Fixing Too Many Ticks in Matplotlib

Introduction

When working with Matplotlib, it is common to encounter unexpected tick behavior, such as too many ticks or ticks that are out of order. This is often caused by passing a list of strings instead of numbers or datetime objects, which Matplotlib treats as categorical variables by default. This lab will provide step-by-step instructions on how to fix too many ticks in Matplotlib.

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Skills Graph

Check the Data Type

The first step is to check the data type of the x-axis values. If it is a list of strings, it is likely that the tick behavior is unexpected. To fix this, we need to convert the strings to numeric types. Here is an example:

import matplotlib.pyplot as plt
import numpy as np

## create example data
x = ['1', '5', '2', '3']
y = [1, 4, 2, 3]

## plot the data with string tick labels
fig, ax = plt.subplots()
ax.plot(x, y, 'd')
ax.set_xlabel('Categories')
plt.show()

In this example, we have a list of strings on the x-axis. When we plot the data, the tick labels are out of order and misplaced.

Convert Strings to Numeric Types

To fix the tick behavior, we need to convert the strings to numeric types. Here is an example:

import matplotlib.pyplot as plt
import numpy as np

## create example data
x = ['1', '5', '2', '3']
y = [1, 4, 2, 3]

## convert strings to floats
x = np.asarray(x, dtype='float')

## plot the data with numeric tick labels
fig, ax = plt.subplots()
ax.plot(x, y, 'd')
ax.set_xlabel('Floats')
plt.show()

In this example, we convert the string values to floats using np.asarray(). When we plot the data again, the tick labels are as expected.

Handle Too Many Ticks

If the x-axis has many elements, all of which are strings, we may end up with too many ticks that are unreadable. In this case, we need to convert the strings to numeric types. Here is an example:

import matplotlib.pyplot as plt
import numpy as np

## create example data with 100 elements
x = [f'{xx}' for xx in np.arange(100)]
y = np.arange(100)

## plot the data with string tick labels
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlabel('Categories')
plt.show()

In this example, we have 100 string values on the x-axis, resulting in too many ticks that are unreadable.

To fix this, we need to convert the strings to floats. Here is an example:

import matplotlib.pyplot as plt
import numpy as np

## create example data with 100 elements
x = [f'{xx}' for xx in np.arange(100)]
y = np.arange(100)

## convert strings to floats
x = np.asarray(x, float)

## plot the data with numeric tick labels
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlabel('Floats')
plt.show()

In this example, we convert the string values to floats using np.asarray(). When we plot the data again, the tick labels are as expected.

Handle DateTime Ticks

When working with datetime values on the x-axis, it is important to convert the strings to datetime objects to get the proper date locators and formatters. Here is an example:

import matplotlib.pyplot as plt
import numpy as np

## create example data with datetime strings
x = ['2021-10-01', '2021-11-02', '2021-12-03', '2021-09-01']
y = [0, 2, 3, 1]

## convert strings to datetime64
x = np.asarray(x, dtype='datetime64[s]')

## plot the data with datetime tick labels
fig, ax = plt.subplots()
ax.plot(x, y, 'd')
ax.tick_params(axis='x', labelrotation=90)
plt.show()

In this example, we convert the string values to datetime64 using np.asarray(). When we plot the data again, the tick labels are as expected.

Summary

In summary, when working with Matplotlib, it is important to check the data type of the x-axis values. If they are strings, we need to convert them to numeric types to fix unexpected tick behavior. If there are too many ticks, we also need to convert the strings to numeric types. When working with datetime values, we need to convert the strings to datetime objects to get the proper date locators and formatters.