)
self.assert_eq(
psdf.melt(
id_vars=[(TEN, "A")],
value_vars=[(TEN, "B")],
var_name=["myV1", "myV2"],
value_name="myValname",
)
.sort_values(["myV1", "myV2", "myValname"])
.reset_index(drop=True),
pdf.melt(
id_vars=[(TEN, "A")],
value_vars=[(TEN, "B")],
var_name=["myV1", "myV2"],
value_name="myValname",
)
.sort_values(["myV1", "myV2", "myValname"])
.rename(columns=name_like_string),
)
columns.names = ["v0", "v1"]
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.melt().sort_values(["v0", "v1", "value"]).reset_index(drop=True),
pdf.melt().sort_values(["v0", "v1", "value"]),
)
self.assertRaises(ValueError, lambda: psdf.melt(id_vars=(TEN, "A")))
self.assertRaises(ValueError, lambda: psdf.melt(value_vars=(TEN, "A")))
self.assertRaises(KeyError, lambda: psdf.melt(id_vars=[TEN]))
self.assertRaises(KeyError, lambda: psdf.melt(id_vars=[(TWELVE, "A")]))
self.assertRaises(KeyError, lambda: psdf.melt(value_vars=[TWELVE]))
self.assertRaises(KeyError, lambda: psdf.melt(value_vars=[(TWELVE, "A")]))
# non-string names
pdf.columns = [10.0, 20.0, 30.0]
psdf.columns = [10.0, 20.0, 30.0]
self.assert_eq(
psdf.melt().sort_values(["variable", "value"]).reset_index(drop=True),
pdf.melt().sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=10.0).sort_values(["variable", "value"]).reset_index(drop=True),
pdf.melt(id_vars=10.0).sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=[10.0, 20.0])
.sort_values(["variable", "value"])
.reset_index(drop=True),
pdf.melt(id_vars=[10.0, 20.0]).sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=(10.0, 20.0))
.sort_values(["variable", "value"])
.reset_index(drop=True),
pdf.melt(id_vars=(10.0, 20.0)).sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(id_vars=[10.0], value_vars=[30.0])
.sort_values(["variable", "value"])
.reset_index(drop=True),
pdf.melt(id_vars=[10.0], value_vars=[30.0]).sort_values(["variable", "value"]),
)
self.assert_eq(
psdf.melt(value_vars=(10.0, 20.0))
.sort_values(["variable", "value"])
.reset_index(drop=True),
pdf.melt(value_vars=(10.0, 20.0)).sort_values(["variable", "value"]),
)
def test_all(self):
pdf = pd.DataFrame(
{
"col1": [False, False, False],
"col2": [True, False, False],
"col3": [0, 0, 1],
"col4": [0, 1, 2],
"col5": [False, False, None],
"col6": [True, False, None],
},
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.all(), pdf.all())
columns = pd.MultiIndex.from_tuples(
[
("a", "col1"),
("a", "col2"),
("a", "col3"),
("b", "col4"),
("b", "col5"),
("c", "col6"),
]
)
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.all(), pdf.all())
columns.names = ["X", "Y"]
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.all(), pdf.all())
with self.assertRaisesRegex(
NotImplementedError, 'axis should be either 0 or "index" currently.'
):
psdf.all(axis=1)
def test_any(self):
pdf = pd.DataFrame(
{
"col1": [False, False, False],
"col2": [True, False, False],
"col3": [0, 0, 1],
"col4": [0, 1, 2],
"col5": [False, False, None],
"col6": [True, False, None],
},
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.any(), pdf.any())
columns = pd.MultiIndex.from_tuples(
[
("a", "col1"),
("a", "col2"),
("a", "col3"),
("b", "col4"),
("b", "col5"),
("c", "col6"),
]
)
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.any(), pdf.any())
columns.names = ["X", "Y"]
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.any(), pdf.any())
with self.assertRaisesRegex(
NotImplementedError, 'axis should be either 0 or "index" currently.'
):
psdf.any(axis=1)
def test_rank(self):
pdf = pd.DataFrame(
data={"col1": [1, 2, 3, 1], "col2": [3, 4, 3, 1]},
columns=["col1", "col2"],
index=np.random.rand(4),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.rank().sort_index(), psdf.rank().sort_index())
self.assert_eq(pdf.rank().sum(), psdf.rank().sum())
self.assert_eq(
pdf.rank(ascending=False).sort_index(), psdf.rank(ascending=False).sort_index()
)
self.assert_eq(pdf.rank(method="min").sort_index(), psdf.rank(method="min").sort_index())
self.assert_eq(pdf.rank(method="max").sort_index(), psdf.rank(method="max").sort_index())
self.assert_eq(
pdf.rank(method="first").sort_index(), psdf.rank(method="first").sort_index()
)
self.assert_eq(
pdf.rank(method="dense").sort_index(), psdf.rank(method="dense").sort_index()
)
msg = "method must be one of 'average', 'min', 'max', 'first', 'dense'"
with self.assertRaisesRegex(ValueError, msg):
psdf.rank(method="nothing")
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "col1"), ("y", "col2")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(pdf.rank().sort_index(), psdf.rank().sort_index())
def test_round(self):
pdf = pd.DataFrame(
{
"A": [0.028208, 0.038683, 0.877076],
"B": [0.992815, 0.645646, 0.149370],
"C": [0.173891, 0.577595, 0.491027],
},
columns=["A", "B", "C"],
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
pser = pd.Series([1, 0, 2], index=["A", "B", "C"])
psser = ps.Series([1, 0, 2], index=["A", "B", "C"])
self.assert_eq(pdf.round(2), psdf.round(2))
self.assert_eq(pdf.round({"A": 1, "C": 2}), psdf.round({"A": 1, "C": 2}))
self.assert_eq(pdf.round({"A": 1, "D": 2}), psdf.round({"A": 1, "D": 2}))
self.assert_eq(pdf.round(pser), psdf.round(psser))
msg = "decimals must be an integer, a dict-like or a Series"
with self.assertRaisesRegex(TypeError, msg):
psdf.round(1.5)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")])
pdf.columns = columns
psdf.columns = columns
pser = pd.Series([1, 0, 2], index=columns)
psser = ps.Series([1, 0, 2], index=columns)
self.assert_eq(pdf.round(2), psdf.round(2))
self.assert_eq(
pdf.round({("X", "A"): 1, ("Y", "C"): 2}), psdf.round({("X", "A"): 1, ("Y", "C"): 2})
)
self.assert_eq(pdf.round({("X", "A"): 1, "Y": 2}), psdf.round({("X", "A"): 1, "Y": 2}))
self.assert_eq(pdf.round(pser), psdf.round(psser))
# non-string names
pdf = pd.DataFrame(
{
10: [0.028208, 0.038683, 0.877076],
20: [0.992815, 0.645646, 0.149370],
30: [0.173891, 0.577595, 0.491027],
},
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.round({10: 1, 30: 2}), psdf.round({10: 1, 30: 2}))
def test_shift(self):
pdf = pd.DataFrame(
{
"Col1": [10, 20, 15, 30, 45],
"Col2": [13, 23, 18, 33, 48],
"Col3": [17, 27, 22, 37, 52],
},
index=np.random.rand(5),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.shift(3), psdf.shift(3))
self.assert_eq(pdf.shift().shift(-1), psdf.shift().shift(-1))
self.assert_eq(pdf.shift().sum().astype(int), psdf.shift().sum())
# Need the expected result since pandas 0.23 does not support `fill_value` argument.
pdf1 = pd.DataFrame(
{"Col1": [0, 0, 0, 10, 20], "Col2": [0, 0, 0, 13, 23], "Col3": [0, 0, 0, 17, 27]},
index=pdf.index,
)
self.assert_eq(pdf1, psdf.shift(periods=3, fill_value=0))
msg = "should be an int"
with self.assertRaisesRegex(TypeError, msg):
psdf.shift(1.5)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "Col1"), ("x", "Col2"), ("y", "Col3")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(pdf.shift(3), psdf.shift(3))
self.assert_eq(pdf.shift().shift(-1), psdf.shift().shift(-1))
def test_diff(self):
pdf = pd.DataFrame(
{"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]},
index=np.random.rand(6),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.diff(), psdf.diff())
self.assert_eq(pdf.diff().diff(-1), psdf.diff().diff(-1))
self.assert_eq(pdf.diff().sum().astype(int), psdf.diff().sum())
msg = "should be an int"
with self.assertRaisesRegex(TypeError, msg):
psdf.diff(1.5)
msg = 'axis should be either 0 or "index" currently.'
with self.assertRaisesRegex(NotImplementedError, msg):
psdf.diff(axis=1)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "Col1"), ("x", "Col2"), ("y", "Col3")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(pdf.diff(), psdf.diff())
def test_duplicated(self):
pdf = pd.DataFrame(
{"a": [1, 1, 2, 3], "b": [1, 1, 1, 4], "c": [1, 1, 1, 5]}, index=np.random.rand(4)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.duplicated().sort_index(), psdf.duplicated().sort_index())
self.assert_eq(
pdf.duplicated(keep="last").sort_index(),
psdf.duplicated(keep="last").sort_index(),
)
self.assert_eq(
pdf.duplicated(keep=False).sort_index(),
psdf.duplicated(keep=False).sort_index(),
)
self.assert_eq(
pdf.duplicated(subset="b").sort_index(),
psdf.duplicated(subset="b").sort_index(),
)
self.assert_eq(
pdf.duplicated(subset=["b"]).sort_index(),
psdf.duplicated(subset=["b"]).sort_index(),
)
with self.assertRaisesRegex(ValueError, "'keep' only supports 'first', 'last' and False"):
psdf.duplicated(keep="false")
with self.assertRaisesRegex(KeyError, "'d'"):
psdf.duplicated(subset=["d"])
pdf.index.name = "x"
psdf.index.name = "x"
self.assert_eq(pdf.duplicated().sort_index(), psdf.duplicated().sort_index())
# multi-index
self.assert_eq(
pdf.set_index("a", append=True).duplicated().sort_index(),
psdf.set_index("a", append=True).duplicated().sort_index(),
)
self.assert_eq(
pdf.set_index("a", append=True).duplicated(keep=False).sort_index(),
psdf.set_index("a", append=True).duplicated(keep=False).sort_index(),
)
self.assert_eq(
pdf.set_index("a", append=True).duplicated(subset=["b"]).sort_index(),
psdf.set_index("a", append=True).duplicated(subset=["b"]).sort_index(),
)
# mutli-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(pdf.duplicated().sort_index(), psdf.duplicated().sort_index())
self.assert_eq(
pdf.duplicated(subset=("x", "b")).sort_index(),
psdf.duplicated(subset=("x", "b")).sort_index(),
)
self.assert_eq(
pdf.duplicated(subset=[("x", "b")]).sort_index(),
psdf.duplicated(subset=[("x", "b")]).sort_index(),
)
# non-string names
pdf = pd.DataFrame(
{10: [1, 1, 2, 3], 20: [1, 1, 1, 4], 30: [1, 1, 1, 5]}, index=np.random.rand(4)
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.duplicated().sort_index(), psdf.duplicated().sort_index())
self.assert_eq(
pdf.duplicated(subset=10).sort_index(),
psdf.duplicated(subset=10).sort_index(),
)
def test_ffill(self):
idx = np.random.rand(6)
pdf = pd.DataFrame(
{
"x": [np.nan, 2, 3, 4, np.nan, 6],
"y": [1, 2, np.nan, 4, np.nan, np.nan],
"z": [1, 2, 3, 4, np.nan, np.nan],
},
index=idx,
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.ffill(), pdf.ffill())
self.assert_eq(psdf.ffill(limit=1), pdf.ffill(limit=1))
pser = pdf.y
psser = psdf.y
psdf.ffill(inplace=True)
pdf.ffill(inplace=True)
self.assert_eq(psdf, pdf)
self.assert_eq(psser, pser)
self.assert_eq(psser[idx[2]], pser[idx[2]])
def test_bfill(self):
idx = np.random.rand(6)
pdf = pd.DataFrame(
{
"x": [np.nan, 2, 3, 4, np.nan, 6],
"y": [1, 2, np.nan, 4, np.nan, np.nan],
"z": [1, 2, 3, 4, np.nan, np.nan],
},
index=idx,
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.bfill(), pdf.bfill())
self.assert_eq(psdf.bfill(limit=1), pdf.bfill(limit=1))
pser = pdf.x
psser = psdf.x
psdf.bfill(inplace=True)
pdf.bfill(inplace=True)
self.assert_eq(psdf, pdf)
self.assert_eq(psser, pser)
self.assert_eq(psser[idx[0]], pser[idx[0]])
def test_filter(self):
pdf = pd.DataFrame(
{
"aa": ["aa", "bd", "bc", "ab", "ce"],
"ba": [1, 2, 3, 4, 5],
"cb": [1.0, 2.0, 3.0, 4.0, 5.0],
"db": [1.0, np.nan, 3.0, np.nan, 5.0],
}
)
pdf = pdf.set_index("aa")
psdf = ps.from_pandas(pdf)
self.assert_eq(
psdf.filter(items=["ab", "aa"], axis=0).sort_index(),
pdf.filter(items=["ab", "aa"], axis=0).sort_index(),
)
self.assert_eq(
psdf.filter(items=["ba", "db"], axis=1).sort_index(),
pdf.filter(items=["ba", "db"], axis=1).sort_index(),
)
self.assert_eq(psdf.filter(like="b", axis="index"), pdf.filter(like="b", axis="index"))
self.assert_eq(psdf.filter(like="c", axis="columns"), pdf.filter(like="c", axis="columns"))
self.assert_eq(
psdf.filter(regex="b.*", axis="index"), pdf.filter(regex="b.*", axis="index")
)
self.assert_eq(
psdf.filter(regex="b.*", axis="columns"), pdf.filter(regex="b.*", axis="columns")
)
pdf = pdf.set_index("ba", append=True)
psdf = ps.from_pandas(pdf)
self.assert_eq(
psdf.filter(items=[("aa", 1), ("bd", 2)], axis=0).sort_index(),
pdf.filter(items=[("aa", 1), ("bd", 2)], axis=0).sort_index(),
)
with self.assertRaisesRegex(TypeError, "Unsupported type list"):
psdf.filter(items=[["aa", 1], ("bd", 2)], axis=0)
with self.assertRaisesRegex(ValueError, "The item should not be empty."):
psdf.filter(items=[(), ("bd", 2)], axis=0)
self.assert_eq(psdf.filter(like="b", axis=0), pdf.filter(like="b", axis=0))
self.assert_eq(psdf.filter(regex="b.*", axis=0), pdf.filter(regex="b.*", axis=0))
with self.assertRaisesRegex(ValueError, "items should be a list-like object"):
psdf.filter(items="b")
with self.assertRaisesRegex(ValueError, "No axis named"):
psdf.filter(regex="b.*", axis=123)
with self.assertRaisesRegex(TypeError, "Must pass either `items`, `like`"):
psdf.filter()
with self.assertRaisesRegex(TypeError, "mutually exclusive"):
psdf.filter(regex="b.*", like="aaa")
# multi-index columns
pdf = pd.DataFrame(
{
("x", "aa"): ["aa", "ab", "bc", "bd", "ce"],
("x", "ba"): [1, 2, 3, 4, 5],
("y", "cb"): [1.0, 2.0, 3.0, 4.0, 5.0],
("z", "db"): [1.0, np.nan, 3.0, np.nan, 5.0],
}
)
pdf = pdf.set_index(("x", "aa"))
psdf = ps.from_pandas(pdf)
self.assert_eq(
psdf.filter(items=["ab", "aa"], axis=0).sort_index(),
pdf.filter(items=["ab", "aa"], axis=0).sort_index(),
)
self.assert_eq(
psdf.filter(items=[("x", "ba"), ("z", "db")], axis=1).sort_index(),
pdf.filter(items=[("x", "ba"), ("z", "db")], axis=1).sort_index(),
)
self.assert_eq(psdf.filter(like="b", axis="index"), pdf.filter(like="b", axis="index"))
self.assert_eq(psdf.filter(like="c", axis="columns"), pdf.filter(like="c", axis="columns"))
self.assert_eq(
psdf.filter(regex="b.*", axis="index"), pdf.filter(regex="b.*", axis="index")
)
self.assert_eq(
psdf.filter(regex="b.*", axis="columns"), pdf.filter(regex="b.*", axis="columns")
)
def test_pipe(self):
psdf = ps.DataFrame(
{"category": ["A", "A", "B"], "col1": [1, 2, 3], "col2": [4, 5, 6]},
columns=["category", "col1", "col2"],
)
self.assertRaisesRegex(
ValueError,
"arg is both the pipe target and a keyword argument",
lambda: psdf.pipe((lambda x: x, "arg"), arg="1"),
)
def test_transform(self):
pdf = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6] * 100,
"b": [1.0, 1.0, 2.0, 3.0, 5.0, 8.0] * 100,
"c": [1, 4, 9, 16, 25, 36] * 100,
},
columns=["a", "b", "c"],
index=np.random.rand(600),
)
psdf = ps.DataFrame(pdf)
self.assert_eq(
psdf.transform(lambda x: x + 1).sort_index(),
pdf.transform(lambda x: x + 1).sort_index(),
)
self.assert_eq(
psdf.transform(lambda x, y: x + y, y=2).sort_index(),
pdf.transform(lambda x, y: x + y, y=2).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.transform(lambda x: x + 1).sort_index(),
pdf.transform(lambda x: x + 1).sort_index(),
)
self.assert_eq(
psdf.transform(lambda x, y: x + y, y=1).sort_index(),
pdf.transform(lambda x, y: x + y, y=1).sort_index(),
)
with self.assertRaisesRegex(AssertionError, "the first argument should be a callable"):
psdf.transform(1)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.transform(lambda x: x + 1).sort_index(),
pdf.transform(lambda x: x + 1).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.transform(lambda x: x + 1).sort_index(),
pdf.transform(lambda x: x + 1).sort_index(),
)
def test_apply(self):
pdf = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6] * 100,
"b": [1.0, 1.0, 2.0, 3.0, 5.0, 8.0] * 100,
"c": [1, 4, 9, 16, 25, 36] * 100,
},
columns=["a", "b", "c"],
index=np.random.rand(600),
)
psdf = ps.DataFrame(pdf)
self.assert_eq(
psdf.apply(lambda x: x + 1).sort_index(), pdf.apply(lambda x: x + 1).sort_index()
)
self.assert_eq(
psdf.apply(lambda x, b: x + b, args=(1,)).sort_index(),
pdf.apply(lambda x, b: x + b, args=(1,)).sort_index(),
)
self.assert_eq(
psdf.apply(lambda x, b: x + b, b=1).sort_index(),
pdf.apply(lambda x, b: x + b, b=1).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.apply(lambda x: x + 1).sort_index(), pdf.apply(lambda x: x + 1).sort_index()
)
self.assert_eq(
psdf.apply(lambda x, b: x + b, args=(1,)).sort_index(),
pdf.apply(lambda x, b: x + b, args=(1,)).sort_index(),
)
self.assert_eq(
psdf.apply(lambda x, b: x + b, b=1).sort_index(),
pdf.apply(lambda x, b: x + b, b=1).sort_index(),
)
# returning a Series
self.assert_eq(
psdf.apply(lambda x: len(x), axis=1).sort_index(),
pdf.apply(lambda x: len(x), axis=1).sort_index(),
)
self.assert_eq(
psdf.apply(lambda x, c: len(x) + c, axis=1, c=100).sort_index(),
pdf.apply(lambda x, c: len(x) + c, axis=1, c=100).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.apply(lambda x: len(x), axis=1).sort_index(),
pdf.apply(lambda x: len(x), axis=1).sort_index(),
)
self.assert_eq(
psdf.apply(lambda x, c: len(x) + c, axis=1, c=100).sort_index(),
pdf.apply(lambda x, c: len(x) + c, axis=1, c=100).sort_index(),
)
with self.assertRaisesRegex(AssertionError, "the first argument should be a callable"):
psdf.apply(1)
with self.assertRaisesRegex(TypeError, "The given function.*1 or 'column'; however"):
def f1(_) -> ps.DataFrame[int]:
pass
psdf.apply(f1, axis=0)
with self.assertRaisesRegex(TypeError, "The given function.*0 or 'index'; however"):
def f2(_) -> ps.Series[int]:
pass
psdf.apply(f2, axis=1)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.apply(lambda x: x + 1).sort_index(), pdf.apply(lambda x: x + 1).sort_index()
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.apply(lambda x: x + 1).sort_index(), pdf.apply(lambda x: x + 1).sort_index()
)
# returning a Series
self.assert_eq(
psdf.apply(lambda x: len(x), axis=1).sort_index(),
pdf.apply(lambda x: len(x), axis=1).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.apply(lambda x: len(x), axis=1).sort_index(),
pdf.apply(lambda x: len(x), axis=1).sort_index(),
)
def test_apply_batch(self):
pdf = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6] * 100,
"b": [1.0, 1.0, 2.0, 3.0, 5.0, 8.0] * 100,
"c": [1, 4, 9, 16, 25, 36] * 100,
},
columns=["a", "b", "c"],
index=np.random.rand(600),
)
psdf = ps.DataFrame(pdf)
self.assert_eq(
psdf.pandas_on_spark.apply_batch(lambda pdf, a: pdf + a, args=(1,)).sort_index(),
(pdf + 1).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.pandas_on_spark.apply_batch(lambda pdf: pdf + 1).sort_index(),
(pdf + 1).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.apply_batch(lambda pdf, b: pdf + b, b=1).sort_index(),
(pdf + 1).sort_index(),
)
with self.assertRaisesRegex(AssertionError, "the first argument should be a callable"):
psdf.pandas_on_spark.apply_batch(1)
with self.assertRaisesRegex(TypeError, "The given function.*frame as its type hints"):
def f2(_) -> ps.Series[int]:
pass
psdf.pandas_on_spark.apply_batch(f2)
with self.assertRaisesRegex(ValueError, "The given function should return a frame"):
psdf.pandas_on_spark.apply_batch(lambda pdf: 1)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.pandas_on_spark.apply_batch(lambda x: x + 1).sort_index(), (pdf + 1).sort_index()
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.pandas_on_spark.apply_batch(lambda x: x + 1).sort_index(),
(pdf + 1).sort_index(),
)
def test_transform_batch(self):
pdf = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6] * 100,
"b": [1.0, 1.0, 2.0, 3.0, 5.0, 8.0] * 100,
"c": [1, 4, 9, 16, 25, 36] * 100,
},
columns=["a", "b", "c"],
index=np.random.rand(600),
)
psdf = ps.DataFrame(pdf)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.c + 1).sort_index(),
(pdf.c + 1).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf, a: pdf + a, 1).sort_index(),
(pdf + 1).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf, a: pdf.c + a, a=1).sort_index(),
(pdf.c + 1).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf + 1).sort_index(),
(pdf + 1).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.b + 1).sort_index(),
(pdf.b + 1).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf, a: pdf + a, 1).sort_index(),
(pdf + 1).sort_index(),
)
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda pdf, a: pdf.c + a, a=1).sort_index(),
(pdf.c + 1).sort_index(),
)
with self.assertRaisesRegex(AssertionError, "the first argument should be a callable"):
psdf.pandas_on_spark.transform_batch(1)
with self.assertRaisesRegex(ValueError, "The given function should return a frame"):
psdf.pandas_on_spark.transform_batch(lambda pdf: 1)
with self.assertRaisesRegex(
ValueError, "transform_batch cannot produce aggregated results"
):
psdf.pandas_on_spark.transform_batch(lambda pdf: pd.Series(1))
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda x: x + 1).sort_index(),
(pdf + 1).sort_index(),
)
with option_context("compute.shortcut_limit", 500):
self.assert_eq(
psdf.pandas_on_spark.transform_batch(lambda x: x + 1).sort_index(),
(pdf + 1).sort_index(),
)
def test_transform_batch_same_anchor(self):
psdf = ps.range(10)
psdf["d"] = psdf.pandas_on_spark.transform_batch(lambda pdf: pdf.id + 1)
self.assert_eq(
psdf,
pd.DataFrame({"id": list(range(10)), "d": list(range(1, 11))}, columns=["id", "d"]),
)
psdf = ps.range(10)
def plus_one(pdf) -> ps.Series[np.int64]:
return pdf.id + 1
psdf["d"] = psdf.pandas_on_spark.transform_batch(plus_one)
self.assert_eq(
psdf,
pd.DataFrame({"id": list(range(10)), "d": list(range(1, 11))}, columns=["id", "d"]),
)
psdf = ps.range(10)
def plus_one(ser) -> ps.Series[np.int64]:
return ser + 1
psdf["d"] = psdf.id.pandas_on_spark.transform_batch(plus_one)
self.assert_eq(
psdf,
pd.DataFrame({"id": list(range(10)), "d": list(range(1, 11))}, columns=["id", "d"]),
)
def test_empty_timestamp(self):
pdf = pd.DataFrame(
{
"t": [
datetime(2019, 1, 1, 0, 0, 0),
datetime(2019, 1, 2, 0, 0, 0),
datetime(2019, 1, 3, 0, 0, 0),
]
},
index=np.random.rand(3),
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf[psdf["t"] != psdf["t"]], pdf[pdf["t"] != pdf["t"]])
self.assert_eq(psdf[psdf["t"] != psdf["t"]].dtypes, pdf[pdf["t"] != pdf["t"]].dtypes)
def test_to_spark(self):
psdf = ps.from_pandas(self.pdf)
with self.assertRaisesRegex(ValueError, "'index_col' cannot be overlapped"):
psdf.to_spark(index_col="a")
with self.assertRaisesRegex(ValueError, "length of index columns.*1.*3"):
psdf.to_spark(index_col=["x", "y", "z"])
def test_keys(self):
pdf = pd.DataFrame(
[[1, 2], [4, 5], [7, 8]],
index=["cobra", "viper", "sidewinder"],
columns=["max_speed", "shield"],
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.keys(), pdf.keys())
def test_quantile(self):
pdf, psdf = self.df_pair
self.assert_eq(psdf.quantile(0.5), pdf.quantile(0.5))
self.assert_eq(psdf.quantile([0.25, 0.5, 0.75]), pdf.quantile([0.25, 0.5, 0.75]))
self.assert_eq(psdf.loc[[]].quantile(0.5), pdf.loc[[]].quantile(0.5))
self.assert_eq(
psdf.loc[[]].quantile([0.25, 0.5, 0.75]), pdf.loc[[]].quantile([0.25, 0.5, 0.75])
)
with self.assertRaisesRegex(
NotImplementedError, 'axis should be either 0 or "index" currently.'
):
psdf.quantile(0.5, axis=1)
with self.assertRaisesRegex(TypeError, "accuracy must be an integer; however"):
psdf.quantile(accuracy="a")
with self.assertRaisesRegex(TypeError, "q must be a float or an array of floats;"):
psdf.quantile(q="a")
with self.assertRaisesRegex(TypeError, "q must be a float or an array of floats;"):
psdf.quantile(q=["a"])
self.assert_eq(
psdf.quantile(0.5, numeric_only=False), pdf.quantile(0.5, numeric_only=False)
)
self.assert_eq(
psdf.quantile([0.25, 0.5, 0.75], numeric_only=False),
pdf.quantile([0.25, 0.5, 0.75], numeric_only=False),
)
# multi-index column
columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.quantile(0.5), pdf.quantile(0.5))
self.assert_eq(psdf.quantile([0.25, 0.5, 0.75]), pdf.quantile([0.25, 0.5, 0.75]))
pdf = pd.DataFrame({"x": ["a", "b", "c"]})
psdf = ps.from_pandas(pdf)
if LooseVersion(pd.__version__) >= LooseVersion("1.0.0"):
self.assert_eq(psdf.quantile(0.5), pdf.quantile(0.5))
self.assert_eq(psdf.quantile([0.25, 0.5, 0.75]), pdf.quantile([0.25, 0.5, 0.75]))
else:
self.assert_eq(psdf.quantile(0.5), pd.Series(name=0.5))
self.assert_eq(psdf.quantile([0.25, 0.5, 0.75]), pd.DataFrame(index=[0.25, 0.5, 0.75]))
with self.assertRaisesRegex(TypeError, "Could not convert object \\(string\\) to numeric"):
psdf.quantile(0.5, numeric_only=False)
with self.assertRaisesRegex(TypeError, "Could not convert object \\(string\\) to numeric"):
psdf.quantile([0.25, 0.5, 0.75], numeric_only=False)
def test_pct_change(self):
pdf = pd.DataFrame(
{"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0], "c": [300, 200, 400, 200]},
index=np.random.rand(4),
)
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.pct_change(2), pdf.pct_change(2), check_exact=False)
self.assert_eq(psdf.pct_change().sum(), pdf.pct_change().sum(), check_exact=False)
def test_where(self):
psdf = ps.from_pandas(self.pdf)
with self.assertRaisesRegex(TypeError, "type of cond must be a DataFrame or Series"):
psdf.where(1)
def test_mask(self):
psdf = ps.from_pandas(self.pdf)
with self.assertRaisesRegex(TypeError, "type of cond must be a DataFrame or Series"):
psdf.mask(1)
def test_query(self):
pdf = pd.DataFrame({"A": range(1, 6), "B": range(10, 0, -2), "C": range(10, 5, -1)})
psdf = ps.from_pandas(pdf)
exprs = ("A > B", "A < C", "C == B")
for expr in exprs:
self.assert_eq(psdf.query(expr), pdf.query(expr))
# test `inplace=True`
for expr in exprs:
dummy_psdf = psdf.copy()
dummy_pdf = pdf.copy()
pser = dummy_pdf.A
psser = dummy_psdf.A
dummy_pdf.query(expr, inplace=True)
dummy_psdf.query(expr, inplace=True)
self.assert_eq(dummy_psdf, dummy_pdf)
self.assert_eq(psser, pser)
# invalid values for `expr`
invalid_exprs = (1, 1.0, (exprs[0],), [exprs[0]])
for expr in invalid_exprs:
with self.assertRaisesRegex(
TypeError,
"expr must be a string to be evaluated, {} given".format(type(expr).__name__),
):
psdf.query(expr)
# invalid values for `inplace`
invalid_inplaces = (1, 0, "True", "False")
for inplace in invalid_inplaces:
with self.assertRaisesRegex(
TypeError,
'For argument "inplace" expected type bool, received type {}.'.format(
type(inplace).__name__
),
):
psdf.query("a < b", inplace=inplace)
# doesn't support for MultiIndex columns
columns = pd.MultiIndex.from_tuples([("A", "Z"), ("B", "X"), ("C", "C")])
psdf.columns = columns
with self.assertRaisesRegex(TypeError, "Doesn't support for MultiIndex columns"):
psdf.query("('A', 'Z') > ('B', 'X')")
def test_take(self):
pdf = pd.DataFrame(
{"A": range(0, 50000), "B": range(100000, 0, -2), "C": range(100000, 50000, -1)}
)
psdf = ps.from_pandas(pdf)
# axis=0 (default)
self.assert_eq(psdf.take([1, 2]).sort_index(), pdf.take([1, 2]).sort_index())
self.assert_eq(psdf.take([-1, -2]).sort_index(), pdf.take([-1, -2]).sort_index())
self.assert_eq(
psdf.take(range(100, 110)).sort_index(), pdf.take(range(100, 110)).sort_index()
)
self.assert_eq(
psdf.take(range(-110, -100)).sort_index(), pdf.take(range(-110, -100)).sort_index()
)
self.assert_eq(
psdf.take([10, 100, 1000, 10000]).sort_index(),
pdf.take([10, 100, 1000, 10000]).sort_index(),
)
self.assert_eq(
psdf.take([-10, -100, -1000, -10000]).sort_index(),
pdf.take([-10, -100, -1000, -10000]).sort_index(),
)
# axis=1
self.assert_eq(
psdf.take([1, 2], axis=1).sort_index(), pdf.take([1, 2], axis=1).sort_index()
)
self.assert_eq(
psdf.take([-1, -2], axis=1).sort_index(), pdf.take([-1, -2], axis=1).sort_index()
)
self.assert_eq(
psdf.take(range(1, 3), axis=1).sort_index(),
pdf.take(range(1, 3), axis=1).sort_index(),
)
self.assert_eq(
psdf.take(range(-1, -3), axis=1).sort_index(),
pdf.take(range(-1, -3), axis=1).sort_index(),
)
self.assert_eq(
psdf.take([2, 1], axis=1).sort_index(),
pdf.take([2, 1], axis=1).sort_index(),
)
self.assert_eq(
psdf.take([-1, -2], axis=1).sort_index(),
pdf.take([-1, -2], axis=1).sort_index(),
)
# MultiIndex columns
columns = pd.MultiIndex.from_tuples([("A", "Z"), ("B", "X"), ("C", "C")])
psdf.columns = columns
pdf.columns = columns
# MultiIndex columns with axis=0 (default)
self.assert_eq(psdf.take([1, 2]).sort_index(), pdf.take([1, 2]).sort_index())
self.assert_eq(psdf.take([-1, -2]).sort_index(), pdf.take([-1, -2]).sort_index())
self.assert_eq(
psdf.take(range(100, 110)).sort_index(), pdf.take(range(100, 110)).sort_index()
)
self.assert_eq(
psdf.take(range(-110, -100)).sort_index(), pdf.take(range(-110, -100)).sort_index()
)
self.assert_eq(
psdf.take([10, 100, 1000, 10000]).sort_index(),
pdf.take([10, 100, 1000, 10000]).sort_index(),
)
self.assert_eq(
psdf.take([-10, -100, -1000, -10000]).sort_index(),
pdf.take([-10, -100, -1000, -10000]).sort_index(),
)
# axis=1
self.assert_eq(
psdf.take([1, 2], axis=1).sort_index(), pdf.take([1, 2], axis=1).sort_index()
)
self.assert_eq(
psdf.take([-1, -2], axis=1).sort_index(), pdf.take([-1, -2], axis=1).sort_index()
)
self.assert_eq(
psdf.take(range(1, 3), axis=1).sort_index(),
pdf.take(range(1, 3), axis=1).sort_index(),
)
self.assert_eq(
psdf.take(range(-1, -3), axis=1).sort_index(),
pdf.take(range(-1, -3), axis=1).sort_index(),
)
self.assert_eq(
psdf.take([2, 1], axis=1).sort_index(),
pdf.take([2, 1], axis=1).sort_index(),
)
self.assert_eq(
psdf.take([-1, -2], axis=1).sort_index(),
pdf.take([-1, -2], axis=1).sort_index(),
)
# Checking the type of indices.
self.assertRaises(TypeError, lambda: psdf.take(1))
self.assertRaises(TypeError, lambda: psdf.take("1"))
self.assertRaises(TypeError, lambda: psdf.take({1, 2}))
self.assertRaises(TypeError, lambda: psdf.take({1: None, 2: None}))
def test_axes(self):
pdf = self.pdf
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.axes, psdf.axes)
# multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(pdf.axes, psdf.axes)
def test_udt(self):
sparse_values = {0: 0.1, 1: 1.1}
sparse_vector = SparseVector(len(sparse_values), sparse_values)
pdf = pd.DataFrame({"a": [sparse_vector], "b": [10]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf, pdf)
def test_eval(self):
pdf = pd.DataFrame({"A": range(1, 6), "B": range(10, 0, -2)})
psdf = ps.from_pandas(pdf)
# operation between columns (returns Series)
self.assert_eq(pdf.eval("A + B"), psdf.eval("A + B"))
self.assert_eq(pdf.eval("A + A"), psdf.eval("A + A"))
# assignment (returns DataFrame)
self.assert_eq(pdf.eval("C = A + B"), psdf.eval("C = A + B"))
self.assert_eq(pdf.eval("A = A + A"), psdf.eval("A = A + A"))
# operation between scalars (returns scalar)
self.assert_eq(pdf.eval("1 + 1"), psdf.eval("1 + 1"))
# complicated operations with assignment
self.assert_eq(
pdf.eval("B = A + B // (100 + 200) * (500 - B) - 10.5"),
psdf.eval("B = A + B // (100 + 200) * (500 - B) - 10.5"),
)
# inplace=True (only support for assignment)
pdf.eval("C = A + B", inplace=True)
psdf.eval("C = A + B", inplace=True)
self.assert_eq(pdf, psdf)
pser = pdf.A
psser = psdf.A
pdf.eval("A = B + C", inplace=True)
psdf.eval("A = B + C", inplace=True)
self.assert_eq(pdf, psdf)
self.assert_eq(pser, psser)
# doesn't support for multi-index columns
columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b"), ("z", "c")])
psdf.columns = columns
self.assertRaises(TypeError, lambda: psdf.eval("x.a + y.b"))
@unittest.skipIf(not have_tabulate, tabulate_requirement_message)
def test_to_markdown(self):
pdf = pd.DataFrame(data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]})
psdf = ps.from_pandas(pdf)
# `to_markdown()` is supported in pandas >= 1.0.0 since it's newly added in pandas 1.0.0.
if LooseVersion(pd.__version__) < LooseVersion("1.0.0"):
self.assertRaises(NotImplementedError, lambda: psdf.to_markdown())
else:
self.assert_eq(pdf.to_markdown(), psdf.to_markdown())
def test_cache(self):
pdf = pd.DataFrame(
[(0.2, 0.3), (0.0, 0.6), (0.6, 0.0), (0.2, 0.1)], columns=["dogs", "cats"]
)
psdf = ps.from_pandas(pdf)
with psdf.spark.cache() as cached_df:
self.assert_eq(isinstance(cached_df, CachedDataFrame), True)
self.assert_eq(
repr(cached_df.spark.storage_level), repr(StorageLevel(True, True, False, True))
)
def test_persist(self):
pdf = pd.DataFrame(
[(0.2, 0.3), (0.0, 0.6), (0.6, 0.0), (0.2, 0.1)], columns=["dogs", "cats"]
)
psdf = ps.from_pandas(pdf)
storage_levels = [
StorageLevel.DISK_ONLY,
StorageLevel.MEMORY_AND_DISK,
StorageLevel.MEMORY_ONLY,
StorageLevel.OFF_HEAP,
]
for storage_level in storage_levels:
with psdf.spark.persist(storage_level) as cached_df:
self.assert_eq(isinstance(cached_df, CachedDataFrame), True)
self.assert_eq(repr(cached_df.spark.storage_level), repr(storage_level))
self.assertRaises(TypeError, lambda: psdf.spark.persist("DISK_ONLY"))
def test_squeeze(self):
axises = [None, 0, 1, "rows", "index", "columns"]
# Multiple columns
pdf = pd.DataFrame([[1, 2], [3, 4]], columns=["a", "b"], index=["x", "y"])
psdf = ps.from_pandas(pdf)
for axis in axises:
self.assert_eq(pdf.squeeze(axis), psdf.squeeze(axis))
# Multiple columns with MultiIndex columns
columns = pd.MultiIndex.from_tuples([("A", "Z"), ("B", "X")])
pdf.columns = columns
psdf.columns = columns
for axis in axises:
self.assert_eq(pdf.squeeze(axis), psdf.squeeze(axis))
# Single column with single value
pdf = pd.DataFrame([[1]], columns=["a"], index=["x"])
psdf = ps.from_pandas(pdf)
for axis in axises:
self.assert_eq(pdf.squeeze(axis), psdf.squeeze(axis))
# Single column with single value with MultiIndex column
columns = pd.MultiIndex.from_tuples([("A", "Z")])
pdf.columns = columns
psdf.columns = columns
for axis in axises:
self.assert_eq(pdf.squeeze(axis), psdf.squeeze(axis))
# Single column with multiple values
pdf = pd.DataFrame([1, 2, 3, 4], columns=["a"])
psdf = ps.from_pandas(pdf)
for axis in axises:
self.assert_eq(pdf.squeeze(axis), psdf.squeeze(axis))
# Single column with multiple values with MultiIndex column
pdf.columns = columns
psdf.columns = columns
for axis in axises:
self.assert_eq(pdf.squeeze(axis), psdf.squeeze(axis))
def test_rfloordiv(self):
pdf = pd.DataFrame(
{"angles": [0, 3, 4], "degrees": [360, 180, 360]},
index=["circle", "triangle", "rectangle"],
columns=["angles", "degrees"],
)
psdf = ps.from_pandas(pdf)
if LooseVersion(pd.__version__) < LooseVersion("1.0.0") and LooseVersion(
pd.__version__
) >= LooseVersion("0.24.0"):
expected_result = pd.DataFrame(
{"angles": [np.inf, 3.0, 2.0], "degrees": [0.0, 0.0, 0.0]},
index=["circle", "triangle", "rectangle"],
columns=["angles", "degrees"],
)
else:
expected_result = pdf.rfloordiv(10)
self.assert_eq(psdf.rfloordiv(10), expected_result)
def test_truncate(self):
pdf1 = pd.DataFrame(
{
"A": ["a", "b", "c", "d", "e", "f", "g"],
"B": ["h", "i", "j", "k", "l", "m", "n"],
"C": ["o", "p", "q", "r", "s", "t", "u"],
},
index=[-500, -20, -1, 0, 400, 550, 1000],
)
psdf1 = ps.from_pandas(pdf1)
pdf2 = pd.DataFrame(
{
"A": ["a", "b", "c", "d", "e", "f", "g"],
"B": ["h", "i", "j", "k", "l", "m", "n"],
"C": ["o", "p", "q", "r", "s", "t", "u"],
},
index=[1000, 550, 400, 0, -1, -20, -500],
)
psdf2 = ps.from_pandas(pdf2)
self.assert_eq(psdf1.truncate(), pdf1.truncate())
self.assert_eq(psdf1.truncate(before=-20), pdf1.truncate(before=-20))
self.assert_eq(psdf1.truncate(after=400), pdf1.truncate(after=400))
self.assert_eq(psdf1.truncate(copy=False), pdf1.truncate(copy=False))
self.assert_eq(psdf1.truncate(-20, 400, copy=False), pdf1.truncate(-20, 400, copy=False))
# The bug for these tests has been fixed in pandas 1.1.0.
if LooseVersion(pd.__version__) >= LooseVersion("1.1.0"):
self.assert_eq(psdf2.truncate(0, 550), pdf2.truncate(0, 550))
self.assert_eq(psdf2.truncate(0, 550, copy=False), pdf2.truncate(0, 550, copy=False))
else:
expected_psdf = ps.DataFrame(
{"A": ["b", "c", "d"], "B": ["i", "j", "k"], "C": ["p", "q", "r"]},
index=[550, 400, 0],
)
self.assert_eq(psdf2.truncate(0, 550), expected_psdf)
self.assert_eq(psdf2.truncate(0, 550, copy=False), expected_psdf)
# axis = 1
self.assert_eq(psdf1.truncate(axis=1), pdf1.truncate(axis=1))
self.assert_eq(psdf1.truncate(before="B", axis=1), pdf1.truncate(before="B", axis=1))
self.assert_eq(psdf1.truncate(after="A", axis=1), pdf1.truncate(after="A", axis=1))
self.assert_eq(psdf1.truncate(copy=False, axis=1), pdf1.truncate(copy=False, axis=1))
self.assert_eq(psdf2.truncate("B", "C", axis=1), pdf2.truncate("B", "C", axis=1))
self.assert_eq(
psdf1.truncate("B", "C", copy=False, axis=1),
pdf1.truncate("B", "C", copy=False, axis=1),
)
# MultiIndex columns
columns = pd.MultiIndex.from_tuples([("A", "Z"), ("B", "X"), ("C", "Z")])
pdf1.columns = columns
psdf1.columns = columns
pdf2.columns = columns
psdf2.columns = columns
self.assert_eq(psdf1.truncate(), pdf1.truncate())
self.assert_eq(psdf1.truncate(before=-20), pdf1.truncate(before=-20))
self.assert_eq(psdf1.truncate(after=400), pdf1.truncate(after=400))
self.assert_eq(psdf1.truncate(copy=False), pdf1.truncate(copy=False))
self.assert_eq(psdf1.truncate(-20, 400, copy=False), pdf1.truncate(-20, 400, copy=False))
# The bug for these tests has been fixed in pandas 1.1.0.
if LooseVersion(pd.__version__) >= LooseVersion("1.1.0"):
self.assert_eq(psdf2.truncate(0, 550), pdf2.truncate(0, 550))
self.assert_eq(psdf2.truncate(0, 550, copy=False), pdf2.truncate(0, 550, copy=False))
else:
expected_psdf.columns = columns
self.assert_eq(psdf2.truncate(0, 550), expected_psdf)
self.assert_eq(psdf2.truncate(0, 550, copy=False), expected_psdf)
# axis = 1
self.assert_eq(psdf1.truncate(axis=1), pdf1.truncate(axis=1))
self.assert_eq(psdf1.truncate(before="B", axis=1), pdf1.truncate(before="B", axis=1))
self.assert_eq(psdf1.truncate(after="A", axis=1), pdf1.truncate(after="A", axis=1))
self.assert_eq(psdf1.truncate(copy=False, axis=1), pdf1.truncate(copy=False, axis=1))
self.assert_eq(psdf2.truncate("B", "C", axis=1), pdf2.truncate("B", "C", axis=1))
self.assert_eq(
psdf1.truncate("B", "C", copy=False, axis=1),
pdf1.truncate("B", "C", copy=False, axis=1),
)
# Exceptions
psdf = ps.DataFrame(
{
"A": ["a", "b", "c", "d", "e", "f", "g"],
"B": ["h", "i", "j", "k", "l", "m", "n"],
"C": ["o", "p", "q", "r", "s", "t", "u"],
},
index=[-500, 100, 400, 0, -1, 550, -20],
)
msg = "truncate requires a sorted index"
with self.assertRaisesRegex(ValueError, msg):
psdf.truncate()
psdf = ps.DataFrame(
{
"A": ["a", "b", "c", "d", "e", "f", "g"],
"B": ["h", "i", "j", "k", "l", "m", "n"],
"C": ["o", "p", "q", "r", "s", "t", "u"],
},
index=[-500, -20, -1, 0, 400, 550, 1000],
)
msg = "Truncate: -20 must be after 400"
with self.assertRaisesRegex(ValueError, msg):
psdf.truncate(400, -20)
msg = "Truncate: B must be after C"
with self.assertRaisesRegex(ValueError, msg):
psdf.truncate("C", "B", axis=1)
def test_explode(self):
pdf = pd.DataFrame({"A": [[-1.0, np.nan], [0.0, np.inf], [1.0, -np.inf]], "B": 1})
pdf.index.name = "index"
pdf.columns.name = "columns"
psdf = ps.from_pandas(pdf)
if LooseVersion(pd.__version__) >= LooseVersion("0.25.0"):
expected_result1 = pdf.explode("A")
expected_result2 = pdf.explode("B")
else:
expected_result1 = pd.DataFrame(
{"A": [-1, np.nan, 0, np.inf, 1, -np.inf], "B": [1, 1, 1, 1, 1, 1]},
index=pd.Index([0, 0, 1, 1, 2, 2]),
)
expected_result1.index.name = "index"
expected_result1.columns.name = "columns"
expected_result2 = pdf
self.assert_eq(psdf.explode("A"), expected_result1, almost=True)
self.assert_eq(psdf.explode("B"), expected_result2)
self.assert_eq(psdf.explode("A").index.name, expected_result1.index.name)
self.assert_eq(psdf.explode("A").columns.name, expected_result1.columns.name)
self.assertRaises(TypeError, lambda: psdf.explode(["A", "B"]))
# MultiIndex
midx = pd.MultiIndex.from_tuples(
[("x", "a"), ("x", "b"), ("y", "c")], names=["index1", "index2"]
)
pdf.index = midx
psdf = ps.from_pandas(pdf)
if LooseVersion(pd.__version__) >= LooseVersion("0.25.0"):
expected_result1 = pdf.explode("A")
expected_result2 = pdf.explode("B")
else:
midx = pd.MultiIndex.from_tuples(
[("x", "a"), ("x", "a"), ("x", "b"), ("x", "b"), ("y", "c"), ("y", "c")],
names=["index1", "index2"],
)
expected_result1.index = midx
expected_result2 = pdf
self.assert_eq(psdf.explode("A"), expected_result1, almost=True)
self.assert_eq(psdf.explode("B"), expected_result2)
self.assert_eq(psdf.explode("A").index.names, expected_result1.index.names)
self.assert_eq(psdf.explode("A").columns.name, expected_result1.columns.name)
self.assertRaises(TypeError, lambda: psdf.explode(["A", "B"]))
# MultiIndex columns
columns = pd.MultiIndex.from_tuples([("A", "Z"), ("B", "X")], names=["column1", "column2"])
pdf.columns = columns
psdf.columns = columns
if LooseVersion(pd.__version__) >= LooseVersion("0.25.0"):
expected_result1 = pdf.explode(("A", "Z"))
expected_result2 = pdf.explode(("B", "X"))
expected_result3 = pdf.A.explode("Z")
else:
expected_result1.columns = columns
expected_result2 = pdf
expected_result3 = pd.DataFrame({"Z": [-1, np.nan, 0, np.inf, 1, -np.inf]}, index=midx)
expected_result3.index.name = "index"
expected_result3.columns.name = "column2"
self.assert_eq(psdf.explode(("A", "Z")), expected_result1, almost=True)
self.assert_eq(psdf.explode(("B", "X")), expected_result2)
self.assert_eq(psdf.explode(("A", "Z")).index.names, expected_result1.index.names)
self.assert_eq(psdf.explode(("A", "Z")).columns.names, expected_result1.columns.names)
self.assert_eq(psdf.A.explode("Z"), expected_result3, almost=True)
self.assertRaises(TypeError, lambda: psdf.explode(["A", "B"]))
self.assertRaises(ValueError, lambda: psdf.explode("A"))
def test_spark_schema(self):
psdf = ps.DataFrame(
{
"a": list("abc"),
"b": list(range(1, 4)),
"c": np.arange(3, 6).astype("i1"),
"d": np.arange(4.0, 7.0, dtype="float64"),
"e": [True, False, True],
"f": pd.date_range("20130101", periods=3),
},
columns=["a", "b", "c", "d", "e", "f"],
)
actual = psdf.spark.schema()
expected = (
StructType()
.add("a", "string", False)
.add("b", "long", False)
.add("c", "byte", False)
.add("d", "double", False)
.add("e", "boolean", False)
.add("f", "timestamp", False)
)
self.assertEqual(actual, expected)
actual = psdf.spark.schema("index")
expected = (
StructType()
.add("index", "long", False)
.add("a", "string", False)
.add("b", "long", False)
.add("c", "byte", False)
.add("d", "double", False)
.add("e", "boolean", False)
.add("f", "timestamp", False)
)
self.assertEqual(actual, expected)
def test_print_schema(self):
psdf = ps.DataFrame(
{"a": list("abc"), "b": list(range(1, 4)), "c": np.arange(3, 6).astype("i1")},
columns=["a", "b", "c"],
)
prev = sys.stdout
try:
out = StringIO()
sys.stdout = out
psdf.spark.print_schema()
actual = out.getvalue().strip()
self.assertTrue("a: string" in actual, actual)
self.assertTrue("b: long" in actual, actual)
self.assertTrue("c: byte" in actual, actual)
out = StringIO()
sys.stdout = out
psdf.spark.print_schema(index_col="index")
actual = out.getvalue().strip()
self.assertTrue("index: long" in actual, actual)
self.assertTrue("a: string" in actual, actual)
self.assertTrue("b: long" in actual, actual)
self.assertTrue("c: byte" in actual, actual)
finally:
sys.stdout = prev
def test_explain_hint(self):
psdf1 = ps.DataFrame(
{"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 5]}, columns=["lkey", "value"]
)
psdf2 = ps.DataFrame(
{"rkey": ["foo", "bar", "baz", "foo"], "value": [5, 6, 7, 8]}, columns=["rkey", "value"]
)
merged = psdf1.merge(psdf2.spark.hint("broadcast"), left_on="lkey", right_on="rkey")
prev = sys.stdout
try:
out = StringIO()
sys.stdout = out
merged.spark.explain()
actual = out.getvalue().strip()
self.assertTrue("Broadcast" in actual, actual)
finally:
sys.stdout = prev
def test_mad(self):
pdf = pd.DataFrame(
{
"A": [1, 2, None, 4, np.nan],
"B": [-0.1, 0.2, -0.3, np.nan, 0.5],
"C": ["a", "b", "c", "d", "e"],
}
)
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.mad(), pdf.mad())
self.assert_eq(psdf.mad(axis=1), pdf.mad(axis=1))
with self.assertRaises(ValueError):
psdf.mad(axis=2)
# MultiIndex columns
columns = pd.MultiIndex.from_tuples([("A", "X"), ("A", "Y"), ("A", "Z")])
pdf.columns = columns
psdf.columns = columns
self.assert_eq(psdf.mad(), pdf.mad())
self.assert_eq(psdf.mad(axis=1), pdf.mad(axis=1))
pdf = pd.DataFrame({"A": [True, True, False, False], "B": [True, False, False, True]})
psdf = ps.from_pandas(pdf)
self.assert_eq(psdf.mad(), pdf.mad())
self.assert_eq(psdf.mad(axis=1), pdf.mad(axis=1))
def test_abs(self):
pdf = pd.DataFrame({"a": [-2, -1, 0, 1]})
psdf = ps.from_pandas(pdf)
self.assert_eq(abs(psdf), abs(pdf))
self.assert_eq(np.abs(psdf), np.abs(pdf))
def test_iteritems(self):
pdf = pd.DataFrame(
{"species": ["bear", "bear", "marsupial"], "population": [1864, 22000, 80000]},
index=["panda", "polar", "koala"],
columns=["species", "population"],
)
psdf = ps.from_pandas(pdf)
for (p_name, p_items), (k_name, k_items) in zip(pdf.iteritems(), psdf.iteritems()):
self.assert_eq(p_name, k_name)
self.assert_eq(p_items, k_items)
def test_tail(self):
pdf = pd.DataFrame({"x": range(1000)})
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.tail(), psdf.tail())
self.assert_eq(pdf.tail(10), psdf.tail(10))
self.assert_eq(pdf.tail(-990), psdf.tail(-990))
self.assert_eq(pdf.tail(0), psdf.tail(0))
self.assert_eq(pdf.tail(-1001), psdf.tail(-1001))
self.assert_eq(pdf.tail(1001), psdf.tail(1001))
self.assert_eq((pdf + 1).tail(), (psdf + 1).tail())
self.assert_eq((pdf + 1).tail(10), (psdf + 1).tail(10))
self.assert_eq((pdf + 1).tail(-990), (psdf + 1).tail(-990))
self.assert_eq((pdf + 1).tail(0), (psdf + 1).tail(0))
self.assert_eq((pdf + 1).tail(-1001), (psdf + 1).tail(-1001))
self.assert_eq((pdf + 1).tail(1001), (psdf + 1).tail(1001))
with self.assertRaisesRegex(TypeError, "bad operand type for unary -: 'str'"):
psdf.tail("10")
def test_last_valid_index(self):
pdf = pd.DataFrame(
{"a": [1, 2, 3, None], "b": [1.0, 2.0, 3.0, None], "c": [100, 200, 400, None]},
index=["Q", "W", "E", "R"],
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.last_valid_index(), psdf.last_valid_index())
self.assert_eq(pdf[[]].last_valid_index(), psdf[[]].last_valid_index())
# MultiIndex columns
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.last_valid_index(), psdf.last_valid_index())
# Empty DataFrame
pdf = pd.Series([]).to_frame()
psdf = ps.Series([]).to_frame()
self.assert_eq(pdf.last_valid_index(), psdf.last_valid_index())
def test_last(self):
index = pd.date_range("2018-04-09", periods=4, freq="2D")
pdf = pd.DataFrame([1, 2, 3, 4], index=index)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.last("1D"), psdf.last("1D"))
self.assert_eq(pdf.last(DateOffset(days=1)), psdf.last(DateOffset(days=1)))
with self.assertRaisesRegex(TypeError, "'last' only supports a DatetimeIndex"):
ps.DataFrame([1, 2, 3, 4]).last("1D")
def test_first(self):
index = pd.date_range("2018-04-09", periods=4, freq="2D")
pdf = pd.DataFrame([1, 2, 3, 4], index=index)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.first("1D"), psdf.first("1D"))
self.assert_eq(pdf.first(DateOffset(days=1)), psdf.first(DateOffset(days=1)))
with self.assertRaisesRegex(TypeError, "'first' only supports a DatetimeIndex"):
ps.DataFrame([1, 2, 3, 4]).first("1D")
def test_first_valid_index(self):
pdf = pd.DataFrame(
{"a": [None, 2, 3, 2], "b": [None, 2.0, 3.0, 1.0], "c": [None, 200, 400, 200]},
index=["Q", "W", "E", "R"],
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.first_valid_index(), psdf.first_valid_index())
self.assert_eq(pdf[[]].first_valid_index(), psdf[[]].first_valid_index())
# MultiIndex columns
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.first_valid_index(), psdf.first_valid_index())
# Empty DataFrame
pdf = pd.Series([]).to_frame()
psdf = ps.Series([]).to_frame()
self.assert_eq(pdf.first_valid_index(), psdf.first_valid_index())
pdf = pd.DataFrame(
{"a": [None, 2, 3, 2], "b": [None, 2.0, 3.0, 1.0], "c": [None, 200, 400, 200]},
index=[
datetime(2021, 1, 1),
datetime(2021, 2, 1),
datetime(2021, 3, 1),
datetime(2021, 4, 1),
],
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.first_valid_index(), psdf.first_valid_index())
def test_product(self):
pdf = pd.DataFrame(
{"A": [1, 2, 3, 4, 5], "B": [10, 20, 30, 40, 50], "C": ["a", "b", "c", "d", "e"]}
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index())
# Named columns
pdf.columns.name = "Koalas"
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index())
# MultiIndex columns
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index())
# Named MultiIndex columns
pdf.columns.names = ["Hello", "Koalas"]
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index())
# No numeric columns
pdf = pd.DataFrame({"key": ["a", "b", "c"], "val": ["x", "y", "z"]})
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index())
# No numeric named columns
pdf.columns.name = "Koalas"
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index(), almost=True)
# No numeric MultiIndex columns
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("b", "y")])
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index(), almost=True)
# No numeric named MultiIndex columns
pdf.columns.names = ["Hello", "Koalas"]
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index(), almost=True)
# All NaN columns
pdf = pd.DataFrame(
{
"A": [np.nan, np.nan, np.nan, np.nan, np.nan],
"B": [10, 20, 30, 40, 50],
"C": ["a", "b", "c", "d", "e"],
}
)
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index(), check_exact=False)
# All NaN named columns
pdf.columns.name = "Koalas"
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index(), check_exact=False)
# All NaN MultiIndex columns
pdf.columns = pd.MultiIndex.from_tuples([("a", "x"), ("b", "y"), ("c", "z")])
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index(), check_exact=False)
# All NaN named MultiIndex columns
pdf.columns.names = ["Hello", "Koalas"]
psdf = ps.from_pandas(pdf)
self.assert_eq(pdf.prod(), psdf.prod().sort_index(), check_exact=False)
def test_from_dict(self):
data = {"row_1": [3, 2, 1, 0], "row_2": [10, 20, 30, 40]}
pdf = pd.DataFrame.from_dict(data)
psdf = ps.DataFrame.from_dict(data)
self.assert_eq(pdf, psdf)
pdf = pd.DataFrame.from_dict(data, dtype="int8")
psdf = ps.DataFrame.from_dict(data, dtype="int8")
self.assert_eq(pdf, psdf)
pdf = pd.DataFrame.from_dict(data, orient="index", columns=["A", "B", "C", "D"])
psdf = ps.DataFrame.from_dict(data, orient="index", columns=["A", "B", "C", "D"])
self.assert_eq(pdf, psdf)
def test_pad(self):
pdf = pd.DataFrame(
{
"A": [None, 3, None, None],
"B": [2, 4, None, 3],
"C": [None, None, None, 1],
"D": [0, 1, 5, 4],
},
columns=["A", "B", "C", "D"],
)
psdf = ps.from_pandas(pdf)
if LooseVersion(pd.__version__) >= LooseVersion("1.1"):
self.assert_eq(pdf.pad(), psdf.pad())
# Test `inplace=True`
pdf.pad(inplace=True)
psdf.pad(inplace=True)
self.assert_eq(pdf, psdf)
else:
expected = ps.DataFrame(
{
"A": [None, 3, 3, 3],
"B": [2.0, 4.0, 4.0, 3.0],
"C": [None, None, None, 1],
"D": [0, 1, 5, 4],
},
columns=["A", "B", "C", "D"],
)
self.assert_eq(expected, psdf.pad())
# Test `inplace=True`
psdf.pad(inplace=True)
self.assert_eq(expected, psdf)
def test_backfill(self):
pdf = pd.DataFrame(
{
"A": [None, 3, None, None],
"B": [2, 4, None, 3],
"C": [None, None, None, 1],
"D": [0, 1, 5, 4],
},
columns=["A", "B", "C", "D"],
)
psdf = ps.from_pandas(pdf)
if LooseVersion(pd.__version__) >= LooseVersion("1.1"):
self.assert_eq(pdf.backfill(), psdf.backfill())
# Test `inplace=True`
pdf.backfill(inplace=True)
psdf.backfill(inplace=True)
self.assert_eq(pdf, psdf)
else:
expected = ps.DataFrame(
{
"A": [3.0, 3.0, None, None],
"B": [2.0, 4.0, 3.0, 3.0],
"C": [1.0, 1.0, 1.0, 1.0],
"D": [0, 1, 5, 4],
},
columns=["A", "B", "C", "D"],
)
self.assert_eq(expected, psdf.backfill())
# Test `inplace=True`
psdf.backfill(inplace=True)
self.assert_eq(expected, psdf)
def test_align(self):
pdf1 = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=[10, 20, 30])
psdf1 = ps.from_pandas(pdf1)
for join in ["outer", "inner", "left", "right"]:
for axis in [None, 0, 1]:
psdf_l, psdf_r = psdf1.align(psdf1[["b"]], join=join, axis=axis)
pdf_l, pdf_r = pdf1.align(pdf1[["b"]], join=join, axis=axis)
self.assert_eq(psdf_l, pdf_l)
self.assert_eq(psdf_r, pdf_r)
psdf_l, psdf_r = psdf1[["a"]].align(psdf1[["b", "a"]], join=join, axis=axis)
pdf_l, pdf_r = pdf1[["a"]].align(pdf1[["b", "a"]], join=join, axis=axis)
self.assert_eq(psdf_l, pdf_l)
self.assert_eq(psdf_r, pdf_r)
psdf_l, psdf_r = psdf1[["b", "a"]].align(psdf1[["a"]], join=join, axis=axis)
pdf_l, pdf_r = pdf1[["b", "a"]].align(pdf1[["a"]], join=join, axis=axis)
self.assert_eq(psdf_l, pdf_l)
self.assert_eq(psdf_r, pdf_r)
psdf_l, psdf_r = psdf1.align(psdf1["b"], axis=0)
pdf_l, pdf_r = pdf1.align(pdf1["b"], axis=0)
self.assert_eq(psdf_l, pdf_l)
self.assert_eq(psdf_r, pdf_r)
psdf_l, psser_b = psdf1[["a"]].align(psdf1["b"], axis=0)
pdf_l, pser_b = pdf1[["a"]].align(pdf1["b"], axis=0)
self.assert_eq(psdf_l, pdf_l)
self.assert_eq(psser_b, pser_b)
self.assertRaises(ValueError, lambda: psdf1.align(psdf1, join="unknown"))
self.assertRaises(ValueError, lambda: psdf1.align(psdf1["b"]))
self.assertRaises(NotImplementedError, lambda: psdf1.align(psdf1["b"], axis=1))
pdf2 = pd.DataFrame({"a": [4, 5, 6], "d": ["d", "e", "f"]}, index=[10, 11, 12])
psdf2 = ps.from_pandas(pdf2)
for join in ["outer", "inner", "left", "right"]:
psdf_l, psdf_r = psdf1.align(psdf2, join=join, axis=1)
pdf_l, pdf_r = pdf1.align(pdf2, join=join, axis=1)
self.assert_eq(psdf_l.sort_index(), pdf_l.sort_index())
self.assert_eq(psdf_r.sort_index(), pdf_r.sort_index())
def test_between_time(self):
idx = pd.date_range("2018-04-09", periods=4, freq="1D20min")
pdf = pd.DataFrame({"A": [1, 2, 3, 4]}, index=idx)
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.between_time("0:15", "0:45").sort_index(),
psdf.between_time("0:15", "0:45").sort_index(),
)
pdf.index.name = "ts"
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.between_time("0:15", "0:45").sort_index(),
psdf.between_time("0:15", "0:45").sort_index(),
)
# Column label is 'index'
pdf.columns = pd.Index(["index"])
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.between_time("0:15", "0:45").sort_index(),
psdf.between_time("0:15", "0:45").sort_index(),
)
# Both index name and column label are 'index'
pdf.index.name = "index"
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.between_time("0:15", "0:45").sort_index(),
psdf.between_time("0:15", "0:45").sort_index(),
)
# Index name is 'index', column label is ('X', 'A')
pdf.columns = pd.MultiIndex.from_arrays([["X"], ["A"]])
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.between_time("0:15", "0:45").sort_index(),
psdf.between_time("0:15", "0:45").sort_index(),
)
with self.assertRaisesRegex(
NotImplementedError, "between_time currently only works for axis=0"
):
psdf.between_time("0:15", "0:45", axis=1)
psdf = ps.DataFrame({"A": [1, 2, 3, 4]})
with self.assertRaisesRegex(TypeError, "Index must be DatetimeIndex"):
psdf.between_time("0:15", "0:45")
def test_at_time(self):
idx = pd.date_range("2018-04-09", periods=4, freq="1D20min")
pdf = pd.DataFrame({"A": [1, 2, 3, 4]}, index=idx)
psdf = ps.from_pandas(pdf)
psdf.at_time("0:20")
self.assert_eq(
pdf.at_time("0:20").sort_index(),
psdf.at_time("0:20").sort_index(),
)
# Index name is 'ts'
pdf.index.name = "ts"
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.at_time("0:20").sort_index(),
psdf.at_time("0:20").sort_index(),
)
# Index name is 'ts', column label is 'index'
pdf.columns = pd.Index(["index"])
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.at_time("0:40").sort_index(),
psdf.at_time("0:40").sort_index(),
)
# Both index name and column label are 'index'
pdf.index.name = "index"
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.at_time("0:40").sort_index(),
psdf.at_time("0:40").sort_index(),
)
# Index name is 'index', column label is ('X', 'A')
pdf.columns = pd.MultiIndex.from_arrays([["X"], ["A"]])
psdf = ps.from_pandas(pdf)
self.assert_eq(
pdf.at_time("0:40").sort_index(),
psdf.at_time("0:40").sort_index(),
)
with self.assertRaisesRegex(NotImplementedError, "'asof' argument is not supported"):
psdf.at_time("0:15", asof=True)
with self.assertRaisesRegex(NotImplementedError, "at_time currently only works for axis=0"):
psdf.at_time("0:15", axis=1)
psdf = ps.DataFrame({"A": [1, 2, 3, 4]})
with self.assertRaisesRegex(TypeError, "Index must be DatetimeIndex"):
psdf.at_time("0:15")
if __name__ == "__main__":
from pyspark.pandas.tests.test_dataframe import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)