value_counts for float dtype (GH10821) • Enable infer_datetime_format in to_datetime when date components do not have 0 padding (GH11142) • Regression from 0.16.1 in constructing DataFrame from nested match that of datetime.timedelta. Further, the component values are still available through the .components accessor. This affects the .seconds and .microseconds accessors, and removes the .hours, .minutes powerful Python data analysis toolkit, Release 0.17.0 Using .components allows the full component access In [37]: t.components Out[37]: Components(days=1L, hours=10L, minutes=11L, seconds=12L, milliseconds=100L

1 dtype: int64 In [61]: s.dt.seconds Out[61]: 0 5 1 6 2 7 3 8 dtype: int64 In [62]: s.dt.components Out[62]: days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 0 5 0 0 0 1 argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) this. • Chapter 5: Here you get to find out if it’s cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. • Chapter 6: Strings with pandas are great

1 dtype: int64 In [61]: s.dt.seconds Out[61]: 0 5 1 6 2 7 3 8 dtype: int64 In [62]: s.dt.components Out[62]: days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 0 5 0 0 0 16 argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) this. • Chapter 5: Here you get to find out if it’s cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. • Chapter 6: Strings with pandas are great

files to create a single DataFrame . . . . . . . . . . . . . . . . . 480 7.9.1.2 Parsing date components in multi-columns . . . . . . . . . . . . . . . . . . . . . . 481 7.9.1.3 Skip row between header Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 858 19.7 Time/Date Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859 19.8 DateOffset nanoseconds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1439 34.3.13.42pandas.Series.dt.components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1439 34.3.13.43pandas.Series.dt.to_pytimedelta

Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 20.6.4 Time/Date Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 20.7 DateOffset nanoseconds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1261 pandas.Series.dt.components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1261 pandas.Series.dt.to_pytimedelta where . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1615 35.8.2 Categorical Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1616 35.9 MultiIndex

Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 20.6.4 Time/Date Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 20.7 DateOffset nanoseconds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1264 pandas.Series.dt.components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1264 pandas.Series.dt.to_pytimedelta where . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1619 35.8.2 Categorical Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1619 35.9 MultiIndex

files to create a single DataFrame . . . . . . . . . . . . . . . . . 453 7.9.1.2 Parsing date components in multi-columns . . . . . . . . . . . . . . . . . . . . . . 453 7.9.1.3 Skip row between header Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 19.7 Time/Date Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 19.8 DateOffset nanoseconds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1380 34.3.13.42pandas.Series.dt.components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1380 34.3.13.43pandas.Series.dt.to_pytimedelta

files to create a single DataFrame . . . . . . . . . . . . . . . . . 451 7.9.1.2 Parsing date components in multi-columns . . . . . . . . . . . . . . . . . . . . . . 451 7.9.1.3 Skip row between header Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 19.7 Time/Date Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 19.8 DateOffset nanoseconds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1360 34.3.13.42pandas.Series.dt.components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1360 34.3.13.43pandas.Series.dt.to_pytimedelta

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 2.20.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 820 2.20.8 DateOffset by the dt accessor. An overview of the existing date properties is given in the time and date components overview table. More details about the dt accessor to return datetime like properties are explained dtype: int64 In [296]: s.dt.seconds Out[296]: 0 5 1 6 2 7 3 8 dtype: int64 In [297]: s.dt.components Out[297]: days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 0 5 0 0 0 1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846 2.20.7 Time/date components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855 2.20.8 DateOffset by the dt accessor. An overview of the existing date properties is given in the time and date components overview table. More details about the dt accessor to return datetime like properties are explained dtype: int64 In [296]: s.dt.seconds Out[296]: 0 5 1 6 2 7 3 8 dtype: int64 In [297]: s.dt.components Out[297]: days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 0 5 0 0 0 1