scalar type. In [48]: df = pd.DataFrame({"A": ['a', 'b']}, dtype='category', ....: index=pd.date_range('2000', periods=2)) ....: In [49]: df Out[49]: A 2000-01-01 a 2000-01-02 b (continues on next previously deprecated keyword “time_rule” from (non-public) offsets.generate_range, which has been moved to core.arrays._ranges.generate_range() (GH24157) • DataFrame.loc() or Series.loc() with listlike indexers from the DatetimeIndex, TimedeltaIndex, and PeriodIndex constructors; use date_range(), timedelta_range(), and period_range() instead (GH23919) • Removed the previously deprecated keyword “verify_integrity”

MultiIndex. (GH13480): The repr now looks like this: In [8]: pd.MultiIndex.from_product([['a', 'abc'], range(500)]) Out[8]: MultiIndex([( 'a', 0), ( 'a', 1), ( 'a', 2), ( 'a', 3), ( 'a', 4), ( 'a', 5), the output more difficult to navigate. For example (limiting the range to 5): In [1]: pd.MultiIndex.from_product([['a', 'abc'], range(5)]) Out[1]: MultiIndex(levels=[['a', 'abc'], [0, 1, 2, 3]], ...: commutative, such that A.union(B) == B.union(A) (GH23525). Previous behavior: In [1]: pd.period_range('19910905', periods=2).union(pd.Int64Index([1, 2, 3])) ... ValueError: can only call with other PeriodIndex-ed

MultiIndex. (GH13480): The repr now looks like this: In [8]: pd.MultiIndex.from_product([['a', 'abc'], range(500)]) Out[8]: MultiIndex([( 'a', 0), ( 'a', 1), ( 'a', 2), ( 'a', 3), ( 'a', 4), ( 'a', 5), the output more difficult to navigate. For example (limiting the range to 5): In [1]: pd.MultiIndex.from_product([['a', 'abc'], range(5)]) Out[1]: MultiIndex(levels=[['a', 'abc'], [0, 1, 2, 3]], ...: commutative, such that A.union(B) == B.union(A) (GH23525). Previous behavior: In [1]: pd.period_range('19910905', periods=2).union(pd.Int64Index([1, 2, 3])) ... ValueError: can only call with other PeriodIndex-ed

analysis toolkit, Release 1.1.1 Data sets do not only contain numerical data. pandas provides a wide range of functions to cleaning textual data and extract useful information from it. To introduction tutorial saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Many of [15]: import string In [16]: baseball = pd.DataFrame( ....: {'team': ["team %d" % (x + 1) for x in range(5)] * 5, ....: 'player': random.sample(list(string.ascii_lowercase), 25), ....: 'batting avg': np

analysis toolkit, Release 1.1.0 Data sets do not only contain numerical data. pandas provides a wide range of functions to cleaning textual data and extract useful information from it. To introduction tutorial saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Many of [15]: import string In [16]: baseball = pd.DataFrame( ....: {'team': ["team %d" % (x + 1) for x in range(5)] * 5, ....: 'player': random.sample(list(string.ascii_lowercase), 25), ....: 'batting avg': np

guide Straight to tutorial... Data sets do not only contain numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it. To introduction tutorial saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting, and lagging. Many of import string In [16]: baseball = pd.DataFrame( ....: { ....: "team": ["team %d" % (x + 1) for x in range(5)] * 5, ....: "player": random.sample(list(string.ascii_lowercase), 25), ....: "batting avg": np

guide Straight to tutorial... Data sets do not only contain numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it. To introduction tutorial saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting, and lagging. 12 Chapter import string In [16]: baseball = pd.DataFrame( ....: { ....: "team": ["team %d" % (x + 1) for x in range(5)] * 5, ....: "player": random.sample(list(string.ascii_lowercase), 25), ....: "batting avg": np

guide Straight to tutorial... Data sets do not only contain numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it. To introduction tutorial saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting, and lagging. 12 Chapter import string In [16]: baseball = pd.DataFrame( ....: { ....: "team": ["team %d" % (x + 1) for x in range(5)] * 5, ....: "player": random.sample(list(string.ascii_lowercase), 25), ....: "batting avg": np

Enhancements 5 pandas: powerful Python data analysis toolkit, Release 0.24.0 In [15]: idx = pd.period_range('2000', periods=4) In [16]: idx.array Out[16]: ['2000-01-01', '2000-01-02', '2000-01-03' pandas: powerful Python data analysis toolkit, Release 0.24.0 In [30]: ser = pd.Series(pd.interval_range(0, 5)) In [31]: ser Out[31]: 0 (0, 1] 1 (1, 2] 2 (2, 3] 3 (3, 4] 4 (4, 5] Length: 5, dtype: \\\\\\\\\\\\\\\\\\Out[32]: ˓→interval[int64] For periods: In [33]: pser = pd.Series(pd.period_range("2000", freq="D", periods=5)) In [34]: pser Out[34]: 0 2000-01-01 1 2000-01-02 2 2000-01-03 3

5 Straight to tutorial... Data sets do not only contain numerical data. Pandas provides a wide range of functions to cleaning textual data and extract useful information from it. To introduction tutorial saving / loading data from the ultrafast HDF5 format • Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Many of DataFrame by passing a NumPy array, with a datetime index and labeled columns: In [5]: dates = pd.date_range('20130101', periods=6) In [6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03'