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TST: add concrete examples of dataframe fixtures to docstrings (pandas-dev#26593)
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‎pandas/tests/frame/conftest.py

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@@ -11,6 +11,25 @@ def float_frame():
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Fixture for DataFrame of floats 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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P7GACiRnxd -0.465578 -0.361863 0.886172 -0.053465
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qZKh6afn8n -0.466693 -0.373773 0.266873 1.673901
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tkp0r6Qble 0.148691 -0.059051 0.174817 1.598433
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wP70WOCtv8 0.133045 -0.581994 -0.992240 0.261651
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M2AeYQMnCz -1.207959 -0.185775 0.588206 0.563938
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QEPzyGDYDo -0.381843 -0.758281 0.502575 -0.565053
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r78Jwns6dn -0.653707 0.883127 0.682199 0.206159
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... ... ... ... ...
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IHEGx9NO0T -0.277360 0.113021 -1.018314 0.196316
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lPMj8K27FA -1.313667 -0.604776 -1.305618 -0.863999
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qa66YMWQa5 1.110525 0.475310 -0.747865 0.032121
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yOa0ATsmcE -0.431457 0.067094 0.096567 -0.264962
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65znX3uRNG 1.528446 0.160416 -0.109635 -0.032987
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eCOBvKqf3e 0.235281 1.622222 0.781255 0.392871
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xSucinXxuV -1.263557 0.252799 -0.552247 0.400426
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[30 rows x 4 columns]
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"""
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return DataFrame(tm.getSeriesData())
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@@ -21,6 +40,25 @@ def float_frame_with_na():
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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
@@ -35,6 +73,25 @@ def bool_frame_with_na():
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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)
@@ -50,6 +107,25 @@ def int_frame():
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Fixture for DataFrame of ints 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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vpBeWjM651 1 0 1 0
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5JyxmrP1En -1 0 0 0
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qEDaoD49U2 -1 1 0 0
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m66TkTfsFe 0 0 0 0
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EHPaNzEUFm -1 0 -1 0
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fpRJCevQhi 2 0 0 0
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OlQvnmfi3Q 0 0 -2 0
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... .. .. .. ..
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uB1FPlz4uP 0 0 0 1
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EcSe6yNzCU 0 0 -1 0
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L50VudaiI8 -1 1 -2 0
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y3bpw4nwIp 0 -1 0 0
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H0RdLLwrCT 1 1 0 0
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rY82K0vMwm 0 0 0 0
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1OPIUjnkjk 2 0 0 0
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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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# force these all to int64 to avoid platform testing issues
@@ -62,6 +138,25 @@ def datetime_frame():
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Fixture for DataFrame of floats with DatetimeIndex
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Columns are ['A', 'B', 'C', 'D']
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A B C D
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2000-01-03 -1.122153 0.468535 0.122226 1.693711
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2000-01-04 0.189378 0.486100 0.007864 -1.216052
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2000-01-05 0.041401 -0.835752 -0.035279 -0.414357
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2000-01-06 0.430050 0.894352 0.090719 0.036939
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2000-01-07 -0.620982 -0.668211 -0.706153 1.466335
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2000-01-10 -0.752633 0.328434 -0.815325 0.699674
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2000-01-11 -2.236969 0.615737 -0.829076 -1.196106
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... ... ... ... ...
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2000-02-03 1.642618 -0.579288 0.046005 1.385249
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2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
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2000-02-07 -2.656149 -0.601387 1.410148 0.444150
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2000-02-08 -1.201881 -1.289040 0.772992 -1.445300
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2000-02-09 1.377373 0.398619 1.008453 -0.928207
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2000-02-10 0.473194 -0.636677 0.984058 0.511519
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2000-02-11 -0.965556 0.408313 -1.312844 -0.381948
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[30 rows x 4 columns]
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"""
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return DataFrame(tm.getTimeSeriesData())
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@@ -72,6 +167,25 @@ def float_string_frame():
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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'
@@ -84,6 +198,25 @@ def mixed_float_frame():
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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')
@@ -99,6 +232,25 @@ def mixed_int_frame():
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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')
@@ -114,6 +266,11 @@ def timezone_frame():
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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({'A': date_range('20130101', periods=3),
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'B': date_range('20130101', periods=3,
@@ -131,6 +288,11 @@ def simple_frame():
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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., 2., 3.],
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[4., 5., 6.],
@@ -147,6 +309,13 @@ def frame_of_index_cols():
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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({'A': ['foo', 'foo', 'foo', 'bar', 'bar'],
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'B': ['one', 'two', 'three', 'one', 'two'],

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