Changing the formatting of a datetime axis in matplotlib

I have a series whose index is datetime that I wish to plot. I want to plot the values of the series on the y axis and the index of the series on the x axis. The Series looks as follows:

2014-01-01     7
2014-02-01     8
2014-03-01     9
2014-04-01     8
...

I generate a graph using plt.plot(series.index, series.values). But the graph looks like:

The problem is that I would like to have only year and month (yyyy-mm or 2016 March). However, the graph contains hours, minutes and seconds. How can I remove them so that I get my desired formatting?

Answers:

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Method 1

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

# sample data
N = 30
drange = pd.date_range("2014-01", periods=N, freq="MS")
np.random.seed(365)  # for a reproducible example of values
values = {'values':np.random.randint(1,20,size=N)}
df = pd.DataFrame(values, index=drange)

fig, ax = plt.subplots()
ax.plot(df.index, df.values)
ax.set_xticks(df.index)

# use formatters to specify major and minor ticks
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
ax.xaxis.set_minor_formatter(mdates.DateFormatter("%Y-%m"))
_ = plt.xticks(rotation=90)

Method 2

You can try something like this:

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
df = pd.DataFrame({'values':np.random.randint(0,1000,36)},index=pd.date_range(start='2014-01-01',end='2016-12-31',freq='M'))
fig,ax1 = plt.subplots()
plt.plot(df.index,df.values)
monthyearFmt = mdates.DateFormatter('%Y %B')
ax1.xaxis.set_major_formatter(monthyearFmt)
_ = plt.xticks(rotation=90)

Method 3

You should check out this native function of matplotlib:

fig.autofmt_xdate()

See examples on the source website Custom tick formatter

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0