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In [27]:

    
"""
simple demo of a horizontal bar chart
"""
import pandas
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline



In [28]:

    
plt.rcdefaults()



In [29]:

    
fig, ax = plt.subplots()
# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))
error = np.random.rand(len(people))



In [30]:

    
ax.barh(y_pos, performance, xerr=error, align='center',
       color='green', ecolor='black')









    Out[30]:





<Container object of 5 artists>



In [31]:

    
ax.set_yticks(y_pos)









    Out[31]:





[<matplotlib.axis.YTick at 0x10fa1f898>,
 <matplotlib.axis.YTick at 0x10fa0afd0>,
 <matplotlib.axis.YTick at 0x10fa1bf28>,
 <matplotlib.axis.YTick at 0x10fa7bf98>,
 <matplotlib.axis.YTick at 0x10fa819b0>]



In [32]:

    
ax.set_yticklabels(people)









    Out[32]:





[<matplotlib.text.Text at 0x10fa23ac8>,
 <matplotlib.text.Text at 0x10fa11438>,
 <matplotlib.text.Text at 0x10fa7bdd8>,
 <matplotlib.text.Text at 0x10fa817f0>,
 <matplotlib.text.Text at 0x10fa85208>]



In [33]:

    
ax.invert_yaxis()



In [34]:

    
ax.set_xlabel('Performance')









    Out[34]:





<matplotlib.text.Text at 0x10fa0aa20>



In [35]:

    
ax.set_title('How fast do you want to go today?')









    Out[35]:





<matplotlib.text.Text at 0x10fa1f5f8>



In [36]:

    
plt.show()



In [37]:

    
x = np.linspace(0, 1, 500)
y = np.sin(4 * np.pi * x) * np.exp(-5 * x)
fig, ax = plt.subplots()
ax.fill(x, y, zorder=10)
ax.grid(True, zorder=5)
plt.show()



In [39]:

    
forest_fires = pandas.read_csv('../data/forestfire/forestfires.csv')



In [40]:

    
forest_fires.head(5)



In [41]:

    
weight = [600, 150, 200, 300, 200, 100, 125, 180]



In [42]:

    
height = [60, 65, 73, 70, 65, 58, 66, 67]



In [43]:

    
len(weight), len(height)









    Out[43]:





(8, 8)



In [44]:

    
plt.scatter(height, weight)
plt.show()



In [47]:

    
plt.scatter(forest_fires["wind"], forest_fires["area"])
plt.title('Wind speed vs fire area')
plt.xlabel('Wind speed when fire started')
plt.ylabel('Area consumed by fire')
plt.show()



In [48]:

    
age = [5, 10, 15, 20 ,25, 30]
height = [25, 45, 65, 75, 75, 75]
plt.plot(age, height)
plt.title('Age vs Height')
plt.xlabel('age')
plt.ylabel('Height')
plt.show()



In [49]:

    
area_by_month = forest_fires.pivot_table(index="month", values="area",aggfunc=np.sum)



In [50]:

    
type(area_by_month)









    Out[50]:





pandas.core.series.Series



In [52]:

    
plt.bar(range(len(area_by_month)), area_by_month)
plt.title('Month vs Area')
plt.xlabel('month')
plt.ylabel('area')
plt.show()



In [53]:

    
plt.barh(range(len(area_by_month)), area_by_month)
plt.title('Month vs Area')
plt.xlabel('month')
plt.ylabel('area')
plt.show()



In [54]:

    
plt.style.use('fivethirtyeight')
plt.plot(forest_fires['rain'], forest_fires['area'])
plt.show()



In [57]:

    
plt.style.use('bmh')
plt.plot(forest_fires['rain'], forest_fires['area'])
plt.show()



In [ ]:

	X	Y	month	day	FFMC	DMC	DC	ISI	temp	RH	wind	rain
0	7	5	mar	fri	86.2	26.2	94.3	5.1	8.2	51	6.7	0.0
1	7	4	oct	tue	90.6	35.4	669.1	6.7	18.0	33	0.9	0.0
2	7	4	oct	sat	90.6	43.7	686.9	6.7	14.6	33	1.3	0.0
3	8	6	mar	fri	91.7	33.3	77.5	9.0	8.3	97	4.0	0.2
4	8	6	mar	sun	89.3	51.3	102.2	9.6	11.4	99	1.8	0.0