A heatmap of Boston temperatures

import numpy as np
import matplotlib.pyplot as plt

# Read in the relevant data from our input file.
dt = np.dtype([('month', np.int), ('day', np.int), ('T', np.float)])
data = np.genfromtxt('boston2019.dat', dtype=dt, usecols=(1, 2, 3),
                     delimiter=(4, 2, 2, 6))

# In our heatmap, nan will mean "no such date", e.g. 31 June.
heatmap = np.empty((12, 31))
heatmap[:] = np.nan

for month, day, T in data:
    # NumPy arrays are zero-indexed; days and months are not!
    heatmap[month-1, day-1] = T

# Plot the heatmap, customize and label the ticks.
fig = plt.figure()
ax = fig.add_subplot()
im = ax.imshow(heatmap, interpolation='nearest')
ax.set_yticks(range(12))
ax.set_yticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                    'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
days = np.array(range(0, 31, 2))
ax.set_xticks(days)
ax.set_xticklabels(['{:d}'.format(day+1) for day in days])
ax.set_xlabel('Day of month')
ax.set_title('Maximum daily temperatures in Boston, 2019')

# Add a colorbar along the bottom and label it.
cbar = fig.colorbar(ax=ax, mappable=im, orientation='horizontal')
cbar.set_label('Temperature, $^\circ\mathrm{C}$')

plt.show()