A tutorial Devert Alexandre - Marmakoidemarmakoide.org/download/teaching/dm/dm-matplotlib.pdf · 2012-11-30 · Custom axis scale Changing the axis scale can improve readability importmath

UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA SCHOOL OF SOFTWARE ENGINEERING OF USTC

MatplotlibA tutorial

Devert AlexandreSchool of Software Engineering of USTC

30 November 2012 — Slide 1/44


Table of Contents

1 First steps

2 Curve plots

3 Scatter plots

4 Boxplots

5 Histograms

6 Usage example

Devert Alexandre (School of Software Engineering of USTC) — Matplotlib — Slide 2/44


Curve plot

Let’s plot a curve

impor t mathimpor t ma t p l o t l i b . p yp l o t as p l t

# Genera te a s i n u s o i dnbSamples = 256xRange = (−math . p i , math . p i )

x , y = [ ] , [ ]f o r n i n x range ( nbSamples ) :

k = (n + 0 . 5 ) / nbSamplesx . append ( xRange [ 0 ] + ( xRange [ 1 ] − xRange [ 0 ] ) ∗ k )y . append (math . s i n ( x [−1]))

# P lo t the s i n u s o i dp l t . p l o t ( x , y )p l t . show ( )



Curve plot

This will show you something like this

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numpy

matplotlib can work with numpy arrays

impor t mathimpor t numpyimpor t ma t p l o t l i b . p yp l o t as p l t

# Genera te a s i n u s o i dnbSamples = 256xRange = (−math . p i , math . p i )

x , y = numpy . z e r o s ( nbSamples ) , numpy . z e r o s ( nbSamples )f o r n i n x range ( nbSamples ) :

k = (n + 0 . 5 ) / nbSamplesx [ n ] = xRange [ 0 ] + ( xRange [ 1 ] − xRange [ 0 ] ) ∗ ky [ n ] = math . s i n ( x [ n ] )


numpy provides a lot of function and is efficient



numpy

• zeros build arrays filled of 0

• linspace build arrays filled with an arithmeticsequence


# Genera te a s i n u s o i dx = numpy . l i n s p a c e (−math . p i , math . p i , num=256)y = numpy . z e r o s ( nbSamples )f o r n i n x range ( nbSamples ) :

y [ n ] = math . s i n ( x [ n ] )




numpy

numpy functions can work on entire arrays


# Genera te a s i n u s o i dx = numpy . l i n s p a c e (−math . p i , math . p i , num=256)y = numpy . s i n ( x )




PDF output

Exporting to a PDF file is just one change


# Genera te a s i n u s o i dx = numpy . l i n s p a c e (−math . p i , math . p i , num=256)y = numpy . s i n ( x )

# P lo t the s i n u s o i dp l t . p l o t ( x , y )p l t . s a v e f i g ( ’ s i n−p l o t . pdf ’ , t r a n s p a r e n t=True )



Table of Contents

1 First steps

2 Curve plots

3 Scatter plots

4 Boxplots

5 Histograms

6 Usage example



Multiple curves

It’s often convenient to show several curves in one figure


# Genera te a s i n u s o i d and a c o s i n o i dx = numpy . l i n s p a c e (−math . p i , math . p i , num=256)y = numpy . s i n ( x )z = numpy . cos ( x )

# P lo t the cu r v e sp l t . p l o t ( x , y )p l t . p l o t ( x , z )p l t . show ( )



Multiple curves

It’s often convenient to show several curves in one figure

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Custom colors

Changing colors can help to make nice documents



# P lo t the cu r v e sp l t . p l o t ( x , y , c=’#FF4500 ’ )p l t . p l o t ( x , z , c=’#4682B4 ’ )p l t . show ( )



Custom colors

Changing colors can help to make nice documents

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Line thickness

Line thickness can be changed as well



# P lo t the cu r v e sp l t . p l o t ( x , y , l i n ew i d t h =3, c=’#FF4500 ’ )p l t . p l o t ( x , z , c=’#4682B4 ’ )p l t . show ( )



Line thickness

Line thickness can be changed as well

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Line patterns

For printed document, colors can be replaced by linepatterns


# L i n e s t y l e s can be ’− ’ , ’−−’, ’− . ’ , ’ : ’


# P lo t the cu r v e sp l t . p l o t ( x , y , l i n e s t y l e=’−− ’ , c=’#000000 ’ )p l t . p l o t ( x , z , c=’#808080 ’ )p l t . show ( )



Line patternsFor printed document, colors can be replaced by linepatterns

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Markers

It sometime relevant to show the data points


# Markers can be ’ . ’ , ’ , ’ , ’ o ’ , ’ 1 ’ and more


# P lo t the cu r v e sp l t . p l o t ( x , y , marker=’ 1 ’ , ma r k e r s i z e =15, c=’#000000 ’ )p l t . p l o t ( x , z , c=’#000000 ’ )p l t . show ( )



Markers

It sometime relevant to show the data points

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Legend

A legend can help to make self–explanatory figures


# l egend l o c a t i o n can be ’ b e s t ’ , ’ c e n t e r ’ , ’ l e f t ’ , ’ r i g h t ’ , e t c .


# P lo t the cu r v e sp l t . p l o t ( x , y , c=’#FF4500 ’ , l a b e l= ’ s i n ( x ) ’ )p l t . p l o t ( x , z , c=’#4682B4 ’ , l a b e l=’ cos ( x ) ’ )p l t . l e g end ( l o c=’ b e s t ’ )p l t . show ( )



Legend

A legend can help to make self–explanatory figures

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sin(x)cos(x)



Custom axis scaleChanging the axis scale can improve readability




# Ax i s s e tupf i g = p l t . f i g u r e ( )a x i s = f i g . a dd subp l o t (111)

a x i s . s e t y l i m (−0.5 ∗ math . p i , 0 . 5 ∗ math . p i )




Custom axis scale

Changing the axis scale can improve readability

4 3 2 1 0 1 2 3 41.5

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1.5sin(x)cos(x)



GridSame goes for a grid, can be helpful





a x i s . s e t y l i m (−0.5 ∗ math . p i , 0 . 5 ∗ math . p i )a x i s . g r i d ( True )




Grid

Same goes for a grid, can be helpful

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1.5sin(x)cos(x)



Error bars

Your data might come with a known measure error

impor t mathimpor t numpyimpor t numpy . randomimpor t ma t p l o t l i b . p yp l o t as p l t

# Genera te a n o i s y s i n u s o i dx = numpy . l i n s p a c e (−math . p i , math . p i , num=48)y = numpy . s i n ( x + 0.05 ∗ numpy . random . s t anda rd no rma l ( l e n ( x ) ) )y e r r o r = 0 .1 ∗ numpy . random . s t anda rd no rma l ( l e n ( x ) )


a x i s . s e t y l i m (−0.5 ∗ math . p i , 0 . 5 ∗ math . p i )

# P lo t the cu r v e sp l t . p l o t ( x , y , c=’#FF4500 ’ )p l t . e r r o r b a r ( x , y , y e r r=y e r r o r )p l t . show ( )



Error bars

Your data might come with a known measure error

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Table of Contents

1 First steps

2 Curve plots

3 Scatter plots

4 Boxplots

5 Histograms

6 Usage example



Scatter plot

A scatter plot just shows one point for each dataset entry

impor t numpyimpor t numpy . randomimpor t ma t p l o t l i b . p yp l o t as p l t

# Genera te a 2d normal d i s t r i b u t i o nnbPo in t s = 100x = numpy . random . s t anda rd no rma l ( nbPo in t s )y = numpy . random . s t anda rd no rma l ( nbPo in t s )

# P lo t the p o i n t sp l t . s c a t t e r ( x , y )p l t . show ( )



Scatter plotA scatter plot just shows one point for each dataset entry

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Aspect ratio

If can be very important to have the same scale on bothaxis


# Genera te a 2d normal d i s t r i b u t i o nnbPo in t s = 100x = numpy . random . s t anda rd no rma l ( nbPo in t s )y = 0 .1 ∗ numpy . random . s t anda rd no rma l ( nbPo in t s )

# Ax i s s e tupf i g = p l t . f i g u r e ( )a x i s = f i g . a dd subp l o t (111 , a sp e c t=’ equa l ’ )

# P lo t the p o i n t sp l t . s c a t t e r ( x , y , c=’#FF4500 ’ )p l t . show ( )



Aspect ratioIf can be very important to have the same scale on bothaxis

3 2 1 0 1 2 30.30.20.10.00.10.20.3



Aspect ratioAlternative way to keep the same scale on both axis


# Genera te a 2d normal d i s t r i b u t i o nnbPo in t s = 100x = numpy . random . s t anda rd no rma l ( nbPo in t s )y = 0 .1 ∗ numpy . random . s t anda rd no rma l ( nbPo in t s )


cmin , cmax = min (min ( x ) , min ( y ) ) , max(max( x ) , max( y ) )cmin −= 0.05 ∗ ( cmax − cmin )cmax += 0.05 ∗ ( cmax − cmin )

a x i s . s e t x l i m ( cmin , cmax )a x i s . s e t y l i m ( cmin , cmax )

# P lo t the p o i n t sp l t . s c a t t e r ( x , y , c=’#FF4500 ’ )p l t . show ( )



Aspect ratio

Alternative way to keep the same scale on both axis

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Multiple scatter plotsAs for curve, you can show 2 datasets on one figure


c o l o r s = ( ’#FF4500 ’ , ’#3CB371 ’ , ’#4682B4 ’ , ’#DB7093 ’ , ’#FFD700 ’ )

# Genera te a 2d normal d i s t r i b u t i o nnbPo in t s = 100

x , y = [ ] , [ ]

x += [ numpy . random . s t anda rd no rma l ( nbPo in t s ) ]y += [ 0 . 2 5 ∗ numpy . random . s t anda rd no rma l ( nbPo in t s ) ]

x += [ 0 . 5 ∗ numpy . random . s t anda rd no rma l ( nbPo in t s ) + 3 . 0 ]y += [2 ∗ numpy . random . s t anda rd no rma l ( nbPo in t s ) + 2 . 0 ]


# P lo t the p o i n t sf o r i i n x range ( l e n ( x ) ) :

p l t . s c a t t e r ( x [ i ] , y [ i ] , c=c o l o r s [ i % l e n ( c o l o r s ) ] )

p l t . show ( )



Multiple scatter plots

As for curve, you can show 2 datasets on one figure

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Showing centersIt can help to see the centers or the median points


c o l o r s = ( ’#FF4500 ’ , ’#3CB371 ’ , ’#4682B4 ’ , ’#DB7093 ’ , ’#FFD700 ’ )


x , y = [ ] , [ ]




# P lo t the p o i n t sf o r i i n x range ( l e n ( x ) ) :

c o l = c o l o r s [ i % l e n ( c o l o r s ) ]p l t . s c a t t e r ( x [ i ] , y [ i ] , c=c o l )p l t . s c a t t e r ( [ numpy . median ( x [ i ] ) ] , [ numpy . median ( y [ i ] ) ] , c=co l , s=250)

p l t . show ( )



Showing centers

It can help to see the centers or the median points

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Marker stylesYou can use different markers styles


markers = ( ’+’ , ’ ˆ ’ , ’ . ’ )


x , y = [ ] , [ ]




# P lo t the p o i n t sf o r i i n x range ( l e n ( x ) ) :m = markers [ i % l e n ( markers ) ]p l t . s c a t t e r ( x [ i ] , y [ i ] , marker=m, c=’#000000 ’ )p l t . s c a t t e r ( [ numpy . median ( x [ i ] ) ] , [ numpy . median ( y [ i ] ) ] , marker=m, s=250 , c=’#000000 ’ )

p l t . show ( )



Marker styles

You can use different markers styles

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Table of Contents

1 First steps

2 Curve plots

3 Scatter plots

4 Boxplots

5 Histograms

6 Usage example



Boxplots

Let’s do a simple boxplot


# Genera te normal d i s t r i b u t i o n datax = numpy . random . s t anda rd no rma l (256)

# Show a boxp l o t o f the datap l t . b o xp l o t ( x )p l t . show ( )



Boxplots

Let’s do a simple boxplot

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Boxplots

You might want to show the original data in the sametime


# Genera te normal d i s t r i b u t i o n datax = numpy . random . s t anda rd no rma l (256)

# Show a boxp l o t o f the datap l t . s c a t t e r ( [ 0 ] ∗ l e n ( x ) , x , c=’#4682B4 ’ )p l t . b o xp l o t ( x , p o s i t i o n s =[0 ] )p l t . show ( )



BoxplotsYou might want to show the original data in the sametime

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Multiple boxplots

Boxplots are often used to show side by side variousdistributions


# Genera te normal d i s t r i b u t i o n datadata = [ ]f o r i i n x range ( 5 ) :

mu = 10 ∗ numpy . random . random sample ( )s igma = 2 ∗ numpy . random . random sample ( ) + 0 .1data . append (numpy . random . normal (mu, sigma , 256))

# Show a boxp l o t o f the datap l t . b o xp l o t ( data )p l t . show ( )



Multiple boxplotsBoxplots are often used to show side by side variousdistributions

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Orientation

Changing the orientation is easily done


# Genera te normal d i s t r i b u t i o n datadata = [ ]f o r i i n x range ( 5 ) :

mu = 10 ∗ numpy . random . random sample ( )s igma = 2 ∗ numpy . random . random sample ( ) + 0 .1data . append (numpy . random . normal (mu, sigma , 256))

# Show a boxp l o t o f the datap l t . b o xp l o t ( data , v e r t =0)p l t . show ( )



Orientation

Changing the orientation is easily done

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LegendGood graphics have a proper legend


# Genera te normal d i s t r i b u t i o n datal a b e l s = [ ’ mercury ’ , ’ l e a d ’ , ’ l i t h i um ’ , ’ t ung s t ene ’ , ’ cadnium ’ ]data = [ ]f o r i i n x range ( l e n ( l a b e l s ) ) :

mu = 10 ∗ numpy . random . random sample ( ) + 100sigma = 2 ∗ numpy . random . random sample ( ) + 0 .1data . append (numpy . random . normal (mu, sigma , 256))


a x i s . s e t t i t l e ( ’ A l i e n nodu l e s compos i t i on ’ )xt ickNames = p l t . s e t p ( a x i s , x t i c k l a b e l s = l a b e l s )a x i s . s e t y l a b e l ( ’ c o n c e n t r a t i o n (ppm) ’ )

# Show a boxp l o t o f the datap l t . b o xp l o t ( data )p l t . show ( )



Legend

Good graphics have a proper legend

mercury lead lithium tungstene cadnium100

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)Alien nodules composition



Table of Contents

1 First steps

2 Curve plots

3 Scatter plots

4 Boxplots

5 Histograms

6 Usage example



Histograms

Histogram are convenient to sum-up results


# Some datadata = numpy . abs (numpy . random . s t anda rd no rma l ( 30 ) )

# Show an h i s tog ramp l t . barh ( range ( l e n ( data ) ) , data , c o l o r=’#4682B4 ’ )p l t . show ( )



Histograms

Histogram are convenient to sum-up results

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Histograms

A variant to show 2 quantities per item on 1 figure


# Some datadata = [ [ ] , [ ] ]f o r i i n x range ( l e n ( data ) ) :

data [ i ] = numpy . abs (numpy . random . s t anda rd no rma l ( 30 ) )

# Show an h i s tog raml a b e l s = range ( l e n ( data [ 0 ] ) )

p l t . barh ( l a b e l s , data [ 0 ] , c o l o r=’#4682B4 ’ )p l t . barh ( l a b e l s , −1 ∗ data [ 1 ] , c o l o r=’#FF4500 ’ )p l t . show ( )



Histograms

A variant to show 2 quantities per item on 1 figure

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LabelsVery often, we need to name items on an histogram


# Some datanames = [ ’Wang Bu ’ , ’ Cheng Cao ’ , ’ Zhang Xue L i ’ , ’ Tang Wei ’ , ’ Sun Wu Kong ’ ]marks = 7 ∗ numpy . random . random sample ( l e n ( names ) ) + 3


a x i s . s e t x l i m (0 , 10)

p l t . y t i c k s ( numpy . a range ( l e n ( marks ) )+0.5 , names )

a x i s . s e t t i t l e ( ’ Data−mining marks ’ )a x i s . s e t x l a b e l ( ’mark ’ )

# Show an h i s tog ramp l t . barh ( range ( l e n ( marks ) ) , marks , c o l o r=’#4682B4 ’ )p l t . show ( )



Labels

Very often, we need to name items on an histogram

0 2 4 6 8 10mark

Wang Bu

Cheng Cao

Zhang Xue Li

Tang Wei

Sun Wu Kong

Data-mining marks



Error barsError bars, to indicate the accuracy of values


# Some datanames = [ ’ 6809 ’ , ’ 6502 ’ , ’ 8086 ’ , ’ Z80 ’ , ’RCA1802 ’ ]speed = 70 ∗ numpy . random . random sample ( l e n ( names ) ) + 30e r r o r = 9 ∗ numpy . random . random sample ( l e n ( names ) ) + 1


p l t . y t i c k s ( numpy . a range ( l e n ( names ))+0.5 , names )

a x i s . s e t t i t l e ( ’ 8 b i t s CPU benchmark − s t r i n g t e s t ’ )a x i s . s e t x l a b e l ( ’ s c o r e ’ )

# Show an h i s tog ramp l t . barh ( range ( l e n ( names ) ) , speed , x e r r=e r r o r , c o l o r=’#4682B4 ’ )p l t . show ( )



Error bars

Error bars, to indicate the accuracy of values

0 20 40 60 80 100score

6809

6502

8086

Z80

RCA1802

8 bits CPU benchmark - string test



Table of Contents

1 First steps

2 Curve plots

3 Scatter plots

4 Boxplots

5 Histograms

6 Usage example



Old Faithful

Let’s display some real data: Old Faithful geyser



Old Faithful

This way works, but good example of half-done job

impor t numpyimpor t ma t p l o t l i b . p yp l o t as p l t

# Read the datadata = numpy . l o a d t x t ( ’ . / d a t a s e t s / g e y s e r . dat ’ )


# P lo t the p o i n t sp l t . s c a t t e r ( data [ : , 0 ] , data [ : , 1 ] , c=’#FF4500 ’ )p l t . show ( )



Old Faithful

This way works, but good example of half-done job

30 40 50 60 70 80 90 1001.52.02.53.03.54.04.55.05.5



Old Faithful

Let’s make a more readable figure


# Read the datadata = numpy . l o a d t x t ( ’ . / d a t a s e t s / g e y s e r . dat ’ )


a x i s . s e t t i t l e ( ’ Old F a i t h f u l g e y s e r d a t a s e t ’ )a x i s . s e t x l a b e l ( ’ w a i t i n g t ime (mn . ) ’ )a x i s . s e t y l a b e l ( ’ e r u p t i o n du r a t i o n (mn . ) ’ )




Old Faithful

Let’s make a more readable figure

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Old Faithful geyser dataset



Mercury & fishes

Let’s display more complex data: fishes and mercurypoisoning



Mercury & fishes

A first try


# Read the datadata = numpy . l o a d t x t ( ’ . / d a t a s e t s / f i s h . dat ’ )


a x i s . s e t t i t l e ( ’ North Ca r o l i n a f i s h e s mercury c o n c e n t r a t i o n ’ )a x i s . s e t x l a b e l ( ’ we ight ( g . ) ’ )a x i s . s e t y l a b e l ( ’ mercury c o n c e n t r a t i o n (ppm) ’ )




Mercury & fishes

A first try

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erc

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)North Carolina fishes mercury concentration



Mercury & fishes

Show more information with subplots

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Mercury & fishes

Data from one river with its own color

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Documents

A tutorial Devert Alexandre - Marmakoidemarmakoide.org/download/teaching/dm/dm-matplotlib.pdf · 2012-11-30 · Custom axis scale Changing the axis scale can improve readability importmath