Matplotlib - highlighting weekends on x axis?-CodePudding

I've a time series (typically energy usage) recorded over a range of days. Since usage tends to be different over the weekend I want to highlight the weekends.

I've done what seems sensible:

import pandas as pd
import matplotlib.pyplot as plt
import datetime
import random

#Create dummy data.

start=datetime.datetime(2022,10,22,0,0)
finish=datetime.datetime(2022,11,7,0,0)

def randomWalk(): 
    i=0
    while True: 
        i=i random.random()-0.5
        yield i

s = pd.Series({i: next(randomWalk()) for i in pd.date_range(start, finish,freq='h')})

# Plot it.

plt.figure(figsize=[12, 8]);
s.plot();

# Color the labels according to the day of week.

for label, day in zip(plt.gca().xaxis.get_ticklabels(which='minor'), 
            pd.date_range(start,finish,freq='d')):
       label.set_color('red' if day.weekday() > 4 else 'black')

But what I get is wrong. Two weekends appear one off, and the third doesn't show at all.

I've explored the 'label' objects, but their X coordinate is just an integer, and doesn't seem meaningful. Using DateFormatter just gives nonsense.

How would be best to fix this, please?

CodePudding user response：

OK - since matplotlib only provides the information we need to the Tick Label Formatter functions, that's what we have to use:

minorLabels=plt.gca().xaxis.get_ticklabels(which='minor')
majorLabels=plt.gca().xaxis.get_ticklabels(which='major')

def MinorFormatter(dateInMinutes, index):
    # Formatter: first param is value (date in minutes, would you believe), second is which item in order.
    day=pd.to_datetime(np.datetime64(int(dateInMinutes),'m'))
    minorLabels[index].set_color('red' if day.weekday()==6 else 'black') # Sunday
    return day.day

def MajorFormatter(dateInMinutes, index):
    day=pd.to_datetime(np.datetime64(int(dateInMinutes),'m'))
    majorLabels[index].set_color('red' if day.weekday()==6 else 'black') # Sunday
    return "" if (index==0 or index==len(majorLabels)-1) else day.strftime("%d\n%b\n%Y")

plt.gca().xaxis.set_minor_formatter(MinorFormatter)
plt.gca().xaxis.set_major_formatter(MajorFormatter)

Pretty clunky, but it works. Could be fragile, though - anyone got a better answer?

CodePudding user response：

Matplotlib is meant for scientific use and although technically styling is possible, it's really hard and not worth the effort.

Consider using Plotly instead of Matplotlib as below:

#pip install plotly in terminal
import plotly.express as px
# read plotly express provided sample dataframe 
df = px.data.tips()
# create plotly figure with color_discrete_map property specifying color per day
fig = px.bar(df, x="day", y="total_bill", color='day', 
            color_discrete_map={"Sat": "orange", "Sun": "orange", "Thur": "blue", "Fri": "blue"}
            )
# send to browser
fig.show()

Solves your problem using a lot fewer lines. Only thing here is you need to make sure your data is in a Pandas DataFrame rather than Series with column names which you can pass into plotly.express.bar or scatter plot.