306 lines
9.2 KiB
Python
306 lines
9.2 KiB
Python
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from datetime import datetime, timedelta
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from io import StringIO
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import warnings
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import numpy as np
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import pytest
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from pandas import (
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Categorical,
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DataFrame,
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MultiIndex,
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NaT,
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PeriodIndex,
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Series,
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Timestamp,
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date_range,
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option_context,
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period_range,
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)
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import pandas._testing as tm
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import pandas.io.formats.format as fmt
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class TestDataFrameReprInfoEtc:
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def test_repr_unicode_level_names(self, frame_or_series):
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index = MultiIndex.from_tuples([(0, 0), (1, 1)], names=["\u0394", "i1"])
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obj = DataFrame(np.random.randn(2, 4), index=index)
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if frame_or_series is Series:
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obj = obj[0]
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repr(obj)
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def test_assign_index_sequences(self):
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# GH#2200
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df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}).set_index(
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["a", "b"]
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)
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index = list(df.index)
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index[0] = ("faz", "boo")
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df.index = index
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repr(df)
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# this travels an improper code path
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index[0] = ["faz", "boo"]
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df.index = index
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repr(df)
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def test_repr_with_mi_nat(self, float_string_frame):
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df = DataFrame({"X": [1, 2]}, index=[[NaT, Timestamp("20130101")], ["a", "b"]])
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result = repr(df)
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expected = " X\nNaT a 1\n2013-01-01 b 2"
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assert result == expected
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def test_multiindex_na_repr(self):
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# only an issue with long columns
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df3 = DataFrame(
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{
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"A" * 30: {("A", "A0006000", "nuit"): "A0006000"},
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"B" * 30: {("A", "A0006000", "nuit"): np.nan},
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"C" * 30: {("A", "A0006000", "nuit"): np.nan},
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"D" * 30: {("A", "A0006000", "nuit"): np.nan},
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"E" * 30: {("A", "A0006000", "nuit"): "A"},
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"F" * 30: {("A", "A0006000", "nuit"): np.nan},
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}
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)
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idf = df3.set_index(["A" * 30, "C" * 30])
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repr(idf)
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def test_repr_name_coincide(self):
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index = MultiIndex.from_tuples(
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[("a", 0, "foo"), ("b", 1, "bar")], names=["a", "b", "c"]
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)
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df = DataFrame({"value": [0, 1]}, index=index)
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lines = repr(df).split("\n")
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assert lines[2].startswith("a 0 foo")
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def test_repr_to_string(
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self,
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multiindex_year_month_day_dataframe_random_data,
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multiindex_dataframe_random_data,
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):
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ymd = multiindex_year_month_day_dataframe_random_data
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frame = multiindex_dataframe_random_data
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repr(frame)
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repr(ymd)
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repr(frame.T)
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repr(ymd.T)
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buf = StringIO()
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frame.to_string(buf=buf)
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ymd.to_string(buf=buf)
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frame.T.to_string(buf=buf)
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ymd.T.to_string(buf=buf)
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def test_repr_empty(self):
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# empty
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repr(DataFrame())
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# empty with index
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frame = DataFrame(index=np.arange(1000))
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repr(frame)
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def test_repr_mixed(self, float_string_frame):
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buf = StringIO()
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# mixed
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repr(float_string_frame)
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float_string_frame.info(verbose=False, buf=buf)
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@pytest.mark.slow
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def test_repr_mixed_big(self):
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# big mixed
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biggie = DataFrame(
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{"A": np.random.randn(200), "B": tm.makeStringIndex(200)}, index=range(200)
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)
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biggie.loc[:20, "A"] = np.nan
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biggie.loc[:20, "B"] = np.nan
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repr(biggie)
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def test_repr(self, float_frame):
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buf = StringIO()
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# small one
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repr(float_frame)
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float_frame.info(verbose=False, buf=buf)
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# even smaller
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float_frame.reindex(columns=["A"]).info(verbose=False, buf=buf)
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float_frame.reindex(columns=["A", "B"]).info(verbose=False, buf=buf)
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# exhausting cases in DataFrame.info
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# columns but no index
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no_index = DataFrame(columns=[0, 1, 3])
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repr(no_index)
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# no columns or index
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DataFrame().info(buf=buf)
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df = DataFrame(["a\n\r\tb"], columns=["a\n\r\td"], index=["a\n\r\tf"])
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assert "\t" not in repr(df)
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assert "\r" not in repr(df)
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assert "a\n" not in repr(df)
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def test_repr_dimensions(self):
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df = DataFrame([[1, 2], [3, 4]])
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with option_context("display.show_dimensions", True):
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assert "2 rows x 2 columns" in repr(df)
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with option_context("display.show_dimensions", False):
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assert "2 rows x 2 columns" not in repr(df)
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with option_context("display.show_dimensions", "truncate"):
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assert "2 rows x 2 columns" not in repr(df)
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@pytest.mark.slow
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def test_repr_big(self):
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# big one
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biggie = DataFrame(np.zeros((200, 4)), columns=range(4), index=range(200))
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repr(biggie)
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def test_repr_unsortable(self, float_frame):
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# columns are not sortable
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warn_filters = warnings.filters
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warnings.filterwarnings("ignore", category=FutureWarning, module=".*format")
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unsortable = DataFrame(
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{
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"foo": [1] * 50,
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datetime.today(): [1] * 50,
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"bar": ["bar"] * 50,
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datetime.today() + timedelta(1): ["bar"] * 50,
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},
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index=np.arange(50),
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)
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repr(unsortable)
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fmt.set_option("display.precision", 3, "display.column_space", 10)
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repr(float_frame)
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fmt.set_option("display.max_rows", 10, "display.max_columns", 2)
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repr(float_frame)
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fmt.set_option("display.max_rows", 1000, "display.max_columns", 1000)
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repr(float_frame)
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tm.reset_display_options()
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warnings.filters = warn_filters
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def test_repr_unicode(self):
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uval = "\u03c3\u03c3\u03c3\u03c3"
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df = DataFrame({"A": [uval, uval]})
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result = repr(df)
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ex_top = " A"
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assert result.split("\n")[0].rstrip() == ex_top
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df = DataFrame({"A": [uval, uval]})
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result = repr(df)
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assert result.split("\n")[0].rstrip() == ex_top
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def test_unicode_string_with_unicode(self):
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df = DataFrame({"A": ["\u05d0"]})
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str(df)
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def test_repr_unicode_columns(self):
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df = DataFrame({"\u05d0": [1, 2, 3], "\u05d1": [4, 5, 6], "c": [7, 8, 9]})
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repr(df.columns) # should not raise UnicodeDecodeError
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def test_str_to_bytes_raises(self):
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# GH 26447
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df = DataFrame({"A": ["abc"]})
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msg = "^'str' object cannot be interpreted as an integer$"
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with pytest.raises(TypeError, match=msg):
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bytes(df)
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def test_very_wide_info_repr(self):
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df = DataFrame(np.random.randn(10, 20), columns=tm.rands_array(10, 20))
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repr(df)
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def test_repr_column_name_unicode_truncation_bug(self):
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# #1906
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df = DataFrame(
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{
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"Id": [7117434],
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"StringCol": (
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"Is it possible to modify drop plot code"
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"so that the output graph is displayed "
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"in iphone simulator, Is it possible to "
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"modify drop plot code so that the "
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"output graph is \xe2\x80\xa8displayed "
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"in iphone simulator.Now we are adding "
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"the CSV file externally. I want to Call "
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"the File through the code.."
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),
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}
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)
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with option_context("display.max_columns", 20):
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assert "StringCol" in repr(df)
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def test_latex_repr(self):
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result = r"""\begin{tabular}{llll}
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\toprule
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{} & 0 & 1 & 2 \\
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\midrule
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0 & $\alpha$ & b & c \\
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1 & 1 & 2 & 3 \\
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\bottomrule
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\end{tabular}
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"""
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with option_context("display.latex.escape", False, "display.latex.repr", True):
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df = DataFrame([[r"$\alpha$", "b", "c"], [1, 2, 3]])
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assert result == df._repr_latex_()
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# GH 12182
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assert df._repr_latex_() is None
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def test_repr_categorical_dates_periods(self):
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# normal DataFrame
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dt = date_range("2011-01-01 09:00", freq="H", periods=5, tz="US/Eastern")
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p = period_range("2011-01", freq="M", periods=5)
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df = DataFrame({"dt": dt, "p": p})
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exp = """ dt p
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0 2011-01-01 09:00:00-05:00 2011-01
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1 2011-01-01 10:00:00-05:00 2011-02
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2 2011-01-01 11:00:00-05:00 2011-03
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3 2011-01-01 12:00:00-05:00 2011-04
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4 2011-01-01 13:00:00-05:00 2011-05"""
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assert repr(df) == exp
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df2 = DataFrame({"dt": Categorical(dt), "p": Categorical(p)})
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assert repr(df2) == exp
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@pytest.mark.parametrize("arg", [np.datetime64, np.timedelta64])
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@pytest.mark.parametrize(
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"box, expected",
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[[Series, "0 NaT\ndtype: object"], [DataFrame, " 0\n0 NaT"]],
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)
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def test_repr_np_nat_with_object(self, arg, box, expected):
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# GH 25445
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result = repr(box([arg("NaT")], dtype=object))
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assert result == expected
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def test_frame_datetime64_pre1900_repr(self):
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df = DataFrame({"year": date_range("1/1/1700", periods=50, freq="A-DEC")})
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# it works!
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repr(df)
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def test_frame_to_string_with_periodindex(self):
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index = PeriodIndex(["2011-1", "2011-2", "2011-3"], freq="M")
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frame = DataFrame(np.random.randn(3, 4), index=index)
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# it works!
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frame.to_string()
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