IPython是一個功能強大的交互式shell
適合進行交互式數(shù)據(jù)可視化和GUI相關(guān)應(yīng)用

IPython的疆导？

變量前或后增加?將顯示一些通用信息包括函數(shù)對應(yīng)的源代碼

In [1]: import numpy as np

In [2]: a=np.arange(10)

In [3]: a
Out[3]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [4]: a?
Type:            ndarray
String form:     [0 1 2 3 4 5 6 7 8 9]
Length:          10
File:            c:\users\summer\anaconda3\lib\site-packages\numpy\__init__.py
Docstring:       <no docstring>
Class docstring:
ndarray(shape, dtype=float, buffer=None, offset=0,
        strides=None, order=None)

An array object represents a multidimensional, homogeneous array
of fixed-size items.  An associated data-type object describes the
format of each element in the array (its byte-order, how many bytes it
occupies in memory, whether it is an integer, a floating point number,
or something else, etc.)

Arrays should be constructed using `array`, `zeros` or `empty` (refer
to the See Also section below).  The parameters given here refer to
a low-level method (`ndarray(...)`) for instantiating an array.

For more information, refer to the `numpy` module and examine the
methods and attributes of an array.

Parameters
----------
(for the __new__ method; see Notes below)

shape : tuple of ints
    Shape of created array.
dtype : data-type, optional
    Any object that can be interpreted as a numpy data type.
buffer : object exposing buffer interface, optional
    Used to fill the array with data.
offset : int, optional
    Offset of array data in buffer.
strides : tuple of ints, optional
    Strides of data in memory.
order : {'C', 'F'}, optional
    Row-major (C-style) or column-major (Fortran-style) order.

Attributes
----------
T : ndarray
    Transpose of the array.
data : buffer
    The array's elements, in memory.
dtype : dtype object
    Describes the format of the elements in the array.
flags : dict
    Dictionary containing information related to memory use, e.g.,
    'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
flat : numpy.flatiter object
    Flattened version of the array as an iterator.  The iterator
    allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
    assignment examples; TODO).
imag : ndarray
    Imaginary part of the array.
real : ndarray
    Real part of the array.
size : int
    Number of elements in the array.
itemsize : int
    The memory use of each array element in bytes.
nbytes : int
    The total number of bytes required to store the array data,
    i.e., ``itemsize * size``.
ndim : int
    The array's number of dimensions.
shape : tuple of ints
    Shape of the array.
strides : tuple of ints
    The step-size required to move from one element to the next in
    memory. For example, a contiguous ``(3, 4)`` array of type
    ``int16`` in C-order has strides ``(8, 2)``.  This implies that
    to move from element to element in memory requires jumps of 2 bytes.
    To move from row-to-row, one needs to jump 8 bytes at a time
    (``2 * 4``).
ctypes : ctypes object
    Class containing properties of the array needed for interaction
    with ctypes.
base : ndarray
    If the array is a view into another array, that array is its `base`
    (unless that array is also a view).  The `base` array is where the
    array data is actually stored.

See Also
--------
array : Construct an array.
zeros : Create an array, each element of which is zero.
empty : Create an array, but leave its allocated memory unchanged (i.e.,
        it contains "garbage").
dtype : Create a data-type.

Notes
-----
There are two modes of creating an array using ``__new__``:

1. If `buffer` is None, then only `shape`, `dtype`, and `order`
   are used.
2. If `buffer` is an object exposing the buffer interface, then
   all keywords are interpreted.

No ``__init__`` method is needed because the array is fully initialized
after the ``__new__`` method.

Examples
--------
These examples illustrate the low-level `ndarray` constructor.  Refer
to the `See Also` section above for easier ways of constructing an
ndarray.

First mode, `buffer` is None:

>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[ -1.13698227e+002,   4.25087011e-303],
       [  2.88528414e-306,   3.27025015e-309]])         #random

Second mode:

>>> np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])

IPython的%run命令

%run用于運行.py程序
注意：%run在一個空的命名空間執(zhí)行%

IPython的%魔術(shù)命令

In [6]: a=np.random.randn(1000,1000)

In [7]: %timeit np.dot(a,a)
32.5 ms ± 3.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [8]: %who
a        np      

In [9]: %hist
import numpy as np
a=np.arange(10)
a
a?
%magic
a=np.random.randn(1000,1000)
%timeit np.dot(a,a)
%who
%hist

In [10]: