In general plotting, dateticks refer to the labels of the tick lines or markers on the axes of a plot with dates or times, replacing the default numeric values. This feature is especially useful when dealing with time series data.

The image below illustrates Dateticks on a plot −

Dateticks in Matplotlib

Matplotlib provides powerful tools for plotting time-series data, allowing users to represent dates or times instead of the default numeric values on the tick lines or markers (dateticks). The library simplifies the process of working with dates by converting date instances into days since the default epoch (1970-01-01T00:00:00). This conversion, along with tick locating and formatting, occurs in the background, making it transparent to the user.

The matplotlib.dates module plays a key role in handling various date-related functionalities. This includes converting data to datetime objects, formatting dateticks labels, and setting the frequency of ticks.

Basic Dateticks with DefaultFormatter

Matplotlib sets the default tick locator and formatter for the axis, using the AutoDateLocator and AutoDateFormatter classes respectively.

Example

This example demonstrates the plotting of time-series data, here Matplotlib handles the date formatting automatically.

import numpy as np
import matplotlib.pylab as plt

# Generate an array of dates
times = np.arange(np.datetime64(''2023-01-02''),
   np.datetime64(''2024-02-03''), np.timedelta64(75, ''m''))
# Generate random data for y axis
y = np.random.randn(len(times))

# Create subplots
fig, ax = plt.subplots(figsize=(7,4), facecolor=''.9'')
ax.plot(times, y)
ax.set_xlabel(''Dataticks'',color=''xkcd:crimson'')
ax.set_ylabel(''Random data'',color=''xkcd:black'')
plt.show()

Output

On executing the above code we will get the following output −

Customizing Dateticks with DateFormatter

For manual customization of date formats, Matplotlib provides the DateFormatter module, which allows users to change the format of dateticks.

Example

This example demonstrates how to manually customize the format of Dateticks.

import numpy as np
import matplotlib.pylab as plt
import matplotlib.dates as mdates

# Generate an array of dates    
times = np.arange(np.datetime64(''2023-01-02''),
   np.datetime64(''2024-02-03''), np.timedelta64(75, ''m''))

# Generate random data for y axis
y = np.random.randn(len(times))

# Create subplots
fig, ax = plt.subplots(figsize=(7,5))
ax.plot(times, y)
ax.set_title(''Customizing Dateticks using DateFormatter'')

ax.xaxis.set_major_formatter(mdates.DateFormatter(''%Y-%b''))
# Rotates and right-aligns the x labels so they don''t overlap each other.
for label in ax.get_xticklabels(which=''major''):
   label.set(rotation=30, horizontalalignment=''right'')

plt.show()

Output

On executing the above code we will get the following output −

Advanced Formatting with ConciseDateFormatter

The ConciseDateFormatter class in Matplotlib simplifies and enhances the appearance of dateticks. This formatter is designed to optimize the choice of strings for tick labels and minimizes their length, which often removes the need to rotate the labels.

Example

This example uses the ConciseDateFormatter and AutoDateLocator classes to set the best tick limits and date formats for the plotting.

import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

# Define a starting date
base_date = datetime.datetime(2023, 12, 31)

# Generate an array of dates with a time delta of 2 hours
num_hours = 732
dates = np.array([base_date + datetime.timedelta(hours=(2 * i)) for i in range(num_hours)])
date_length = len(dates)

# Generate random data for the y-axis
np.random.seed(1967801)
y_axis = np.cumsum(np.random.randn(date_length))

# Define different date ranges
date_ranges = [
   (np.datetime64(''2024-01''), np.datetime64(''2024-03'')),
   (np.datetime64(''2023-12-31''), np.datetime64(''2024-01-31'')),
   (np.datetime64(''2023-12-31 23:59''), np.datetime64(''2024-01-01 13:20''))
]

# Create subplots for each date range
figure, axes = plt.subplots(3, 1, constrained_layout=True, figsize=(6, 6))

for nn, ax in enumerate(axes):
   # AutoDateLocator and ConciseDateFormatter for date formatting
   locator = mdates.AutoDateLocator(minticks=3, maxticks=7)
   formatter = mdates.ConciseDateFormatter(locator)

   ax.xaxis.set_major_locator(locator)
   ax.xaxis.set_major_formatter(formatter)

   # Plot the random data within the specified date range
   ax.plot(dates, y_axis)
   ax.set_xlim(date_ranges[nn])

axes[0].set_title(''Concise Date Formatter'')

# Show the plot
plt.show()

Output

On executing the above code we will get the following output −