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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Compute the average temperature by season ('season_desc'). (The temperatures are numbers between 0 and 1, but don't worry about that. Let's say that's the Shellman temperature scale.)
Step1:  Various of the columns represent dates or datetimes, but out of the box pd.read_table won't treat them correctly. This makes it hard to (for example) compute the number of rentals by month. Fix the dates and compute the number of rentals by month.
Step2:  weather[['total_riders', 'temp']].corr()
3.Investigate how the number of rentals varies with temperature. Is this trend constant across seasons? Across months?
Step3:  weather[['total_riders', 'temp', 'season_desc']].groupby('season_desc').corr()
Step4:  4.There are various types of users in the usage data sets. What sorts of things can you say about how they use the bikes differently?
Step5:  Part 2
Step6:  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.
Step7:  Use a scatterplot to show how the daily rental volume varies with temperature. Use a different series (with different colors) for each season.
Step8:  Create another scatterplot to show how daily rental volume varies with windspeed.  As above, use a different series for each season.
Step9:  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.
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Python Code:
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
weather = pd.read_table('daily_weather.tsv')
weather.groupby('season_desc').agg({'temp': np.mean})
fix = weather.replace("Fall", "Summer_").replace("Summer", "Spring_").replace("Winter", "Fall_").replace("Spring", "Winter_")
weather.groupby('season_desc').agg({'temp': np.mean})
Explanation: Compute the average temperature by season ('season_desc'). (The temperatures are numbers between 0 and 1, but don't worry about that. Let's say that's the Shellman temperature scale.)
End of explanation
weather['months'] = pd.DatetimeIndex(weather.date).month
weather.groupby('months').agg({'total_riders': np.sum})
Explanation: Various of the columns represent dates or datetimes, but out of the box pd.read_table won't treat them correctly. This makes it hard to (for example) compute the number of rentals by month. Fix the dates and compute the number of rentals by month.
End of explanation
weather[['total_riders', 'temp', 'months']].groupby('months').corr()
Explanation: weather[['total_riders', 'temp']].corr()
3.Investigate how the number of rentals varies with temperature. Is this trend constant across seasons? Across months?
End of explanation
weather[['no_casual_riders', 'no_reg_riders', 'temp']].corr()
Explanation: weather[['total_riders', 'temp', 'season_desc']].groupby('season_desc').corr()
End of explanation
weather[['no_casual_riders', 'no_reg_riders']].corr()
weather[['is_holiday', 'total_riders']].sum()
weather[['is_holiday', 'total_riders']].corr()
Explanation: 4.There are various types of users in the usage data sets. What sorts of things can you say about how they use the bikes differently?
End of explanation
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Part 2
End of explanation
plt.plot(weather['months'], weather['temp'])
plt.xlabel("This is just an x-axis")
plt.ylabel("This is just a y-axis")
plt.show()
x = weather.groupby('months').agg({"humidity":np.mean})
plt.bar([n for n in range(1, 13)], x['humidity'])
plt.title("weather and humidity by months")
plt.show()
Explanation: 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.
End of explanation
xs = range(10)
plt.scatter(xs, 5 * np.random.rand(10) + xs, color='r', marker='*', label='series1')
plt.scatter(xs, 5 * np.random.rand(10) + xs, color='g', marker='o', label='series2')
plt.title("A scatterplot with two series")
plt.legend(loc=9)
plt.show()
w = weather[['season_desc', 'temp', 'total_riders']]
fall = w.loc[w['season_desc'] == 'Fall']
winter = w.loc[w['season_desc'] == 'Winter']
spring = w.loc[w['season_desc'] == 'Spring']
summer = w.loc[w['season_desc'] == 'Summer']
plt.scatter(fall['temp'], fall['total_riders'], color='orange', marker='^', label='fall', s=100, alpha=.41)
plt.scatter(winter['temp'], winter['total_riders'], color='blue', marker='*', label='winter', s=100, alpha=.41)
plt.scatter(spring['temp'], spring['total_riders'], color='purple', marker='d', label='spring', s=100, alpha=.41)
plt.scatter(summer['temp'], summer['total_riders'], color='red', marker='o', label='summer', s=100, alpha=.41)
plt.legend(loc='lower right')
plt.xlabel('temperature')
plt.ylabel('rental volume')
plt.show()
Explanation: Use a scatterplot to show how the daily rental volume varies with temperature. Use a different series (with different colors) for each season.
End of explanation
w = weather[['season_desc', 'windspeed', 'total_riders']]
fall = w.loc[w['season_desc'] == 'Fall']
winter = w.loc[w['season_desc'] == 'Winter']
spring = w.loc[w['season_desc'] == 'Spring']
summer = w.loc[w['season_desc'] == 'Summer']
plt.scatter(fall['windspeed'], fall['total_riders'], color='orange', marker='^', label='fall', s=100, alpha=.41)
plt.scatter(winter['windspeed'], winter['total_riders'], color='blue', marker='*', label='winter', s=100, alpha=.41)
plt.scatter(spring['windspeed'], spring['total_riders'], color='purple', marker='d', label='spring', s=100, alpha=.41)
plt.scatter(summer['windspeed'], summer['total_riders'], color='red', marker='o', label='summer', s=100, alpha=.41)
plt.legend(loc='lower right')
plt.xlabel('windspeed x1000 mph')
plt.ylabel('rental volume')
Explanation: Create another scatterplot to show how daily rental volume varies with windspeed.  As above, use a different series for each season.
End of explanation
usage = pd.read_table('usage_2012.tsv')
stations = pd.read_table('stations.tsv')
stations.head()
c = DataFrame(counts.index, columns=['station'])
c['counts'] = counts.values
s = stations[['station','lat','long']]
u = pd.concat([usage['station_start']], axis=1, keys=['station'])
counts = u['station'].value_counts()
m = pd.merge(s, c, on='station')
 
plt.scatter(m['long'], m['lat'], c='b', label='Location', s=(m['counts'] * .05), alpha=.2)
plt.legend(loc='lower right')
plt.xlabel('longitude')
plt.ylabel('latitude')
plt.show()
Explanation: 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.
End of explanation
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