133 lines
4.9 KiB
Python
133 lines
4.9 KiB
Python
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# -*- coding: utf-8 -*-
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from __future__ import print_function
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import numpy as np
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import pandas as pd
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import pandas.util.testing as tm
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from pandas.compat import (is_platform_windows,
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is_platform_32bit)
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from pandas.core.config import option_context
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use_32bit_repr = is_platform_windows() or is_platform_32bit()
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class TestSparseSeriesFormatting(object):
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@property
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def dtype_format_for_platform(self):
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return '' if use_32bit_repr else ', dtype=int32'
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def test_sparse_max_row(self):
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s = pd.Series([1, np.nan, np.nan, 3, np.nan]).to_sparse()
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result = repr(s)
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dfm = self.dtype_format_for_platform
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exp = ("0 1.0\n1 NaN\n2 NaN\n3 3.0\n"
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"4 NaN\ndtype: float64\nBlockIndex\n"
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"Block locations: array([0, 3]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dfm))
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assert result == exp
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with option_context("display.max_rows", 3):
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# GH 10560
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result = repr(s)
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exp = ("0 1.0\n ... \n4 NaN\n"
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"Length: 5, dtype: float64\nBlockIndex\n"
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"Block locations: array([0, 3]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dfm))
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assert result == exp
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def test_sparse_mi_max_row(self):
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idx = pd.MultiIndex.from_tuples([('A', 0), ('A', 1), ('B', 0),
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('C', 0), ('C', 1), ('C', 2)])
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s = pd.Series([1, np.nan, np.nan, 3, np.nan, np.nan],
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index=idx).to_sparse()
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result = repr(s)
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dfm = self.dtype_format_for_platform
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exp = ("A 0 1.0\n 1 NaN\nB 0 NaN\n"
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"C 0 3.0\n 1 NaN\n 2 NaN\n"
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"dtype: float64\nBlockIndex\n"
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"Block locations: array([0, 3]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dfm))
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assert result == exp
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with option_context("display.max_rows", 3,
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"display.show_dimensions", False):
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# GH 13144
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result = repr(s)
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exp = ("A 0 1.0\n ... \nC 2 NaN\n"
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"dtype: float64\nBlockIndex\n"
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"Block locations: array([0, 3]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dfm))
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assert result == exp
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def test_sparse_bool(self):
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# GH 13110
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s = pd.SparseSeries([True, False, False, True, False, False],
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fill_value=False)
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result = repr(s)
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dtype = '' if use_32bit_repr else ', dtype=int32'
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exp = ("0 True\n1 False\n2 False\n"
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"3 True\n4 False\n5 False\n"
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"dtype: bool\nBlockIndex\n"
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"Block locations: array([0, 3]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dtype))
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assert result == exp
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with option_context("display.max_rows", 3):
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result = repr(s)
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exp = ("0 True\n ... \n5 False\n"
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"Length: 6, dtype: bool\nBlockIndex\n"
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"Block locations: array([0, 3]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dtype))
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assert result == exp
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def test_sparse_int(self):
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# GH 13110
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s = pd.SparseSeries([0, 1, 0, 0, 1, 0], fill_value=False)
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result = repr(s)
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dtype = '' if use_32bit_repr else ', dtype=int32'
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exp = ("0 0\n1 1\n2 0\n3 0\n4 1\n"
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"5 0\ndtype: int64\nBlockIndex\n"
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"Block locations: array([1, 4]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dtype))
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assert result == exp
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with option_context("display.max_rows", 3,
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"display.show_dimensions", False):
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result = repr(s)
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exp = ("0 0\n ..\n5 0\n"
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"dtype: int64\nBlockIndex\n"
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"Block locations: array([1, 4]{0})\n"
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"Block lengths: array([1, 1]{0})".format(dtype))
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assert result == exp
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class TestSparseDataFrameFormatting(object):
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def test_sparse_frame(self):
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# GH 13110
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df = pd.DataFrame({'A': [True, False, True, False, True],
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'B': [True, False, True, False, True],
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'C': [0, 0, 3, 0, 5],
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'D': [np.nan, np.nan, np.nan, 1, 2]})
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sparse = df.to_sparse()
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assert repr(sparse) == repr(df)
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with option_context("display.max_rows", 3):
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assert repr(sparse) == repr(df)
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def test_sparse_repr_after_set(self):
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# GH 15488
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sdf = pd.SparseDataFrame([[np.nan, 1], [2, np.nan]])
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res = sdf.copy()
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# Ignore the warning
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with pd.option_context('mode.chained_assignment', None):
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sdf[0][1] = 2 # This line triggers the bug
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repr(sdf)
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tm.assert_sp_frame_equal(sdf, res)
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