pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame this version. • Highlights include: – Officially support Python 3.4 – SQL interfaces updated to use sqlalchemy, See Here. – Display interface changes, See Here – MultiIndexing Using Slicers, See Here pandas about am- biguity of the name a. – The top-level pandas.eval() function does not allow you use the ’@’ prefix and provides you with an error message telling you so. – NameResolutionError was removed

pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame the SQL type of columns when writing a DataFrame to a database (GH8778). For example, specifying to use the sqlalchemy String type instead of the default Text type for string columns: from sqlalchemy.types index contained DST days (GH8772). • Bug where index name was still used when plotting a series with use_index=False (GH8558). • Bugs when trying to stack multiple columns, when some (or all) of the level

pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame np.array_equal(np.array([0, np.nan]), np.array([0, np.nan])) Out[30]: False • DataFrame.apply will use the reduce argument to determine whether a Series or a DataFrame should be returned when the DataFrame you would have set levels or labels directly index.levels = [[1, 2, 3, 4], [1, 2, 4, 4]] # now, you use the set_levels or set_labels methods index = index.set_levels([[1, 2, 3, 4], [1, 2, 4, 4]]) # similarly

pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame in Panel indexing with a list-like (GH8710) • Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722) • Bug in read_csv, dialect parameter would not take a string (:issue: pandas >= 0.15.0 will no longer support compatibility with NumPy versions < 1.7.0. If you want to use the latest versions of pandas, please upgrade to NumPy >= 1.7.0 (GH7711) • Highlights include: –

. . . . . . . . . . . . . . . . . . . 891 29 rpy2 / R interface 893 29.1 Updating your code to use rpy2 functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 29.2 R interface pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame customizing plot types by supplying the kind keyword arguments. Unfortunately, many of these kinds of plots use different required and optional keyword arguments, which makes it difficult to discover what any given

query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 12.16.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 646 12.16.3 query() in operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 12.16.5 Special use of the == operator with list objects . . . . . . . . . . . . . . . . . . . . . . . 649 12.16.6 Boolean pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame

query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614 12.15.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 12.15.3 query() in operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 12.15.5 Special use of the == operator with list objects . . . . . . . . . . . . . . . . . . . . . . . 619 12.15.6 Boolean pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame

query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553 13.14.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 13.14.3 query() in operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556 13.14.5 Special use of the == operator with list objects . . . . . . . . . . . . . . . . . . . . . . . 557 13.14.6 Boolean . . . . . . . . . . . . . . . . . . 1044 30 rpy2 / R interface 1045 30.1 Updating your code to use rpy2 functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 30.2 R interface

query() Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 13.14.2 query() Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552 13.14.3 query() in operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 13.14.5 Special use of the == operator with list objects . . . . . . . . . . . . . . . . . . . . . . . 555 13.14.6 Boolean . . . . . . . . . . . . . . . . . . 1042 30 rpy2 / R interface 1043 30.1 Updating your code to use rpy2 functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1043 30.2 R interface

pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users, DataFrame [12]: mask A True B False C True D False E True Name: a, dtype: bool # this is what you should use In [13]: df.loc[mask] a A 0 C 2 E 4 # this will work as well In [14]: df.iloc[mask.values] a see Colormaps for more information. (GH3860) • DataFrame.interpolate() is now deprecated. Please use DataFrame.fillna() and DataFrame.replace() instead. (GH3582, GH3675, GH3676) • the method and axis