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Numpy与Pandas学习记录

Numpy为Python下的科学计算库,用于快速处理数据、科学计算等。

pandas 是基于 Numpy 构建的含有更高级数据结构和工具的数据分析包


Numpy 库文档:https://docs.scipy.org/doc/numpy-dev/user/quickstart.html

Pandas 库文档:http://pandas.pydata.org/pandas-docs/version/0.17.0/


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import numpy as np

Numpy的基本使用

创建数组

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array = np.array([1, 4, 5, 8], float)
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print array

output>>[ 1. 4. 5. 8.]

np.array最后的参数表示数据类型为浮点。因此显示数组时整数后会有一个小数点。

重建的数组中所有数据必须为相同的数据类型

The array object class is the foundation of Numpy, and Numpy arrays are like lists in Python, except that every thing inside an array must be of the same type, like int or float.

创建二维数组

同理:

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array = np.array([[1, 2, 3], [4, 5, 6]], float)  # a 2D array/Matrix
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print array

output>>[[ 1. 2. 3.][ 4. 5. 6.]]

存取元素

与Python基本用法一致

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array = np.array([1, 4, 5, 8], float)
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print array

output>>[ 1. 4. 5. 8.]

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print array[1]##显示数组中下标为1的数据

output>>4.0

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print array[:2]##显示数组从下标0开始向后的2个数据

output>>[ 1. 4.]

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array[1]=5.0##通过下标修改对应数据
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print array[1]

output>>5.0


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two_D_array=np.array([[1, 2, 3], [4, 5, 6]], float)##创建2d数组
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print two_D_array

output>>[[ 1. 2. 3.][ 4. 5. 6.]]

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print two_D_array[1][1]##显示下标为1的数据组中的下标为1的数据

output>>5.0

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print two_D_array[1, :]##显示下标为1的数据组中所有数据

output>>[ 4. 5. 6.]

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print two_D_array[:, 2]##显示所有数据组中的下标为2的数据

output>>[ 3. 6.]

数组运算

数组间的四则运算

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array_1 = np.array([1, 2, 3], float)
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array_2 = np.array([5, 2, 6], float)

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print array_1 + array_2
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print array_1 - array_2
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print array_1 * array_2
4
print array_1 / array_2

output>>[ 6. 4. 9.]

output>>[-4. 0. -3.]

output>>[ 5. 4. 18.]

output>>[ 0.2 1. 0.5]

二维数组间的四则运算

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array_1 = np.array([[1, 2], [3, 4]], float)
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array_2 = np.array([[5, 6], [7, 8]], float)

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print array_1 + array_2
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print array_1 - array_2
3
print array_1 * array_2
4
print array_1 / array_2

output>>[[ 6. 8.][ 10. 12.]]

output>>[[-4. -4.][-4. -4.]]

output>>[[ 5. 12.][ 21. 32.]]

output>>[[ 0.2 0.33333333][ 0.42857143 0.5]]

数据快速处理

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array_1 = np.array([1, 2, 3], float)
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array_2 = np.array([[6], [7], [8]], float)

求和:

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print np.sum(array_1)
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print np.sum(array_2)

output>>6.0

output>>21.0

求均值:

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print np.mean(array_1)
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print np.mean(array_2)

output>>2.0

output>>7.0

求方差:

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print np.var(array_1)
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print np.var(array_2)

output>>0.666666666667

output>>0.666666666667

求标准差:

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print np.std(array_1)
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print np.std(array_2)

output>>0.816496580928

output>>0.816496580928

求内积:

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print np.dot(array_1, array_2)

output>>[ 44.]


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import pandas as pd

Pandas的基本使用

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series = pd.Series(['Dave', 'Cheng-Han', 'Udacity', 42, -1789710578])
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print series
index column
0 Dave
1 Cheng-Han
2 Udacity
3 42
4 -1789710578

dtype: object

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##通过Python字典对象创建一个数据系列,自动使用递增整型作为索引。
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series = pd.Series(['Dave', 'Cheng-Han', 359, 9001],
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                     index=['Instructor', 'Curriculum Manager',
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                            'Course Number', 'Power Level'])
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print series
column0 column1
Instructor Dave
Curriculum Manager Cheng-Han
Course Number 359
Power Level 9001

dtype: object

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##为数据添加了索引,此时可以通过索引来选择数据:
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print series['Instructor']

output>>Dave

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print series[['Instructor', 'Curriculum Manager', 'Course Number']]

column0 column1
Instructor Dave
Curriculum Manager Cheng-Han
Course Number 359

dtype: object

通过bool来显示数据是否满足筛选条件:

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cuteness = pd.Series([1, 2, 3, 4, 5], index=['Cockroach', 'Fish', 'Mini Pig',
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                          'Puppy', 'Kitten'])

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print cuteness > 3

column0 column1
Cockroach False
Fish False
Mini Pig False
Puppy True
Kitten True

dtype: bool

只显示满足条件的数据:

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print cuteness[cuteness > 3]

column0 column1
Puppy True
Kitten True

dtype: int64

使用数据框架:

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data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],
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     'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions',
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          'Lions', 'Lions'],
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     'wins': [11, 8, 10, 15, 11, 6, 10, 4],
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     'losses': [5, 8, 6, 1, 5, 10, 6, 12]}
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football = pd.DataFrame(data)
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print football

output>>

upload successful

显示数据框架各列数据格式:

to get the datatype for each column

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print football.dtypes

output>>

losses int64

team object

wins int64

year int64

dtype: object

对数据列进行快速统计与汇总:

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## useful for seeing basic statistics of the dataframe's numerical columns
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print football.describe()

output>>

 losses        wins       year 

count 8.000000 8.000000 8.000000##总数

mean 6.625000 9.375000 2011.125000##均值

std 3.377975 3.377975 0.834523##标准差

min 1.000000 4.000000 2010.000000##最小值

25% 5.000000 7.500000 2010.750000

50% 6.000000 10.000000 2011.000000

75% 8.500000 11.000000 2012.000000

max 12.000000 15.000000 2012.000000##最大值

显示前5行

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print football.head()

output>>

upload successful

显示后5行

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print football.tail()

output>>

upload successful

通过索引来访问对应列

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print football['year']##显示year这一列
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##也可用print football.year

output>>

0 2010
1 2011
2 2012
3 2011
4 2012
5 2010
6 2011
7 2012
Name: year, dtype: int64

显示year wins losses三列

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print football[['year', 'wins', 'losses']]

output>>

year wins losses
0 2010 11 5
1 2011 8 8
2 2012 10 6
3 2011 15 1
4 2012 11 5
5 2010 6 10
6 2011 10 6
7 2012 4 12

显示第[0]行数据

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print football.loc[[0]]

output>>
losses team wins year
0 5 Bears 11 2010

显示从第[3]行到第[5]行

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print football[3:5]

output>>

losses team wins year
3 1 Packers 15 2011
4 5 Packers 11 2012

显示wins中大于10的行

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print football[football.wins > 10]

output>>

upload successful

显示wins大于10且team为Packers的行

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print football[(football.wins > 10) & (football.team == "Packers")]

output>>

upload successful