arrays.IntervalArray.from_tuples([(0, 1), (2, 3)]) Out[2]: IntervalArray([(0, 1], (2, 3]], closed='right', dtype='interval[int64]') pandas 1.0.0 1.5. Backwards incompatible API changes 9 pandas: powerful IntervalArray.from_tuples([(0, 1), (2, 3)]) Out[30]: [(0, 1], (2, 3]] Length: 2, closed: right, dtype: interval[int64] 1.5.3 DataFrame.rename now only accepts one positional argument DataFrame name references variable before it is as- signed (GH29142) • Bug in Series.var() not computing the right value with a nullable integer dtype series not passing through ddof argument (GH29128) • Improved

from_tuples([(0, 4), (1, 5), (5, 8)]) In [43]: ii Out[43]: IntervalIndex([(0, 4], (1, 5], (5, 8]], closed='right', dtype='interval[int64]') 1.2. Backwards incompatible API changes 15 pandas: powerful Python data --------------------------------------------------------------------------- KeyError: Interval(2, 6, closed='right') Likewise, get_indexer() and get_indexer_non_unique() will also only return locations for exact Interval(2, 3, closed='right') In [7]: s.loc[pd.Interval(2, 3)] --------------------------------------------------------------------------- KeyError: Interval(2, 3, closed='right') The overlaps() method

from_tuples([(0, 4), (1, 5), (5, 8)]) In [43]: ii Out[43]: IntervalIndex([(0, 4], (1, 5], (5, 8]], closed='right', dtype='interval[int64]') 1.2. Backwards incompatible API changes 15 pandas: powerful Python data --------------------------------------------------------------------------- KeyError: Interval(2, 6, closed='right') Likewise, get_indexer() and get_indexer_non_unique() will also only return locations for exact Interval(2, 3, closed='right') In [7]: s.loc[pd.Interval(2, 3)] --------------------------------------------------------------------------- KeyError: Interval(2, 3, closed='right') The overlaps() method

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data. In pandas, a data table lend more semantic meaning to the data; i.e., for a particular data set, there is likely to be a “right” way to orient the data. The goal, then, is to reduce the amount of mental effort required to code air_quality = pd.merge(air_quality, air_quality_parameters, ....: how='left', left_on='parameter', right_on='id') ....: In [25]: air_quality.head() Out[25]: date.utc location parameter value ... coordinates

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data. In pandas, a data table lend more semantic meaning to the data; i.e., for a particular data set, there is likely to be a “right” way to orient the data. The goal, then, is to reduce the amount of mental effort required to code air_quality = pd.merge(air_quality, air_quality_parameters, ....: how='left', left_on='parameter', right_on='id') ....: In [25]: air_quality.head() Out[25]: date.utc location parameter ... id ␣ ˓→ description

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean and process your data. In pandas, a data table lend more semantic meaning to the data; i.e., for a particular data set there is likely to be a “right” way to orient the data. The goal, then, is to reduce the amount of mental effort required to code air_quality = pd.merge(air_quality, air_quality_parameters, ....: how='left', left_on='parameter', right_on='id') ....: In [25]: air_quality.head() Out[25]: date.utc location parameter ... id ˓→ description

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data. In pandas, a data table lend more semantic meaning to the data; i.e., for a particular data set, there is likely to be a “right” way to orient the data. The goal, then, is to reduce the amount of mental effort required to code air_quality = pd.merge(air_quality, air_quality_parameters, ....: how='left', left_on='parameter', right_on='id') ....: In [25]: air_quality.head() Out[25]: date.utc location parameter ... id ˓→ description

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean and process your data. In pandas, a data table lend more semantic meaning to the data; i.e., for a particular data set there is likely to be a “right” way to orient the data. The goal, then, is to reduce the amount of mental effort required to code air_quality = pd.merge(air_quality, air_quality_parameters, ....: how='left', left_on='parameter', right_on='id') ....: In [25]: air_quality.head() Out[25]: date.utc location parameter value coordinates

Series: In [36]: ser.array Out[36]: IntervalArray([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]], closed='right', dtype='interval[int64]') In [37]: pser.array \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ index=index_left) ....: In [40]: index_right = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'), ....: ('K2', 'Y2'), ('K2', 'Y3')], ....: names=['key', 'Y']) ....: In [41]: right = pd.DataFrame({'C': ['C0' ['C0', 'C1', 'C2', 'C3'], ....: 'D': ['D0', 'D1', 'D2', 'D3']}, index=index_right) ....: In [42]: left.join(right) Out[42]: A B C D key X Y K0 X0 Y0 A0 B0 C0 D0 X1 Y0 A1 B1 C0 D0 K1 X2 Y1 A2 B2 C1

OUTER JOIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1201 32.4.3 RIGHT JOIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1201 32 Reshaping • Error message in pd.merge_asof() for key datatype mismatch now includes datatype of left and right key (GH18068) • Bug in pd.concat when empty and non-empty DataFrames or Series are concatenated (GH18178 interval_range(start=0, end=4, periods=6) Out[2]: IntervalIndex([(0, 1], (1, 2], (2, 3]] closed='right', dtype='interval[int64]') In [3]: pd.period_range(start='2017Q1', end='2017Q4', periods=6, freq='Q')