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takin' care of imports





In [1]:



    


from pandas import Series, DataFrame



    











In [2]:



    


import pandas as pd



    











In [3]:



    


import numpy as np



    











In [4]:



    


import matplotlib.pyplot as plt



    











In [32]:



    


import matplotlib



    











In [5]:



    


%matplotlib inline

read in data





In [6]:



    


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



    











In [7]:



    


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



    











In [8]:



    


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

repeat data fixing from previous exercise





In [9]:



    


weather['date'] = pd.to_datetime(weather['date'])



    











In [10]:



    


weather.loc[weather['season_code'] == 1, 'season_desc'] = 'winter'
weather.loc[weather['season_code'] == 2, 'season_desc'] = 'spring'
weather.loc[weather['season_code'] == 3, 'season_desc'] = 'summer'
weather.loc[weather['season_code'] == 4, 'season_desc'] = 'fall'



    











In [11]:



    


weather.loc[weather['season_desc'] == 'winter', 'season_code'] = 4
weather.loc[weather['season_desc'] == 'spring', 'season_code'] = 1
weather.loc[weather['season_desc'] == 'summer', 'season_code'] = 2
weather.loc[weather['season_desc'] == 'fall', 'season_code'] = 3

1a. Plot the daily temperature over the course of the year. (This should probably be a line chart.)





In [12]:



    


weather.plot(x='date', y='temp')
plt.show()

1b. Create a bar chart that shows the average temperature and humidity by month.





In [13]:



    


temp_humid = weather[['temp', 'humidity']].groupby(weather['date'].dt.month).mean()



    











In [14]:



    


temp_humid.plot(kind='bar', width=0.75, color=['#EE4444','#4444EE'])
plt.show()

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 [15]:



    


spring_daily_vol = weather.loc[weather['season_desc'] == 'spring']
summer_daily_vol = weather.loc[weather['season_desc'] == 'summer']
fall_daily_vol = weather.loc[weather['season_desc'] == 'fall']
winter_daily_vol = weather.loc[weather['season_desc'] == 'winter']



    











In [16]:



    


spr_ax = spring_daily_vol.plot(kind='scatter', x='temp', y='total_riders', c='yellow', s=50, alpha=.4)
sum_ax = summer_daily_vol.plot(kind='scatter', x='temp', y='total_riders', c='lightgreen', s=50, alpha=.4, ax=spr_ax)
fal_ax = fall_daily_vol.plot(kind='scatter', x='temp', y='total_riders', c='#ee5555', s=50, alpha=.4, ax=sum_ax)
win_ax = winter_daily_vol.plot(kind='scatter', x='temp', y='total_riders', c='lightblue', s=50, alpha=.4, ax=fal_ax)
plt.title('Temp vs Daily Rental Volume')
plt.show()

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





In [17]:



    


spr_ax = spring_daily_vol.plot(kind='scatter', x='windspeed', y='total_riders', c='yellow', s=50, alpha=.4)
sum_ax = summer_daily_vol.plot(kind='scatter', x='windspeed', y='total_riders', c='lightgreen', s=50, alpha=.4, ax=spr_ax)
fal_ax = fall_daily_vol.plot(kind='scatter', x='windspeed', y='total_riders', c='#ee5555', s=50, alpha=.4, ax=sum_ax)
win_ax = winter_daily_vol.plot(kind='scatter', x='windspeed', y='total_riders', c='lightblue', s=50, alpha=.4, ax=fal_ax)
plt.title('Windspeed vs Daily Rental Volume')
plt.show()

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.

_pull out just the station_start column from 'usage' file data, turn it into a new dataframe 'usagestations'





In [18]:



    


usage_stations = usage[['station_start']]



    











In [19]:



    


usage_stations.head()



    


















    
Out[19]:










  
    

      
      station_start
    

  
  
    

      0
      7th & R St NW / Shaw Library
    

    

      1
      Georgia & New Hampshire Ave NW
    

    

      2
      Georgia & New Hampshire Ave NW
    

    

      3
      14th & V St NW
    

    

      4
      11th & Kenyon St NW

_pull out the lat and long from 'stations' file data, turn it into a new dataframe 'stationsgeo'

_set the index of 'stationsgeo' to be the values of the 'stations' column from the same 'stations' file data; this is important because we want both of our dataframes to have similar indices





In [20]:



    


stations_geo = DataFrame({'lat': stations.lat, 'long': stations.long})
stations_geo.index = stations.station.values



    











In [21]:



    


stations_geo.head()



    


















    
Out[21]:










  
    

      
      lat
      long
    

  
  
    

      20th & Bell St
      38.856100
      -77.051200
    

    

      18th & Eads St.
      38.857250
      -77.053320
    

    

      20th & Crystal Dr
      38.856400
      -77.049200
    

    

      15th & Crystal Dr
      38.860170
      -77.049593
    

    

      Aurora Hills Community Ctr/18th & Hayes St
      38.857866
      -77.059490

_make a new dataframe 'station_count' that sums the occurrences of each station name in 'usagestations'





In [22]:



    


station_count = DataFrame(usage_stations['station_start'].value_counts())



    











In [23]:



    


station_count.head()



    


















    
Out[23]:










  
    

      
      0
    

  
  
    

      Massachusetts Ave & Dupont Circle NW
      69850
    

    

      Columbus Circle / Union Station
      55146
    

    

      15th & P St NW
      49416
    

    

      17th & Corcoran St NW
      43547
    

    

      14th & V St NW
      40242

_create a new colum 'rides' in 'stations_geo' which is populated with the data from 'stationcount'





In [24]:



    


stations_geo['rides'] = station_count



    











In [25]:



    


stations_geo.head()



    


















    
Out[25]:










  
    

      
      lat
      long
      rides
    

  
  
    

      20th & Bell St
      38.856100
      -77.051200
      1688
    

    

      18th & Eads St.
      38.857250
      -77.053320
      NaN
    

    

      20th & Crystal Dr
      38.856400
      -77.049200
      5113
    

    

      15th & Crystal Dr
      38.860170
      -77.049593
      3094
    

    

      Aurora Hills Community Ctr/18th & Hayes St
      38.857866
      -77.059490
      1986

get rid of all thye 'NaN' rows, then plot the data on a scatterplot where the radius of each point is the total number of rides at that location divided by the 366 days of the year





In [26]:



    


cleared = stations_geo.dropna()



    











In [27]:



    


cleared.head()



    


















    
Out[27]:










  
    

      
      lat
      long
      rides
    

  
  
    

      20th & Bell St
      38.856100
      -77.051200
      1688
    

    

      20th & Crystal Dr
      38.856400
      -77.049200
      5113
    

    

      15th & Crystal Dr
      38.860170
      -77.049593
      3094
    

    

      Aurora Hills Community Ctr/18th & Hayes St
      38.857866
      -77.059490
      1986
    

    

      Pentagon City Metro / 12th & S Hayes St
      38.862303
      -77.059936
      4231
    

  




















In [77]:



    


cleared.plot(kind='scatter', x='long', y='lat', s=(cleared['rides'] / 366) * 5, alpha=0.6, figsize=(10, 10))
plt.show()

Content source: tlake/bikeshare

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