import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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NaT,
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date_range,
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)
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import pandas._testing as tm
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@pytest.fixture
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def float_frame_with_na():
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"""
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Fixture for DataFrame of floats with index of unique strings
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Columns are ['A', 'B', 'C', 'D']; some entries are missing
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A B C D
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ABwBzA0ljw -1.128865 -0.897161 0.046603 0.274997
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DJiRzmbyQF 0.728869 0.233502 0.722431 -0.890872
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neMgPD5UBF 0.486072 -1.027393 -0.031553 1.449522
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0yWA4n8VeX -1.937191 -1.142531 0.805215 -0.462018
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3slYUbbqU1 0.153260 1.164691 1.489795 -0.545826
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soujjZ0A08 NaN NaN NaN NaN
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7W6NLGsjB9 NaN NaN NaN NaN
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... ... ... ... ...
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uhfeaNkCR1 -0.231210 -0.340472 0.244717 -0.901590
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n6p7GYuBIV -0.419052 1.922721 -0.125361 -0.727717
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ZhzAeY6p1y 1.234374 -1.425359 -0.827038 -0.633189
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uWdPsORyUh 0.046738 -0.980445 -1.102965 0.605503
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3DJA6aN590 -0.091018 -1.684734 -1.100900 0.215947
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2GBPAzdbMk -2.883405 -1.021071 1.209877 1.633083
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sHadBoyVHw -2.223032 -0.326384 0.258931 0.245517
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[30 rows x 4 columns]
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"""
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df = DataFrame(tm.getSeriesData())
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# set some NAs
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df.iloc[5:10] = np.nan
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df.iloc[15:20, -2:] = np.nan
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return df
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@pytest.fixture
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def bool_frame_with_na():
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"""
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Fixture for DataFrame of booleans with index of unique strings
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Columns are ['A', 'B', 'C', 'D']; some entries are missing
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A B C D
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zBZxY2IDGd False False False False
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IhBWBMWllt False True True True
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ctjdvZSR6R True False True True
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AVTujptmxb False True False True
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G9lrImrSWq False False False True
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sFFwdIUfz2 NaN NaN NaN NaN
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s15ptEJnRb NaN NaN NaN NaN
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... ... ... ... ...
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UW41KkDyZ4 True True False False
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l9l6XkOdqV True False False False
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X2MeZfzDYA False True False False
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xWkIKU7vfX False True False True
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QOhL6VmpGU False False False True
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22PwkRJdat False True False False
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kfboQ3VeIK True False True False
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[30 rows x 4 columns]
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"""
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df = DataFrame(tm.getSeriesData()) > 0
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df = df.astype(object)
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# set some NAs
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df.iloc[5:10] = np.nan
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df.iloc[15:20, -2:] = np.nan
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# For `any` tests we need to have at least one True before the first NaN
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# in each column
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for i in range(4):
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df.iloc[i, i] = True
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return df
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@pytest.fixture
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def float_string_frame():
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"""
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Fixture for DataFrame of floats and strings with index of unique strings
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Columns are ['A', 'B', 'C', 'D', 'foo'].
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A B C D foo
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w3orJvq07g -1.594062 -1.084273 -1.252457 0.356460 bar
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PeukuVdmz2 0.109855 -0.955086 -0.809485 0.409747 bar
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ahp2KvwiM8 -1.533729 -0.142519 -0.154666 1.302623 bar
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3WSJ7BUCGd 2.484964 0.213829 0.034778 -2.327831 bar
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khdAmufk0U -0.193480 -0.743518 -0.077987 0.153646 bar
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LE2DZiFlrE -0.193566 -1.343194 -0.107321 0.959978 bar
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HJXSJhVn7b 0.142590 1.257603 -0.659409 -0.223844 bar
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... ... ... ... ... ...
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9a1Vypttgw -1.316394 1.601354 0.173596 1.213196 bar
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h5d1gVFbEy 0.609475 1.106738 -0.155271 0.294630 bar
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mK9LsTQG92 1.303613 0.857040 -1.019153 0.369468 bar
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oOLksd9gKH 0.558219 -0.134491 -0.289869 -0.951033 bar
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9jgoOjKyHg 0.058270 -0.496110 -0.413212 -0.852659 bar
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jZLDHclHAO 0.096298 1.267510 0.549206 -0.005235 bar
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lR0nxDp1C2 -2.119350 -0.794384 0.544118 0.145849 bar
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[30 rows x 5 columns]
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"""
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df = DataFrame(tm.getSeriesData())
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df["foo"] = "bar"
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return df
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@pytest.fixture
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def mixed_float_frame():
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"""
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Fixture for DataFrame of different float types with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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A B C D
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GI7bbDaEZe -0.237908 -0.246225 -0.468506 0.752993
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KGp9mFepzA -1.140809 -0.644046 -1.225586 0.801588
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VeVYLAb1l2 -1.154013 -1.677615 0.690430 -0.003731
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kmPME4WKhO 0.979578 0.998274 -0.776367 0.897607
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CPyopdXTiz 0.048119 -0.257174 0.836426 0.111266
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0kJZQndAj0 0.274357 -0.281135 -0.344238 0.834541
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tqdwQsaHG8 -0.979716 -0.519897 0.582031 0.144710
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... ... ... ... ...
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7FhZTWILQj -2.906357 1.261039 -0.780273 -0.537237
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4pUDPM4eGq -2.042512 -0.464382 -0.382080 1.132612
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B8dUgUzwTi -1.506637 -0.364435 1.087891 0.297653
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hErlVYjVv9 1.477453 -0.495515 -0.713867 1.438427
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1BKN3o7YLs 0.127535 -0.349812 -0.881836 0.489827
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9S4Ekn7zga 1.445518 -2.095149 0.031982 0.373204
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xN1dNn6OV6 1.425017 -0.983995 -0.363281 -0.224502
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[30 rows x 4 columns]
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"""
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df = DataFrame(tm.getSeriesData())
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df.A = df.A.astype("float32")
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df.B = df.B.astype("float32")
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df.C = df.C.astype("float16")
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df.D = df.D.astype("float64")
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return df
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@pytest.fixture
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def mixed_int_frame():
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"""
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Fixture for DataFrame of different int types with index of unique strings
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Columns are ['A', 'B', 'C', 'D'].
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A B C D
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mUrCZ67juP 0 1 2 2
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rw99ACYaKS 0 1 0 0
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7QsEcpaaVU 0 1 1 1
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xkrimI2pcE 0 1 0 0
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dz01SuzoS8 0 1 255 255
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ccQkqOHX75 -1 1 0 0
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DN0iXaoDLd 0 1 0 0
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... .. .. ... ...
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Dfb141wAaQ 1 1 254 254
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IPD8eQOVu5 0 1 0 0
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CcaKulsCmv 0 1 0 0
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rIBa8gu7E5 0 1 0 0
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RP6peZmh5o 0 1 1 1
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NMb9pipQWQ 0 1 0 0
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PqgbJEzjib 0 1 3 3
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[30 rows x 4 columns]
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"""
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df = DataFrame({k: v.astype(int) for k, v in tm.getSeriesData().items()})
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df.A = df.A.astype("int32")
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df.B = np.ones(len(df.B), dtype="uint64")
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df.C = df.C.astype("uint8")
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df.D = df.C.astype("int64")
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return df
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@pytest.fixture
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def timezone_frame():
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"""
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Fixture for DataFrame of date_range Series with different time zones
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Columns are ['A', 'B', 'C']; some entries are missing
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A B C
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0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00
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1 2013-01-02 NaT NaT
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2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00
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"""
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df = DataFrame(
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{
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"A": date_range("20130101", periods=3),
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"B": date_range("20130101", periods=3, tz="US/Eastern"),
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"C": date_range("20130101", periods=3, tz="CET"),
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}
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)
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df.iloc[1, 1] = NaT
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df.iloc[1, 2] = NaT
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return df
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@pytest.fixture
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def uint64_frame():
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"""
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Fixture for DataFrame with uint64 values
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Columns are ['A', 'B']
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"""
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return DataFrame(
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{"A": np.arange(3), "B": [2**63, 2**63 + 5, 2**63 + 10]}, dtype=np.uint64
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)
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@pytest.fixture
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def simple_frame():
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"""
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Fixture for simple 3x3 DataFrame
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Columns are ['one', 'two', 'three'], index is ['a', 'b', 'c'].
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one two three
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a 1.0 2.0 3.0
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b 4.0 5.0 6.0
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c 7.0 8.0 9.0
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"""
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arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
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return DataFrame(arr, columns=["one", "two", "three"], index=["a", "b", "c"])
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@pytest.fixture
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def frame_of_index_cols():
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"""
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Fixture for DataFrame of columns that can be used for indexing
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Columns are ['A', 'B', 'C', 'D', 'E', ('tuple', 'as', 'label')];
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'A' & 'B' contain duplicates (but are jointly unique), the rest are unique.
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A B C D E (tuple, as, label)
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0 foo one a 0.608477 -0.012500 -1.664297
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1 foo two b -0.633460 0.249614 -0.364411
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2 foo three c 0.615256 2.154968 -0.834666
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3 bar one d 0.234246 1.085675 0.718445
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4 bar two e 0.533841 -0.005702 -3.533912
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"""
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df = DataFrame(
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{
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"A": ["foo", "foo", "foo", "bar", "bar"],
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"B": ["one", "two", "three", "one", "two"],
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"C": ["a", "b", "c", "d", "e"],
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"D": np.random.randn(5),
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"E": np.random.randn(5),
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("tuple", "as", "label"): np.random.randn(5),
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}
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)
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return df
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