Changing the "Tick Frequency" on X or Y Axis in Matplotlib

Changing the tick frequency on x or y axis in matplotlib

You could explicitly set where you want to tick marks with plt.xticks:

plt.xticks(np.arange(min(x), max(x)+1, 1.0))

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For example,

import numpy as np
import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
plt.show()

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(np.arange was used rather than Python's range function just in case min(x) and max(x) are floats instead of ints.)

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The plt.plot (or ax.plot) function will automatically set default x and y limits. If you wish to keep those limits, and just change the stepsize of the tick marks, then you could use ax.get_xlim() to discover what limits Matplotlib has already set.

start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, stepsize))

The default tick formatter should do a decent job rounding the tick values to a sensible number of significant digits. However, if you wish to have more control over the format, you can define your own formatter. For example,

ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))

Here's a runnable example:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.show()

Change frequency of ticks matplotlib

This issue is solved with a more recent version of matplotlib. Your code in matplotlib 3.4.2 works fine.

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If you cannot update your environment, you should treat x axis format as datetime (tested with matplotlib 2.1.1).

In order to do this, first of all you have to convert x axis from str to datetime:

x_time = [datetime.strptime(x_i, '%H:%M') for x_i in x]

Then you plot your data replacing x with x_time:

ax2.plot(x_time, y, label="test2")

Now matplotlib knows your x axis is a datetime format. You still need to format properly ticks:

ax2.xaxis.set_major_locator(md.MinuteLocator(interval = 1))
ax2.xaxis.set_major_formatter(md.DateFormatter('%H:%M'))

MinuteLocator places ticks each interval value (1 minute in this case) and DateFormatter formats ticks in '%H:%M' format.

Finally you can set the space between each tick with:

ax2.set_xticks(ax2.get_xticks()[::2])

You could also avoid the last line and control space between ticks with interval parameter:

ax2.xaxis.set_major_locator(md.MinuteLocator(interval = 2))

Complete Code

import matplotlib.pyplot as plt
from datetime import datetime
import matplotlib.dates as md

f = plt.figure(figsize=(5, 5))
ax1 = f.add_subplot(211)
ax2 = f.add_subplot(212)

x = ["10:31","10:32","10:33","10:34","10:35","10:36","10:37"]
y = [13,15,17,14,17,20,21]

x_time = [datetime.strptime(x_i, '%H:%M') for x_i in x]

ax1.plot(x, y, label="test1")
ax2.plot(x_time, y, label="test2")

ax2.xaxis.set_major_locator(md.MinuteLocator(interval = 1))
ax2.xaxis.set_major_formatter(md.DateFormatter('%H:%M'))

ax2.set_xticks(ax2.get_xticks()[::2])

plt.subplots_adjust(hspace=1)
plt.show()

How to adjust 'tick frequency' for string x-axis?

One way you can do this is to reduce the number of ticks on the x axis. You can set the ticks using ax.set_xticks(). Here you can slice the x list to set a ticks at every 2nd entry using the slice notation [::2]. Then set the x tick labels using ax.set_xticklabels() using the same slice when setting the ticks.

For example:

x = ["Ant", "Bob", "Crab", "Donkey", "Elephant", "Fire", "Giant","Hello",
     "Igloo", "Jump", "Kellogg","Llama", "More", "Night"]
y = np.random.randint(0,10,14)

fig, (ax1, ax2) = plt.subplots(1,2, figsize=(9,5))
ax1.plot(x,y)
ax1.set_title("Crowded x axis")

ax2.plot(x,y)
ax2.set_xticks(x[::2])
ax2.set_xticklabels(x[::2], rotation=45)
ax2.set_title("Every 2nd ticks on x axis")

plt.show()

Y-axis tick values rounded up to the same value due to formatting in matplotlib

For each of the y-axes (ax1 and ax2), you should set the y-ticks. The ax.plot function will automatically set x and y limits. You can use the same limits, and just change the stepsize of the tick marks, then you could use ax.get_xlim() to discover what limits Matplotlib has already set.

start, end = ax2.get_ylim()
ax2.yaxis.set_ticks(np.arange(start, end, 1.0))  #as you want to set ax2 ticks to 1

Add this code right after ax2.set_ylim(ymin=0) and that should work

My output for some random numbers....as I had set mean for random number between 0 and 1 while the median line was set to random number between 0 and 4, matplotlib chose those limits and the stepsize of 1.0 along with your other code ensured that the ticks were 1 unit apart.

Matplotlib x axis date tick frequency

Check this code:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as md

x = range(2000, 2018, 1)
year = [f'{str(y)}-01-01' for y in x]

df = pd.DataFrame({'Year': year,
                   'A': np.sin(x),
                   'B': np.cos(x)})

df['Year'] = pd.to_datetime(df['Year'], format = '%Y-%m-%d').dt.date
df.set_index('Year', inplace = True)

fig, ax = plt.subplots(1, 1, figsize = (6, 4))

df['A'].plot()
df['B'].plot()

step = 2
ax.xaxis.set_major_locator(md.YearLocator(step, month = 1, day = 1))
ax.xaxis.set_major_formatter(md.DateFormatter('%Y'))

plt.legend()
plt.show()

You can manage the number of ticks with md.YearLocator(), in particular with the step value. I report the documentation for this method.

Pay attention to the type of the df index: in order to make the code work properly, the dataframe index column has to be a datetime.date, so I used the .dt.date method to convert from pandas._libs.tslibs.timestamps.Timestamp (because I built the dataframe in that way) to datetime.date. This depends on the type of data you have.

Some examples:

step = 2

step = 4

step = 10

Change y-axis ticks only on RHS

One possible solution is to "twin" the axis:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 3)
y = np.linspace(1, 3)

fig, ax1 = plt.subplots()
ax1.plot(x, y)

ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
# set the new ylimits to the be the same as the other
ax2.set_ylim(ax1.get_ylim())
ax2.set_yticks([1, 2, 3])

fig.tight_layout()  # otherwise the right y-label is slightly clipped
plt.show()

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