PERF: better perf on _ensure_data in core/algorithms, helping perf of unique, duplicated, factorize

[jreback]

xref #16043

   before     after       ratio
  [6f525eec] [3cc49f80]
+   82.43μs   115.72μs      1.40  timeseries.DatetimeIndex.time_unique
+  120.41ms   161.89ms      1.34  frame_methods.frame_duplicated.time_frame_duplicated_wide
+    7.78ms    10.43ms      1.34  groupby.GroupBySuite.time_unique('int', 100)
+   12.13ms    15.91ms      1.31  groupby.GroupBySuite.time_unique('float', 100)
+  707.18ms   916.55ms      1.30  groupby.GroupBySuite.time_unique('int', 10000)
+     1.24s      1.49s      1.20  groupby.GroupBySuite.time_unique('float', 10000)
+  400.17μs   448.59μs      1.12  groupby.GroupBySuite.time_nunique('float', 100)
-    8.64ms     2.46ms      0.28  period.Algorithms.time_drop_duplicates_pseries

so lowers the diff with 0.19.2 a bit.

[codecov]

Codecov Report

Merging #16046 into master will increase coverage by 0.02%.
The diff coverage is 98.57%.

@@            Coverage Diff             @@
##           master   #16046      +/-   ##
==========================================
+ Coverage   90.76%   90.79%   +0.02%     
==========================================
  Files         155      156       +1     
  Lines       50484    50534      +50     
==========================================
+ Hits        45824    45883      +59     
+ Misses       4660     4651       -9

Flag	Coverage Δ
#multiple	88.56% <98.57%> (+0.02%)	⬆️
#single	40.44% <65.71%> (+0.02%)	⬆️

Impacted Files	Coverage Δ
pandas/core/algorithms.py	94.41% <100%> (-0.05%)	⬇️
pandas/core/dtypes/common.py	93.63% <98%> (+0.36%)	⬆️
pandas/core/dtypes/cast.py	86.89% <0%> (-1.37%)	⬇️
pandas/util/testing.py	79.63% <0%> (-0.19%)	⬇️
pandas/compat/pickle_compat.py	69.51% <0%> (ø)	⬆️
pandas/core/indexes/base.py	96.19% <0%> (ø)	⬆️
pandas/core/api.py	100% <0%> (ø)	⬆️
pandas/tseries/api.py	100% <0%> (ø)	⬆️
pandas/tseries/period.py
pandas/tseries/tdi.py
... and 18 more

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