Created
February 3, 2017 14:19
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# Let's make a graph to visualize the data | |
# Bet you haven't had a title quite like this before | |
import matplotlib.pyplot as plt | |
from pandasql import * | |
import pandas as pd | |
pysqldf = lambda q: sqldf(q, globals()) | |
q = """ | |
SELECT | |
m.date | |
, m.beef | |
, b.births | |
FROM | |
meat m | |
LEFT JOIN | |
births b | |
ON m.date = b.date | |
WHERE | |
m.date > '1974-12-31'; | |
""" | |
meat = load_meat() | |
births = load_births() | |
df = pysqldf(q) | |
df.births = df.births.fillna(method='backfill') | |
fig = plt.figure() | |
ax1 = fig.add_subplot(111) | |
ax1.plot(pd.rolling_mean(df['beef'], 12), color='b') | |
ax1.set_xlabel('months since 1975') | |
ax1.set_ylabel('cattle slaughtered', color='b') | |
ax2 = ax1.twinx() | |
ax2.plot(pd.rolling_mean(df['births'], 12), color='r') | |
ax2.set_ylabel('babies born', color='r') | |
plt.title("Beef Consumption and the Birth Rate") | |
plt.show() |
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