99
1010from pandas import NaT , Series , Timestamp , date_range
1111from pandas .api .types import CategoricalDtype
12- from pandas .tests .series .common import TestData
1312import pandas .util .testing as tm
1413from pandas .util .testing import assert_series_equal
1514
1615
17- class TestSeriesRank ( TestData ) :
16+ class TestSeriesRank :
1817 s = Series ([1 , 3 , 4 , 2 , np .nan , 2 , 1 , 5 , np .nan , 3 ])
1918
2019 results = {
@@ -25,20 +24,20 @@ class TestSeriesRank(TestData):
2524 "dense" : np .array ([1 , 3 , 4 , 2 , np .nan , 2 , 1 , 5 , np .nan , 3 ]),
2625 }
2726
28- def test_rank (self ):
27+ def test_rank (self , datetime_series ):
2928 pytest .importorskip ("scipy.stats.special" )
3029 rankdata = pytest .importorskip ("scipy.stats.rankdata" )
3130
32- self . ts [::2 ] = np .nan
33- self . ts [:10 ][::3 ] = 4.0
31+ datetime_series [::2 ] = np .nan
32+ datetime_series [:10 ][::3 ] = 4.0
3433
35- ranks = self . ts .rank ()
36- oranks = self . ts .astype ("O" ).rank ()
34+ ranks = datetime_series .rank ()
35+ oranks = datetime_series .astype ("O" ).rank ()
3736
3837 assert_series_equal (ranks , oranks )
3938
40- mask = np .isnan (self . ts )
41- filled = self . ts .fillna (np .inf )
39+ mask = np .isnan (datetime_series )
40+ filled = datetime_series .fillna (np .inf )
4241
4342 # rankdata returns a ndarray
4443 exp = Series (rankdata (filled ), index = filled .index , name = "ts" )
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