working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has specifying na_filter=False (GH5239) • Bug in read_csv() when reading numeric category fields with high cardinality (GH18186) • Bug in DataFrame.to_csv() when the table had MultiIndex columns, and a list basically identical to calling .asfreq(). OHLC upsampling now returns a DataFrame with columns open, high, low and close (GH13083). This is consistent with downsampling and DatetimeIndex behavior. Previous

functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 22.4 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has specify strings to be recognized as True/False • Ability to yield NumPy record arrays (as_recarray) • High performance delim_whitespace option • Decimal format (e.g. European format) specification • Easier

functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 772 28.4 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has documentation in Remote Data • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as date- times. This is activated once pandas is imported. In previous versions

functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 758 28.4 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has documentation in Remote Data • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as date- times. This is activated once pandas is imported. In previous versions

functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895 29.6 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has documentation in Remote Data • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as date- times. This is activated once pandas is imported. In previous versions

working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has AAPL141128C00110000 1.32 • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as datetimes. This is activated once pandas is imported. In previous versions resulted in plotted integer values. To keep the previous behaviour, you can do del matplotlib. units.registry[np.datetime64] (GH8614). 1.15.2 Enhancements • concat permits a wider variety of iterables of

functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 30.6 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has AAPL141128C00110000 1.32 • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as date- times. This is activated once pandas is imported. In previous versions

functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047 30.6 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has AAPL141128C00110000 1.32 • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as date- times. This is activated once pandas is imported. In previous versions

working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has AAPL141128C00110000 1.32 • pandas now also registers the datetime64 dtype in matplotlib’s units registry to plot such values as datetimes. This is activated once pandas is imported. In previous versions resulted in plotted integer values. To keep the previous behaviour, you can do del matplotlib. units.registry[np.datetime64] (GH8614). 1.14.2 Enhancements • concat permits a wider variety of iterables of

functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562 24.4 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has extract from, and load data into, Google’s BigQuery Data Sets by way of pandas DataFrames. BigQuery is a high performance SQL-like database service, useful for performing ad-hoc queries against extremely large