Matplotlib and Numpy - Create a calendar heatmap

Question

Is it possible to create a calendar heatmap without using pandas? If so, can someone post a simple example?

I have dates like Aug-16 and a count value like 16 and I

Answer 1

I was looking to create a calendar heatmap where each month is displayed separately. I also needed to annotate each day with the day number (day_of_month) and it's value label.

I've been inspired by the answers posted here and also the following sites:

Here, although in R

Heatmap using pcolormesh

However I didn't seem to find something exactly as I was looking for, so I've decided to post my solution here to perhaps save others wanting the same kind of plot some time.

My example uses a bit of Pandas simply to generate some dummy data, so you can easily plug your own data source instead. Other than that it's just matplotlib.

Output from the code is given below. For my needs I also wanted to highlight days where the data was 0 (see 1st January).

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon

# Settings
years = [2018] # [2018, 2019, 2020]
weeks = [1, 2, 3, 4, 5, 6]
days = ['M', 'T', 'W', 'T', 'F', 'S', 'S']
month_names = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August',
               'September', 'October', 'November', 'December']

def generate_data():
    idx = pd.date_range('2018-01-01', periods=365, freq='D')
    return pd.Series(range(len(idx)), index=idx)


def split_months(df, year):
    """
    Take a df, slice by year, and produce a list of months,
    where each month is a 2D array in the shape of the calendar
    :param df: dataframe or series
    :return: matrix for daily values and numerals
    """
    df = df[df.index.year == year]


    # Empty matrices
    a = np.empty((6, 7))
    a[:] = np.nan

    day_nums = {m:np.copy(a) for m in range(1,13)}  # matrix for day numbers
    day_vals = {m:np.copy(a) for m in range(1,13)}  # matrix for day values

    # Logic to shape datetimes to matrices in calendar layout
    for d in df.iteritems():  # use iterrows if you have a DataFrame

        day = d[0].day
        month = d[0].month
        col = d[0].dayofweek

        if d[0].is_month_start:
            row = 0

        day_nums[month][row, col] = day  # day number (0-31)
        day_vals[month][row, col] = d[1] # day value (the heatmap data)

        if col == 6:
            row += 1

    return day_nums, day_vals


def create_year_calendar(day_nums, day_vals):
    fig, ax = plt.subplots(3, 4, figsize=(14.85, 10.5))

    for i, axs in enumerate(ax.flat):

        axs.imshow(day_vals[i+1], cmap='viridis', vmin=1, vmax=365)  # heatmap
        axs.set_title(month_names[i])

        # Labels
        axs.set_xticks(np.arange(len(days)))
        axs.set_xticklabels(days, fontsize=10, fontweight='bold', color='#555555')
        axs.set_yticklabels([])

        # Tick marks
        axs.tick_params(axis=u'both', which=u'both', length=0)  # remove tick marks
        axs.xaxis.tick_top()

        # Modify tick locations for proper grid placement
        axs.set_xticks(np.arange(-.5, 6, 1), minor=True)
        axs.set_yticks(np.arange(-.5, 5, 1), minor=True)
        axs.grid(which='minor', color='w', linestyle='-', linewidth=2.1)

        # Despine
        for edge in ['left', 'right', 'bottom', 'top']:
            axs.spines[edge].set_color('#FFFFFF')

        # Annotate
        for w in range(len(weeks)):
            for d in range(len(days)):
                day_val = day_vals[i+1][w, d]
                day_num = day_nums[i+1][w, d]

                # Value label
                axs.text(d, w+0.3, f"{day_val:0.0f}",
                         ha="center", va="center",
                         fontsize=7, color="w", alpha=0.8)

                # If value is 0, draw a grey patch
                if day_val == 0:
                    patch_coords = ((d - 0.5, w - 0.5),
                                    (d - 0.5, w + 0.5),
                                    (d + 0.5, w + 0.5),
                                    (d + 0.5, w - 0.5))

                    square = Polygon(patch_coords, fc='#DDDDDD')
                    axs.add_artist(square)

                # If day number is a valid calendar day, add an annotation
                if not np.isnan(day_num):
                    axs.text(d+0.45, w-0.31, f"{day_num:0.0f}",
                             ha="right", va="center",
                             fontsize=6, color="#003333", alpha=0.8)  # day

                # Aesthetic background for calendar day number
                patch_coords = ((d-0.1, w-0.5),
                                (d+0.5, w-0.5),
                                (d+0.5, w+0.1))

                triangle = Polygon(patch_coords, fc='w', alpha=0.7)
                axs.add_artist(triangle)

    # Final adjustments
    fig.suptitle('Calendar', fontsize=16)
    plt.subplots_adjust(left=0.04, right=0.96, top=0.88, bottom=0.04)

    # Save to file
    plt.savefig('calendar_example.pdf')


for year in years:
    df = generate_data()
    day_nums, day_vals = split_months(df, year)
    create_year_calendar(day_nums, day_vals)

There is probably a lot of room for optimisation, but this gets what I need done.