New benchmarks for parsing dates with dayfirst=True and format='%d-%m-%Y'

asv_bench/benchmarks/io/csv.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 import pandas.util.testing as tm
-from pandas import DataFrame, Categorical, date_range, read_csv
+from pandas import DataFrame, Categorical, date_range, read_csv, to_datetime
 from pandas.io.parsers import _parser_defaults
 from io import StringIO
 
@@ -273,7 +273,7 @@ def mem_parser_chunks(self):
 
 class ReadCSVParseSpecialDate(StringIORewind):
     params = (['mY', 'mdY', 'hm'],)
-    params_name = ['value']
+    param_names = ['value']
     objects = {
         'mY': '01-2019\n10-2019\n02/2000\n',
         'mdY': '12/02/2010\n',
@@ -290,4 +290,29 @@ def time_read_special_date(self, value):
                  names=['Date'], parse_dates=['Date'])
 
 
+class ParseDateComparison(StringIORewind):
+    params = ([False, True],)
+    param_names = ['cache_dates']
+
+    def setup(self, cache_dates):
+        count_elem = 10000
+        data = '12-02-2010\n' * count_elem
+        self.StringIO_input = StringIO(data)
+
+    def time_read_csv_dayfirst(self, cache_dates):
+        read_csv(self.data(self.StringIO_input), sep=',', header=None,
+                 names=['Date'], parse_dates=['Date'], cache_dates=cache_dates,
+                 dayfirst=True)
+
+    def time_to_datetime_dayfirst(self, cache_dates):
+        df = read_csv(self.data(self.StringIO_input),
+                      dtype={'date': str}, names=['date'])
+        to_datetime(df['date'], cache=cache_dates, dayfirst=True)
+
+    def time_to_datetime_format_DD_MM_YYYY(self, cache_dates):
+        df = read_csv(self.data(self.StringIO_input),
+                      dtype={'date': str}, names=['date'])
+        to_datetime(df['date'], cache=cache_dates, format='%d-%m-%Y')
+
+
 from ..pandas_vb_common import setup  # noqa: F401