from datetime import datetime from io import StringIO from textwrap import dedent import numpy as np import pytest from pandas import ( DataFrame, Series, option_context, to_datetime, ) def test_repr_embedded_ndarray(): arr = np.empty(10, dtype=[("err", object)]) for i in range(len(arr)): arr["err"][i] = np.random.default_rng(2).standard_normal(i) df = DataFrame(arr) repr(df["err"]) repr(df) df.to_string() def test_repr_tuples(): buf = StringIO() df = DataFrame({"tups": list(zip(range(10), range(10)))}) repr(df) df.to_string(col_space=10, buf=buf) def test_to_string_truncate(): # GH 9784 - dont truncate when calling DataFrame.to_string df = DataFrame( [ { "a": "foo", "b": "bar", "c": "let's make this a very VERY long line that is longer " "than the default 50 character limit", "d": 1, }, {"a": "foo", "b": "bar", "c": "stuff", "d": 1}, ] ) df.set_index(["a", "b", "c"]) assert df.to_string() == ( " a b " " c d\n" "0 foo bar let's make this a very VERY long line t" "hat is longer than the default 50 character limit 1\n" "1 foo bar " " stuff 1" ) with option_context("max_colwidth", 20): # the display option has no effect on the to_string method assert df.to_string() == ( " a b " " c d\n" "0 foo bar let's make this a very VERY long line t" "hat is longer than the default 50 character limit 1\n" "1 foo bar " " stuff 1" ) assert df.to_string(max_colwidth=20) == ( " a b c d\n" "0 foo bar let's make this ... 1\n" "1 foo bar stuff 1" ) @pytest.mark.parametrize( "input_array, expected", [ ("a", "a"), (["a", "b"], "a\nb"), ([1, "a"], "1\na"), (1, "1"), ([0, -1], " 0\n-1"), (1.0, "1.0"), ([" a", " b"], " a\n b"), ([".1", "1"], ".1\n 1"), (["10", "-10"], " 10\n-10"), ], ) def test_format_remove_leading_space_series(input_array, expected): # GH: 24980 s = Series(input_array).to_string(index=False) assert s == expected @pytest.mark.parametrize( "input_array, expected", [ ({"A": ["a"]}, "A\na"), ({"A": ["a", "b"], "B": ["c", "dd"]}, "A B\na c\nb dd"), ({"A": ["a", 1], "B": ["aa", 1]}, "A B\na aa\n1 1"), ], ) def test_format_remove_leading_space_dataframe(input_array, expected): # GH: 24980 df = DataFrame(input_array).to_string(index=False) assert df == expected @pytest.mark.parametrize( "max_cols, max_rows, expected", [ ( 10, None, " 0 1 2 3 4 ... 6 7 8 9 10\n" " 0 0 0 0 0 ... 0 0 0 0 0\n" " 0 0 0 0 0 ... 0 0 0 0 0\n" " 0 0 0 0 0 ... 0 0 0 0 0\n" " 0 0 0 0 0 ... 0 0 0 0 0", ), ( None, 2, " 0 1 2 3 4 5 6 7 8 9 10\n" " 0 0 0 0 0 0 0 0 0 0 0\n" " .. .. .. .. .. .. .. .. .. .. ..\n" " 0 0 0 0 0 0 0 0 0 0 0", ), ( 10, 2, " 0 1 2 3 4 ... 6 7 8 9 10\n" " 0 0 0 0 0 ... 0 0 0 0 0\n" " .. .. .. .. .. ... .. .. .. .. ..\n" " 0 0 0 0 0 ... 0 0 0 0 0", ), ( 9, 2, " 0 1 2 3 ... 7 8 9 10\n" " 0 0 0 0 ... 0 0 0 0\n" " .. .. .. .. ... .. .. .. ..\n" " 0 0 0 0 ... 0 0 0 0", ), ( 1, 1, " 0 ...\n 0 ...\n.. ...", ), ], ) def test_truncation_no_index(max_cols, max_rows, expected): df = DataFrame([[0] * 11] * 4) assert df.to_string(index=False, max_cols=max_cols, max_rows=max_rows) == expected def test_to_string_unicode_columns(float_frame): df = DataFrame({"\u03c3": np.arange(10.0)}) buf = StringIO() df.to_string(buf=buf) buf.getvalue() buf = StringIO() df.info(buf=buf) buf.getvalue() result = float_frame.to_string() assert isinstance(result, str) def test_to_string_utf8_columns(): n = "\u05d0".encode() with option_context("display.max_rows", 1): df = DataFrame([1, 2], columns=[n]) repr(df) def test_to_string_unicode_two(): dm = DataFrame({"c/\u03c3": []}) buf = StringIO() dm.to_string(buf) def test_to_string_unicode_three(): dm = DataFrame(["\xc2"]) buf = StringIO() dm.to_string(buf) def test_to_string_with_formatters(): df = DataFrame( { "int": [1, 2, 3], "float": [1.0, 2.0, 3.0], "object": [(1, 2), True, False], }, columns=["int", "float", "object"], ) formatters = [ ("int", lambda x: f"0x{x:x}"), ("float", lambda x: f"[{x: 4.1f}]"), ("object", lambda x: f"-{x!s}-"), ] result = df.to_string(formatters=dict(formatters)) result2 = df.to_string(formatters=list(zip(*formatters))[1]) assert result == ( " int float object\n" "0 0x1 [ 1.0] -(1, 2)-\n" "1 0x2 [ 2.0] -True-\n" "2 0x3 [ 3.0] -False-" ) assert result == result2 def test_to_string_with_datetime64_monthformatter(): months = [datetime(2016, 1, 1), datetime(2016, 2, 2)] x = DataFrame({"months": months}) def format_func(x): return x.strftime("%Y-%m") result = x.to_string(formatters={"months": format_func}) expected = dedent( """\ months 0 2016-01 1 2016-02""" ) assert result.strip() == expected def test_to_string_with_datetime64_hourformatter(): x = DataFrame( {"hod": to_datetime(["10:10:10.100", "12:12:12.120"], format="%H:%M:%S.%f")} ) def format_func(x): return x.strftime("%H:%M") result = x.to_string(formatters={"hod": format_func}) expected = dedent( """\ hod 0 10:10 1 12:12""" ) assert result.strip() == expected def test_to_string_with_formatters_unicode(): df = DataFrame({"c/\u03c3": [1, 2, 3]}) result = df.to_string(formatters={"c/\u03c3": str}) expected = dedent( """\ c/\u03c3 0 1 1 2 2 3""" ) assert result == expected def test_to_string_complex_number_trims_zeros(): s = Series([1.000000 + 1.000000j, 1.0 + 1.0j, 1.05 + 1.0j]) result = s.to_string() expected = dedent( """\ 0 1.00+1.00j 1 1.00+1.00j 2 1.05+1.00j""" ) assert result == expected def test_nullable_float_to_string(float_ea_dtype): # https://github.com/pandas-dev/pandas/issues/36775 dtype = float_ea_dtype s = Series([0.0, 1.0, None], dtype=dtype) result = s.to_string() expected = dedent( """\ 0 0.0 1 1.0 2 """ ) assert result == expected def test_nullable_int_to_string(any_int_ea_dtype): # https://github.com/pandas-dev/pandas/issues/36775 dtype = any_int_ea_dtype s = Series([0, 1, None], dtype=dtype) result = s.to_string() expected = dedent( """\ 0 0 1 1 2 """ ) assert result == expected @pytest.mark.parametrize("na_rep", ["NaN", "Ted"]) def test_to_string_na_rep_and_float_format(na_rep): # GH 13828 df = DataFrame([["A", 1.2225], ["A", None]], columns=["Group", "Data"]) result = df.to_string(na_rep=na_rep, float_format="{:.2f}".format) expected = dedent( f"""\ Group Data 0 A 1.22 1 A {na_rep}""" ) assert result == expected @pytest.mark.parametrize( "data,expected", [ ( {"col1": [1, 2], "col2": [3, 4]}, " col1 col2\n0 1 3\n1 2 4", ), ( {"col1": ["Abc", 0.756], "col2": [np.nan, 4.5435]}, " col1 col2\n0 Abc NaN\n1 0.756 4.5435", ), ( {"col1": [np.nan, "a"], "col2": [0.009, 3.543], "col3": ["Abc", 23]}, " col1 col2 col3\n0 NaN 0.009 Abc\n1 a 3.543 23", ), ], ) def test_to_string_max_rows_zero(data, expected): # GH35394 result = DataFrame(data=data).to_string(max_rows=0) assert result == expected def test_to_string_string_dtype(): # GH#50099 pytest.importorskip("pyarrow") df = DataFrame({"x": ["foo", "bar", "baz"], "y": ["a", "b", "c"], "z": [1, 2, 3]}) df = df.astype( {"x": "string[pyarrow]", "y": "string[python]", "z": "int64[pyarrow]"} ) result = df.dtypes.to_string() expected = dedent( """\ x string[pyarrow] y string[python] z int64[pyarrow]""" ) assert result == expected