sample

Github contrib map is such a great visualization to see the activity over a year. There are several javascript versions of this that provide interactive visualizations on the data; but when you don't need interactivity and want to just visualize multiple data points over same time axis to see any trends, Calmap is a super simple library that can generate those.

Continue to see what we can make with our own data!

desktop screenshot

How?

Let us say our data is in a csv file that has 3 columns viz., dt, cat and y which respectively indicate date, category and the actual value. For now, assume date is in yymmdd format.

I made this in an sqlite database and queried into a file. Hence no header row; and the default field separator is a pipe symbol.

Sample Data

190518|carts|1223
190411|carts|1447
191111|orders|204
190524|carts|1181
181111|gtv|332
...
  • cart = how many shopping carts were opened up
  • orders = how many actual orders were placed
  • gtv = what was the order amount

All grouped up by dates.

Code

I am assuming that you've a python3 environment with pandas, numpy, matplotlib etc and calmap installed.

import numpy as np
import pandas as pd
import calmap
from matplotlib import pyplot as plt

def parser(x):
  """
  parse yymmdd into DateTime. Used in read_csv
  """
	return pd.datetime.strptime(x, '%y%m%d')


# pipe sep, no header row, custom date parser, trim column values
df = pd.read_csv('databydate.csv',      \
        sep="|" ,header=None,           \
        parse_dates=[0],  squeeze=True, \
        date_parser=parser)

# since there isn't a header, let us name the columns thus. _ds_ and _y_ are conventions followed by
# facebook's nice prophet library - so using it here as well
df.columns = ['ds', 'cat', 'y']

# let us set ds as datetime index
df["ds"] = pd.to_datetime(df.ds, format="%y%m%d")
df = df.set_index("ds")

## -- now comes the main part of making visualizations
ax= {}; fig = {} #each plot is a different figure - keep those and axes separately

# if you have more kinds of data, get more colormaps from
# https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
cmaps = """Greens Oranges Reds""".split()

# I want to print 2019 and 2020 data only and for 3 categories one below the other
# to see how this year is trending compared to last.
for i, yr in enumerate((2019, 2020)):
    for j,cat in enumerate("carts orders gtv".split()):
        #we take the events as a series; and fill dates for which
        #there is no data available with 0
        events = df[df.cat == cat].y.resample("D").asfreq().fillna(0)
        # make the plot and set title
        k = "{0}_{1}".format(yr, cat)
        fig[k], ax[k] = plt.subplots(1, 1, figsize = (18, 2)) #tweak figsize x,y
        calmap.yearplot(events, year=yr, cmap=cmaps[j],
                  daylabels='MTWTFSS',linewidth=1,  ax=ax[k])
        fig[k].suptitle(k) 
        #up to here is enough to plot in Jupyter notebook
        #I wanted to save the plot as pngs too so that those can be
        #embedded in an html page/email -- the next line saves those
        plt.savefig("/tmp/{0}_{1}_{2}.png".format(i,j,k))

Done! Works very well for comparative visualizations.

Note - Calmap documentation has examples on how to make random timeseries data.