TST: add concrete examples of dataframe fixtures to docstrings (#26593)

pandas/tests/frame/conftest.py

@@ -11,6 +11,25 @@ def float_frame():
     Fixture for DataFrame of floats with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D'].
+
+                       A         B         C         D
+    P7GACiRnxd -0.465578 -0.361863  0.886172 -0.053465
+    qZKh6afn8n -0.466693 -0.373773  0.266873  1.673901
+    tkp0r6Qble  0.148691 -0.059051  0.174817  1.598433
+    wP70WOCtv8  0.133045 -0.581994 -0.992240  0.261651
+    M2AeYQMnCz -1.207959 -0.185775  0.588206  0.563938
+    QEPzyGDYDo -0.381843 -0.758281  0.502575 -0.565053
+    r78Jwns6dn -0.653707  0.883127  0.682199  0.206159
+    ...              ...       ...       ...       ...
+    IHEGx9NO0T -0.277360  0.113021 -1.018314  0.196316
+    lPMj8K27FA -1.313667 -0.604776 -1.305618 -0.863999
+    qa66YMWQa5  1.110525  0.475310 -0.747865  0.032121
+    yOa0ATsmcE -0.431457  0.067094  0.096567 -0.264962
+    65znX3uRNG  1.528446  0.160416 -0.109635 -0.032987
+    eCOBvKqf3e  0.235281  1.622222  0.781255  0.392871
+    xSucinXxuV -1.263557  0.252799 -0.552247  0.400426
+
+    [30 rows x 4 columns]
     """
     return DataFrame(tm.getSeriesData())
 
@@ -21,6 +40,25 @@ def float_frame_with_na():
     Fixture for DataFrame of floats with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D']; some entries are missing
+
+                       A         B         C         D
+    ABwBzA0ljw -1.128865 -0.897161  0.046603  0.274997
+    DJiRzmbyQF  0.728869  0.233502  0.722431 -0.890872
+    neMgPD5UBF  0.486072 -1.027393 -0.031553  1.449522
+    0yWA4n8VeX -1.937191 -1.142531  0.805215 -0.462018
+    3slYUbbqU1  0.153260  1.164691  1.489795 -0.545826
+    soujjZ0A08       NaN       NaN       NaN       NaN
+    7W6NLGsjB9       NaN       NaN       NaN       NaN
+    ...              ...       ...       ...       ...
+    uhfeaNkCR1 -0.231210 -0.340472  0.244717 -0.901590
+    n6p7GYuBIV -0.419052  1.922721 -0.125361 -0.727717
+    ZhzAeY6p1y  1.234374 -1.425359 -0.827038 -0.633189
+    uWdPsORyUh  0.046738 -0.980445 -1.102965  0.605503
+    3DJA6aN590 -0.091018 -1.684734 -1.100900  0.215947
+    2GBPAzdbMk -2.883405 -1.021071  1.209877  1.633083
+    sHadBoyVHw -2.223032 -0.326384  0.258931  0.245517
+
+    [30 rows x 4 columns]
     """
     df = DataFrame(tm.getSeriesData())
     # set some NAs
@@ -35,6 +73,25 @@ def bool_frame_with_na():
     Fixture for DataFrame of booleans with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D']; some entries are missing
+
+                    A      B      C      D
+    zBZxY2IDGd  False  False  False  False
+    IhBWBMWllt  False   True   True   True
+    ctjdvZSR6R   True  False   True   True
+    AVTujptmxb  False   True  False   True
+    G9lrImrSWq  False  False  False   True
+    sFFwdIUfz2    NaN    NaN    NaN    NaN
+    s15ptEJnRb    NaN    NaN    NaN    NaN
+    ...           ...    ...    ...    ...
+    UW41KkDyZ4   True   True  False  False
+    l9l6XkOdqV   True  False  False  False
+    X2MeZfzDYA  False   True  False  False
+    xWkIKU7vfX  False   True  False   True
+    QOhL6VmpGU  False  False  False   True
+    22PwkRJdat  False   True  False  False
+    kfboQ3VeIK   True  False   True  False
+
+    [30 rows x 4 columns]
     """
     df = DataFrame(tm.getSeriesData()) > 0
     df = df.astype(object)
@@ -50,6 +107,25 @@ def int_frame():
     Fixture for DataFrame of ints with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D']
+
+                A  B  C  D
+    vpBeWjM651  1  0  1  0
+    5JyxmrP1En -1  0  0  0
+    qEDaoD49U2 -1  1  0  0
+    m66TkTfsFe  0  0  0  0
+    EHPaNzEUFm -1  0 -1  0
+    fpRJCevQhi  2  0  0  0
+    OlQvnmfi3Q  0  0 -2  0
+    ...        .. .. .. ..
+    uB1FPlz4uP  0  0  0  1
+    EcSe6yNzCU  0  0 -1  0
+    L50VudaiI8 -1  1 -2  0
+    y3bpw4nwIp  0 -1  0  0
+    H0RdLLwrCT  1  1  0  0
+    rY82K0vMwm  0  0  0  0
+    1OPIUjnkjk  2  0  0  0
+
+    [30 rows x 4 columns]
     """
     df = DataFrame({k: v.astype(int) for k, v in tm.getSeriesData().items()})
     # force these all to int64 to avoid platform testing issues
@@ -62,6 +138,25 @@ def datetime_frame():
     Fixture for DataFrame of floats with DatetimeIndex
 
     Columns are ['A', 'B', 'C', 'D']
+
+                       A         B         C         D
+    2000-01-03 -1.122153  0.468535  0.122226  1.693711
+    2000-01-04  0.189378  0.486100  0.007864 -1.216052
+    2000-01-05  0.041401 -0.835752 -0.035279 -0.414357
+    2000-01-06  0.430050  0.894352  0.090719  0.036939
+    2000-01-07 -0.620982 -0.668211 -0.706153  1.466335
+    2000-01-10 -0.752633  0.328434 -0.815325  0.699674
+    2000-01-11 -2.236969  0.615737 -0.829076 -1.196106
+    ...              ...       ...       ...       ...
+    2000-02-03  1.642618 -0.579288  0.046005  1.385249
+    2000-02-04 -0.544873 -1.160962 -0.284071 -1.418351
+    2000-02-07 -2.656149 -0.601387  1.410148  0.444150
+    2000-02-08 -1.201881 -1.289040  0.772992 -1.445300
+    2000-02-09  1.377373  0.398619  1.008453 -0.928207
+    2000-02-10  0.473194 -0.636677  0.984058  0.511519
+    2000-02-11 -0.965556  0.408313 -1.312844 -0.381948
+
+    [30 rows x 4 columns]
     """
     return DataFrame(tm.getTimeSeriesData())
 
@@ -72,6 +167,25 @@ def float_string_frame():
     Fixture for DataFrame of floats and strings with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D', 'foo'].
+
+                       A         B         C         D  foo
+    w3orJvq07g -1.594062 -1.084273 -1.252457  0.356460  bar
+    PeukuVdmz2  0.109855 -0.955086 -0.809485  0.409747  bar
+    ahp2KvwiM8 -1.533729 -0.142519 -0.154666  1.302623  bar
+    3WSJ7BUCGd  2.484964  0.213829  0.034778 -2.327831  bar
+    khdAmufk0U -0.193480 -0.743518 -0.077987  0.153646  bar
+    LE2DZiFlrE -0.193566 -1.343194 -0.107321  0.959978  bar
+    HJXSJhVn7b  0.142590  1.257603 -0.659409 -0.223844  bar
+    ...              ...       ...       ...       ...  ...
+    9a1Vypttgw -1.316394  1.601354  0.173596  1.213196  bar
+    h5d1gVFbEy  0.609475  1.106738 -0.155271  0.294630  bar
+    mK9LsTQG92  1.303613  0.857040 -1.019153  0.369468  bar
+    oOLksd9gKH  0.558219 -0.134491 -0.289869 -0.951033  bar
+    9jgoOjKyHg  0.058270 -0.496110 -0.413212 -0.852659  bar
+    jZLDHclHAO  0.096298  1.267510  0.549206 -0.005235  bar
+    lR0nxDp1C2 -2.119350 -0.794384  0.544118  0.145849  bar
+
+    [30 rows x 5 columns]
     """
     df = DataFrame(tm.getSeriesData())
     df['foo'] = 'bar'
@@ -84,6 +198,25 @@ def mixed_float_frame():
     Fixture for DataFrame of different float types with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D'].
+
+                       A         B         C         D
+    GI7bbDaEZe -0.237908 -0.246225 -0.468506  0.752993
+    KGp9mFepzA -1.140809 -0.644046 -1.225586  0.801588
+    VeVYLAb1l2 -1.154013 -1.677615  0.690430 -0.003731
+    kmPME4WKhO  0.979578  0.998274 -0.776367  0.897607
+    CPyopdXTiz  0.048119 -0.257174  0.836426  0.111266
+    0kJZQndAj0  0.274357 -0.281135 -0.344238  0.834541
+    tqdwQsaHG8 -0.979716 -0.519897  0.582031  0.144710
+    ...              ...       ...       ...       ...
+    7FhZTWILQj -2.906357  1.261039 -0.780273 -0.537237
+    4pUDPM4eGq -2.042512 -0.464382 -0.382080  1.132612
+    B8dUgUzwTi -1.506637 -0.364435  1.087891  0.297653
+    hErlVYjVv9  1.477453 -0.495515 -0.713867  1.438427
+    1BKN3o7YLs  0.127535 -0.349812 -0.881836  0.489827
+    9S4Ekn7zga  1.445518 -2.095149  0.031982  0.373204
+    xN1dNn6OV6  1.425017 -0.983995 -0.363281 -0.224502
+
+    [30 rows x 4 columns]
     """
     df = DataFrame(tm.getSeriesData())
     df.A = df.A.astype('float32')
@@ -99,6 +232,25 @@ def mixed_int_frame():
     Fixture for DataFrame of different int types with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D'].
+
+                A  B    C    D
+    mUrCZ67juP  0  1    2    2
+    rw99ACYaKS  0  1    0    0
+    7QsEcpaaVU  0  1    1    1
+    xkrimI2pcE  0  1    0    0
+    dz01SuzoS8  0  1  255  255
+    ccQkqOHX75 -1  1    0    0
+    DN0iXaoDLd  0  1    0    0
+    ...        .. ..  ...  ...
+    Dfb141wAaQ  1  1  254  254
+    IPD8eQOVu5  0  1    0    0
+    CcaKulsCmv  0  1    0    0
+    rIBa8gu7E5  0  1    0    0
+    RP6peZmh5o  0  1    1    1
+    NMb9pipQWQ  0  1    0    0
+    PqgbJEzjib  0  1    3    3
+
+    [30 rows x 4 columns]
     """
     df = DataFrame({k: v.astype(int) for k, v in tm.getSeriesData().items()})
     df.A = df.A.astype('int32')
@@ -114,6 +266,11 @@ def timezone_frame():
     Fixture for DataFrame of date_range Series with different time zones
 
     Columns are ['A', 'B', 'C']; some entries are missing
+
+               A                         B                         C
+    0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00
+    1 2013-01-02                       NaT                       NaT
+    2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00
     """
     df = DataFrame({'A': date_range('20130101', periods=3),
                     'B': date_range('20130101', periods=3,
@@ -131,6 +288,11 @@ def simple_frame():
     Fixture for simple 3x3 DataFrame
 
     Columns are ['one', 'two', 'three'], index is ['a', 'b', 'c'].
+
+       one  two  three
+    a  1.0  2.0    3.0
+    b  4.0  5.0    6.0
+    c  7.0  8.0    9.0
     """
     arr = np.array([[1., 2., 3.],
                     [4., 5., 6.],
@@ -147,6 +309,13 @@ def frame_of_index_cols():
 
     Columns are ['A', 'B', 'C', 'D', 'E', ('tuple', 'as', 'label')];
     'A' & 'B' contain duplicates (but are jointly unique), the rest are unique.
+
+         A      B  C         D         E  (tuple, as, label)
+    0  foo    one  a  0.608477 -0.012500           -1.664297
+    1  foo    two  b -0.633460  0.249614           -0.364411
+    2  foo  three  c  0.615256  2.154968           -0.834666
+    3  bar    one  d  0.234246  1.085675            0.718445
+    4  bar    two  e  0.533841 -0.005702           -3.533912
     """
     df = DataFrame({'A': ['foo', 'foo', 'foo', 'bar', 'bar'],
                     'B': ['one', 'two', 'three', 'one', 'two'],