文章目錄 第1章 數(shù)據(jù)與統(tǒng)計(jì)資料 1.1 統(tǒng)計(jì)學(xué)在商務(wù)經(jīng)濟(jì)中的應(yīng)用 1.2 數(shù)據(jù) 1.3 數(shù)據(jù)來(lái)源 1.4 描述統(tǒng)計(jì) 1.5 統(tǒng)計(jì)推斷 1.6 邏輯分析方法 1.7 大數(shù)據(jù)與數(shù)據(jù)挖掘 1.8 計(jì)算機(jī)與統(tǒng)計(jì)分析 1.9 統(tǒng)計(jì)實(shí)踐的道德準(zhǔn)則 第2章 描述統(tǒng)計(jì)學(xué)1:表格法和圖形法 2.1 匯總分類(lèi)變量的數(shù)據(jù) 條形圖及樣例（bar chart） 餅形圖及樣例（pie chart） 2.2 匯總數(shù)量變量的數(shù)據(jù) 單變量：打點(diǎn)圖（dot plot） 單變量：直方圖（histogram） 單變量：累積分布（displot） 單變量：莖葉顯示（stem-and-leaf display） 2.3 用表格方法匯總兩個(gè)變量的數(shù)據(jù) 2.4 用圖形顯示方法匯總兩個(gè)變量的數(shù)據(jù) 散點(diǎn)圖（scatter diagram)和趨勢(shì)線(trendline) 復(fù)合條形圖（side-by-side bar chart)和結(jié)構(gòu)條形圖(stacked chart) 2.5 數(shù)據(jù)可視化：創(chuàng)建有效圖形顯示的最佳實(shí)踐 第一次讀本書(shū)的時(shí)候，因?yàn)橛写髮W(xué)課程的基礎(chǔ)，更關(guān)注于技術(shù)性的內(nèi)容和理解，而忽略了看似簡(jiǎn)單的基礎(chǔ)知識(shí)。實(shí)際上這應(yīng)該是入門(mén)新手的通病，總是著眼于實(shí)用性?xún)?nèi)容，而忽略基礎(chǔ)知識(shí)。雖然這樣做有助于維持學(xué)習(xí)興趣，幫助新人堅(jiān)持到入門(mén)，然后在實(shí)踐之中反過(guò)來(lái)學(xué)習(xí)基礎(chǔ)知識(shí)。但是最好在第一次學(xué)習(xí)就能認(rèn)識(shí)到基礎(chǔ)知識(shí)的重要性，并且盡量掌握。最好的辦法就是做習(xí)題。
最初是為了學(xué)習(xí)數(shù)據(jù)分析，然而當(dāng)業(yè)內(nèi)人士說(shuō)數(shù)據(jù)分析最重要的知識(shí)是‘描述統(tǒng)計(jì)學(xué)’，我記憶中卻是將其歸為顯淺知識(shí)，囫圇吞棗。
第1章 數(shù)據(jù)與統(tǒng)計(jì)資料
1.1 統(tǒng)計(jì)學(xué)在商務(wù)經(jīng)濟(jì)中的應(yīng)用
會(huì)計(jì)、財(cái)務(wù)、市場(chǎng)營(yíng)銷(xiāo)、生產(chǎn)、經(jīng)濟(jì)、信息系統(tǒng)
1.2 數(shù)據(jù)
數(shù)據(jù)、數(shù)據(jù)集、個(gè)體、變量、觀測(cè)值、分類(lèi)型數(shù)據(jù)、分類(lèi)變量、數(shù)量型數(shù)據(jù)、數(shù)量變量、截面數(shù)據(jù)、時(shí)間序列數(shù)據(jù)
1.3 數(shù)據(jù)來(lái)源
來(lái)源有：現(xiàn)有來(lái)源、觀測(cè)性研究、實(shí)驗(yàn)，需要注意：時(shí)間與成本問(wèn)題、數(shù)據(jù)采集誤差
1.4 描述統(tǒng)計(jì)
將數(shù)據(jù)以表格、圖形或數(shù)值形式匯總的統(tǒng)計(jì)方法
1.5 統(tǒng)計(jì)推斷
總體、樣本、普查、抽樣調(diào)查
1.6 邏輯分析方法
邏輯分析方法包括：
1.7 大數(shù)據(jù)與數(shù)據(jù)挖掘
大數(shù)據(jù)容量（volume）、速度（velocity）、種類(lèi)（variety），3V
1.8 計(jì)算機(jī)與統(tǒng)計(jì)分析
1.9 統(tǒng)計(jì)實(shí)踐的道德準(zhǔn)則
統(tǒng)計(jì)是搜集、分析、表述、和解析數(shù)據(jù)的藝術(shù)和科學(xué)
第2章 描述統(tǒng)計(jì)學(xué)1:表格法和圖形法
2.1 匯總分類(lèi)變量的數(shù)據(jù)
頻數(shù)分布、相對(duì)頻數(shù)分布、百分比頻數(shù)分布
條形圖及樣例（bar chart）
條形圖（bar chat）描述：頻數(shù)分布、相對(duì)頻數(shù)分布、百分比頻數(shù)分布，分類(lèi)變量的條形圖，應(yīng)該有一定的間隔
from matplotlib
import pyplot
as plt
x
, y
, x2
, y2
= [ 5 , 8 , 10 ] , [ 12 , 16 , 6 ] , [ 6 , 9 , 11 ] , [ 6 , 15 , 7 ]
plt
. bar
( x
, y
, align
= 'center' )
plt
. bar
( x2
, y2
, color
= 'g' , align
= 'center' )
plt
. title
( 'Bar graph' )
plt
. ylabel
( 'Y axis' )
plt
. xlabel
( 'X axis' )
plt
. show
( )
import numpy
as np
import matplotlib
. pyplot
as plt
np
. random
. seed
( 19680801 )
N
= 20
theta
= np
. linspace
( 0.0 , 2 * np
. pi
, N
, endpoint
= False )
radii
= 10 * np
. random
. rand
( N
)
width
= np
. pi
/ 4 * np
. random
. rand
( N
)
colors
= plt
. cm
. viridis
( radii
/ 10 . )
ax
= plt
. subplot
( 111 , projection
= 'polar' )
ax
. bar
( theta
, radii
, width
= width
, bottom
= 0.0 , color
= colors
, alpha
= 0.5 )
plt
. show
( )
ax
= sns
. barplot
( x
= "day" , y
= "total_bill" , hue
= "sex" , data
= tips
)
餅形圖及樣例（pie chart）
餅形圖（pie chat）描述：相對(duì)頻數(shù)分布、百分比頻數(shù)分布（相對(duì)角度差異，人更能判斷長(zhǎng)度間的差異，所以最好標(biāo)注比例）
import matplotlib
. pyplot
as plt
labels
= 'Frogs' , 'Hogs' , 'Dogs' , 'Logs'
sizes
= [ 15 , 30 , 45 , 10 ]
explode
= ( 0 , 0.1 , 0 , 0 )
fig1
, ax1
= plt
. subplots
( )
ax1
. pie
( sizes
, explode
= explode
, labels
= labels
, autopct
= '%1.1f%%' , shadow
= True , startangle
= 90 )
ax1
. axis
( 'equal' )
plt
. show
( )
import numpy
as np
import matplotlib
. pyplot
as plt
fig
, ax
= plt
. subplots
( figsize
= ( 6 , 3 ) , subplot_kw
= dict ( aspect
= "equal" ) )
recipe
= [ "375 g flour" , "75 g sugar" , "250 g butter" , "300 g berries" ]
data
= [ float ( x
. split
( ) [ 0 ] ) for x
in recipe
]
ingredients
= [ x
. split
( ) [ - 1 ] for x
in recipe
]
def func ( pct
, allvals
) : absolute
= int ( pct
/ 100 . * np
. sum ( allvals
) ) return "{:.1f}%\n({:d} g)" . format ( pct
, absolute
)
wedges
, texts
, autotexts
= ax
. pie
( data
, autopct
= lambda pct
: func
( pct
, data
) , textprops
= dict ( color
= "w" ) )
ax
. legend
( wedges
, ingredients
, title
= "Ingredients" , loc
= "center left" , bbox_to_anchor
= ( 1 , 0 , 0.5 , 1 ) )
plt
. setp
( autotexts
, size
= 8 , weight
= "bold" )
ax
. set_title
( "Matplotlib bakery: A pie" )
plt
. show
( )
fig
, ax
= plt
. subplots
( figsize
= ( 6 , 3 ) , subplot_kw
= dict ( aspect
= "equal" ) )
recipe
= [ "225 g flour" , "90 g sugar" , "1 egg" , "60 g butter" , "100 ml milk" , "1/2 package of yeast" ]
data
= [ 225 , 90 , 50 , 60 , 100 , 5 ]
wedges
, texts
= ax
. pie
( data
, wedgeprops
= dict ( width
= 0.5 ) , startangle
= - 40 )
bbox_props
= dict ( boxstyle
= "square,pad=0.3" , fc
= "w" , ec
= "k" , lw
= 0.72 )
kw
= dict ( arrowprops
= dict ( arrowstyle
= "-" ) , bbox
= bbox_props
, zorder
= 0 , va
= "center" )
for i
, p
in enumerate ( wedges
) : ang
= ( p
. theta2
- p
. theta1
) / 2 . + p
. theta1y
= np
. sin
( np
. deg2rad
( ang
) ) x
= np
. cos
( np
. deg2rad
( ang
) ) horizontalalignment
= { - 1 : "right" , 1 : "left" } [ int ( np
. sign
( x
) ) ] connectionstyle
= "angle,angleA=0,angleB={}" . format ( ang
) kw
[ "arrowprops" ] . update
( { "connectionstyle" : connectionstyle
} ) ax
. annotate
( recipe
[ i
] , xy
= ( x
, y
) , xytext
= ( 1.35 * np
. sign
( x
) , 1.4 * y
) , horizontalalignment
= horizontalalignment
, ** kw
)
ax
. set_title
( "Matplotlib bakery: A donut" )
plt
. show
( )
Pandas 畫(huà)圖一個(gè)函數(shù)應(yīng)該夠用了，參數(shù)詳解
DataFrame
. plot
( x
= None , y
= None , kind
= 'line' , ax
= None , subplots
= False , sharex
= None , sharey
= False , layout
= None , figsize
= None , use_index
= True , title
= None , grid
= None , legend
= True , style
= None , logx
= False , logy
= False , loglog
= False , xticks
= None , yticks
= None , xlim
= None , ylim
= None , rot
= None , xerr
= None , secondary_y
= False , sort_columns
= False , ** kwds
)
樣例 Matplotlib examples
2.2 匯總數(shù)量變量的數(shù)據(jù)
組數(shù)、組寬、組限、組中值、相對(duì)頻數(shù)分布、百分比頻數(shù)分布、累積頻數(shù)分布
單變量：打點(diǎn)圖（dot plot）
使用 matplotlib.scatter,seaborn.swarmplot模擬
import numpy
as np
import matplotlib
. pyplot
as plt
import seaborn
as sns
import pandas
as pd
from matplotlib
. pyplot
import MultipleLocator
fig
, ax
= plt
. subplots
( 1 , 2 , figsize
= ( 12 , 2 ) )
np
. random
. seed
( 1900 )
x
= np
. random
. randint
( 1 , 99 , size
= 20 )
data
= pd
. DataFrame
( x
, columns
= [ 'x' ] )
data
[ 'y' ] = 1
for i
in range ( len ( data
) ) : data
[ 'y' ] . at
[ i
] = data
[ 'x' ] . iloc
[ : i
+ 1 ] [ data
[ 'x' ] . iloc
[ : i
+ 1 ] == data
[ 'x' ] . at
[ i
] ] . count
( )
plt
. subplot
( 121 ) plt
. scatter
( data
[ 'x' ] , data
[ 'y' ] )
plt
. tick_params
( axis
= 'both' , which
= 'major' )
sns
. swarmplot
( x
= "x" , y
= "y" , palette
= [ "r" , "c" , "y" ] , data
= data
, ax
= ax
[ 1 ] )
plt
. show
( )
單變量：直方圖（histogram）
與條形圖原理一樣，只是數(shù)量型變量進(jìn)行分組，方條之間無(wú)間隔
from matplotlib
import pyplot
as plt
import numpy
as np
np
. random
. seed
( 1900 )
x
= np
. random
. randint
( 1 , 99 , size
= 50 )
plt
. hist
( x
, bins
= [ 0 , 20 , 40 , 60 , 80 , 100 ] )
plt
. show
( )
單變量：累積分布（displot）
累積分布如果使用matplotlib則需要計(jì)算累積量，使用seaborn.displot，一口氣能畫(huà)4張圖Distribution plot options
import numpy
as np
import seaborn
as sns
import matplotlib
. pyplot
as plt
sns
. set ( style
= "white" , palette
= "muted" , color_codes
= True )
rs
= np
. random
. RandomState
( 10 )
f
, axes
= plt
. subplots
( 2 , 2 , figsize
= ( 7 , 7 ) , sharex
= True )
sns
. despine
( left
= True )
d
= rs
. normal
( size
= 100 )
sns
. distplot
( d
, kde
= False , color
= "b" , ax
= axes
[ 0 , 0 ] )
sns
. distplot
( d
, hist
= False , rug
= True , color
= "r" , ax
= axes
[ 0 , 1 ] )
sns
. distplot
( d
, hist
= False , color
= "g" , kde_kws
= { "shade" : True } , ax
= axes
[ 1 , 0 ] )
sns
. distplot
( d
, color
= "m" , ax
= axes
[ 1 , 1 ] )
plt
. setp
( axes
, yticks
= [ ] )
plt
. tight_layout
( )
單變量：莖葉顯示（stem-and-leaf display）
暫時(shí)沒(méi)找到莖葉圖的庫(kù)，手動(dòng)實(shí)現(xiàn)
0 | 6 9 8 4
import numpy
as np
np
. random
. seed
( 2019 )
data
= np
. random
. randint
( 1 , 99 , size
= 50 )
_stem
= [ ]
for x
in data
: _stem
. append
( x
// 10 ) stem
= list ( set ( _stem
) )
for m
in stem
: leaf
= [ ] leaf
. append
( m
) for n
in data
: if n
// 10 == m
: leaf
. append
( n
% 10 ) print ( leaf
[ 0 ] , '|' , end
= ' ' ) for i
in range ( 1 , len ( leaf
) ) : print ( leaf
[ i
] , end
= ' ' ) print ( '\n' )
2.3 用表格方法匯總兩個(gè)變量的數(shù)據(jù)
辛普森悖論：依據(jù)綜合和未綜合的數(shù)據(jù)得到相反的結(jié)論。（原因是未綜合的變量，本身權(quán)重不等）
交叉分組表（crosstabulation）
使用pandas.corsstab模擬了一下書(shū)上的表格:
import numpy
as np
import pandas
as pd
np
. random
. seed
( 900 )
y
= np
. random
. randint
( 0 , 3 , size
= 300 )
z
= np
. random
. randint
( 11 , 49 , size
= 300 )
data
= pd
. DataFrame
( { '質(zhì)量等級(jí)' : y
, '餐價(jià)' : z
} )
data
[ '質(zhì)量等級(jí)' ] . replace
( { 0 : '好' , 1 : '很好' , 2 : '優(yōu)秀' } , inplace
= True )
bins
= [ 10 , 19 , 29 , 39 , 49 ]
quartiles
= pd
. cut
( data
[ '餐價(jià)' ] , bins
, labels
= [ '10~19' , '20~29' , '30~39' , '40~49' ] )
data
[ '餐價(jià)' ] = quartiles
pd
. crosstab
( data
[ '質(zhì)量等級(jí)' ] , data
[ '餐價(jià)' ] , margins
= True , margins_name
= '總計(jì)' )
2.4 用圖形顯示方法匯總兩個(gè)變量的數(shù)據(jù)
散點(diǎn)圖（scatter diagram)和趨勢(shì)線(trendline)
帥氣的散點(diǎn)圖（matplotlib中，趨勢(shì)線要用numpy.ployfit函數(shù)）：
import matplotlib
. pyplot
as plt
import numpy
as np
np
. random
. seed
( 19680801 )
x
= np
. arange
( 0.0 , 50.0 , 2.0 )
y
= x
** 1.3 + np
. random
. rand
( * x
. shape
) * 30.0
s
= np
. random
. rand
( * x
. shape
) * 800 + 500
colors
= np
. random
. rand
( * x
. shape
)
plt
. figure
( figsize
= ( 12 , 6 ) )
plt
. scatter
( x
, y
, s
, c
= colors
, alpha
= 0.5 , marker
= r
'$\clubsuit$' , label
= "Luck" )
p1
= np
. poly1d
( np
. polyfit
( x
, y
, 1 ) )
l1
= plt
. plot
( x
, p1
( x
) , 'r--' , label
= 'trendline' )
plt
. xlabel
( "Leprechauns" )
plt
. ylabel
( "Gold" )
plt
. legend
( loc
= 'upper left' )
plt
. show
( )
import seaborn
as sns
; sns
. set ( )
import matplotlib
. pyplot
as plt
fig
, axes
= plt
. subplots
( 2 , 2 , figsize
= ( 12 , 6 ) )
tips
= sns
. load_dataset
( "tips" )
cmap
= sns
. cubehelix_palette
( dark
= .3 , light
= .8 , as_cmap
= True )
sns
. scatterplot
( x
= "total_bill" , y
= "tip" , hue
= "time" , data
= tips
, ax
= axes
[ 0 , 0 ] )
sns
. residplot
( x
= "total_bill" , y
= "tip" , data
= tips
, ax
= axes
[ 0 , 1 ] )
sns
. regplot
( x
= "size" , y
= "total_bill" , data
= tips
, x_jitter
= .1 , ax
= axes
[ 1 , 1 ] )
sns
. lmplot
( x
= "size" , y
= "total_bill" , hue
= "day" , col
= "day" , data
= tips
, height
= 6 , aspect
= .4 , x_jitter
= .1 )
復(fù)合條形圖（side-by-side bar chart)和結(jié)構(gòu)條形圖(stacked chart)
matplotlib做這種復(fù)合圖，有點(diǎn)復(fù)雜，附上鏈接
import numpy
as np
import pandas
as pd
import matplotlib
. pyplot
as plt
pd
. set_option
( 'precision' , 1 )
np
. random
. seed
( 900 )
y
= np
. random
. randint
( 0 , 3 , size
= 300 )
z
= np
. random
. randint
( 11 , 49 , size
= 300 )
data
= pd
. DataFrame
( { '質(zhì)量等級(jí)' : y
, '餐價(jià)' : z
} )
data
[ '質(zhì)量等級(jí)' ] . replace
( { 0 : '好' , 1 : '很好' , 2 : '優(yōu)秀' } , inplace
= True )
bins
= [ 10 , 19 , 29 , 39 , 49 ]
quartiles
= pd
. cut
( data
[ '餐價(jià)' ] , bins
, labels
= [ '10~19' , '20~29' , '30~39' , '40~49' ] )
df
= data
. groupby
( [ '質(zhì)量等級(jí)' , quartiles
] ) . count
( ) . unstack
( )
df
= df
. apply ( lambda x
: x
/ x
. sum ( ) * 100 )
df
. loc
[ '總計(jì)' ] = df
. apply ( lambda x
: x
. sum ( ) )
df
. T
. plot
( kind
= 'bar' , stacked
= True )
import matplotlib
. pyplot
as plt
import seaborn
as sns
sns
. set ( style
= "darkgrid" )
fig
, ( ax1
, ax2
) = plt
. subplots
( 1 , 2 , figsize
= ( 12 , 6 ) )
tips
= sns
. load_dataset
( "tips" )
sns
. countplot
( y
= "day" , hue
= "sex" , data
= tips
, ax
= ax1
)
sns
. barplot
( x
= "day" , y
= "total_bill" , data
= tips
, ax
= ax2
)
sns
. catplot
( x
= "sex" , y
= "total_bill" , hue
= "smoker" , col
= "time" , data
= tips
, kind
= "bar" , height
= 4 , aspect
= .7 )
g
= sns
. FacetGrid
( tips
, row
= "sex" , col
= "time" , margin_titles
= True )
bins
= np
. linspace
( 0 , 60 , 13 )
g
. map ( plt
. hist
, "total_bill" , color
= "steelblue" , bins
= bins
)
import seaborn
as sns
import matplotlib
. pyplot
as plt
sns
. set ( style
= "whitegrid" )
f
, ax
= plt
. subplots
( figsize
= ( 15 , 6 ) )
crashes
= sns
. load_dataset
( "car_crashes" ) . sort_values
( "total" , ascending
= False )
sns
. set_color_codes
( "pastel" )
sns
. barplot
( y
= "total" , x
= "abbrev" , data
= crashes
, label
= "Total" , color
= "b" )
sns
. set_color_codes
( "muted" )
sns
. barplot
( y
= "alcohol" , x
= "abbrev" , data
= crashes
, label
= "Alcohol-involved" , color
= "b" )
ax
. legend
( ncol
= 2 , loc
= "lower right" , frameon
= True )
ax
. set ( xlim
= ( 0 , 24 ) , ylabel
= "" , xlabel
= "Automobile collisions per billion miles" )
sns
. despine
( left
= True , bottom
= True )
2.5 數(shù)據(jù)可視化：創(chuàng)建有效圖形顯示的最佳實(shí)踐
創(chuàng)建有效的圖形顯示
1、給予圖形顯示一個(gè)清晰、簡(jiǎn)明的標(biāo)題。
總結(jié)
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