E. Tufte Slope Graphs contest¶

So here is my entry for the slope Graph contest. (You can find the initial bounty description here )

Installation¶

Dependancies¶

This script is written in Python and relies on Numpy, Pandas and Matplotlib.
The easiest way to have a clean and robust install is to download one the great Scientific Python distribution, namely :

Everything you'll need is included. I personally use Anaconda from the guys at Continuum Analytics. All of them should work on Linux, Windows and Mac.

Sources¶

Grab the sources at https://github.com/pascal-schetelat/Slope
Launch Spyder, the Scientific python IDE bundled with Anaconda
Set the Spyder working directory where plotSlope.py is and import it in the console :

>>> from plotSlope import slope
    >>> import pandas as pd

You are good to go.

Interactive example¶

In [14]:

# Initial import. Load code to the console
from plotSlope import slope
import pandas as pd
import os

In [20]:

# Load data from a csv file using pandas and display it 
data = pd.read_csv(os.path.join('Data','EU_GDP_2007_2013.csv'),index_col=0,na_values='-')
data.head()/1000

Out[20]:

	2007	2008	2009	2010	2011	2012	2013
Austria	274.0198	282.7460	274.8182	286.1973	300.8913	310.1333	322.1904
Belgium	335.6100	346.1300	340.3980	354.3780	370.4364	381.7799	396.2738
Bulgaria	30.7724	35.4305	34.9328	36.0335	38.9899	NaN	NaN
Croatia	43.3804	47.7602	45.6661	45.8992	46.0216	46.7810	48.1752
Cyprus	15.9015	17.1571	16.8535	17.3336	17.9286	18.4096	19.1675

In [24]:

# Specify an optional dictionnary of color 
color  = {"France":'blue',
          'Germany':'red',
          'Ireland':'chocolate',
          'United Kingdom': 'purple', 
          'Poland':'green'}
color

Out[24]:

{'France': 'blue',
 'Germany': 'red',
 'Ireland': 'chocolate',
 'Poland': 'green',
 'United Kingdom': 'purple'}

In [32]:

f = slope(data/1000,kind='interval',color=color,
          height= 18,width=20,font_size=15,font_family='GillSans',dpi=150,savename='test.png',
          title = u'European GPD until 2010 and forecasts at market prices (billions of Euro) source : EUROSTAT') 

Notice that when the vertical density of lines is greater than the maximal density of text, labels are lumped together.
The layout is mainly controlled by the ratio of the font size and of the figure size.
For instance lets increase the font size from 15 to 20 :

In [34]:

# Changing font and figure size.
# Notice how some label get lumped together
f = slope(data/1000,width =30,height= 12,kind='interval',font_size=20,marker='%0.f',color=None,savename=None,dpi=200) 

The only issue is that lumped labels tends to be very long and make the figure very large.
Another option is to suppress the Marker. In the mean time, lets reduce the figure width:

In [37]:

# Without numbers
f = slope(data/1000,width =15,height= 12,kind='interval',marker = None,font_size=12,color=color,savename=None,dpi=200) 