python数据分析学习笔记一
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第一章 Python程序库入门
Numpy 数值数组和函数
Scipy 科学计算库
Matplotlib 基于numpy绘图库
Ipython 交互是计算
示例代码如下:
# -*- coding: utf-8 -*-# @Time : 2016/12/6 19:38# @Author : Retacn# @Site : 计算两个向量的和# @File : numpyTest.py# @Software: PyCharm__author__ = "retacn"__copyright__ = "property of mankind."__license__ = "CN"__version__ = "0.0.1"__maintainer__ = "retacn"__email__ = "zhenhuayue@sina.com"__status__ = "Development"import sysfrom datetime import datetimeimport numpy as np# 使用numpy计算两个向量的和def numpysum(n): a = np.arange(n) ** 2 b = np.arange(n) ** 3 c = a + b return c# 使用python原生方法def pythonsum(n): a = list(range(n)) b = list(range(n)) c = [] print(len(a)) for i in range(len(a)): a[i] = i ** 2 b[i] = i ** 3 c.append(a[i] + b[i]) return csize = int(sys.argv[1])start = datetime.now()c = pythonsum(size)delta = datetime.now() - startprint('最后两个元素的和', c[-2:])print('使用pthon原生计算的时间:', delta.microseconds)start = datetime.now()c = numpysum(size)delta = datetime.now() - startprint('最后两个元素的和', c[-2:])print('使用numpy计算的时间:', delta.microseconds)
Pycharm在运行程序时添加参数
在run菜单中的edit confignurations中的script parameters中添加
运行结果如下:
最后两个元素的和 [995007996, 998001000]
使用pthon原生计算的时间: 1000
最后两个元素的和 [995007996 998001000]
使用numpy计算的时间: 56004
将ipython 用作shell
#Pylab参数,将自动导入scipy numpy matplotlib
C:\Users\Administrator>ipython -pylab
d:\python27\lib\site-packages\IPython\terminal\ipapp.py:291:UserWarning: `-pylab` flag has been deprecated.
Use `--matplotlib <backend>` and import pylab manually.
warnings.warn("`-pylab` flag has been deprecated.\n"
Python 2.7.12 (v2.7.12:d33e0cf91556, Jun 272016, 15:19:22) [MSC v.1500 32 bit (Intel)]
Type "copyright","credits" or "license" for more information.
IPython 5.1.0 -- An enhanced InteractivePython.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
Using matplotlib backend: TkAgg
#退出ipython
In [1]: quit()
#保存会话
In [1]: %logstart
Activating auto-logging. Current sessionstate plus future input saved.
Filename : ipython_log.py
Mode : rotate
Output logging : False
Raw input log : False
Timestamping : False
State : active
In [2]: print('hello world')
hello world
#关闭会话
In [3]: %logoff
Switching logging OFF
#得到当前日期
In [5]: !date
当前日期: 2016/12/06 周二
输入新日期: (年月日)
#显示历史上用过的命令
In [10]: %hist
%logstart
print('hello world')
%logoff
ipython_log.py
!date
thedata=!date
thedata
a=2+2
a
%hist
#显示历史上用过的命令,并作过滤
In [11]: %hist -g a=2
8:a=2+2
11:%hist -g a=2
学习手册页
使用帮助功能
In [13]: help ar
arange arcsin arctan2 argmin argwhere array2string array_repr arrow
arccos arcsinh arctanh argpartition around array_equal array_split
arccosh arctan argmax argsort array array_equiv array_str
In [14]: help(arange)
Help on built-in function arange in modulenumpy.core.multiarray:
arange(...)
arange([start,] stop[, step,], dtype=None)
Return evenly spaced values within a given interval.
Values are generated within the half-open interval ``[start, stop)``
(in other words, the interval including `start` but excluding `stop`).
For integer arguments the function is equivalent to the Python built-in
`range <http://docs.python.org/lib/built-in-funcs.html>`_function,
but returns an ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not
be consistent. It is better touse ``linspace`` for these cases.
Parameters
----------
start : number, optional
Start of interval. The intervalincludes this value. The default
start value is 0.
stop : number
End of interval. The intervaldoes not include this value, except
in some cases where `step` is not an integer and floating point
round-off affects the length of `out`.
step : number, optional
Spacing between values. For anyoutput `out`, this is the distance
between two adjacent values, ``out[i+1] - out[i]``. The default
step size is 1. If `step` isspecified, `start` must also be given.
dtype : dtype
The type of the output array. If`dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
``ceil((stop - start)/step)``. Because of floating point overflow,
this rule may result in the last element of `out` being greater
than `stop`.
See Also
--------
linspace : Evenly spaced numbers with careful handling of endpoints.
ogrid: Arrays of evenly spaced numbers in N-dimensions.
mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
Examples
--------
>>> np.arange(3)
array([0, 1, 2])
>>> np.arange(3.0)
array([ 0., 1., 2.])
>>> np.arange(3,7)
array([3, 4, 5, 6])
>>> np.arange(3,7,2)
array([3, 5])
#使用问号进行查询:
In [15]: arange?
Docstring:
arange([start,]stop[, step,], dtype=None)
Return evenlyspaced values within a given interval.
Values aregenerated within the half-open interval ``[start, stop)``
(in other words,the interval including `start` but excluding `stop`).
For integerarguments the function is equivalent to the Python built-in
`range<http://docs.python.org/lib/built-in-funcs.html>`_ function,
but returns anndarray rather than a list.
When using anon-integer step, such as 0.1, the results will often not
beconsistent. It is better to use``linspace`` for these cases.
Parameters
----------
start : number,optional
Start of interval. The interval includes this value. The default
start value is 0.
stop : number
End of interval. The interval does not include this value,except
in some cases where `step` is not aninteger and floating point
round-off affects the length of `out`.
step : number,optional
Spacing between values. For any output `out`, this is the distance
between two adjacent values, ``out[i+1] -out[i]``. The default
step size is 1. If `step` is specified, `start` must also begiven.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length ofthe result is
``ceil((stop - start)/step)``. Because of floating point overflow,
this rule may result in the last element of`out` being greater
than `stop`.
See Also
--------
linspace :Evenly spaced numbers with careful handling of endpoints.
ogrid: Arrays ofevenly spaced numbers in N-dimensions.
mgrid:Grid-shaped arrays of evenly spaced numbers in N-dimensions.
Examples
--------
>>>np.arange(3)
array([0, 1, 2])
>>>np.arange(3.0)
array([ 0., 1., 2.])
>>>np.arange(3,7)
array([3, 4, 5,6])
>>>np.arange(3,7,2)
array([3, 5])
Type: builtin_function_or_method
#使用tab自动补全.前提是安装readline
In [16]: randn
rand random ranf
randint random_integersrange
randn random_sample rank
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