import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('AmesHousing.tsv', delimiter="\t")
financial_crisis = df[['Order','SalePrice']].copy()
financial_crisis['Time'] = df['Order']
financial_crisis['rolling_mean'] = df['SalePrice']
financial_crisis.head()
Order | SalePrice | Time | rolling_mean | |
---|---|---|---|---|
0 | 1 | 215000 | 1 | 215000 |
1 | 2 | 105000 | 2 | 105000 |
2 | 3 | 172000 | 3 | 172000 |
3 | 4 | 244000 | 4 | 244000 |
4 | 5 | 189900 | 5 | 189900 |
mean = financial_crisis['rolling_mean'].mean()
# # ZOOM IN:
financial_crisis = financial_crisis[:200]
### Adding the FiveThirtyEight style
import matplotlib.style as style
style.use('fivethirtyeight')
### Adding the plot
fig, (ax1, ax2) = plt.subplots(nrows = 2, ncols = 1,figsize=(12,6))
plt.subplots_adjust(hspace = 0, wspace=0)
ax1.plot(financial_crisis['Time'],
financial_crisis['rolling_mean'],
linewidth=1, color='#e23d28')
ax1.set_ylim(bottom=mean)
ax1.set_xticklabels([])
ax1.spines['bottom'].set_visible(False)
#ax1.get_xaxis().set_visible(False)
ax2.plot(financial_crisis['Time'],
financial_crisis['rolling_mean'],
linewidth=1, color='#A6D785')
ax2.set_ylim(top=mean)
ax2.spines['top'].set_visible(False)
plt.show()
# # ZOOM IN:
financial_crisis = financial_crisis[:200]
### Adding the FiveThirtyEight style
import matplotlib.style as style
style.use('fivethirtyeight')
### Adding the plot
fig = plt.figure(figsize=(12, 6))
ax1 =fig.add_axes([0, .5, 1, .5])
ax1.plot(financial_crisis['Time'],
financial_crisis['rolling_mean'],
linewidth=1, color='#A6D785')
ax1.set_ylim(mean)
ax1.set_xticklabels([])
ax1.spines['bottom'].set_visible(False)
ax2 = fig.add_axes([0, 0, 1, 0.5])
ax2.plot(financial_crisis['Time'],
financial_crisis['rolling_mean'],
linewidth=1, color='#e23d28')
ax2.set_ylim(top=mean)
ax2.spines['top'].set_visible(False)
plt.show()