《利用Python進行資料分析》第五章 pandas的基本功能

weixin_34138377發表於2017-12-12

介紹操作Series和DataFrame中的資料的基本功能

重新索引
pandas物件的一個重要方法是reindex,其作用是建立一個適應新索引的新物件。以之前的一個簡單示例來說

In [1]: from pandas import Series,DataFrame

In [2]: import pandas as pd

In [3]: import numpy as np

In [4]: obj=Series([6.5,7.8,-5.9,8.6],index=['d','b','a','c'])

In [5]: obj
Out[5]: 
d 6.5
b 7.8
a -5.9
c 8.6
dtype: float64

呼叫該Series的reindex將會根據新索引進行重排。如果某個索引值當前不存在,就引入缺失值

In [6]: obj2=obj.reindex(['a', 'b', 'c', 'd', 'e'])

In [7]: obj2
Out[7]: 
a -5.9
b 7.8
c 8.6
d 6.5
e NaN
dtype: float64

In [8]: obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)
Out[8]: 
a -5.9
b 7.8
c 8.6
d 6.5
e 0.0
dtype: float64

In [9]: obj3=Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])

In [10]: obj3.reindex(range(6), method='ffill')
Out[10]: 
0 blue
1 blue
2 purple
3 purple
4 yellow
5 yellow
dtype: object

In [11]: obj3.reindex(range(6), method='bfill')
Out[11]: 
0 blue
1 purple
2 purple
3 yellow
4 yellow
5 NaN
dtype: object

In [12]: obj3.reindex(range(6), method='pad')
Out[12]: 
0 blue
1 blue
2 purple
3 purple
4 yellow
5 yellow
dtype: object
6784893-c18ebd22ae6cf49a.png

對於DataFrame,reindex可以修改(行)索引、列,或兩個都修改。如果僅傳入一個序列,則會重新索引行

In [13]: frame = DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'c', 'd'],columns=['Ohio', 'Texas', 'California'])

In [14]: frame
Out[14]: 
Ohio Texas California
a 0 1 2
c 3 4 5
d 6 7 8

In [15]: frame2=frame.reindex(['a', 'b', 'c', 'd'])

In [16]: frame2
Out[16]: 
Ohio Texas California
a 0.0 1.0 2.0
b NaN NaN NaN
c 3.0 4.0 5.0
d 6.0 7.0 8.0

使用columns關鍵字即可重新索引列

In [17]: states = ['Texas', 'Utah', 'California']

In [18]: frame.reindex(columns=states)
Out[18]: 
Texas Utah California
a 1 NaN 2
c 4 NaN 5
d 7 NaN 8

利用ix的標籤索引功能

In [28]: frame
Out[28]: 
Ohio Texas California
a 0 1 2
c 3 4 5
d 6 7 8

In [31]: states = ['Texas', 'Utah', 'California']

In [32]: frame.ix[['a', 'b', 'c', 'd'], states]
Out[32]: 
Texas Utah California
a 1.0 NaN 2.0
b NaN NaN NaN
c 4.0 NaN 5.0
d 7.0 NaN 8.0
6784893-db671a469d1adee6.png

丟棄某條軸上的一個或多個項很簡單,只要有一個索引陣列或列表即可。由於需要執行一些資料整理和集合邏輯,所以drop方法返回的是一個在指定軸上刪除了指定值的新物件

In [33]: obj=Series(np.arange(5.),index=['a', 'b', 'c', 'd', 'e'])

In [34]: obj
Out[34]: 
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
dtype: float64

In [35]: new_obj=obj.drop('c')

In [36]: new_obj
Out[36]: 
a 0.0
b 1.0
d 3.0
e 4.0
dtype: float64

In [37]: obj.drop(['d','b'])
Out[37]: 
a 0.0
c 2.0
e 4.0
dtype: float64

對於DataFrame,可以刪除任意軸上的索引值

In [41]: data = DataFrame(np.arange(16).reshape((4, 4)),
    ...: index=['Ohio', 'Colorado', 'Utah', 'New York'], 
    ...: columns=['one', 'two', 'three', 'four'])

In [42]: data
Out[42]: 
one two three four
Ohio 0 1 2 3
Colorado 4 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

In [43]: data.drop(['Colorado', 'Ohio'])
Out[43]: 
one two three four
Utah 8 9 10 11
New York 12 13 14 15

In [44]: data.drop('two',axis=1)
Out[44]: 
one three four
Ohio 0 2 3
Colorado 4 6 7
Utah 8 10 11
New York 12 14 15

In [45]: data.drop(['two', 'four'], axis=1)
Out[45]: 
one three
Ohio 0 2
Colorado 4 6
Utah 8 10
New York 12 14

索引、選取和過濾
Series的索引值不只是整數

In [47]: obj=Series(np.arange(5.),index=['a', 'b', 'c', 'd','e'])

In [48]: obj
Out[48]: 
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
dtype: float64

In [49]: obj['b']
Out[49]: 1.0

In [50]: obj[3]
Out[50]: 3.0

In [51]: obj[3:5]
Out[51]: 
d 3.0
e 4.0
dtype: float64

In [52]: obj[['b','e','d']]
Out[52]: 
b 1.0
e 4.0
d 3.0
dtype: float64

In [53]: obj[[1,4]]
Out[53]: 
b 1.0
e 4.0
dtype: float64

In [54]: obj[obj<3]
Out[54]: 
a 0.0
b 1.0
c 2.0
dtype: float64

利用標籤的切片運算與普通的Python切片運算不同,其末端是包含的(inclusive)

In [55]: obj['b':'d']
Out[55]: 
b 1.0
c 2.0
d 3.0
dtype: float64

In [56]: obj['b':'d']=6

In [57]: obj
Out[57]: 
a 0.0
b 6.0
c 6.0
d 6.0
e 4.0
dtype: float64

DataFrame進行索引其實就是獲取一個或多個列

In [60]: data = DataFrame(np.arange(16).reshape((4, 4)),
    ...: index=['Ohio', 'Colorado', 'Utah', 'New York'],
    ...: columns=['one', 'two', 'three', 'four'])

In [61]: data
Out[61]: 
one two three four
Ohio 0 1 2 3
Colorado 4 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

In [62]: data['two']
Out[62]: 
Ohio 1
Colorado 5
Utah 9
New York 13
Name: two, dtype: int32

In [63]: data[['three','one']]
Out[63]: 
three one
Ohio 2 0
Colorado 6 4
Utah 10 8
New York 14 12

通過切片或布林型陣列選取行

In [64]: data[:3]
Out[64]: 
one two three four
Ohio 0 1 2 3
Colorado 4 5 6 7
Utah 8 9 10 11

In [65]: data[data['three']>5]
Out[65]: 
one two three four
Colorado 4 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

通過布林型DataFrame(比如下面由標量比較運算得出的)進行索引

In [66]: data<6
Out[66]: 
one two three four
Ohio True True True True
Colorado True True False False
Utah False False False False
New York False False False False

In [67]: data[data<5]=0

In [68]: data
Out[68]: 
one two three four
Ohio 0 0 0 0
Colorado 0 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

利用索引欄位ix,它可以通過NumPy式的標記法以及軸標籤從DataFrame中選取行和列的子集。其中:ix is deprecated,可以使用loc

In [69]: data.ix['Colorado', ['two', 'three']]
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
"""Entry point for launching an IPython kernel.
Out[69]: 
two 5
three 6
Name: Colorado, dtype: int32

In [70]: data.loc['Colorado', ['two', 'three']]
Out[70]: 
two 5
three 6
Name: Colorado, dtype: int32

In [71]: data.ix[['Colorado', 'Utah'], [3, 0, 1]]
Out[71]: 
four one two
Colorado 7 0 5
Utah 11 8 9

In [72]: data.ix[2]
Out[72]: 
one 8
two 9
three 10
four 11
Name: Utah, dtype: int32

In [73]: data.loc[:'Utah','two']
Out[73]: 
Ohio 0
Colorado 5
Utah 9
Name: two, dtype: int32

In [74]: data.ix[data.three>5]
Out[74]: 
one two three four
Colorado 0 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

In [75]: data.ix[data.three > 5, :3]
Out[75]: 
one two three
Colorado 0 5 6
Utah 8 9 10
New York 12 13 14

對pandas物件中的資料的選取和重排方式有很多


6784893-5670e249410699e9.png

為什麼不是輸出7 4 5,而輸出的是7 0 5,是不能理解的小地方,只能慢慢體會其中的用法。


6784893-0ee96e990dd3da1b.png

其中,get_value方法是選取,set-value方法是設定

算術運算和資料對齊
Pandas可以對不同索引的物件進行算術運算。在將物件相加時,如果存在不同的索引對,則結果的索引就是該索引對的並集。

In [77]: s1=Series([6.8,-4.5,3.6,5.6],index=['a','c','d','e'])

In [78]: s2 = Series([-6.5, 3.6, -5.6, 4, 3.1], index=['a', 'c', 'e', 'f', 'g'])

In [79]: s1
Out[79]: 
a 6.8
c -4.5
d 3.6
e 5.6
dtype: float64

In [80]: s2
Out[80]: 
a -6.5
c 3.6
e -5.6
f 4.0
g 3.1
dtype: float64

In [81]: s1+s2
Out[81]: 
a 0.3
c -0.9
d NaN
e 0.0
f NaN
g NaN
dtype: float64

自動的資料對齊操作在不重疊的索引處引入了NA值。缺失值會在算術運算過程中傳播。對於DataFrame,對齊操作會同時發生在行和列上。相加後將會返回一個新的DataFrame,其索引和列為原來那兩個DataFrame的並集

In [85]: df1 = DataFrame(np.arange(9.).reshape((3, 3)), columns=list('bcd'),
    ...: index=['Ohio', 'Texas', 'Colorado'])

In [86]: df2 = DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),
    ...: index=['Utah', 'Ohio', 'Texas', 'Oregon'])

In [87]: df1
Out[87]: 
b c d
Ohio 0.0 1.0 2.0
Texas 3.0 4.0 5.0
Colorado 6.0 7.0 8.0

In [88]: df2
Out[88]: 
b d e
Utah 0.0 1.0 2.0
Ohio 3.0 4.0 5.0
Texas 6.0 7.0 8.0
Oregon 9.0 10.0 11.0

In [89]: df1+df2
Out[89]: 
b c d e
Colorado NaN NaN NaN NaN
Ohio 3.0 NaN 6.0 NaN
Oregon NaN NaN NaN NaN
Texas 9.0 NaN 12.0 NaN
Utah NaN NaN NaN NaN

在算術方法中填充值
在對不同索引的物件進行算術運算時,你可能希望當一個物件中某個軸標籤在另一個物件中找不到時填充一個特殊值(比如0),相加時,沒有重疊的位置就會產生NA值。

In [95]: f1 = DataFrame(np.arange(12.).reshape((3, 4)), columns=list('abcd'))

In [96]: f2 = DataFrame(np.arange(20.).reshape((4, 5)), columns=list('abcde'))

In [97]: f1
Out[97]: 
a b c d
0 0.0 1.0 2.0 3.0
1 4.0 5.0 6.0 7.0
2 8.0 9.0 10.0 11.0

In [98]: f2
Out[98]: 
a b c d e
0 0.0 1.0 2.0 3.0 4.0
1 5.0 6.0 7.0 8.0 9.0
2 10.0 11.0 12.0 13.0 14.0
3 15.0 16.0 17.0 18.0 19.0

In [99]: f1+f2
Out[99]: 
a b c d e
0 0.0 2.0 4.0 6.0 NaN
1 9.0 11.0 13.0 15.0 NaN
2 18.0 20.0 22.0 24.0 NaN
3 NaN NaN NaN NaN NaN

使用add方法,傳入f2以及一個fill_value引數

In [102]: f1.add(f2, fill_value=0)
Out[102]: 
a b c d e
0 0.0 2.0 4.0 6.0 4.0
1 9.0 11.0 13.0 15.0 9.0
2 18.0 20.0 22.0 24.0 14.0
3 15.0 16.0 17.0 18.0 19.0

在對Series或DataFrame重新索引時,也可以指定一個填充值

In [103]: f1.reindex(columns=f2.columns, fill_value=0)
Out[103]: 
a b c d e
0 0.0 1.0 2.0 3.0 0
1 4.0 5.0 6.0 7.0 0
2 8.0 9.0 10.0 11.0 0

In [105]: f1*f2
Out[105]: 
a b c d e
0 0.0 1.0 4.0 9.0 NaN
1 20.0 30.0 42.0 56.0 NaN
2 80.0 99.0 120.0 143.0 NaN
3 NaN NaN NaN NaN NaN
6784893-d75594258b90c272.png

DataFrame和Series之間的運算
計算一個二維陣列與其某行之間的差,出現的結果這就叫做廣播(broadcasting),如下:

In [106]: arr=np.arange(12.).reshape((3,4))

In [107]: arr
Out[107]: 
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]])

In [108]: arr[0]
Out[108]: array([ 0., 1., 2., 3.])

In [109]: arr-arr[0]
Out[109]: 
array([[ 0., 0., 0., 0.],
[ 4., 4., 4., 4.],
[ 8., 8., 8., 8.]])

預設情況下,DataFrame和Series之間的算術運算會將Series的索引匹配到DataFrame的列,然後沿著行一直向下廣播

In [110]: frame = DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),
     ...: index=['Utah', 'Ohio', 'Texas', 'Oregon'])

In [111]: frame
Out[111]: 
b d e
Utah 0.0 1.0 2.0
Ohio 3.0 4.0 5.0
Texas 6.0 7.0 8.0
Oregon 9.0 10.0 11.0

In [112]: series = frame.ix[0]

In [113]: series
Out[113]: 
b 0.0
d 1.0
e 2.0
Name: Utah, dtype: float64

In [114]: frame-series
Out[114]: 
b d e
Utah 0.0 0.0 0.0
Ohio 3.0 3.0 3.0
Texas 6.0 6.0 6.0
Oregon 9.0 9.0 9.0

如果某個索引值在DataFrame的列或Series的索引中找不到,則參與運算的兩個物件就會被重新索引以形成並集

In [115]: series2 = Series(range(3), index=['b', 'e', 'f'])

In [116]: series2
Out[116]: 
b 0
e 1
f 2
dtype: int32

In [117]: frame + series2
Out[117]: 
b d e f
Utah 0.0 NaN 3.0 NaN
Ohio 3.0 NaN 6.0 NaN
Texas 6.0 NaN 9.0 NaN
Oregon 9.0 NaN 12.0 NaN

如果你希望匹配行且在列上廣播,則必須使用算術運算方法。

In [118]: series3 = frame['d']

In [119]: series3
Out[119]: 
Utah 1.0
Ohio 4.0
Texas 7.0
Oregon 10.0
Name: d, dtype: float64

In [120]: frame
Out[120]: 
b d e
Utah 0.0 1.0 2.0
Ohio 3.0 4.0 5.0
Texas 6.0 7.0 8.0
Oregon 9.0 10.0 11.0

In [121]: frame.sub(series3, axis=0)
Out[121]: 
b d e
Utah -1.0 0.0 1.0
Ohio -1.0 0.0 1.0
Texas -1.0 0.0 1.0
Oregon -1.0 0.0 1.0

傳入的軸號就是希望匹配的軸。

函式應用和對映
NumPy的ufuncs(元素級陣列方法)也可用於操作pandas物件

In [122]: frame = DataFrame(np.random.randn(4, 3), columns=list('bde'),
     ...: index=['Utah', 'Ohio', 'Texas', 'Oregon'])

In [123]: frame
Out[123]: 
b d e
Utah -0.976531 -1.511940 -0.018721
Ohio 0.598117 0.047678 -0.058404
Texas 2.469704 0.027215 1.154004
Oregon 1.308615 -1.634739 0.096210

In [124]: np.abs(frame)
Out[124]: 
b d e
Utah 0.976531 1.511940 0.018721
Ohio 0.598117 0.047678 0.058404
Texas 2.469704 0.027215 1.154004
Oregon 1.308615 1.634739 0.096210

將函式應用到由各列或行所形成的一維陣列上。DataFrame的apply方法即可實現此功能。

In [125]: f = lambda x: x.max() - x.min()

In [126]: frame.apply(f)
Out[126]: 
b 3.446234
d 1.682417
e 1.212408
dtype: float64

In [127]: frame.apply(f,axis=1)
Out[127]: 
Utah 1.493219
Ohio 0.656521
Texas 2.442489
Oregon 2.943355
dtype: float64

sum和mean方法

In [128]: def f(x):
     ...: return Series([x.min(), x.max()], index=['min', 'max'])
     ...: 

In [129]: frame.apply(f)
Out[129]: 
b d e
min -0.976531 -1.634739 -0.058404
max 2.469704 0.047678 1.154004

得到frame中各個浮點值的格式化字串,使用applymap

In [128]: def f(x):
     ...:
return Series([x.min(), x.max()], index=['min', 'max'])
  

In [129]: frame.apply(f)
Out[129]: 
b d e
min -0.976531 -1.634739 -0.058404
max 2.469704 0.047678 1.154004

In [130]: format = lambda x: '%.2f' % x

In [131]: frame.applymap(format)
Out[131]: 
b d e
Utah -0.98 -1.51 -0.02
Ohio 0.60 0.05 -0.06
Texas 2.47 0.03 1.15
Oregon 1.31 -1.63 0.10

Series有一個用於應用元素級函式的map方法

In [132]: frame['e'].map(format)
Out[132]: 
Utah -0.02
Ohio -0.06
Texas 1.15
Oregon 0.10
Name: e, dtype: object

排序和排名
根據條件對資料集排序(sorting)也是一種重要的內建運算。要對行或列索引進行排序(按字典順序),可使用sort_index方法,它將返回一個已排序的新物件

In [133]: obj = Series(range(4), index=['d', 'a', 'b', 'c'])

In [134]: obj
Out[134]: 
d 0
a 1
b 2
c 3
dtype: int32

In [135]: obj.sort_index()
Out[135]: 
a 1
b 2
c 3
d 0
dtype: int32

DataFrame,則可以根據任意一個軸上的索引進行排序

In [136]: frame = DataFrame(np.arange(8).reshape((2, 4)), index=['three', 'one'],
     ...: columns=['d', 'a', 'b', 'c'])

In [137]: frame
Out[137]: 
d a b c
three 0 1 2 3
one 4 5 6 7

In [138]: frame.sort_index()
Out[138]: 
d a b c
one 4 5 6 7
three 0 1 2 3

In [139]: frame.sort_index(axis=1)
Out[139]: 
a b c d
three 1 2 3 0
one 5 6 7 4

資料預設是按升序排序的,但也可以降序排序

In [140]: frame.sort_index(axis=1,ascending=False)
Out[140]: 
d c b a
three 0 3 2 1
one 4 7 6 5

series通過索引進行排序

In [148]: obj = Series([6, 9, -8, 3])

In [149]: obj.sort_index()
Out[149]: 
0 6
1 9
2 -8
3 3
dtype: int64

series通過升值進行排序

In [150]: obj.sort_values()
Out[150]: 
2 -8
3 3
0 6
1 9
dtype: int64

在排序時,任何缺失值預設都會被放到Series的末尾,其中order不能排序,使用sort_values進行排序

In [151]: obj = Series([4, np.nan, 6, np.nan, -3, 3])

In [152]: obj.order()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-152-4fc888977b98> in <module>()
----> 1 obj.order()

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
2968 if name in self._info_axis:
2969 return self[name]
-> 2970 return object.__getattribute__(self, name)
2971 
2972 def __setattr__(self, name, value):

AttributeError: 'Series' object has no attribute 'order'

In [153]: obj.sort_values()
Out[153]: 
4 -3.0
5 3.0
0 4.0
2 6.0
1 NaN
3 NaN
dtype: float64

希望根據一個或多個列中的值進行排序,可以將一個或多個列的名字傳遞給by選項

In [154]: frame = DataFrame({'b': [5, 8, -6, 3], 'a': [0, 1, 0, 1]})

In [155]: frame
Out[155]: 
a b
0 0 5
1 1 8
2 0 -6
3 1 3

In [156]: frame.sort_index(by='b')
Out[156]: 
a b
2 0 -6
3 1 3
0 0 5
1 1 8

In [157]: frame.sort_index(by=['a','b'])
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)
"""Entry point for launching an IPython kernel.
Out[157]: 
a b
2 0 -6
0 0 5
3 1 3
1 1 8

要根據多個列進行排序,傳入名稱的列表

In [158]: frame.sort_values(by=['a','b'])
Out[158]: 
a b
2 0 -6
0 0 5
3 1 3
1 1 8

排名(ranking)跟排序關係密切,且它會增設一個排名值(從1開始,一直到陣列中有效資料的數量)。它跟numpy.argsort產生的間接排序索引差不多,只不過它可以根據某種規則破壞平級關係。預設情況下,rank是通過“為各組分配一個平均排名”的方式破壞平級關係的:

In [159]: obj = Series([8, -6, 5, 4, 2, 0, 4])

In [160]: obj
Out[160]: 
0 8
1 -6
2 5
3 4
4 2
5 0
6 4
dtype: int64

In [161]: obj.rank()
Out[161]: 
0 7.0
1 1.0
2 6.0
3 4.5
4 3.0
5 2.0
6 4.5
dtype: float64

可以根據值在原資料中出現的順序給出排名

In [162]: obj.rank(method='first')
Out[162]: 
0 7.0
1 1.0
2 6.0
3 4.0
4 3.0
5 2.0
6 5.0
dtype: float64

按降序進行排名

In [163]: obj.rank(ascending=False, method='max')
Out[163]: 
0 1.0
1 7.0
2 2.0
3 4.0
4 5.0
5 6.0
6 4.0
dtype: float64

在行或列上計算排名

In [164]: frame = DataFrame({'b': [4.3, 7, -3, 2], 'a': [0, 1, 0, 1],
     ...: 'c': [-2, 5, 8, -2.5]})

In [165]: frame
Out[165]: 
a b c
0 0 4.3 -2.0
1 1 7.0 5.0
2 0 -3.0 8.0
3 1 2.0 -2.5

In [166]: frame.rank(axis=1)
Out[166]: 
a b c
0 2.0 3.0 1.0
1 1.0 3.0 2.0
2 2.0 1.0 3.0
3 2.0 3.0 1.0
6784893-861f40410b68f256.png

帶有重複值的軸索引值的Series

In [167]: obj = Series(range(5), index=['a', 'a', 'b', 'b', 'c'])

In [168]: obj
Out[168]: 
a 0
a 1
b 2
b 3
c 4
dtype: int32

is_unique屬性可以告訴它的值是否是唯一的

In [169]: obj.index.is_unique
Out[169]: False

對於帶有重複值的索引,資料選取的行為將會有些不同。如果某個索引對應多個值,則返回一個Series;而對應單個值的,則返回一個標量值

In [170]: obj['a']
Out[170]: 
a 0
a 1
dtype: int32

In [171]: obj['c']
Out[171]: 4

In [172]: df = DataFrame(np.random.randn(4, 3), index=['a', 'a', 'b', 'b'])

In [173]: df
Out[173]: 
0 1 2
a -0.199619 -0.871154 -0.674903
a 1.573516 0.558822 0.511055
b 0.029318 -0.654353 -0.682175
b -0.563794 1.756565 0.105016

In [174]: df.ix['b']
Out[174]: 
0 1 2
b 0.029318 -0.654353 -0.682175
b -0.563794 1.756565 0.105016

通過練習認識到有些地方不能很好的理解,以後學習中慢慢理解各種函式的使用。

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