. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 13.19 Set / Reset Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . dimensional objects • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align 0 (October 9, 2015) 11 pandas: powerful Python data analysis toolkit, Release 0.17.0 In [38]: pd.set_option('display.unicode.east_asian_width', True) In [39]: df; For further details, see here Other

dimensional objects • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align accepts regular expressions. 1.1.1 API changes • The I/O API is now much more consistent with a set of top level reader functions accessed like pd.read_csv() that generally return a pandas object. – names of the returned DataFrame. • pd.set_option() now allows N option, value pairs (GH3667). Let’s say that we had an option ’a.b’ and another option ’b.c’. We can set them at the same time: In [31]: pd

dimensional objects • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align date_range(’20130101’,periods=10))) ...: In [9]: df.iloc[3:6,[0,2]] = np.nan # set to not display the null counts In [10]: pd.set_option(’max_info_rows’,0) In [11]: df.info() 4 Chapter 1. What’s New pandas: datetime64[ns] dtypes: datetime64[ns](1), float64(2) # this is the default (same as in 0.13.0) In [12]: pd.set_option(’max_info_rows’,max_info_rows) In [13]: df.info() Int64Index:

objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.22 Set / reset index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. 4 Chapter 1. Getting started pandas:

objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 2.5.22 Set / reset index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. 4 Chapter 1. Getting started pandas:

counts...) are easily calculable. These or custom aggregations can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine user guide Straight to tutorial... pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data. 4 Chapter 1. Getting started pandas: JOIN, etc.? Most of these SQL manipulations do have equivalents in pandas. Learn more The data set included in the STATA statistical software suite corresponds to the pandas DataFrame. Many of the operations

dimensional objects • Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align Index.astype() In [9]: i[[0,1,2]].astype(np.int_) Out[9]: Int64Index([1, 2, 3], dtype=’int32’) • set_index no longer converts MultiIndexes to an Index of tuples. For example, the old behavior returned (May 31 , 2014) 5 pandas: powerful Python data analysis toolkit, Release 0.14.0 In [11]: df_multi.set_index(tuple_ind) Out[11]: 0 1 (a, c) 0.471435 -1.190976 (a, d) 1.432707 -0.312652 (b, c) -0.720589

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Index + / - no longer used for set operations . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Index.difference and .symmetric_difference returns Index . . . . . . . . . . . . . . . . . . . . . . . . . 29 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 30 read_csv will progressively enumerate chunks . . . . metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 13.19.2 Set operations on Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 13

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Index + / - no longer used for set operations . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Index.difference and .symmetric_difference returns Index . . . . . . . . . . . . . . . . . . . . . . . . . 28 MultiIndex constructors, groupby and set_index preserve categorical dtypes . . . . 28 read_csv will progressively enumerate chunks . . . . metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 13.19.2 Set operations on Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564 13

. . . . . . . . . . . . . . . . . . . . . . . . . . . 74 1.6.2.8 Index + / - no longer used for set operations . . . . . . . . . . . . . . . . . . . . . 76 1.6.2.9 Index.difference and .symmetric_difference returns Index . . . . . . . . . . . . . . . . . . . . 77 1.6.2.11 MultiIndex constructors, groupby and set_index preserve categorical dtypes 77 1.6.2.12 read_csv will progressively enumerate chunks . . . metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 12.20.2 Set operations on Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628 12