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I am plotting time series using pandas .plot() and want to see every month shown as an x-tick.
Here is the dataset structure 
Here is the result of the .plot()
I was trying to use examples from other posts and matplotlib documentation and do something like
ax.xaxis.set_major_locator( dates.MonthLocator(revenue_pivot.index, bymonthday=1,interval=1)) But that removed all the ticks :(
I also tried to pass xticks = df.index, but it has not changed anything.
What would be the rigth way to show more ticks on x-axis?
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Setting Figure-Level Tick Frequency in Matplotlib You can use the xticks() and yticks() functions and pass in an array denoting the actual ticks. On the X-axis, this array starts on 0 and ends at the length of the x array. On the Y-axis, it starts at 0 and ends at the max value of y .
Locator_params() function that lets us change the tightness and number of ticks in the plots. This is made for customizing the subplots in matplotlib, where we need the ticks packed a little tighter and limited. So, we can use this function to control the number of ticks on the plots.
To show all X coordinates (or Y coordinates), we can use xticks() method (or yticks()).
No need to pass any args to MonthLocator. Make sure to use x_compat in the df.plot() call per @Rotkiv's answer.
import pandas as pd import numpy as np import matplotlib.pylab as plt import matplotlib.dates as mdates df = pd.DataFrame(np.random.rand(100,2), index=pd.date_range('1-1-2018', periods=100)) ax = df.plot(x_compat=True) ax.xaxis.set_major_locator(mdates.MonthLocator()) plt.show() set_major_locator 
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