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Corrected import statements and references.
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‎doc/source/whatsnew/v0.14.0.rst

+62-69
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@@ -5,11 +5,6 @@ v0.14.0 (May 31 , 2014)
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{{ header }}
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.. ipython:: python
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:suppress:
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This is a major release from 0.13.1 and includes a small number of API changes, several new features,
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enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
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users upgrade to this version.
@@ -63,8 +58,9 @@ API changes
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.. ipython:: python
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import pandas.DataFrame as DataFrame
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dfl = DataFrame(np.random.randn(5, 2), columns=list('AB'))
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import pandas as pd
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dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))
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dfl
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dfl.iloc[:, 2:3]
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dfl.iloc[:, 1:3]
@@ -138,13 +134,11 @@ API changes
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.. ipython:: python
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:suppress:
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import pandas.MultiIndex as MultiIndex
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import pandas.Series as Series
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np.random.seed(1234)
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from itertools import product
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tuples = list(product(('a', 'b'), ('c', 'd')))
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mi = MultiIndex.from_tuples(tuples)
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df_multi = DataFrame(np.random.randn(4, 2), index=mi)
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mi = pd.MultiIndex.from_tuples(tuples)
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df_multi = pd.DataFrame(np.random.randn(4, 2), index=mi)
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tuple_ind = pd.Index(tuples, tupleize_cols=False)
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df_multi.index
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@@ -183,7 +177,7 @@ API changes
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.. code-block:: ipython
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In [1]: df = DataFrame(np.random.randn(10, 4), columns=list('ABCD'))
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In [1]: df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))
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In [4]: covs = pd.rolling_cov(df[['A', 'B', 'C']],
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....: df[['B', 'C', 'D']],
@@ -356,7 +350,7 @@ More consistent behaviour for some groupby methods:
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.. ipython:: python
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df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
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df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])
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g = df.groupby('A')
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g.nth(0)
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@@ -379,7 +373,7 @@ More consistent behaviour for some groupby methods:
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.. ipython:: python
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df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
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df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
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g = df.groupby('A')
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g.count()
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g.describe()
@@ -388,7 +382,7 @@ More consistent behaviour for some groupby methods:
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.. ipython:: python
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df = DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
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df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6], [5, 8]], columns=['A', 'B'])
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g = df.groupby('A', as_index=False)
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g.count()
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g.describe()
@@ -524,17 +518,17 @@ See also issues (:issue:`6134`, :issue:`4036`, :issue:`3057`, :issue:`2598`, :is
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def mklbl(prefix, n):
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return ["%s%s" % (prefix, i) for i in range(n)]
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index = MultiIndex.from_product([mklbl('A', 4),
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mklbl('B', 2),
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mklbl('C', 4),
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mklbl('D', 2)])
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columns = MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'),
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('b', 'foo'), ('b', 'bah')],
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names=['lvl0', 'lvl1'])
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df = DataFrame(np.arange(len(index) * len(columns)).reshape((len(index),
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len(columns))),
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index=index,
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columns=columns).sort_index().sort_index(axis=1)
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index = pd.MultiIndex.from_product([mklbl('A', 4),
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mklbl('B', 2),
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mklbl('C', 4),
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mklbl('D', 2)])
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columns = pd.MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'),
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('b', 'foo'), ('b', 'bah')],
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names=['lvl0', 'lvl1'])
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df = pd.DataFrame(np.arange(len(index) * len(columns)).reshape((len(index),
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len(columns))),
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index=index,
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columns=columns).sort_index().sort_index(axis=1)
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df
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Basic MultiIndex slicing using slices, lists, and labels.
@@ -682,25 +676,25 @@ Deprecations
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.. code-block:: ipython
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# non-floating point indexes can only be indexed by integers / labels
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In [1]: Series(1, np.arange(5))[3.0]
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In [1]: pd.Series(1, np.arange(5))[3.0]
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pandas/core/index.py:469: FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point
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Out[1]: 1
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In [2]: Series(1, np.arange(5)).iloc[3.0]
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In [2]: pd.Series(1, np.arange(5)).iloc[3.0]
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pandas/core/index.py:469: FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point
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Out[2]: 1
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In [3]: Series(1, np.arange(5)).iloc[3.0:4]
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In [3]: pd.Series(1, np.arange(5)).iloc[3.0:4]
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pandas/core/index.py:527: FutureWarning: slice indexers when using iloc should be integers and not floating point
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Out[3]:
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3 1
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dtype: int64
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# these are Float64Indexes, so integer or floating point is acceptable
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In [4]: Series(1, np.arange(5.))[3]
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In [4]: pd.Series(1, np.arange(5.))[3]
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Out[4]: 1
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In [5]: Series(1, np.arange(5.))[3.0]
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In [5]: pd.Series(1, np.arange(5.))[3.0]
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Out[6]: 1
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- Numpy 1.9 compat w.r.t. deprecation warnings (:issue:`6960`)
@@ -753,13 +747,13 @@ Enhancements
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.. ipython:: python
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Series({('a', 'b'): 1, ('a', 'a'): 0,
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('a', 'c'): 2, ('b', 'a'): 3, ('b', 'b'): 4})
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DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
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('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},
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('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},
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('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},
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('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
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pd.Series({('a', 'b'): 1, ('a', 'a'): 0,
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('a', 'c'): 2, ('b', 'a'): 3, ('b', 'b'): 4})
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pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
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('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},
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('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},
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('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},
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('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
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- Added the ``sym_diff`` method to ``Index`` (:issue:`5543`)
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- ``DataFrame.to_latex`` now takes a longtable keyword, which if True will return a table in a longtable environment. (:issue:`6617`)
@@ -772,34 +766,34 @@ Enhancements
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.. ipython:: python
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household = DataFrame({
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'household_id': [1, 2, 3],
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'male': [0, 1, 0],
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'wealth': [196087.3, 316478.7, 294750]
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},
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columns=['household_id', 'male', 'wealth']
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).set_index('household_id')
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household = pd.DataFrame({
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'household_id': [1, 2, 3],
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'male': [0, 1, 0],
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'wealth': [196087.3, 316478.7, 294750]
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},
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columns=['household_id', 'male', 'wealth']
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).set_index('household_id')
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household
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portfolio = DataFrame({
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'household_id': [1, 2, 2, 3, 3, 3, 4],
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'asset_id': ["nl0000301109",
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"nl0000289783",
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"gb00b03mlx29",
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"gb00b03mlx29",
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"lu0197800237",
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"nl0000289965",
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np.nan],
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'name': ["ABN Amro",
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"Robeco",
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"Royal Dutch Shell",
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"Royal Dutch Shell",
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"AAB Eastern Europe Equity Fund",
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"Postbank BioTech Fonds",
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np.nan],
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'share': [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]
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},
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columns=['household_id', 'asset_id', 'name', 'share']
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).set_index(['household_id', 'asset_id'])
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portfolio = pd.DataFrame({
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'household_id': [1, 2, 2, 3, 3, 3, 4],
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'asset_id': ["nl0000301109",
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"nl0000289783",
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"gb00b03mlx29",
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"gb00b03mlx29",
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"lu0197800237",
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"nl0000289965",
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np.nan],
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'name': ["ABN Amro",
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"Robeco",
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"Royal Dutch Shell",
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"Royal Dutch Shell",
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"AAB Eastern Europe Equity Fund",
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"Postbank BioTech Fonds",
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np.nan],
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'share': [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0]
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},
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columns=['household_id', 'asset_id', 'name', 'share']
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).set_index(['household_id', 'asset_id'])
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portfolio
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household.join(portfolio, how='inner')
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.. ipython:: python
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import datetime
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df = DataFrame({
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df = pd.DataFrame({
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'Branch' : 'A A A A A B'.split(),
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'Buyer': 'Carl Mark Carl Carl Joe Joe'.split(),
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'Quantity': [1, 3, 5, 1, 8, 1],
@@ -857,9 +851,8 @@ Enhancements
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.. ipython:: python
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import pandas.period_range as period_range
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prng = period_range('2013-01-01 09:00', periods=100, freq='H')
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ps = Series(np.random.randn(len(prng)), index=prng)
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prng = pd.period_range('2013-01-01 09:00', periods=100, freq='H')
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ps = pd.Series(np.random.randn(len(prng)), index=prng)
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ps
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ps['2013-01-02']
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‎doc/source/whatsnew/v0.14.1.rst

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@@ -5,10 +5,6 @@ v0.14.1 (July 11, 2014)
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{{ header }}
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.. ipython:: python
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:suppress:
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This is a minor release from 0.14.0 and includes a small number of API changes, several new features,
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enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
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users upgrade to this version.
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.. code-block:: ipython
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In [5]: import pandas as pd
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In [6]: from pandas.tseries import offsets
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In [7]: d = pd.Timestamp('2014-01-01 09:00')
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# old behaviour < 0.14.1
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In [8]: d + offsets.MonthEnd()
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Out[8]: Timestamp('2014-01-31 00:00:00')
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Out[8]: pd.Timestamp('2014-01-31 00:00:00')
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Starting from 0.14.1 all offsets preserve time by default. The old
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behaviour can be obtained with ``normalize=True``
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import pandas as pd
103102
import pandas.tseries.offsets as offsets
104-
import pandas.Timestamp as Timestamp
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day = offsets.Day()
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day.apply(Timestamp('2014-01-01 09:00'))
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day.apply(pd.Timestamp('2014-01-01 09:00'))
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day = offsets.Day(normalize=True)
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day.apply(Timestamp('2014-01-01 09:00'))
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day.apply(pd.Timestamp('2014-01-01 09:00'))
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- ``PeriodIndex`` is represented as the same format as ``DatetimeIndex`` (:issue:`7601`)
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- ``StringMethods`` now work on empty Series (:issue:`7242`)
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pytz timezones across pandas. (:issue:`4688`)
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import pandas.date_range
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rng = pandas.date_range('3/6/2012 00:00', periods=10, freq='D',
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tz='dateutil/Europe/London')
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rng = pd.date_range('3/6/2012 00:00', periods=10, freq='D',
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tz='dateutil/Europe/London')
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rng.tz
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See :ref:`the docs <timeseries.timezone>`.

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