LSR/env/lib/python3.6/site-packages/pandas/tests/extension/base/reshaping.py
2020-06-04 17:24:47 +02:00

327 lines
12 KiB
Python

import itertools
import numpy as np
import pytest
import pandas as pd
from pandas.core.internals import ExtensionBlock
from .base import BaseExtensionTests
class BaseReshapingTests(BaseExtensionTests):
"""Tests for reshaping and concatenation."""
@pytest.mark.parametrize("in_frame", [True, False])
def test_concat(self, data, in_frame):
wrapped = pd.Series(data)
if in_frame:
wrapped = pd.DataFrame(wrapped)
result = pd.concat([wrapped, wrapped], ignore_index=True)
assert len(result) == len(data) * 2
if in_frame:
dtype = result.dtypes[0]
else:
dtype = result.dtype
assert dtype == data.dtype
assert isinstance(result._data.blocks[0], ExtensionBlock)
@pytest.mark.parametrize("in_frame", [True, False])
def test_concat_all_na_block(self, data_missing, in_frame):
valid_block = pd.Series(data_missing.take([1, 1]), index=[0, 1])
na_block = pd.Series(data_missing.take([0, 0]), index=[2, 3])
if in_frame:
valid_block = pd.DataFrame({"a": valid_block})
na_block = pd.DataFrame({"a": na_block})
result = pd.concat([valid_block, na_block])
if in_frame:
expected = pd.DataFrame({"a": data_missing.take([1, 1, 0, 0])})
self.assert_frame_equal(result, expected)
else:
expected = pd.Series(data_missing.take([1, 1, 0, 0]))
self.assert_series_equal(result, expected)
def test_concat_mixed_dtypes(self, data):
# https://github.com/pandas-dev/pandas/issues/20762
df1 = pd.DataFrame({"A": data[:3]})
df2 = pd.DataFrame({"A": [1, 2, 3]})
df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
dfs = [df1, df2, df3]
# dataframes
result = pd.concat(dfs)
expected = pd.concat([x.astype(object) for x in dfs])
self.assert_frame_equal(result, expected)
# series
result = pd.concat([x["A"] for x in dfs])
expected = pd.concat([x["A"].astype(object) for x in dfs])
self.assert_series_equal(result, expected)
# simple test for just EA and one other
result = pd.concat([df1, df2])
expected = pd.concat([df1.astype("object"), df2.astype("object")])
self.assert_frame_equal(result, expected)
result = pd.concat([df1["A"], df2["A"]])
expected = pd.concat([df1["A"].astype("object"), df2["A"].astype("object")])
self.assert_series_equal(result, expected)
def test_concat_columns(self, data, na_value):
df1 = pd.DataFrame({"A": data[:3]})
df2 = pd.DataFrame({"B": [1, 2, 3]})
expected = pd.DataFrame({"A": data[:3], "B": [1, 2, 3]})
result = pd.concat([df1, df2], axis=1)
self.assert_frame_equal(result, expected)
result = pd.concat([df1["A"], df2["B"]], axis=1)
self.assert_frame_equal(result, expected)
# non-aligned
df2 = pd.DataFrame({"B": [1, 2, 3]}, index=[1, 2, 3])
expected = pd.DataFrame(
{
"A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
"B": [np.nan, 1, 2, 3],
}
)
result = pd.concat([df1, df2], axis=1)
self.assert_frame_equal(result, expected)
result = pd.concat([df1["A"], df2["B"]], axis=1)
self.assert_frame_equal(result, expected)
def test_concat_extension_arrays_copy_false(self, data, na_value):
# GH 20756
df1 = pd.DataFrame({"A": data[:3]})
df2 = pd.DataFrame({"B": data[3:7]})
expected = pd.DataFrame(
{
"A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
"B": data[3:7],
}
)
result = pd.concat([df1, df2], axis=1, copy=False)
self.assert_frame_equal(result, expected)
def test_align(self, data, na_value):
a = data[:3]
b = data[2:5]
r1, r2 = pd.Series(a).align(pd.Series(b, index=[1, 2, 3]))
# Assumes that the ctor can take a list of scalars of the type
e1 = pd.Series(data._from_sequence(list(a) + [na_value], dtype=data.dtype))
e2 = pd.Series(data._from_sequence([na_value] + list(b), dtype=data.dtype))
self.assert_series_equal(r1, e1)
self.assert_series_equal(r2, e2)
def test_align_frame(self, data, na_value):
a = data[:3]
b = data[2:5]
r1, r2 = pd.DataFrame({"A": a}).align(pd.DataFrame({"A": b}, index=[1, 2, 3]))
# Assumes that the ctor can take a list of scalars of the type
e1 = pd.DataFrame(
{"A": data._from_sequence(list(a) + [na_value], dtype=data.dtype)}
)
e2 = pd.DataFrame(
{"A": data._from_sequence([na_value] + list(b), dtype=data.dtype)}
)
self.assert_frame_equal(r1, e1)
self.assert_frame_equal(r2, e2)
def test_align_series_frame(self, data, na_value):
# https://github.com/pandas-dev/pandas/issues/20576
ser = pd.Series(data, name="a")
df = pd.DataFrame({"col": np.arange(len(ser) + 1)})
r1, r2 = ser.align(df)
e1 = pd.Series(
data._from_sequence(list(data) + [na_value], dtype=data.dtype),
name=ser.name,
)
self.assert_series_equal(r1, e1)
self.assert_frame_equal(r2, df)
def test_set_frame_expand_regular_with_extension(self, data):
df = pd.DataFrame({"A": [1] * len(data)})
df["B"] = data
expected = pd.DataFrame({"A": [1] * len(data), "B": data})
self.assert_frame_equal(df, expected)
def test_set_frame_expand_extension_with_regular(self, data):
df = pd.DataFrame({"A": data})
df["B"] = [1] * len(data)
expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
self.assert_frame_equal(df, expected)
def test_set_frame_overwrite_object(self, data):
# https://github.com/pandas-dev/pandas/issues/20555
df = pd.DataFrame({"A": [1] * len(data)}, dtype=object)
df["A"] = data
assert df.dtypes["A"] == data.dtype
def test_merge(self, data, na_value):
# GH-20743
df1 = pd.DataFrame({"ext": data[:3], "int1": [1, 2, 3], "key": [0, 1, 2]})
df2 = pd.DataFrame({"int2": [1, 2, 3, 4], "key": [0, 0, 1, 3]})
res = pd.merge(df1, df2)
exp = pd.DataFrame(
{
"int1": [1, 1, 2],
"int2": [1, 2, 3],
"key": [0, 0, 1],
"ext": data._from_sequence(
[data[0], data[0], data[1]], dtype=data.dtype
),
}
)
self.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
res = pd.merge(df1, df2, how="outer")
exp = pd.DataFrame(
{
"int1": [1, 1, 2, 3, np.nan],
"int2": [1, 2, 3, np.nan, 4],
"key": [0, 0, 1, 2, 3],
"ext": data._from_sequence(
[data[0], data[0], data[1], data[2], na_value], dtype=data.dtype
),
}
)
self.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
def test_merge_on_extension_array(self, data):
# GH 23020
a, b = data[:2]
key = type(data)._from_sequence([a, b], dtype=data.dtype)
df = pd.DataFrame({"key": key, "val": [1, 2]})
result = pd.merge(df, df, on="key")
expected = pd.DataFrame({"key": key, "val_x": [1, 2], "val_y": [1, 2]})
self.assert_frame_equal(result, expected)
# order
result = pd.merge(df.iloc[[1, 0]], df, on="key")
expected = expected.iloc[[1, 0]].reset_index(drop=True)
self.assert_frame_equal(result, expected)
def test_merge_on_extension_array_duplicates(self, data):
# GH 23020
a, b = data[:2]
key = type(data)._from_sequence([a, b, a], dtype=data.dtype)
df1 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
df2 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
result = pd.merge(df1, df2, on="key")
expected = pd.DataFrame(
{
"key": key.take([0, 0, 0, 0, 1]),
"val_x": [1, 1, 3, 3, 2],
"val_y": [1, 3, 1, 3, 2],
}
)
self.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"columns",
[
["A", "B"],
pd.MultiIndex.from_tuples(
[("A", "a"), ("A", "b")], names=["outer", "inner"]
),
],
)
def test_stack(self, data, columns):
df = pd.DataFrame({"A": data[:5], "B": data[:5]})
df.columns = columns
result = df.stack()
expected = df.astype(object).stack()
# we need a second astype(object), in case the constructor inferred
# object -> specialized, as is done for period.
expected = expected.astype(object)
if isinstance(expected, pd.Series):
assert result.dtype == df.iloc[:, 0].dtype
else:
assert all(result.dtypes == df.iloc[:, 0].dtype)
result = result.astype(object)
self.assert_equal(result, expected)
@pytest.mark.parametrize(
"index",
[
# Two levels, uniform.
pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"]),
# non-uniform
pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "b")]),
# three levels, non-uniform
pd.MultiIndex.from_product([("A", "B"), ("a", "b", "c"), (0, 1, 2)]),
pd.MultiIndex.from_tuples(
[
("A", "a", 1),
("A", "b", 0),
("A", "a", 0),
("B", "a", 0),
("B", "c", 1),
]
),
],
)
@pytest.mark.parametrize("obj", ["series", "frame"])
def test_unstack(self, data, index, obj):
data = data[: len(index)]
if obj == "series":
ser = pd.Series(data, index=index)
else:
ser = pd.DataFrame({"A": data, "B": data}, index=index)
n = index.nlevels
levels = list(range(n))
# [0, 1, 2]
# [(0,), (1,), (2,), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
combinations = itertools.chain.from_iterable(
itertools.permutations(levels, i) for i in range(1, n)
)
for level in combinations:
result = ser.unstack(level=level)
assert all(
isinstance(result[col].array, type(data)) for col in result.columns
)
expected = ser.astype(object).unstack(level=level)
result = result.astype(object)
self.assert_frame_equal(result, expected)
def test_ravel(self, data):
# as long as EA is 1D-only, ravel is a no-op
result = data.ravel()
assert type(result) == type(data)
# Check that we have a view, not a copy
result[0] = result[1]
assert data[0] == data[1]
def test_transpose(self, data):
df = pd.DataFrame({"A": data[:4], "B": data[:4]}, index=["a", "b", "c", "d"])
result = df.T
expected = pd.DataFrame(
{
"a": type(data)._from_sequence([data[0]] * 2, dtype=data.dtype),
"b": type(data)._from_sequence([data[1]] * 2, dtype=data.dtype),
"c": type(data)._from_sequence([data[2]] * 2, dtype=data.dtype),
"d": type(data)._from_sequence([data[3]] * 2, dtype=data.dtype),
},
index=["A", "B"],
)
self.assert_frame_equal(result, expected)
self.assert_frame_equal(np.transpose(np.transpose(df)), df)
self.assert_frame_equal(np.transpose(np.transpose(df[["A"]])), df[["A"]])