Visualizing the Temperature in Cambridge, UK

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import Normalize
from mpl_toolkits.mplot3d import Axes3D
plt.rcParams['text.usetex'] = True

# Read the data into a DataFrame, using the timestamp as the index and selecting
# the temperature column.
df = pd.read_csv('weather-raw.csv', names=['timestamp', 'T'], usecols=(0,1),
                 index_col=[0])
df.index = pd.to_datetime(df.index)

# The temperature in the data file is encoded as 10 * T, so retrieve the real
# temperature, in degC.
df['T'] /= 10

# Select the maximum temperature on each day of the year.
df2 = df.groupby(pd.Grouper(level='timestamp', freq='D')).max()

# Convert the timestamp to the number of seconds since the start of the year.
df2['secs'] = (df2.index - pd.to_datetime(df2.index.year, format='%Y')).total_seconds()
# Approximate the angle as the number of seconds for the timestamp divide by
# the number of seconds in an average year.
df2['angle'] = df2['secs'] / (365.25 * 24 * 60 * 60) * 2 * np.pi

# For the colourmap, the minimum is the largest multiple of 5 not greater than
# the smallest value of T; the maximum is the smallest multiple of 5 not less
# than the largest value of T, e.g. (-3.2, 40.2) -> (-5, 45).
Tmin = 5 * np.floor(df2['T'].min() / 5)
Tmax = 5 * np.ceil(df2['T'].max() / 5)

# Normalization of the colourmap.
norm = Normalize(vmin=Tmin, vmax=Tmax)
c = norm(df2['T'])

def plot_polar():
    fig = plt.figure()
    ax = fig.add_subplot(projection='polar')
    # We prefer 1 January (0 deg) on the left, but the default is to the
    # right, so offset by 180 deg.
    ax.set_theta_offset(np.pi)
    cmap = cm.turbo
    ax.scatter(df2['angle'], df2['T'], c=cmap(c), s=2)

    # Tick labels.
    ax.set_xticks(np.arange(0, 2 * np.pi, np.pi / 6))
    ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                        'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
    ax.set_yticks([])

    # Add and title the colourbar.
    cbar = fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap),
                        ax=ax, orientation='vertical', pad=0.1)
    cbar.ax.set_title(r'$T\;/^\circ\mathrm{C}$')

    plt.show()

def plot_3d():
    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')
    cmap = cm.turbo
    X = df2['T'] * np.cos(df2['angle'] + np.pi)
    Y = df2['T'] * np.sin(df2['angle'] + np.pi)
    z = df2.index.year
    ax.scatter(X, Y, z, c=cmap(c), s=2)

    ax.set_xticks([])
    ax.set_yticks([])
    cbar = fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap),
                        ax=ax, orientation='horizontal', pad=-0.02, shrink=0.6)
    cbar.ax.set_title(r'$T\;/^\circ\mathrm{C}$')
    plt.show()

plot_polar()
plot_3d()