Changing the default scale of a plot to a "custom scale"

Question

I am trying to change the scale of the x_axis for a plot.

The default is generating divisions of 20 units (0-20-40-60-80-100-120).

I tried changing to log, but then I get 0-10-100, which helps understand the data, but I would like to try to see divisions with 10 units because my data is not logarithmic (its linear, but there are many values in the 0-10 range, which results in a plot that is very hard to read).

How can I achieve that effect, or another one that will help me visually the data?

The code is below:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
sns.set() 
plot = data3.plot(kind="scatter", x="A", y="B") # data3 is the name of my data frame

plt.xscale('log') # this was my change (plt is correct)

plot

I also tried the following (instead of the 'log' line above), but it gives the default (0-20-40-etc):

plt.xticks = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

Anandologist · Accepted Answer · 2020-04-20 16:43:20Z

You are almost there when you tried to set the xticks values.

Try

ticks = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
plt.xticks(ticks, ticks)

The two parameters are the tick locations and the tick labels and they could be different, if you wanted.

Here's a full sample code

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random

point_min = 0 # low value for random data
point_max = 101 # high value for random data
point_count = 100 # number of data points

# generate a DataFrame
data3 = pd.DataFrame({'A':random.sample(range(point_min, point_max), point_count)
                    , 'B':random.sample(range(point_min, point_max), point_count)}
                    )

tick_freq = 10 # define how far apart you want your ticks

data3.plot(x='A', y='B', kind='scatter'
         , c=np.repeat(np.atleast_2d([.5, .6, .7]), data3.shape[0], axis=0) # marker color
         , figsize=(15, 5)) # wide plot to show all tick labels
plt.xticks(ticks, ticks)
plot;

The resulting graph looks as below

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