Change Tick Frequency on X (Time, Not Number) Frequency in Matplotlib

Changing the tick frequency on the x or y axis

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()

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

How to change the datetime tick label frequency for matplotlib plots

First of all you have to convert pandas date objects to python date objects. This conversion is needed because of matplotlib internal date conversion functions. Then use functions from matplotlib.dates to set desired formatter and tick positions like here:

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

# convert date objects from pandas format to python datetime
index = pd.date_range(start = "2015-07-01", end = "2017-01-01", freq = "D")
index = [pd.to_datetime(date, format='%Y-%m-%d').date() for date in index]
data = np.random.randint(1,100, size=len(index))
df = pd.DataFrame(data=data,index=index, columns=['data'])
print (df.head())

ax = df.plot()
# set monthly locator
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))
# set formatter
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
# set font and rotation for date tick labels
plt.gcf().autofmt_xdate()

plt.show()

For season labels you have to construct it by yourself and then set it with plt.setp function (for month 02 set label winter, 04 - spring etc.):
plt.setp(new_labels, rotation=90, fontsize=9).

head of df:

            data
2015-07-01    26
2015-07-02    33
2015-07-03    46
2015-07-04    69
2015-07-05    17

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()

Changing the tick frequency on the x or y axis

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()

Changing tick frequency for x-axis in a two y-axis with imported data from .csv

I managed to do what I wanted :D it appears it had something to do with the order in which I put the things. Now it looks like this:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('.../example.csv', delimiter=';')

#get data
x = df.Time
y1 = df.Sensor1
y2 = df.Sensor2

#create a figure
plt.figure(figsize=(20,10))

#plot axes
#plot y1
plt.plot(x,y1,color='red',label='Sensor1', linestyle='-', linewidth=0.8, marker='.', markersize=10)
plt.ylim(0,40)
plt.ylabel('%')
plt.legend(loc='upper left')

plt.xlabel('Time')
plt.grid(True)
plt.xticks(rotation=45)

#plot y2
#create twin axes
ax2=plt.gca().twinx()

plt.plot(x,y2,color='blue',label='Sensor2', linestyle='-', linewidth=0.8, marker='.', markersize=10)
plt.ylabel('ppb')
plt.legend(loc='upper right')

plt.grid(True)
plt.xticks(voc_tt.Zeit[::6])

plt.show()

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