Question 1

Plot the daily temperature over the course of the year. (This should probably be a line chart.) Create a bar chart that shows the average temperature and humidity by month.



In [6]:

    
from pandas import DataFrame, Series



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import pandas as pd



In [9]:

    
import numpy as np



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import matplotlib.pyplot as plt



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%matplotlib inline



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weather = pd.read_table('data/daily_weather.tsv')



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season_mapping = {'Spring': 'Winter', 'Winter': 'Fall', 'Fall': 'Summer', 'Summer': 'Spring'}



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weather['season_desc'] = weather['season_desc'].map(season_mapping)



In [32]:

    
plt.plot(weather['temp'], 'b-')
plt.axis(xmax=365)
plt.axis(ymin=0, ymax=1)
plt.xlabel('Days')
plt.ylabel('Temperature')
plt.show()



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weather['Month'] = pd.DatetimeIndex(weather_data.date).month



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weather.groupby('Month')['temp', 'humidity'].mean().plot(kind='bar')









    Out[48]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fbdfe8023d0>

Question 2

Use a scatterplot to show how the daily rental volume varies with temperature. Use a different series (with different colors) for each season.



In [73]:

    
plt.scatter(
    weather[weather.season_desc == 'Winter']['temp'],
    weather[weather.season_desc == 'Winter']['total_riders'],
    s=20, c='b', alpha=.3, label='Winter'
)
plt.scatter(
    weather[weather.season_desc == 'Spring']['temp'],
    weather[weather.season_desc == 'Spring']['total_riders'],
    s=20, c='g', alpha=.3, label='Spring'
)
plt.scatter(
    weather[weather.season_desc == 'Summer']['temp'],
    weather[weather.season_desc == 'Summer']['total_riders'],
    s=20, c='y', alpha=.3, label='Summer'
)
plt.scatter(
    weather[weather.season_desc == 'Fall']['temp'],
    weather[weather.season_desc == 'Fall']['total_riders'],
    s=20, c='r', alpha=.3, label='Fall'
)
plt.title("Daily Rental Volume Versus Temperature")
plt.legend(loc=4)
plt.show()

Question 3

Create another scatterplot to show how daily rental volume varies with windspeed. As above, use a different series for each season.



In [74]:

    
plt.scatter(
    weather[weather.season_desc == 'Winter']['windspeed'],
    weather[weather.season_desc == 'Winter']['total_riders'],
    s=20, c='b', alpha=.3, label='Winter'
)
plt.scatter(
    weather[weather.season_desc == 'Spring']['windspeed'],
    weather[weather.season_desc == 'Spring']['total_riders'],
    s=20, c='g', alpha=.3, label='Spring'
)
plt.scatter(
    weather[weather.season_desc == 'Summer']['windspeed'],
    weather[weather.season_desc == 'Summer']['total_riders'],
    s=20, c='y', alpha=.3, label='Summer'
)
plt.scatter(
    weather[weather.season_desc == 'Fall']['windspeed'],
    weather[weather.season_desc == 'Fall']['total_riders'],
    s=20, c='r', alpha=.3, label='Fall'
)
plt.title("Daily Rental Volume Versus Windspeed")
plt.legend(loc=4)
plt.show()

Question 4

How do the rental volumes vary with geography? Compute the average daily rentals for each station and use this as the radius for a scatterplot of each station's latitude and longitude.



In [81]:

    
stations = pd.read_table('data/stations.tsv')



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usage = pd.read_table('data/usage_2012.tsv')



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s = stations[['station', 'lat', 'long']]



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u = pd.concat([usage['station_start']], axis=1, keys=['station'])



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counts = u['station'].value_counts()



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c = DataFrame(counts.index, columns=['station'])



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c['counts'] = counts.values



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c['counts'] = c['counts'].apply(lambda x: x / 365)



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m = pd.merge(s, c, on='station')



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plt.scatter(m['long'], m['lat'], c='b', label='Location', s=(m['counts'] * 10), alpha=.1)

plt.legend(loc='lower right')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()