In this post you can find how to change the date frequency of date x-axis labels in Matplotlib and Python.

Notebook

We are going to see the default behaviour and the changed date frequency.

So default behaviour of the Matplotlib and bar plot depends highly on the data that you have.

In this example we will use data for Bitcoin price from it's start. You can find the data below:

Symbol Date High Low Open
BTC 2013-04-29 23:59:59 147.488007 134.000000 134.444000
BTC 2013-04-30 23:59:59 146.929993 134.050003 144.000000
BTC 2013-05-01 23:59:59 139.889999 107.720001 139.000000
BTC 2013-05-02 23:59:59 125.599998 92.281898 116.379997
BTC 2013-05-03 23:59:59 108.127998 79.099998 106.250000

We can plot the price for each date with bar plot by method: plt.bar:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

df = pd.read_csv('../../data/coin_Bitcoin.csv')

df['Date'] = pd.to_datetime(df['Date'])

plt.bar(df['Date'], df['High'])

result:

change-date-formatting-x-axis-tick-labels-matplotlib-python

If you like to get a bigger plot and different x axis frequency you can use the code below. Date interval frequency is set to 120:

plt.figure(figsize=(20,8))
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator())

plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=120))
plt.gcf().autofmt_xdate()

plt.bar(df['Date'], df['High'])

plt.xticks(rotation=90)

plt.show()

result:

And testing with frequency 60:

plt.figure(figsize=(20,8))
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator())

plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=60))
plt.gcf().autofmt_xdate()

plt.bar(df['Date'], df['High'])

plt.xticks(rotation=90)

plt.show()

this will result in a ticker date labels on the x-axis:

change-frequency-date-x-axis-matplotlib-python_ticker