__unicode__ method, gets python 2 and python 3 compatible string methods (__str__, __bytes__, and __repr__). Plus string safety throughout. Now employed in many places throughout the pandas library. (GH4090, GH4092) object In [53]: s.dropna().values.item() == ’w’ True The last element yielded by the iterator will be a Series containing the last element of the longest string in the Series with all other elements being decorator to allow explicitly checking a website as a proxy for seeing if there is network connectivity. Plus, new optional_args decorator factory for decorators. (GH3910, GH3914) • Fixed testing issue where

245373 [5 rows x 4 columns] Specifying an apply that operates on a Series (to return a single element) In [43]: panel.apply(lambda x: x.dtype, axis=’items’) Out[43]: A B C D 2000-01-03 float64 float64 allowing 2/3 compatibility. It contains both list and itera- tor versions of range, filter, map and zip, plus other necessary elements for Python 3 compatibility. lmap, lzip, lrange and lfilter all produce lists and df2 s1 and s2 Added the .bool() method to NDFrame objects to facilitate evaluating of single-element boolean Series: In [1]: Series([True]).bool() Out[1]: True In [2]: Series([False]).bool() Out[2]:

result, but retains in result’s Index. (GH10150) • Bug in pd.eval using numexpr engine coerces 1 element numpy array to scalar (GH10546) • Bug in pd.concat with axis=0 when column is of dtype category length_of_indexer returns wrong results (GH9995) • Bug in csv parser causing lines with initial whitespace plus one non-space character to be skipped. (GH9710) • Bug in C csv parser causing spurious NaNs when utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect as it didn’t

utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect as it didn’t e.g. list(Series(...)) of timedelta64[ns] would prior to v0.15.0 return np.timedelta64 for each element. These will now be wrapped in Timedelta. Memory Usage Implemented methods to find memory usage analysis toolkit, Release 0.15.2 Specifying an apply that operates on a Series (to return a single element) In [44]: panel.apply(lambda x: x.dtype, axis=’items’) Out[44]: A B C D 2000-01-03 float64 float64

utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect as it didn’t e.g. list(Series(...)) of timedelta64[ns] would prior to v0.15.0 return np.timedelta64 for each element. These will now be wrapped in Timedelta. Memory Usage Implemented methods to find memory usage analysis toolkit, Release 0.15.1 Specifying an apply that operates on a Series (to return a single element) In [44]: panel.apply(lambda x: x.dtype, axis=’items’) Out[44]: A B C D 2000-01-03 float64 float64

591863 [5 rows x 4 columns] Specifying an apply that operates on a Series (to return a single element) In [43]: panel.apply(lambda x: x.dtype, axis=’items’) Out[43]: A B C D 34 Chapter 1. What’s New allowing 2/3 compatibility. It contains both list and itera- tor versions of range, filter, map and zip, plus other necessary elements for Python 3 compatibility. lmap, lzip, lrange and lfilter all produce lists and df2 s1 and s2 Added the .bool() method to NDFrame objects to facilitate evaluating of single-element boolean Series: In [1]: Series([True]).bool() Out[1]: True In [2]: Series([False]).bool() Out[2]:

applicable on both Series and DataFrames • By default, each of the columns is plotted as a different element (line, boxplot,...) • Any plot created by pandas is a Matplotlib object. A full overview of plotting new column name at the left side of the assignment. Note: The calculation of the values is done element_wise. This means all values in the given column are multiplied 34 Chapter 1. Getting started pandas: calculation is again element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, \*, /) or logical operators (<, >, =,...) work element wise. The latter was

applicable on both Series and DataFrames • By default, each of the columns is plotted as a different element (line, boxplot,...) • Any plot created by pandas is a Matplotlib object. A full overview of plotting new column name at the left side of the assignment. Note: The calculation of the values is done element_wise. This means all values in the given column are multiplied 34 Chapter 1. Getting started pandas: calculation is again element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, \*, /) or logical operators (<, >, =,...) work element wise. The latter was

would incorrectly raise a MemoryError (GH16798). • Bug in read_csv() when called with a single-element list header would return a DataFrame of all NaN values (GH7757) • Bug in DataFrame.to_csv() defaulting whatever parameter provided. (GH16707) • Fixed an issue with DataFrame.style() where generated element ids were not unique (GH16780) • Fixed loading a DataFrame with a PeriodIndex, from a format='fixed' and copy.deepcopy() functions on NDFrame objects (GH15444) • Series.sort_values() accepts a one element list of bool for consistency with the behavior of DataFrame.sort_values() (GH15604) • .merge() and

Python data analysis toolkit, Release 0.19.0 • groupby() will now accept a scalar and a single-element list for specifying level on a non-MultiIndex grouper. (GH13907) • Non-convertible dates in an excel union() and .difference() methods), and is now disabled. When possible, + and - are now used for element-wise operations, for example for concatenating strings or subtracting datetimes (GH8227, GH14127) perform element-wise addition: In [129]: pd.Index(['a', 'b']) + pd.Index(['a', 'c']) Out[129]: Index([u'aa', u'bc'], dtype='object') Note that numeric Index objects already performed element-wise operations