

In [2]:

    
# import * is a personal choice
from ggplot import *
# our trusty old friends
import pandas as pd
import numpy as np



In [1]:

    
%matplotlib inline

Built in datasets



In [3]:

    
meat.head()









    Out[3]:






  
    
      
      date
      beef
      veal
      pork
      lamb_and_mutton
      broilers
      other_chicken
      turkey
    
  
  
    
      0
      1944-01-01
       751
        85
       1280
       89
      NaN
      NaN
      NaN
    
    
      1
      1944-02-01
       713
        77
       1169
       72
      NaN
      NaN
      NaN
    
    
      2
      1944-03-01
       741
        90
       1128
       75
      NaN
      NaN
      NaN
    
    
      3
      1944-04-01
       650
        89
        978
       66
      NaN
      NaN
      NaN
    
    
      4
      1944-05-01
       681
       106
       1029
       78
      NaN
      NaN
      NaN
    
  

5 rows × 8 columns



In [4]:

    
diamonds.head()









    Out[4]:






  
    
      
      carat
      cut
      color
      clarity
      depth
      table
      price
      x
      y
      z
    
  
  
    
      0
       0.23
         Ideal
       E
       SI2
       61.5
       55
       326
       3.95
       3.98
       2.43
    
    
      1
       0.21
       Premium
       E
       SI1
       59.8
       61
       326
       3.89
       3.84
       2.31
    
    
      2
       0.23
          Good
       E
       VS1
       56.9
       65
       327
       4.05
       4.07
       2.31
    
    
      3
       0.29
       Premium
       I
       VS2
       62.4
       58
       334
       4.20
       4.23
       2.63
    
    
      4
       0.31
          Good
       J
       SI2
       63.3
       58
       335
       4.34
       4.35
       2.75
    
  

5 rows × 10 columns



In [5]:

    
mtcars.head()









    Out[5]:






  
    
      
      name
      mpg
      cyl
      disp
      hp
      drat
      wt
      qsec
      vs
      am
      gear
      carb
    
  
  
    
      0
               Mazda RX4
       21.0
       6
       160
       110
       3.90
       2.620
       16.46
       0
       1
       4
       4
    
    
      1
           Mazda RX4 Wag
       21.0
       6
       160
       110
       3.90
       2.875
       17.02
       0
       1
       4
       4
    
    
      2
              Datsun 710
       22.8
       4
       108
        93
       3.85
       2.320
       18.61
       1
       1
       4
       1
    
    
      3
          Hornet 4 Drive
       21.4
       6
       258
       110
       3.08
       3.215
       19.44
       1
       0
       3
       1
    
    
      4
       Hornet Sportabout
       18.7
       8
       360
       175
       3.15
       3.440
       17.02
       0
       0
       3
       2
    
  

5 rows × 12 columns



In [7]:

    
pageviews.head()









    Out[7]:






  
    
      
      date_hour
      pageviews
    
  
  
    
      0
      2013-02-11 21:00:00
       8860.982383
    
    
      1
      2013-02-11 22:00:00
       8637.474753
    
    
      2
      2013-02-11 23:00:00
       9020.593099
    
    
      3
      2013-02-12 00:00:00
       8437.500380
    
    
      4
      2013-02-12 01:00:00
       9157.399672

The API

`ggplot`

ggplot's API revolves around the ggplot class. It's class that behaves much more like a function (you don't really operate on ggplot methods).



In [9]:

    
?ggplot

ggplots take 2 arguments: a data frame and accompanying "aesthetics" or aes. These are equivalent.



In [6]:

    
p = ggplot(aes(x='wt'), data=mtcars)



In [7]:

    
p = ggplot(mtcars, aes(x='wt'))

A ggplot is a "base layer". It won't create any aesthetics but think of it as a canvas. Watch what happens when you render it.



In [8]:

    
p









    












    Out[8]:





<ggplot: (281151189)>

`aes`

Aesthetics or aes define how ggplot with extract data from your data frame and render it. Think of it as the instructions for creating x, y, color, etc. components.

aes is just a dictionary with keys being an aesthetic property and values being strings or formulas--for more on formulas read this--relating to data in your data frame.



In [9]:

    
aes(x='date', y='price')









    Out[9]:





{'y': 'price', 'x': 'date'}



In [10]:

    
# shorthand
aes('date', 'price')









    Out[10]:





{u'y': 'price', u'x': 'date'}



In [11]:

    
# shorthand
aes('date', 'price', 'name')









    Out[11]:





{u'y': 'price', u'x': 'date', u'color': 'name'}



In [12]:

    
# formula
aes(x='date', y='price', color='date * price', shape='factor(name)')









    Out[12]:





{'color': 'date * price', 'y': 'price', 'shape': 'factor(name)', 'x': 'date'}

Your first `ggplot`

So taking everything that we've learned, let's use the mtcars dataset to plot the relationship between car weight (wt) and miles per gallon (mpg). First create a ggplot object with the proper aes and name it p.



In [30]:

    
p = ggplot(aes(x='wt', y='mpg'), data=mtcars)
p









    












    Out[30]:





<ggplot: (274847889)>

Now let's (quite literally) add a scatterplot (geom_point) to our plot. We'll get into more detail on how this works later.



In [31]:

    
p + geom_point()









    












    Out[31]:





<ggplot: (275439357)>



In [ ]:

	date	beef	veal	pork	lamb_and_mutton	broilers	other_chicken	turkey
0	1944-01-01	751	85	1280	89	NaN	NaN	NaN
1	1944-02-01	713	77	1169	72	NaN	NaN	NaN
2	1944-03-01	741	90	1128	75	NaN	NaN	NaN
3	1944-04-01	650	89	978	66	NaN	NaN	NaN
4	1944-05-01	681	106	1029	78	NaN	NaN	NaN

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75

	name	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
0	Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
1	Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
2	Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
3	Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
4	Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2

	date_hour	pageviews
0	2013-02-11 21:00:00	8860.982383
1	2013-02-11 22:00:00	8637.474753
2	2013-02-11 23:00:00	9020.593099
3	2013-02-12 00:00:00	8437.500380
4	2013-02-12 01:00:00	9157.399672

Built in datasets

The API

ggplot

aes

Your first ggplot

`ggplot`

`aes`

Your first `ggplot`