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‎doc/source/whatsnew/v0.19.0.rst

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@@ -76,51 +76,51 @@ This also illustrates using the ``by`` parameter to group data before merging.
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.. ipython:: python
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trades = pd.DataFrame({
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'time': pd.to_datetime(['20160525 13:30:00.023',
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'20160525 13:30:00.038',
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'20160525 13:30:00.048',
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'20160525 13:30:00.048',
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'20160525 13:30:00.048']),
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'ticker': ['MSFT', 'MSFT',
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'GOOG', 'GOOG', 'AAPL'],
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'price': [51.95, 51.95,
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720.77, 720.92, 98.00],
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'quantity': [75, 155,
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100, 100, 100]},
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columns=['time', 'ticker', 'price', 'quantity'])
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quotes = pd.DataFrame({
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'time': pd.to_datetime(['20160525 13:30:00.023',
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'20160525 13:30:00.023',
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'20160525 13:30:00.030',
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'20160525 13:30:00.041',
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'20160525 13:30:00.048',
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'20160525 13:30:00.049',
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'20160525 13:30:00.072',
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'20160525 13:30:00.075']),
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'ticker': ['GOOG', 'MSFT', 'MSFT', 'MSFT',
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'GOOG', 'AAPL', 'GOOG', 'MSFT'],
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'bid': [720.50, 51.95, 51.97, 51.99,
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720.50, 97.99, 720.50, 52.01],
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'ask': [720.93, 51.96, 51.98, 52.00,
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720.93, 98.01, 720.88, 52.03]},
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columns=['time', 'ticker', 'bid', 'ask'])
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trades = pd.DataFrame({
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'time': pd.to_datetime(['20160525 13:30:00.023',
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'20160525 13:30:00.038',
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'20160525 13:30:00.048',
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'20160525 13:30:00.048',
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'20160525 13:30:00.048']),
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'ticker': ['MSFT', 'MSFT',
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'GOOG', 'GOOG', 'AAPL'],
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'price': [51.95, 51.95,
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720.77, 720.92, 98.00],
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'quantity': [75, 155,
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100, 100, 100]},
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columns=['time', 'ticker', 'price', 'quantity'])
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quotes = pd.DataFrame({
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'time': pd.to_datetime(['20160525 13:30:00.023',
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'20160525 13:30:00.023',
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'20160525 13:30:00.030',
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'20160525 13:30:00.041',
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'20160525 13:30:00.048',
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'20160525 13:30:00.049',
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'20160525 13:30:00.072',
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'20160525 13:30:00.075']),
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'ticker': ['GOOG', 'MSFT', 'MSFT', 'MSFT',
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'GOOG', 'AAPL', 'GOOG', 'MSFT'],
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'bid': [720.50, 51.95, 51.97, 51.99,
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720.50, 97.99, 720.50, 52.01],
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'ask': [720.93, 51.96, 51.98, 52.00,
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720.93, 98.01, 720.88, 52.03]},
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columns=['time', 'ticker', 'bid', 'ask'])
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.. ipython:: python
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trades
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quotes
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trades
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quotes
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An asof merge joins on the ``on``, typically a datetimelike field, which is ordered, and
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in this case we are using a grouper in the ``by`` field. This is like a left-outer join, except
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that forward filling happens automatically taking the most recent non-NaN value.
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.. ipython:: python
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pd.merge_asof(trades, quotes,
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on='time',
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by='ticker')
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pd.merge_asof(trades, quotes,
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on='time',
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by='ticker')
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This returns a merged DataFrame with the entries in the same order as the original left
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passed DataFrame (``trades`` in this case), with the fields of the ``quotes`` merged.
@@ -135,17 +135,17 @@ See the full documentation :ref:`here <stats.moments.ts>`.
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.. ipython:: python
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dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
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index=pd.date_range('20130101 09:00:00',
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periods=5, freq='s'))
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dft
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dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
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index=pd.date_range('20130101 09:00:00',
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periods=5, freq='s'))
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dft
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This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.
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.. ipython:: python
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dft.rolling(2).sum()
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dft.rolling(2, min_periods=1).sum()
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dft.rolling(2).sum()
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dft.rolling(2, min_periods=1).sum()
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Specifying an offset allows a more intuitive specification of the rolling frequency.
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@@ -271,10 +271,10 @@ Categorical Concatenation
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.. ipython:: python
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from pandas.api.types import union_categoricals
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a = pd.Categorical(["b", "c"])
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b = pd.Categorical(["a", "b"])
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union_categoricals([a, b])
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from pandas.api.types import union_categoricals
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a = pd.Categorical(["b", "c"])
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b = pd.Categorical(["a", "b"])
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union_categoricals([a, b])
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- ``concat`` and ``append`` now can concat ``category`` dtypes with different ``categories`` as ``object`` dtype (:issue:`13524`)
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@@ -287,14 +287,14 @@ Categorical Concatenation
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.. code-block:: ipython
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In [1]: pd.concat([s1, s2])
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ValueError: incompatible categories in categorical concat
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In [1]: pd.concat([s1, s2])
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ValueError: incompatible categories in categorical concat
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**New behavior**:
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.. ipython:: python
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pd.concat([s1, s2])
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pd.concat([s1, s2])
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.. _whatsnew_0190.enhancements.semi_month_offsets:
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@@ -307,31 +307,31 @@ These provide date offsets anchored (by default) to the 15th and end of month, a
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.. ipython:: python
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from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin
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from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin
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**SemiMonthEnd**:
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.. ipython:: python
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pd.Timestamp('2016-01-01') + SemiMonthEnd()
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pd.Timestamp('2016-01-01') + SemiMonthEnd()
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pd.date_range('2015-01-01', freq='SM', periods=4)
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pd.date_range('2015-01-01', freq='SM', periods=4)
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**SemiMonthBegin**:
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.. ipython:: python
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pd.Timestamp('2016-01-01') + SemiMonthBegin()
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pd.Timestamp('2016-01-01') + SemiMonthBegin()
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pd.date_range('2015-01-01', freq='SMS', periods=4)
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pd.date_range('2015-01-01', freq='SMS', periods=4)
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Using the anchoring suffix, you can also specify the day of month to use instead of the 15th.
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.. ipython:: python
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pd.date_range('2015-01-01', freq='SMS-16', periods=4)
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pd.date_range('2015-01-01', freq='SMS-16', periods=4)
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pd.date_range('2015-01-01', freq='SM-14', periods=4)
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pd.date_range('2015-01-01', freq='SM-14', periods=4)
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.. _whatsnew_0190.enhancements.index:
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@@ -360,11 +360,11 @@ For ``MultiIndex``, values are dropped if any level is missing by default. Speci
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.. ipython:: python
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midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4],
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[1, 2, np.nan, np.nan]])
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midx
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midx.dropna()
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midx.dropna(how='all')
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midx = pd.MultiIndex.from_arrays([[1, 2, np.nan, 4],
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[1, 2, np.nan, np.nan]])
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midx
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midx.dropna()
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midx.dropna(how='all')
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``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see the :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`)
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@@ -464,23 +464,24 @@ Other enhancements
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.. ipython:: python
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pd.Timestamp(2012, 1, 1)
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pd.Timestamp(2012, 1, 1)
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pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30)
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pd.Timestamp(year=2012, month=1, day=1, hour=8, minute=30)
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- The ``.resample()`` function now accepts a ``on=`` or ``level=`` parameter for resampling on a datetimelike column or ``MultiIndex`` level (:issue:`13500`)
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.. ipython:: python
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df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
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'a': np.arange(5)},
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df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W', periods=5),
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'a': np.arange(5)},
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index=pd.MultiIndex.from_arrays([[1, 2, 3, 4, 5],
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pd.date_range('2015-01-01', freq='W', periods=5)],
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names=['v', 'd'])
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)
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df
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df.resample('M', on='date').sum()
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df.resample('M', level='d').sum()
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pd.date_range('2015-01-01',
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freq='W',
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periods=5)
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], names=['v', 'd']))
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df
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df.resample('M', on='date').sum()
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df.resample('M', level='d').sum()
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- The ``.get_credentials()`` method of ``GbqConnector`` can now first try to fetch `the application default credentials <https://developers.google.com/identity/protocols/application-default-credentials>`__. See the docs for more details (:issue:`13577`).
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- The ``.tz_localize()`` method of ``DatetimeIndex`` and ``Timestamp`` has gained the ``errors`` keyword, so you can potentially coerce nonexistent timestamps to ``NaT``. The default behavior remains to raising a ``NonExistentTimeError`` (:issue:`13057`)
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.. code-block:: ipython
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In [1]: pd.Index(['a', 'b']) + pd.Index(['a', 'c'])
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FutureWarning: using '+' to provide set union with Indexes is deprecated, use '|' or .union()
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Out[1]: Index(['a', 'b', 'c'], dtype='object')
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In [1]: pd.Index(['a', 'b']) + pd.Index(['a', 'c'])
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FutureWarning: using '+' to provide set union with Indexes is deprecated, use '|' or .union()
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Out[1]: Index(['a', 'b', 'c'], dtype='object')
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**New behavior**: the same operation will now perform element-wise addition:
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.. ipython:: python
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pd.Index(['a', 'b']) + pd.Index(['a', 'c'])
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pd.Index(['a', 'b']) + pd.Index(['a', 'c'])
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Note that numeric Index objects already performed element-wise operations.
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For example, the behavior of adding two integer Indexes is unchanged.
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The base ``Index`` is now made consistent with this behavior.
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.. ipython:: python
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pd.Index([1, 2, 3]) + pd.Index([2, 3, 4])
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pd.Index([1, 2, 3]) + pd.Index([2, 3, 4])
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Further, because of this change, it is now possible to subtract two
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DatetimeIndex objects resulting in a TimedeltaIndex:
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.. code-block:: ipython
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In [1]: pd.Index([1, 2, 3]).unique()
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Out[1]: array([1, 2, 3])
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In [1]: pd.Index([1, 2, 3]).unique()
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Out[1]: array([1, 2, 3])
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In [2]: pd.DatetimeIndex(['2011-01-01', '2011-01-02',
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...: '2011-01-03'], tz='Asia/Tokyo').unique()
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Out[2]:
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DatetimeIndex(['2011-01-01 00:00:00+09:00', '2011-01-02 00:00:00+09:00',
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'2011-01-03 00:00:00+09:00'],
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dtype='datetime64[ns, Asia/Tokyo]', freq=None)
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In [2]: pd.DatetimeIndex(['2011-01-01', '2011-01-02',
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...: '2011-01-03'], tz='Asia/Tokyo').unique()
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Out[2]:
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DatetimeIndex(['2011-01-01 00:00:00+09:00', '2011-01-02 00:00:00+09:00',
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'2011-01-03 00:00:00+09:00'],
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dtype='datetime64[ns, Asia/Tokyo]', freq=None)
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**New behavior**:
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.. ipython:: python
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pd.Index([1, 2, 3]).unique()
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pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'],
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tz='Asia/Tokyo').unique()
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pd.Index([1, 2, 3]).unique()
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pd.DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'],
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tz='Asia/Tokyo').unique()
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.. _whatsnew_0190.api.multiindex:
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.. ipython:: python
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s = pd.SparseSeries([0, 2, 0, 1], fill_value=0, dtype=np.int64)
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s.dtype
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s = pd.SparseSeries([0, 2, 0, 1], fill_value=0, dtype=np.int64)
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s.dtype
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s + 1
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s + 1
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- Sparse data structure now support ``astype`` to convert internal ``dtype`` (:issue:`13900`)
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.. ipython:: python
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s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
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s
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s.astype(np.int64)
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s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
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s
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s.astype(np.int64)
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``astype`` fails if data contains values which cannot be converted to specified ``dtype``.
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Note that the limitation is applied to ``fill_value`` which default is ``np.nan``.
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.. code-block:: ipython
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In [7]: pd.SparseSeries([1., np.nan, 2., np.nan], fill_value=np.nan).astype(np.int64)
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Out[7]:
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ValueError: unable to coerce current fill_value nan to int64 dtype
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In [7]: pd.SparseSeries([1., np.nan, 2., np.nan], fill_value=np.nan).astype(np.int64)
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Out[7]:
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ValueError: unable to coerce current fill_value nan to int64 dtype
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Other sparse fixes
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""""""""""""""""""

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