H a n d s o n, p r o j e c t b a s e d

Plotting Dates 
Now we can improve our temperature data plot by extracting dates for the 
daily highs and passing those highs and dates to 
plot()
, as shown here:
import csv
from datetime import datetime
import matplotlib.pyplot as plt
filename = 'data/sitka_weather_07-2018_simple.csv'
sitka_highs.py

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Downloading Data
339
with open(filename) as f:
reader = csv.reader(f)
header_row = next(reader)
# Get dates and high temperatures from this file.
u
dates, highs = [], []
for row in reader:
v
current_date = datetime.strptime(row[2], '%Y-%m-%d')
high = int(row[5])
dates.append(current_date)
highs.append(high)
# Plot the high temperatures.
plt.style.use('seaborn')
fig, ax = plt.subplots()
w
ax.plot(dates, highs, c='red')
# Format plot.
plt.title("Daily high temperatures, July 2018", fontsize=24)
plt.xlabel('', fontsize=16)
x
fig.autofmt_xdate()
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis='both', which='major', labelsize=16)
plt.show()
We create two empty lists to store the dates and high temperatures from 
the file u. We then convert the data containing the date information (
row[2]
) 
to a 
datetime
object v and append it to 
dates
. We pass the dates and the high 
temperature values to 
plot()
w. The call to 
fig.autofmt_xdate()
x draws the 
date labels diagonally to prevent them from overlapping. Fig ure 16-2 shows 
the improved graph.
Figure 16-2: The graph is more meaningful now that it has dates on the x-axis.

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340
Chapter 16