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| """
| Tests dtype specification during parsing
| for all of the parsers defined in parsers.py
| """
| from io import StringIO
| import os
|
| import numpy as np
| import pytest
|
| from pandas.core.dtypes.dtypes import CategoricalDtype
|
| import pandas as pd
| from pandas import (
| Categorical,
| DataFrame,
| Timestamp,
| )
| import pandas._testing as tm
|
| xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
| skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
|
|
| @xfail_pyarrow
| @pytest.mark.parametrize(
| "dtype",
| [
| "category",
| CategoricalDtype(),
| {"a": "category", "b": "category", "c": CategoricalDtype()},
| ],
| )
| def test_categorical_dtype(all_parsers, dtype):
| # see gh-10153
| parser = all_parsers
| data = """a,b,c
| 1,a,3.4
| 1,a,3.4
| 2,b,4.5"""
| expected = DataFrame(
| {
| "a": Categorical(["1", "1", "2"]),
| "b": Categorical(["a", "a", "b"]),
| "c": Categorical(["3.4", "3.4", "4.5"]),
| }
| )
| actual = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(actual, expected)
|
|
| @skip_pyarrow # Flaky
| @pytest.mark.parametrize("dtype", [{"b": "category"}, {1: "category"}])
| def test_categorical_dtype_single(all_parsers, dtype):
| # see gh-10153
| parser = all_parsers
| data = """a,b,c
| 1,a,3.4
| 1,a,3.4
| 2,b,4.5"""
| expected = DataFrame(
| {"a": [1, 1, 2], "b": Categorical(["a", "a", "b"]), "c": [3.4, 3.4, 4.5]}
| )
| actual = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(actual, expected)
|
|
| @xfail_pyarrow
| def test_categorical_dtype_unsorted(all_parsers):
| # see gh-10153
| parser = all_parsers
| data = """a,b,c
| 1,b,3.4
| 1,b,3.4
| 2,a,4.5"""
| expected = DataFrame(
| {
| "a": Categorical(["1", "1", "2"]),
| "b": Categorical(["b", "b", "a"]),
| "c": Categorical(["3.4", "3.4", "4.5"]),
| }
| )
| actual = parser.read_csv(StringIO(data), dtype="category")
| tm.assert_frame_equal(actual, expected)
|
|
| @xfail_pyarrow
| def test_categorical_dtype_missing(all_parsers):
| # see gh-10153
| parser = all_parsers
| data = """a,b,c
| 1,b,3.4
| 1,nan,3.4
| 2,a,4.5"""
| expected = DataFrame(
| {
| "a": Categorical(["1", "1", "2"]),
| "b": Categorical(["b", np.nan, "a"]),
| "c": Categorical(["3.4", "3.4", "4.5"]),
| }
| )
| actual = parser.read_csv(StringIO(data), dtype="category")
| tm.assert_frame_equal(actual, expected)
|
|
| @xfail_pyarrow
| @pytest.mark.slow
| def test_categorical_dtype_high_cardinality_numeric(all_parsers):
| # see gh-18186
| parser = all_parsers
| data = np.sort([str(i) for i in range(524289)])
| expected = DataFrame({"a": Categorical(data, ordered=True)})
|
| actual = parser.read_csv(StringIO("a\n" + "\n".join(data)), dtype="category")
| actual["a"] = actual["a"].cat.reorder_categories(
| np.sort(actual.a.cat.categories), ordered=True
| )
| tm.assert_frame_equal(actual, expected)
|
|
| def test_categorical_dtype_utf16(all_parsers, csv_dir_path):
| # see gh-10153
| pth = os.path.join(csv_dir_path, "utf16_ex.txt")
| parser = all_parsers
| encoding = "utf-16"
| sep = "\t"
|
| expected = parser.read_csv(pth, sep=sep, encoding=encoding)
| expected = expected.apply(Categorical)
|
| actual = parser.read_csv(pth, sep=sep, encoding=encoding, dtype="category")
| tm.assert_frame_equal(actual, expected)
|
|
| @xfail_pyarrow
| def test_categorical_dtype_chunksize_infer_categories(all_parsers):
| # see gh-10153
| parser = all_parsers
| data = """a,b
| 1,a
| 1,b
| 1,b
| 2,c"""
| expecteds = [
| DataFrame({"a": [1, 1], "b": Categorical(["a", "b"])}),
| DataFrame({"a": [1, 2], "b": Categorical(["b", "c"])}, index=[2, 3]),
| ]
| with parser.read_csv(
| StringIO(data), dtype={"b": "category"}, chunksize=2
| ) as actuals:
| for actual, expected in zip(actuals, expecteds):
| tm.assert_frame_equal(actual, expected)
|
|
| @xfail_pyarrow
| def test_categorical_dtype_chunksize_explicit_categories(all_parsers):
| # see gh-10153
| parser = all_parsers
| data = """a,b
| 1,a
| 1,b
| 1,b
| 2,c"""
| cats = ["a", "b", "c"]
| expecteds = [
| DataFrame({"a": [1, 1], "b": Categorical(["a", "b"], categories=cats)}),
| DataFrame(
| {"a": [1, 2], "b": Categorical(["b", "c"], categories=cats)},
| index=[2, 3],
| ),
| ]
| dtype = CategoricalDtype(cats)
| with parser.read_csv(StringIO(data), dtype={"b": dtype}, chunksize=2) as actuals:
| for actual, expected in zip(actuals, expecteds):
| tm.assert_frame_equal(actual, expected)
|
|
| def test_categorical_dtype_latin1(all_parsers, csv_dir_path):
| # see gh-10153
| pth = os.path.join(csv_dir_path, "unicode_series.csv")
| parser = all_parsers
| encoding = "latin-1"
|
| expected = parser.read_csv(pth, header=None, encoding=encoding)
| expected[1] = Categorical(expected[1])
|
| actual = parser.read_csv(pth, header=None, encoding=encoding, dtype={1: "category"})
| tm.assert_frame_equal(actual, expected)
|
|
| @pytest.mark.parametrize("ordered", [False, True])
| @pytest.mark.parametrize(
| "categories",
| [["a", "b", "c"], ["a", "c", "b"], ["a", "b", "c", "d"], ["c", "b", "a"]],
| )
| def test_categorical_category_dtype(all_parsers, categories, ordered):
| parser = all_parsers
| data = """a,b
| 1,a
| 1,b
| 1,b
| 2,c"""
| expected = DataFrame(
| {
| "a": [1, 1, 1, 2],
| "b": Categorical(
| ["a", "b", "b", "c"], categories=categories, ordered=ordered
| ),
| }
| )
|
| dtype = {"b": CategoricalDtype(categories=categories, ordered=ordered)}
| result = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(result, expected)
|
|
| def test_categorical_category_dtype_unsorted(all_parsers):
| parser = all_parsers
| data = """a,b
| 1,a
| 1,b
| 1,b
| 2,c"""
| dtype = CategoricalDtype(["c", "b", "a"])
| expected = DataFrame(
| {
| "a": [1, 1, 1, 2],
| "b": Categorical(["a", "b", "b", "c"], categories=["c", "b", "a"]),
| }
| )
|
| result = parser.read_csv(StringIO(data), dtype={"b": dtype})
| tm.assert_frame_equal(result, expected)
|
|
| def test_categorical_coerces_numeric(all_parsers):
| parser = all_parsers
| dtype = {"b": CategoricalDtype([1, 2, 3])}
|
| data = "b\n1\n1\n2\n3"
| expected = DataFrame({"b": Categorical([1, 1, 2, 3])})
|
| result = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow # Flaky
| def test_categorical_coerces_datetime(all_parsers):
| parser = all_parsers
| dti = pd.DatetimeIndex(["2017-01-01", "2018-01-01", "2019-01-01"], freq=None)
| dtype = {"b": CategoricalDtype(dti)}
|
| data = "b\n2017-01-01\n2018-01-01\n2019-01-01"
| expected = DataFrame({"b": Categorical(dtype["b"].categories)})
|
| result = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(result, expected)
|
|
| def test_categorical_coerces_timestamp(all_parsers):
| parser = all_parsers
| dtype = {"b": CategoricalDtype([Timestamp("2014")])}
|
| data = "b\n2014-01-01\n2014-01-01"
| expected = DataFrame({"b": Categorical([Timestamp("2014")] * 2)})
|
| result = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(result, expected)
|
|
| def test_categorical_coerces_timedelta(all_parsers):
| parser = all_parsers
| dtype = {"b": CategoricalDtype(pd.to_timedelta(["1H", "2H", "3H"]))}
|
| data = "b\n1H\n2H\n3H"
| expected = DataFrame({"b": Categorical(dtype["b"].categories)})
|
| result = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(result, expected)
|
|
| @pytest.mark.parametrize(
| "data",
| [
| "b\nTrue\nFalse\nNA\nFalse",
| "b\ntrue\nfalse\nNA\nfalse",
| "b\nTRUE\nFALSE\nNA\nFALSE",
| "b\nTrue\nFalse\nNA\nFALSE",
| ],
| )
| def test_categorical_dtype_coerces_boolean(all_parsers, data):
| # see gh-20498
| parser = all_parsers
| dtype = {"b": CategoricalDtype([False, True])}
| expected = DataFrame({"b": Categorical([True, False, None, False])})
|
| result = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(result, expected)
|
|
| def test_categorical_unexpected_categories(all_parsers):
| parser = all_parsers
| dtype = {"b": CategoricalDtype(["a", "b", "d", "e"])}
|
| data = "b\nd\na\nc\nd" # Unexpected c
| expected = DataFrame({"b": Categorical(list("dacd"), dtype=dtype["b"])})
|
| result = parser.read_csv(StringIO(data), dtype=dtype)
| tm.assert_frame_equal(result, expected)
|
|
