Mdates.DayLocator() adds a tick for each day. But if you wanted to add day ticks to a plot that did have minor ticks turned “on” you would use:Īx.t_minor_locator(mdates.DayLocator()) Given we are using seaborn to customize the look of our plot, minor ticks are not rendered. You can add minor ticks to your plot too. WeekdayLocator ( interval = 2 )) Scatterplot showing daily precipitation with the x-axis dates cleaned up and the format customized so they are easier to read. set_major_formatter ( date_form ) # Ensure ticks fall once every other week (interval=2)Īx. set ( xlabel = "Date", ylabel = "Precipitation (Inches)", title = "Daily Precipitation (inches) \n Boulder, Colorado 2013" ) # Define the date formatĭate_form = DateFormatter ( "%m/%d" ) ax. Since the x-axis of the scatter plot will contain timestamp values, the lines List which currently contains string values, needs to be converted into a List. This applies the date format that you defined above to the plot.įig, ax = plt. Then you call the format that you defined using the set_major_formatter() method. If a plot with data that contains dates, you can use plotdate. Here you can customize the date to look like whatever format you want. This a date format that is month/day so it will look like this: 10/05 which represents October 5th. To implement the custom date, you then: define the date format: myFmt = DateFormatter("%m/%d") since the minimum date in date column is and maximum date is. for this purpose I am trying to plot x-asis ticks as date. %Y - 4 digit year %y - 2 digit year %m - month %d - day I am trying to plot date data because there are multiple values for same date. Then you specify the format that you want to use for the date DateFormatter using the syntax: ("%m/%d") where each %m element represents a part of the date as follows: To begin you need to import DateFormatter from matplotlib. You can change the format of a date on a plot axis too in matplotlib using the DateFormatter module. show () Scatterplot showing daily precipitation in Boulder, Colorado. base datetime.datetime(2005, 2, 1) dates base + datetime.timedelta(hours(2 i)) for i in range(732) N len(dates) np.ed(19680801) y np.cumsum(np.random.randn(N)) fig, axs plt.subplots(3, 1, layout'constrained', figsize(6, 6)) lims (np.datetime64('2005-02'), np.datetime64('2005-04')), (np.datetime64(''), np.d. set_major_formatter ( DateFormatter ( "%m-%d" )) plt. import datetime import matplotlib.pyplot as plt x datetime.date(2014, 1, 29), datetime.date(2014, 1, 29), datetime.date(2014, 1, 29) y 2, 4, 1 fig, ax plt. set ( xlabel = "Date", ylabel = "Precipitation (Inches)", title = "Daily Precipitation \n Boulder, Colorado 2013" ) # Format the x axisĪx.
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