如何在绘制所有数据集时使用 matplotlib 在 x 轴上仅使用 label 特定日期

Question

I want to plot this data frame, with dates on the x-axes and values on the y-axes.

f_index=pd.date_range(start='1/1/2020', end='1/12/2020')
f_data=np.arange(0,len(f_index))
df=pd.DataFrame(data=f_data, index=f_index,columns=['Example'])

On the x ticks, I want to show only two dates like 2020,3,2 , and 2020,6,8 because are the relevant ones.

So I was thinking about something like that:

x1=(pd.Timestamp(2020,3,2)-f_index[0]).days
x2=(pd.Timestamp(2020,6,8)-f_index[0]).days
xx=[x1,x2]
fig2, ax2= plt.subplots(figsize=(6,4),
                          facecolor='white', dpi=300)
ax2.plot(df.index,df.Example)
#ax2.set_xticks(xx)
#ax2.set_xlabel(xx)
plt.show()

but it doesn't work.

I tried different methods and read different questions, but I did not find one with my specific answer.

This is what can I get at the moment for simplicity I rotated the dates with ax2.tick_params(axis='x',rotation=90) but is not in the code above.

This is what I would like to get

For me is important to understand how dates work because then I want to plot two straight lines in correspondence of the specific dates, something like this.

Answer 1

Your code looks fine:

from matplotlib import pyplot as plt
import datetime

# create dummy data
dates = []
data = []
for i in range(1,24):
    dates.append( datetime.date(2020,12,i) )
    data.append( i )
# open figure + axis
fig, ax = plt.subplots()
# plot data
ax.plot( dates, data)
# rotate x-tick-labels by 90°
ax.tick_params(axis='x',rotation=90)

create this output while adding

ax.set_xticks( [dates[5],dates[6],dates[16]] )

leads to this graph

If you would like to have a grid, you can simply switch it on via the ax.grid() method (you can also say, on which axis; axis='both is default):

ax.grid(True)

Now, if you want to have minor ticks in between (let's say, you want to display every tick-date), it gets a bit trickier because you need to set a multiplicator of the ticks... in our case the non-equidistant spacing makes this a little non-intuitive, but let's see:

from matplotlib.ticker import AutoMinorLocator
minor_locator = AutoMinorLocator(1)
ax.xaxis.set_minor_locator(minor_locator)
ax.grid(which='minor',linestyle=':')