.3/lib/python2.7/httplib.pyc in connect(self) 755 """Connect to the host and port specified in __init__.""" 756 self.sock = socket.create_connection((self.host,self.port), --> 757 self.timeout, self or object value : Returns boolean ndarray or boolean : 21.2.2 Conversion / Constructors Series.__init__([data, index, dtype, name, copy]) One-dimensional ndarray with axis labels (including time series) ndarray.astype Series.copy() Return new Series with copy of underlying values pandas.Series.__init__ Series.__init__(data=None, index=None, dtype=None, name=None, copy=False) One-dimensional ndarray with

or object value : Returns boolean ndarray or boolean : 21.2.2 Conversion / Constructors Series.__init__([data, index, dtype, name, copy]) One-dimensional ndarray with axis labels (including time series) specified type. Series.copy() Return new Series with copy of underlying values pandas.Series.__init__ Series.__init__(data=None, index=None, dtype=None, name=None, copy=False) One-dimensional ndarray with DataFrame.ndim pandas.DataFrame.shape DataFrame.shape 21.3.2 Conversion / Constructors DataFrame.__init__([data, index, columns, ...]) Two-dimensional size-mutable, potentially heterogeneous tabular data

or object value : Returns boolean ndarray or boolean : 21.2.2 Conversion / Constructors Series.__init__([data, index, dtype, name, copy]) One-dimensional ndarray with axis labels (including time series) specified type. Series.copy() Return new Series with copy of underlying values pandas.Series.__init__ Series.__init__(data=None, index=None, dtype=None, name=None, copy=False) One-dimensional ndarray with DataFrame.ndim pandas.DataFrame.shape DataFrame.shape 21.3.2 Conversion / Constructors DataFrame.__init__([data, index, columns, ...]) Two-dimensional size-mutable, potentially heterogeneous tabular data

44 return FastParquetImpl() ~/sandbox/pandas-release/pandas/pandas/io/parquet.py in __init__(self) 76 def __init__(self): 77 pyarrow = import_optional_dependency( ---> 78 "pyarrow", extra="pyarrow is ---> 44 return FastParquetImpl() 45 46 ~/sandbox/pandas-release/pandas/pandas/io/parquet.py in __init__(self) 4.1. IO tools (text, CSV, HDF5, ) 283 pandas: powerful Python data analysis toolkit, Release ---> 44 return FastParquetImpl() 45 46 ~/sandbox/pandas-release/pandas/pandas/io/parquet.py in __init__(self) 139 # we need to import on first use 140 fastparquet = import_optional_dependency( --> 141

Series.__init__([data, index, dtype, name, copy]) Series.astype(dtype) See numpy.ndarray.astype Series.copy([order]) Return new Series with copy of underlying values pandas.Series.__init__ Series.__init__(data=None __init__(data=None, index=None, dtype=None, name=None, copy=False) pandas.Series.astype Series.astype(dtype) See numpy.ndarray.astype 476 Chapter 25. API Reference pandas: powerful Python data analysis DataFrame.ndim pandas.DataFrame.shape DataFrame.shape 25.4.2 Conversion / Constructors DataFrame.__init__([data, index, columns, ...]) DataFrame.astype(dtype[, copy, raise_on_error]) Cast object to input

the index or columns Each of these can be specified in two ways: • A keyword argument to Styler.__init__ • A call to one of the .set_ or .hide_ methods, e.g. .set_caption or .hide_columns The best method so the Jinja environment needs to be able to find it. Now we can use that custom styler. It’s __init__ takes a DataFrame. [44]: MyStyler(df) [44]: <__main__.MyStyler at 0x7f0848b983d0> Our custom 3 0.000 0.000 0.003 0.001 frame.py:2767(__getitem__) 3 0.000 0.000 0.000 0.000 managers.py:1467(__init__) As one might expect, the majority of the time is now spent in apply_integrate_f, so if we wanted

the index or columns Each of these can be specified in two ways: • A keyword argument to Styler.__init__ • A call to one of the .set_ or .hide_ methods, e.g. .set_caption or .hide_columns The best method so the Jinja environment needs to be able to find it. Now we can use that custom styler. It’s __init__ takes a DataFrame. [41]: MyStyler(df) [41]: <__main__.MyStyler at 0x7f053bf260f0> Our custom be initialized with the pandas object the user is interacting with. So the signature must be def __init__(self, pandas_object): # noqa: E999 ... For consistency with pandas methods, you should raise an

the index or columns Each of these can be specified in two ways: • A keyword argument to Styler.__init__ • A call to one of the .set_ or .hide_ methods, e.g. .set_caption or .hide_columns The best method so the Jinja environment needs to be able to find it. Now we can use that custom styler. It’s __init__ takes a DataFrame. [41]: MyStyler(df) [41]: <__main__.MyStyler at 0x7f393cd95c10> Our custom be initialized with the pandas object the user is interacting with. So the signature must be def __init__(self, pandas_object): # noqa: E999 ... For consistency with pandas methods, you should raise an

the index or columns Each of these can be specified in two ways: • A keyword argument to Styler.__init__ • A call to one of the .set_ or .hide_ methods, e.g. .set_caption or .hide_columns The best method so the Jinja environment needs to be able to find it. Now we can use that custom styler. It’s __init__ takes a DataFrame. [44]: MyStyler(df) [44]: <__main__.MyStyler at 0x7f0fe0029090> Our custom be initialized with the pandas object the user is interacting with. So the signature must be def __init__(self, pandas_object): # noqa: E999 ... For consistency with pandas methods, you should raise an

the index or columns Each of these can be specified in two ways: • A keyword argument to Styler.__init__ • A call to one of the .set_ or .hide_ methods, e.g. .set_caption or .hide_columns The best method so the Jinja environment needs to be able to find it. Now we can use that custom styler. It’s __init__ takes a DataFrame. [44]: MyStyler(df) [44]: <__main__.MyStyler at 0x7fc7100b5fd0> Our custom be initialized with the pandas object the user is interacting with. So the signature must be def __init__(self, pandas_object): # noqa: E999 ... For consistency with pandas methods, you should raise an