Up and Down the Python Data and Web Visualization Stack¶

USGS dataset listing every wind turbine in the United States:

In [35]:

from collections import OrderedDict

import bearcart
import bokeh
import bokeh.plotting as bp
from bokeh.plotting import output_notebook
import folium
import ggplot as gg
from ggplot import ggplot
from IPython.html.widgets import interact
import matplotlib.pyplot as plt
import mpld3
import numpy as np
import pandas as pd
import vincent

%matplotlib inline
mpld3.enable_notebook()
bearcart.initialize_notebook()
vincent.initialize_notebook()
folium.initialize_notebook()

# axis_color = 'black'
axis_color = '#d0d0d0'

In [36]:

df = pd.read_csv('USGS_WindTurbine_201307_cleaned.csv')
df.head()

Out[36]:

	Unnamed: 0	Unique ID	Site Name	Online Year	Turbine Manufacturer	Turbine Model	Tower Type	Turbine MW	Total Height	Tower/Hub Height	Blade Length	Rotor Diameter	Rotor Swept Area	Latitude-Decimal Degrees	Longitude-Decimal Degrees	State	County	Attribute Confidence	Location Confidence	WENDI Site Name
0	0	1836	Sand Point	2012	Vestas	V39	monopole	0.500	59.5	40	19.5	39	1194.59	55.3436	-160.4891	AK	Aleutians East	2	2	unknown	...
1	1	1837	Sand Point	2012	Vestas	V39	monopole	0.500	59.5	40	19.5	39	1194.59	55.3448	-160.4906	AK	Aleutians East	2	2	unknown	...
2	2	1838	St. Paul Island	2007	Vestas	V27	monopole	0.225	50.5	37	13.5	27	572.55	57.1571	-170.2352	AK	Aleutians West	2	2	St. Paul Island	...
3	3	1839	St. Paul Island	1999	Vestas	V27	monopole	0.225	50.5	37	13.5	27	572.55	57.1576	-170.2359	AK	Aleutians West	2	2	St. Paul Island	...
4	4	1840	St. Paul Island	2007	Vestas	V27	monopole	0.225	50.5	37	13.5	27	572.55	57.1576	-170.2373	AK	Aleutians West	2	2	St. Paul Island	...

5 rows × 27 columns

In [37]:

ws = pd.read_table('CO_WS_2011_2012.txt')
ws = ws.set_index('Date & Time Stamp')
ws.index = ws.index.to_datetime()
ws.head()

Out[37]:

	WS1_50mMean	WS1_50mStdev	WS1_50mMax	WS1_50mMin	WS2_50mMean	WS2_50mStDev	WS2_50mMax	WS2_50mMin	WS3_30mMean	WS3_30mStDev	WS3_30mMax	WS3_30mMin	WS4_40mMean	WS4_40mStDev	WS4_40mMax	WS4_40mMin	WD1_49mMean	WD1_49mStDev	WD1_49mMax	WD1_49mMin
2011-06-03 00:00:00	9.50	1.12	11.70	6.40	9.39	1.03	11.39	6.43	7.96	1.33	10.97	4.51	8.76	1.17	11.01	4.95	172	7	173	240	...
2011-06-03 00:10:00	8.37	0.56	10.19	6.78	8.27	0.56	9.86	6.43	6.83	0.76	9.06	3.75	7.60	0.64	9.49	5.70	172	5	169	240	...
2011-06-03 00:20:00	8.38	0.43	9.44	7.17	8.28	0.41	9.48	7.20	7.13	0.64	8.69	4.89	7.81	0.45	9.10	6.83	169	5	177	240	...
2011-06-03 00:30:00	7.48	1.17	9.44	3.76	7.43	1.12	9.48	3.77	6.43	1.03	8.29	3.37	6.93	1.03	9.10	3.43	163	11	166	240	...
2011-06-03 00:40:00	7.13	0.79	9.06	5.26	7.10	0.76	9.09	5.28	6.21	0.67	7.91	2.99	6.61	0.66	8.34	3.81	172	12	166	240	...

5 rows × 24 columns

In [38]:

# Rotor Diameter vs. Turbine Manufacturer
mf_grouped = df.groupby('Turbine Manufacturer')
mean_grouped = mf_grouped.mean().dropna()
mean_rd = mean_grouped.sort('Rotor Diameter')['Rotor Diameter']
rotor_diam = vincent.Bar(mean_rd)
rotor_diam.axis_titles(x='Turbine Manufacturer', y='Rotor Diameter')
# The Hard Way
from vincent.axes import AxisProperties
from vincent.properties import PropertySet
from vincent.values import ValueRef
for axis in rotor_diam.axes:
    axis.properties = AxisProperties()
    for prop in ['ticks', 'axis', 'major_ticks', 'minor_ticks']:
        setattr(axis.properties, prop, PropertySet(stroke=ValueRef(value=axis_color)))
    axis.properties.title = PropertySet(font_size=ValueRef(value=20), 
                                        fill=ValueRef(value=axis_color))
    axis.properties.labels = PropertySet(fill=ValueRef(value=axis_color))
rotor_diam.axes[0].properties.labels.angle = ValueRef(value=50)
rotor_diam.axes[0].properties.labels.align = ValueRef(value='left')
rotor_diam.axes[0].properties.title.dy = ValueRef(value=115)
rotor_diam.scales[2].range = ['#b48ead']
rotor_diam

Out[38]:

In [39]:

# Total Turbine Count
turbine_ct = mf_grouped.count().dropna().sort('Unique ID', ascending=False)['Unique ID']
num_turbines = (vincent.Bar(turbine_ct[:25])
                       .axis_titles(x='Turbine Manufacturer', 
                                    y='Number of Turbines in the US')
                       .colors(range_=['#6a9fb5']))
# Shortcuts!
def lighten_axes(vis, x_offset=50):
    (vis.common_axis_properties(color=axis_color, title_size=20)
        .x_axis_properties(label_angle=50, label_align='left', 
                           title_offset=x_offset)
        .y_axis_properties(title_offset=-40))
# If Area Chart
# num_turbines.scales[0].type = 'ordinal'
lighten_axes(num_turbines)
num_turbines

Out[39]:

In [40]:

# Turbine Count vs. Date
grouped_date = df.groupby(['Online Year', 'Turbine Manufacturer'])
by_year = grouped_date.count()['Unique ID'].reset_index()
by_year['Online Year'] = pd.to_datetime(by_year['Online Year'], coerce=True)
by_year = by_year.rename(columns={'Unique ID': 'Turbine Count'}).dropna()
by_year = by_year.pivot(index='Online Year', columns='Turbine Manufacturer', values='Turbine Count')
by_year = by_year[turbine_ct[:10].index.tolist()]

online_by_year = (vincent.StackedArea(by_year)
                         .axis_titles(x='Date', y='Turbine Count')
                         .legend(title='Turbine Manufacturer', text_color=axis_color)
                         .colors(range_=['#ac4142', '#d28445', '#f4bf75', '#90a959', 
                                         '#75b5aa', '#6a9fb5', '#aa759f', '#8f5536']))
lighten_axes(online_by_year, x_offset=30)
online_by_year

Out[40]:

In [41]:

height_diam = (vincent.GroupedBar(mean_grouped[['Tower/Hub Height', 'Rotor Diameter']]
                                  .sort(['Rotor Diameter', 'Tower/Hub Height'], ascending=False))
                      .axis_titles(x='Turbine Manufacturer', y='Meters')
                      .legend(title='Parameters', text_color=axis_color)
                      .colors(range_=['#f4bf75', '#75b5aa']))
lighten_axes(height_diam, 100)
height_diam

Out[41]:

In [42]:

november_2011 = ws['2011-11-15':'2011-12-01']
ws_line = (vincent.Line(november_2011['WS1_50mMean'])
                  .axis_titles(x='Date', y='Wind Speed (m/s)')
                  .colors(range_=['#d28445']))
lighten_axes(ws_line, x_offset=30)
ws_line

Out[42]:

In [43]:

# Rotor Diameter vs. Power
min_heights = df[df['Rotor Diameter'] > 10]
diameter_vs_mw = (vincent.Scatter(min_heights[['Turbine MW', 'Rotor Diameter']], iter_idx='Turbine MW')
                         .axis_titles(x='Power (MW)', y='Rotor Diameter (m)')
                         .colors(range_=['#75b5aa']))
lighten_axes(diameter_vs_mw, x_offset=30)
diameter_vs_mw

Out[43]: