python数据分析实战-第4章-pandas库

Posted 2020-12-24 learnfromnow

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第4章　pandas库简介　　56

4.1　pandas：Python数据分析库　　56

4.2　安装　　57

4.2.1　用Anaconda安装　　57

4.2.2　用PyPI安装　　58

4.2.3　在Linux系统的安装方法　　58

4.2.4　用源代码安装　　58

4.2.5　Windows模块仓库　　59

4.3　测试pandas是否安装成功　　59

4.4　开始pandas之旅　　59

1 2	import pandas as pd import numpy as np

4.5　pandas数据结构简介　　60

4.5.1　Series对象　　60

1 2	s = pd.Series([12,-4,7,9]) s

0    12
1    -4
2     7
3     9
dtype: int64

1 2	s = pd.Series([12,-4,7,9], index=[‘a‘,‘b‘,‘c‘,‘d‘]) s

a    12
b    -4
c     7
d     9
dtype: int64

s.values

array([12, -4,  7,  9])

s.index

Index([‘a‘, ‘b‘, ‘c‘, ‘d‘], dtype=‘object‘)

1	s[2], s[‘b‘]

(7, -4)

s[0:2]

a    12
b    -4
dtype: int64

1	s[[‘b‘,‘c‘]]

b   -4
c    7
dtype: int64

1 2	s[1] = 0 s

a    12
b     0
c     7
d     9
dtype: int64

1 2	s[‘b‘] = 1 s

a    12
b     1
c     7
d     9
dtype: int64

1
2
3

arr = np.array([1,2,3,4])
s3 = pd.Series(arr)
s3

0    1
1    2
2    3
3    4
dtype: int64

1 2	s4 = pd.Series(s) s4

a    12
b     1
c     7
d     9
dtype: int64

1 2	arr[2] = -2 s3

0    1
1    2
2   -2
3    4
dtype: int64

s[s > 8]

a    12
d     9
dtype: int64

s / 2

a    6.0
b    0.5
c    3.5
d    4.5
dtype: float64

np.log(s)

a    2.484907
b    0.000000
c    1.945910
d    2.197225
dtype: float64

1	serd = pd.Series([1,0,2,1,2,3], index=[‘white‘,‘white‘,‘blue‘,‘green‘,‘green‘,‘yellow‘])

serd

white     1
white     0
blue      2
green     1
green     2
yellow    3
dtype: int64

1	serd.unique()

array([1, 0, 2, 3])

1	serd.value_counts()

2    2
1    2
3    1
0    1
dtype: int64

1	serd.isin([0,3])

white     False
white      True
blue      False
green     False
green     False
yellow     True
dtype: bool

1	serd[serd.isin([0,3])]

white     0
yellow    3
dtype: int64

1 2	s2 = pd.Series([5,-3,np.NaN,14]) s2

0     5.0
1    -3.0
2     NaN
3    14.0
dtype: float64

1	s2.isnull()

0    False
1    False
2     True
3    False
dtype: bool

1	s2.notnull()

0     True
1     True
2    False
3     True
dtype: bool

1	s2[s2.notnull()]

0     5.0
1    -3.0
3    14.0
dtype: float64

1	s2[s2.isnull()]

2   NaN
dtype: float64

1
2
3

mydict = {‘red‘: 2000, ‘blue‘: 1000, ‘yellow‘: 500, ‘orange‘: 1000}
myseries = pd.Series(mydict)
myseries

blue      1000
orange    1000
red       2000
yellow     500
dtype: int64

1
2
3

colors = [‘red‘,‘yellow‘,‘orange‘,‘blue‘,‘green‘]
myseries = pd.Series(mydict, index=colors)
myseries

red       2000.0
yellow     500.0
orange    1000.0
blue      1000.0
green        NaN
dtype: float64

1
2
3

mydict2 = {‘red‘:400,‘yellow‘:1000,‘black‘:700}
myseries2 = pd.Series(mydict2)
myseries2

black      700
red        400
yellow    1000
dtype: int64

1	myseries + myseries2

black        NaN
blue         NaN
green        NaN
orange       NaN
red       2400.0
yellow    1500.0
dtype: float64

4.5.2　DataFrame对象　　66

data = {‘color‘ : [‘blue‘,‘green‘,‘yellow‘,‘red‘,‘white‘],
‘object‘ : [‘ball‘,‘pen‘,‘pencil‘,‘paper‘,‘mug‘],
‘price‘ : [1.2,1.0,0.6,0.9,1.7]}
frame = pd.DataFrame(data)
frame

….	color	object	price
0	blue	ball	1.2
1	green	pen	1.0
2	yellow	pencil	0.6
3	red	paper	0.9
4	white	mug	1.7

1 2	frame2 = pd.DataFrame(data, columns=[‘object‘,‘price‘]) frame2

…..	object	price
0	ball	1.2
1	pen	1.0
2	pencil	0.6
3	paper	0.9
4	mug	1.7

1 2	frame2 = pd.DataFrame(data, index=[‘one‘,‘two‘,‘three‘,‘four‘,‘five‘]) frame2

….	color	object	price
one	blue	ball	1.2
two	green	pen	1.0
three	yellow	pencil	0.6
four	red	paper	0.9
five	white	mug	1.7

frame3 = pd.DataFrame(np.arange(16).reshape((4,4)),
    index=[‘red‘,‘blue‘,‘yellow‘,‘white‘],
    columns=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘])
frame3

…..	ball	pen	pencil	paper
red	0	1	2	3
blue	4	5	6	7
yellow	8	9	10	11
white	12	13	14	15

1	frame.columns

Index([‘color‘, ‘object‘, ‘price‘], dtype=‘object‘)

1	frame.index

RangeIndex(start=0, stop=5, step=1)

1	frame.values

array([[‘blue‘, ‘ball‘, 1.2],
       [‘green‘, ‘pen‘, 1.0],
       [‘yellow‘, ‘pencil‘, 0.6],
       [‘red‘, ‘paper‘, 0.9],
       [‘white‘, ‘mug‘, 1.7]], dtype=object)

1	frame[‘price‘]

0    1.2
1    1.0
2    0.6
3    0.9
4    1.7
Name: price, dtype: float64

1	frame.price

0    1.2
1    1.0
2    0.6
3    0.9
4    1.7
Name: price, dtype: float64

1	frame.ix[2]

color     yellow
object    pencil
price        0.6
Name: 2, dtype: object

1	frame.ix[[2,4]]

1	frame[0:1]

color    object    price

0 blue ball 1.2

1	frame[1:3]

color    object    price

1 green pen 1.0
2 yellow pencil 0.6

1	frame[‘object‘][3]

‘paper‘

1
2
3

frame.index.name = ‘id‘; 
frame.columns.name = ‘item‘
frame

item color object price
id
0 blue ball 1.2
1 green pen 1.0
2 yellow pencil 0.6
3 red paper 0.9
4 white mug 1.7

1 2	frame[‘new‘] = 12 frame

item color object price new
id
0 blue ball 1.2 12
1 green pen 1.0 12
2 yellow pencil 0.6 12
3 red paper 0.9 12
4 white mug 1.7 12

1 2	frame[‘new‘] = [3.0,1.3,2.2,0.8,1.1] frame

item color object price new
id
0 blue ball 1.2 3.0
1 green pen 1.0 1.3
2 yellow pencil 0.6 2.2
3 red paper 0.9 0.8
4 white mug 1.7 1.1

1 2	ser = pd.Series(np.arange(5)) ser

0    0
1    1
2    2
3    3
4    4
dtype: int64

1 2	frame[‘new‘] = ser frame

item color object price new
id
0 blue ball 1.2 0
1 green pen 1.0 1
2 yellow pencil 0.6 2
3 red paper 0.9 3
4 white mug 1.7 4

1 2	frame[‘price‘][2] = 3.3 frame

/home/vivoadmin/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

item color object price new
id
0 blue ball 1.2 0
1 green pen 1.0 1
2 yellow pencil 3.3 2
3 red paper 0.9 3
4 white mug 1.7 4

1	frame.isin([1.0,‘pen‘])

item color object price new
id
0 False False False False
1 False True True True
2 False False False False
3 False False False False
4 False False False False

1	frame[frame.isin([1.0,‘pen‘])]

item color object price new
id
0 NaN NaN NaN NaN
1 NaN pen 1.0 1.0
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN

1 2	del frame[‘new‘] frame

item color object price
id
0 blue ball 1.2
1 green pen 1.0
2 yellow pencil 3.3
3 red paper 0.9
4 white mug 1.7

1	frame[frame < 1]

item color object price
id
0 blue ball NaN
1 green pen NaN
2 yellow pencil NaN
3 red paper 0.9
4 white mug NaN

nestdict = {‘red‘:{2012: 22, 2013: 33},
    ‘white‘:{2011: 13, 2012: 22, 2013: 16},
    ‘blue‘: {2011: 17, 2012: 27, 2013: 18}}
frame2 = pd.DataFrame(nestdict)
frame2

blue    red    white

2011 17 NaN 13
2012 27 22.0 22
2013 18 33.0 16

frame2.T

2011    2012    2013

blue 17.0 27.0 18.0
red NaN 22.0 33.0
white 13.0 22.0 16.0

4.5.3　Index对象　　72

1 2	ser = pd.Series([5,0,3,8,4], index=[‘red‘,‘blue‘,‘yellow‘,‘white‘,‘green‘]) ser.index

Index([‘red‘, ‘blue‘, ‘yellow‘, ‘white‘, ‘green‘], dtype=‘object‘)

1	ser.idxmin(), ser.idxmax()

(‘blue‘, ‘white‘)

1 2	serd = pd.Series(range(6), index=[‘white‘,‘white‘,‘blue‘,‘green‘,‘green‘,‘yellow‘]) serd

white     0
white     1
blue      2
green     3
green     4
yellow    5
dtype: int64

1	serd[‘white‘]

white    0
white    1
dtype: int64

1	serd.index.is_unique

False

1	frame.index.is_unique

True

4.6　索引对象的其他功能　　74

4.6.1　更换索引　　74

1 2	ser = pd.Series([2,5,7,4], index=[‘one‘,‘two‘,‘three‘,‘four‘]) ser

one      2
two      5
three    7
four     4
dtype: int64

1	ser.reindex([‘three‘,‘four‘,‘five‘,‘one‘])

three    7.0
four     4.0
five     NaN
one      2.0
dtype: float64

1 2	ser3 = pd.Series([1,5,6,3],index=[0,3,5,6]) ser3

0    1
3    5
5    6
6    3
dtype: int64

1	ser3.reindex(range(6),method=‘ffill‘)

0    1
1    1
2    1
3    5
4    5
5    6
dtype: int64

1	ser3.reindex(range(6),method=‘bfill‘)

0    1
1    5
2    5
3    5
4    6
5    6
dtype: int64

item colors price new object
id
0 blue 1.2 blue ball
1 green 1.0 green pen
2 yellow 3.3 yellow pencil
3 red 0.9 red paper
4 white 1.7 white mug

4.6.2　删除　　75

1 2	ser = pd.Series(np.arange(4.), index=[‘red‘,‘blue‘,‘yellow‘,‘white‘]) ser

red       0.0
blue      1.0
yellow    2.0
white     3.0
dtype: float64

1	ser.drop(‘yellow‘)

red      0.0
blue     1.0
white    3.0
dtype: float64

1	ser.drop([‘blue‘,‘white‘])

red       0.0
yellow    2.0
dtype: float64

frame = pd.DataFrame(np.arange(16).reshape((4,4)),
    index=[‘red‘,‘blue‘,‘yellow‘,‘white‘],
    columns=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘])
frame

ball    pen    pencil    paper

red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

1	frame.drop([‘blue‘,‘yellow‘])

ball pen pencil paper
red 0 1 2 3
white 12 13 14 15

1	frame.drop([‘pen‘,‘pencil‘],axis=1)

ball    paper

red 0 3
blue 4 7
yellow 8 11
white 12 15

4.6.3　算术和数据对齐　　77

1 2	s1 = pd.Series([3,2,5,1],[‘white‘,‘yellow‘,‘green‘,‘blue‘]) s2 = pd.Series([1,4,7,2,1],[‘white‘,‘yellow‘,‘black‘,‘blue‘,‘brown‘])

s1 + s2

black     NaN
blue      3.0
brown     NaN
green     NaN
white     4.0
yellow    6.0
dtype: float64

frame1 = pd.DataFrame(np.arange(16).reshape((4,4)),
    index=[‘red‘,‘blue‘,‘yellow‘,‘white‘],
    columns=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘])
frame2 = pd.DataFrame(np.arange(12).reshape((4,3)),
    index=[‘blue‘,‘green‘,‘white‘,‘yellow‘],
    columns=[‘mug‘,‘pen‘,‘ball‘])

frame1

ball    pen    pencil    paper

red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

frame2

mug    pen    ball

blue 0 1 2
green 3 4 5
white 6 7 8
yellow 9 10 11

1	frame1 + frame2

ball    mug    paper    pen    pencil

blue 6.0 NaN NaN 6.0 NaN
green NaN NaN NaN NaN NaN
red NaN NaN NaN NaN NaN
white 20.0 NaN NaN 20.0 NaN
yellow 19.0 NaN NaN 19.0 NaN

4.7　数据结构之间的运算　　78

4.7.1　灵活的算术运算方法　　78

1	frame1.add(frame2)

ball    mug    paper    pen    pencil

blue 6.0 NaN NaN 6.0 NaN
green NaN NaN NaN NaN NaN
red NaN NaN NaN NaN NaN
white 20.0 NaN NaN 20.0 NaN
yellow 19.0 NaN NaN 19.0 NaN

4.7.2　DataFrame和Series对象之间的运算　　78

frame = pd.DataFrame(np.arange(16).reshape((4,4)),
    index=[‘red‘,‘blue‘,‘yellow‘,‘white‘],
    columns=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘])
frame

ball    pen    pencil    paper

red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

1 2	ser = pd.Series(np.arange(4), index=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘]) ser

ball      0
pen       1
pencil    2
paper     3
dtype: int64

1	frame - ser

ball    pen    pencil    paper

red 0 0 0 0
blue 4 4 4 4
yellow 8 8 8 8
white 12 12 12 12

1 2	ser[‘mug‘] = 9 ser

ball      0
pen       1
pencil    2
paper     3
mug       9
dtype: int64

1	frame - ser

ball    mug    paper    pen    pencil

red 0 NaN 0 0 0
blue 4 NaN 4 4 4
yellow 8 NaN 8 8 8
white 12 NaN 12 12 12

4.8　函数应用和映射　　79

4.8.1　操作元素的函数　　79

frame = pd.DataFrame(np.arange(16).reshape((4,4)),
    index=[‘red‘,‘blue‘,‘yellow‘,‘white‘],
    columns=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘])
frame

ball    pen    pencil    paper

red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

1	np.sqrt(frame)

ball    pen    pencil    paper

red 0.000000 1.000000 1.414214 1.732051
blue 2.000000 2.236068 2.449490 2.645751
yellow 2.828427 3.000000 3.162278 3.316625
white 3.464102 3.605551 3.741657 3.872983

4.8.2　按行或列执行操作的函数　　80

1
2
3

f = lambda x: x.max() - x.min()
def f(x):
    return x.max() - x.min()

1	frame.apply(f)

ball      12
pen       12
pencil    12
paper     12
dtype: int64

1	frame.apply(f, axis=1)

red       3
blue      3
yellow    3
white     3
dtype: int64

1 2	def f(x): return pd.Series([x.min(), x.max()], index=[‘min‘,‘max‘])

1	frame.apply(f)

ball    pen    pencil    paper

min 0 1 2 3
max 12 13 14 15

4.8.3　统计函数　　81

1	frame.sum()

ball      24
pen       28
pencil    32
paper     36
dtype: int64

1	frame.mean()

ball      6.0
pen       7.0
pencil    8.0
paper     9.0
dtype: float64

1	frame.describe()

ball    pen    pencil    paper

count 4.000000 4.000000 4.000000 4.000000
mean 6.000000 7.000000 8.000000 9.000000
std 5.163978 5.163978 5.163978 5.163978
min 0.000000 1.000000 2.000000 3.000000
25% 3.000000 4.000000 5.000000 6.000000
50% 6.000000 7.000000 8.000000 9.000000
75% 9.000000 10.000000 11.000000 12.000000
max 12.000000 13.000000 14.000000 15.000000

4.9　排序和排位次　　81

1 2	ser = pd.Series([5,0,3,8,4], index=[‘red‘,‘blue‘,‘yellow‘,‘white‘,‘green‘]) ser

red       5
blue      0
yellow    3
white     8
green     4
dtype: int64

1	ser.sort_index()

blue      0
green     4
red       5
white     8
yellow    3
dtype: int64

1	ser.sort_index(ascending=False)

yellow    3
white     8
red       5
green     4
blue      0
dtype: int64

frame = pd.DataFrame(np.arange(16).reshape((4,4)),
    index=[‘red‘,‘blue‘,‘yellow‘,‘white‘],
    columns=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘])
frame

ball    pen    pencil    paper

red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

1	frame.sort_index()

ball    pen    pencil    paper

blue 4 5 6 7
red 0 1 2 3
white 12 13 14 15
yellow 8 9 10 11

1	frame.sort_index(axis=1)

ball    paper    pen    pencil

red 0 3 1 2
blue 4 7 5 6
yellow 8 11 9 10
white 12 15 13 14

1	ser.order()

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-131-bb828ac4164f> in <module>()
----> 1 ser.order()


~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)
   3612             if name in self._info_axis:
   3613                 return self[name]
-> 3614             return object.__getattribute__(self, name)
   3615 
   3616     def __setattr__(self, name, value):


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

1	frame.sort_index(by=‘pen‘)

/home/vivoadmin/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: by argument to sort_index is deprecated, please use .sort_values(by=...)
  """Entry point for launching an IPython kernel.



ball    pen    pencil    paper

red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

1	frame.sort_index(by=[‘pen‘,‘pencil‘])

/home/vivoadmin/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: by argument to sort_index is deprecated, please use .sort_values(by=...)
  """Entry point for launching an IPython kernel.

ball pen pencil paper
red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

1	ser.rank()

red       4.0
blue      1.0
yellow    2.0
white     5.0
green     3.0
dtype: float64

1	ser.rank(method=‘first‘)

red       4.0
blue      1.0
yellow    2.0
white     5.0
green     3.0
dtype: float64

1	ser.rank(ascending=False)

red       2.0
blue      5.0
yellow    4.0
white     1.0
green     3.0
dtype: float64

4.10　相关性和协方差　　84

1
2

seq2 = pd.Series([3,4,3,4,5,4,3,2],[‘2006‘,‘2007‘,‘2008‘,‘2009‘,‘2010‘,‘2011‘,‘2012‘,‘2013‘])
seq = pd.Series([1,2,3,4,4,3,2,1],[‘2006‘,‘2007‘,‘2008‘,‘2009‘,‘2010‘,‘2011‘,‘2012‘,‘2013‘])

1	seq.corr(seq2)

0.7745966692414835

1	seq.cov(seq2)

0.8571428571428571

frame2 = pd.DataFrame([[1,4,3,6],[4,5,6,1],[3,3,1,5],[4,1,6,4]],
    index=[‘red‘,‘blue‘,‘yellow‘,‘white‘],
    columns=[‘ball‘,‘pen‘,‘pencil‘,‘paper‘])
frame2

ball    pen    pencil    paper

red 1 4 3 6
blue 4 5 6 1
yellow 3 3 1 5
white 4 1 6 4

1	frame2.corr()

ball    pen    pencil    paper

ball 1.000000 -0.276026 0.577350 -0.763763
pen -0.276026 1.000000 -0.079682 -0.361403
pencil 0.577350 -0.079682 1.000000 -0.692935
paper -0.763763 -0.361403 -0.692935 1.000000

1	frame2.cov()

ball    pen    pencil    paper

ball 2.000000 -0.666667 2.000000 -2.333333
pen -0.666667 2.916667 -0.333333 -1.333333
pencil 2.000000 -0.333333 6.000000 -3.666667
paper -2.333333 -1.333333 -3.666667 4.666667

ser

red       5
blue      0
yellow    3
white     8
green     4
dtype: int64

1	frame2.corrwith(ser)

ball     -0.140028
pen      -0.869657
pencil    0.080845
paper     0.595854
dtype: float64

1	frame2.corrwith(frame)

ball      0.730297
pen      -0.831522
pencil    0.210819
paper    -0.119523
dtype: float64

4.11　NaN数据　　85

4.11.1　为元素赋NaN值　　85

1 2	ser = pd.Series([0,1,2,np.NaN,9], index=[‘red‘,‘blue‘,‘yellow‘,‘white‘,‘green‘]) ser

red       0.0
blue      1.0
yellow    2.0
white     NaN
green     9.0
dtype: float64

1 2	ser[‘white‘] = None ser

red       0.0
blue      1.0
yellow    2.0
white     NaN
green     9.0
dtype: float64

4.11.2　过滤NaN　　86

1	ser.dropna()

red       0.0
blue      1.0
yellow    2.0
green     9.0
dtype: float64

1	ser[ser.notnull()]

red       0.0
blue      1.0
yellow    2.0
green     9.0
dtype: float64

frame3 = pd.DataFrame([[6,np.nan,6],[np.nan,np.nan,np.nan],[2,np.nan,5]],
    index = [‘blue‘,‘green‘,‘red‘],
    columns = [‘ball‘,‘mug‘,‘pen‘])
frame3

ball    mug    pen

blue 6.0 NaN 6.0
green NaN NaN NaN
red 2.0 NaN 5.0

1	frame3.dropna()

ball mug pen

1	frame3.dropna(how=‘all‘)

ball    mug    pen

blue 6.0 NaN 6.0
red 2.0 NaN 5.0

4.11.3　为NaN元素填充其他值　　86

1	frame3.fillna(0)

ball    mug    pen

blue 6.0 0.0 6.0
green 0.0 0.0 0.0
red 2.0 0.0 5.0

1	frame3.fillna({‘ball‘:1,‘mug‘:0,‘pen‘:99})

ball    mug    pen

blue 6.0 0.0 6.0
green 1.0 0.0 99.0
red 2.0 0.0 5.0

4.12　等级索引和分级　　87

mser = pd.Series(np.random.rand(8),
    index=[[‘white‘,‘white‘,‘white‘,‘blue‘,‘blue‘,‘red‘,‘red‘,‘red‘],
    [‘up‘,‘down‘,‘right‘,‘up‘,‘down‘,‘up‘,‘down‘,‘left‘]])
mser

white  up       0.941438
       down     0.165728
       right    0.492898
blue   up       0.902410
       down     0.879385
red    up       0.646074
       down     0.210103
       left     0.476333
dtype: float64

1	mser.index

MultiIndex(levels=[[‘blue‘, ‘red‘, ‘white‘], [‘down‘, ‘left‘, ‘right‘, ‘up‘]],
           labels=[[2, 2, 2, 0, 0, 1, 1, 1], [3, 0, 2, 3, 0, 3, 0, 1]])

1	mser[‘white‘]

up       0.941438
down     0.165728
right    0.492898
dtype: float64

1	mser[:,‘up‘]

white    0.941438
blue     0.902410
red      0.646074
dtype: float64

1	mser[‘white‘,‘up‘]

0.9414384939430696

1	mser.unstack()

down    left    right    up

blue 0.879385 NaN NaN 0.902410
red 0.210103 0.476333 NaN 0.646074
white 0.165728 NaN 0.492898 0.941438

frame

ball    pen    pencil    paper

red 0 1 2 3
blue 4 5 6 7
yellow 8 9 10 11
white 12 13 14 15

1	frame.stack()

red     ball       0
        pen        1
        pencil     2
        paper      3
blue    ball       4
        pen        5
        pencil     6
        paper      7
yellow  ball       8
        pen        9
        pencil    10
        paper     11
white   ball      12
        pen       13
        pencil    14
        paper     15
dtype: int64

mframe = pd.DataFrame(np.random.randn(16).reshape(4,4),
    index=[[‘white‘,‘white‘,‘red‘,‘red‘], [‘up‘,‘down‘,‘up‘,‘down‘]],
    columns=[[‘pen‘,‘pen‘,‘paper‘,‘paper‘],[1,2,1,2]])
mframe

    pen    paper

1 2 1 2
white up 0.992202 -1.037634 -0.083882 -0.028204
down -1.758579 1.832742 -0.167739 0.174567
red up 0.983876 2.306897 -1.003258 -0.591767
down 1.398166 0.054964 -1.705692 -0.375007

4.12.1　重新调整顺序和为层级排序　　89

1
2
3

mframe.columns.names = [‘objects‘,‘id‘]
mframe.index.names = [‘colors‘,‘status‘]
mframe

objects    pen    paper

id 1 2 1 2
colors status
white up 0.992202 -1.037634 -0.083882 -0.028204
down -1.758579 1.832742 -0.167739 0.174567
red up 0.983876 2.306897 -1.003258 -0.591767
down 1.398166 0.054964 -1.705692 -0.375007

1	mframe.swaplevel(‘colors‘,‘status‘)

objects    pen    paper

id 1 2 1 2
status colors
up white 0.992202 -1.037634 -0.083882 -0.028204
down white -1.758579 1.832742 -0.167739 0.174567
up red 0.983876 2.306897 -1.003258 -0.591767
down red 1.398166 0.054964 -1.705692 -0.375007

1	mframe.sortlevel(‘colors‘)

/home/vivoadmin/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: sortlevel is deprecated, use sort_index(level= ...)
  """Entry point for launching an IPython kernel.



objects    pen    paper

id 1 2 1 2
colors status
red down 1.398166 0.054964 -1.705692 -0.375007
up 0.983876 2.306897 -1.003258 -0.591767
white down -1.758579 1.832742 -0.167739 0.174567
up 0.992202 -1.037634 -0.083882 -0.028204

4.12.2　按层级统计数据　　89

1	mframe.sum(level=‘colors‘)

objects pen paper
id 1 2 1 2
colors
white -0.766377 0.795107 -0.251622 0.146363
red 2.382042 2.361861 -2.708950 -0.966774

1	mframe.sum(level=‘id‘, axis=1)

id    1    2

colors status
white up 0.908320 -1.065838
down -1.926319 2.007309
red up -0.019382 1.715131
down -0.307526 -0.320044

4.13　小结　　90

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第4章 pandas库简介 56

4.1 pandas：Python数据分析库 56

4.2 安装 57

4.2.1 用Anaconda安装 57

4.2.2 用PyPI安装 58

4.2.3 在Linux系统的安装方法 58

4.2.4 用源代码安装 58

4.2.5 Windows模块仓库 59

4.3 测试pandas是否安装成功 59

4.4 开始pandas之旅 59

4.5 pandas数据结构简介 60

4.5.1 Series对象 60

4.5.2 DataFrame对象 66

4.5.3 Index对象 72

4.6 索引对象的其他功能 74

4.6.1 更换索引 74

4.6.2 删除 75

4.6.3 算术和数据对齐 77

4.7 数据结构之间的运算 78

4.7.1 灵活的算术运算方法 78

4.7.2 DataFrame和Series对象之间的运算 78

4.8 函数应用和映射 79

4.8.1 操作元素的函数 79

4.8.2 按行或列执行操作的函数 80

4.8.3 统计函数 81

4.9 排序和排位次 81

4.10 相关性和协方差 84

4.11 NaN数据 85

4.11.1 为元素赋NaN值 85

4.11.2 过滤NaN 86

4.11.3 为NaN元素填充其他值 86

4.12 等级索引和分级 87

4.12.1 重新调整顺序和为层级排序 89

4.12.2 按层级统计数据 89

4.13 小结 90

第4章　pandas库简介　　56

4.1　pandas：Python数据分析库　　56

4.2　安装　　57

4.2.1　用Anaconda安装　　57

4.2.2　用PyPI安装　　58

4.2.3　在Linux系统的安装方法　　58

4.2.4　用源代码安装　　58

4.2.5　Windows模块仓库　　59

4.3　测试pandas是否安装成功　　59

4.4　开始pandas之旅　　59

4.5　pandas数据结构简介　　60

4.5.1　Series对象　　60

4.5.2　DataFrame对象　　66

4.5.3　Index对象　　72

4.6　索引对象的其他功能　　74

4.6.1　更换索引　　74

4.6.2　删除　　75

4.6.3　算术和数据对齐　　77

4.7　数据结构之间的运算　　78

4.7.1　灵活的算术运算方法　　78

4.7.2　DataFrame和Series对象之间的运算　　78

4.8　函数应用和映射　　79

4.8.1　操作元素的函数　　79

4.8.2　按行或列执行操作的函数　　80

4.8.3　统计函数　　81

4.9　排序和排位次　　81

4.10　相关性和协方差　　84

4.11　NaN数据　　85

4.11.1　为元素赋NaN值　　85

4.11.2　过滤NaN　　86

4.11.3　为NaN元素填充其他值　　86

4.12　等级索引和分级　　87

4.12.1　重新调整顺序和为层级排序　　89

4.12.2　按层级统计数据　　89

4.13　小结　　90