**Problem :**

I am trying to fix how python plots my data.

Say:

```
x = [0,5,9,10,15]
y = [0,1,2,3,4]
matplotlib.pyplot.plot(x,y)
matplotlib.pyplot.show()
```

The x axis’ ticks are plotted in intervals of 5. Is there a way to make it show intervals of 1?

**Solution :**

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

:

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

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()
```

(`np.arange`

was used rather than Python’s `range`

function just in case `min(x)`

and `max(x)`

are floats instead of ints.)

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()
```

Another approach is to set the axis locator:

```
import matplotlib.ticker as plticker
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
```

There are several different types of locator depending upon your needs.

Here is a full example:

```
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.show()
```

I like this solution (from the Matplotlib Plotting Cookbook):

```
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
x = [0,5,9,10,15]
y = [0,1,2,3,4]
tick_spacing = 1
fig, ax = plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()
```

This solution give you explicit control of the tick spacing via the number given to `ticker.MultipleLocater()`

, allows automatic limit determination, and is easy to read later.

In case anyone is interested in a general one-liner, simply get the current ticks and use it to set the new ticks by sampling every other tick.

```
ax.set_xticks(ax.get_xticks()[::2])
```

This is a bit hacky, but by far the cleanest/easiest to understand example that I’ve found to do this. It’s from an answer on SO here:

Cleanest way to hide every nth tick label in matplotlib colorbar?

```
for label in ax.get_xticklabels()[::2]:
label.set_visible(False)
```

Then you can loop over the labels setting them to visible or not depending on the density you want.

edit: note that sometimes matplotlib sets labels == `''`

, so it might look like a label is not present, when in fact it is and just isn’t displaying anything. To make sure you’re looping through actual visible labels, you could try:

```
visible_labels = [lab for lab in ax.get_xticklabels() if lab.get_visible() is True and lab.get_text() != '']
plt.setp(visible_labels[::2], visible=False)
```

if you just want to set the spacing a simple one liner with minimal boilerplate:

```
plt.gca().xaxis.set_major_locator(plt.MultipleLocator(1))
```

also works easily for minor ticks:

```
plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(1))
```

a bit of a mouthfull, but pretty compact

This is an old topic, but I stumble over this every now and then and made this function. It’s very convenient:

```
import matplotlib.pyplot as pp
import numpy as np
def resadjust(ax, xres=None, yres=None):
"""
Send in an axis and I fix the resolution as desired.
"""
if xres:
start, stop = ax.get_xlim()
ticks = np.arange(start, stop + xres, xres)
ax.set_xticks(ticks)
if yres:
start, stop = ax.get_ylim()
ticks = np.arange(start, stop + yres, yres)
ax.set_yticks(ticks)
```

One caveat of controlling the ticks like this is that one does no longer enjoy the interactive automagic updating of max scale after an added line. Then do

```
gca().set_ylim(top=new_top) # for example
```

and run the resadjust function again.

I developed an inelegant solution. Consider that we have the X axis and also a list of labels for each point in X.

Example:

```
import matplotlib.pyplot as plt
x = [0,1,2,3,4,5]
y = [10,20,15,18,7,19]
xlabels = ['jan','feb','mar','apr','may','jun']
```

Let’s say that I want to show ticks labels only for ‘feb’ and ‘jun’

```
xlabelsnew = []
for i in xlabels:
if i not in ['feb','jun']:
i = ' '
xlabelsnew.append(i)
else:
xlabelsnew.append(i)
```

Good, now we have a fake list of labels. First, we plotted the original version.

```
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabels,rotation=45)
plt.show()
```

Now, the modified version.

```
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabelsnew,rotation=45)
plt.show()
```

# Pure Python Implementation

Below’s a pure python implementation of the desired functionality that handles any numeric series (int or float) with positive, negative, or mixed values and allows for the user to specify the desired step size:

```
import math
def computeTicks (x, step = 5):
"""
Computes domain with given step encompassing series x
@ params
x - Required - A list-like object of integers or floats
step - Optional - Tick frequency
"""
xMax, xMin = math.ceil(max(x)), math.floor(min(x))
dMax, dMin = xMax + abs((xMax % step) - step) + (step if (xMax % step != 0) else 0), xMin - abs((xMin % step))
return range(dMin, dMax, step)
```

# Sample Output

```
# Negative to Positive
series = [-2, 18, 24, 29, 43]
print(list(computeTicks(series)))
[-5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
# Negative to 0
series = [-30, -14, -10, -9, -3, 0]
print(list(computeTicks(series)))
[-30, -25, -20, -15, -10, -5, 0]
# 0 to Positive
series = [19, 23, 24, 27]
print(list(computeTicks(series)))
[15, 20, 25, 30]
# Floats
series = [1.8, 12.0, 21.2]
print(list(computeTicks(series)))
[0, 5, 10, 15, 20, 25]
# Step – 100
series = [118.3, 293.2, 768.1]
print(list(computeTicks(series, step = 100)))
[100, 200, 300, 400, 500, 600, 700, 800]
```

# Sample Usage

```
import matplotlib.pyplot as plt
x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(computeTicks(x))
plt.show()
```

Notice the x-axis has integer values all evenly spaced by 5, whereas the y-axis has a different interval (the `matplotlib`

default behavior, because the ticks weren’t specified).

Generalisable one liner, with only Numpy imported:

```
ax.set_xticks(np.arange(min(x),max(x),1))
```

Set in the context of the question:

```
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x = [0,5,9,10,15]
y = [0,1,2,3,4]
ax.plot(x,y)
ax.set_xticks(np.arange(min(x),max(x),1))
plt.show()
```

How it works:

`fig, ax = plt.subplots()`

gives the ax object which contains the axes.`np.arange(min(x),max(x),1)`

gives an array of interval 1 from the min of x to the max of x. This is the new x ticks that we want.`ax.set_xticks()`

changes the ticks on the ax object.

```
xmarks=[i for i in range(1,length+1,1)]
plt.xticks(xmarks)
```

Since **None** of the above solutions worked for my usecase, here I provide a solution using `None`

(pun!) which can be adapted to a wide variety of scenarios.

Here is a sample piece of code that produces cluttered ticks on both `X`

and `Y`

axes.

```
# Note the super cluttered ticks on both X and Y axis.
# inputs
x = np.arange(1, 101)
y = x * np.log(x)
fig = plt.figure() # create figure
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_xticks(x) # set xtick values
ax.set_yticks(y) # set ytick values
plt.show()
```

Now, we clean up the clutter with a new plot that shows only a sparse set of values on both x and y axes as ticks.

```
# inputs
x = np.arange(1, 101)
y = x * np.log(x)
fig = plt.figure() # create figure
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_xticks(x)
ax.set_yticks(y)
# which values need to be shown?
# here, we show every third value from `x` and `y`
show_every = 3
sparse_xticks = [None] * x.shape[0]
sparse_xticks[::show_every] = x[::show_every]
sparse_yticks = [None] * y.shape[0]
sparse_yticks[::show_every] = y[::show_every]
ax.set_xticklabels(sparse_xticks, fontsize=6) # set sparse xtick values
ax.set_yticklabels(sparse_yticks, fontsize=6) # set sparse ytick values
plt.show()
```

Depending on the usecase, one can adapt the above code simply by changing `show_every`

and using that for sampling tick values for X or Y or both the axes.

If this stepsize based solution doesn’t fit, then one can also populate the values of `sparse_xticks`

or `sparse_yticks`

at irregular intervals, if that is what is desired.

You can loop through labels and show or hide those you want:

```
for i, label in enumerate(ax.get_xticklabels()):
if i % interval != 0:
label.set_visible(False)
```