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 otherwise (GH6290). • When converting a dataframe to HTML it used to return Empty DataFrame. This special case has been removed, instead a header with the column names is returned (GH6062). • Series and Index

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 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: –

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 level keyword to drop for dropping values from a level (GH159) 1.1.2 Performance improvements • Use khash for Series.value_counts, add raw function to algorithms.py (GH861) • Intercept __builtin__.sum performing full gamut of database / relational-algebra operations. Refactored existing join methods to use the new infrastructure, resulting in substantial performance gains (GH220, GH249, GH267) • New unified

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 performing full gamut of database / relational-algebra operations. Refactored existing join methods to use the new infrastructure, resulting in substantial performance gains (GH220, GH249, GH267) 3 pandas: and __setitem__ methods). The behavior will be the same as passing similar input to ix except in the case of integer indexing: In [915]: s = Series(randn(6), index=list(’acegkm’)) In [916]: s Out[916]:

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 level keyword to drop for dropping values from a level (GH159) 1.2.2 Performance improvements • Use khash for Series.value_counts, add raw function to algorithms.py (GH861) • Intercept __builtin__.sum performing full gamut of database / relational-algebra operations. Refactored existing join methods to use the new infrastructure, resulting in substantial performance gains (GH220, GH249, GH267) • New unified

. . . . . . . . . . . . . . . . . . . 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

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 Since iloc is purely positional based, the labels on the Series are not alignable (GH3631) This case is rarely used, and there are plently of alternatives. This preserves the iloc API to be purely positional [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

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

. . . . . . . . . . . . . . . 913 2.24.2 Use efficient datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914 2.24.3 Use chunking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916 2.24.4 Use other libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917 2.25 Sparse data structures . . . | Mailing List pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New