I want to plot, in a specific few ways, dates on the x axis and time of day on the y axis, and then have either line plots or interval (floating bar) plots.

But I have a few differences from that plot and I can't get it to work. I'm actually getting the y axis to plot both the DATES as well as the time, so it is scrunching months' worth of timestamps on the y axis, when I just need about one day's worth. That example claims in the comments, "base date for yaxis can be anything, since information is in the time", but I don't understand how he is "throwing away" the base date information.

In any case, my needs are:

I'd like the option for either 24 hour time (00:00 to 24:00) or am/pm style time for the y axis, with the axis ticks looking like 3:00pm, 11:00am, etc. This will be done with a FuncFormatter, I guess.

For the intervals (time ranges) I don't want to use the error bars--the lines are way too thin. I'd like to use a (floating) bar/column plot.

My data are datetime strings of the format '2010-12-20 05:00:00'

Thanks for any help.

I think you're slightly confused as to exactly how matplotlib handles times and dates behind the scenes.

All datetimes in matplotlib are represented as simple floats. 1 day corresponds to a difference of 1.0, and the dates are in days since 1900 (if I remember correctly, anyway).

So, in order to plot just the time of a given date, you need to use a `% 1`

.

I'm going to use points, but you can easily use bars. Look into using the `bottom`

keyword argument if you do use `plt.bar`

, so that the bottom of the bars will start at the start time of your intervals (and remember that the second argument is the *height* of the bar, not its top y-value).

For example:

```
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import datetime as dt
# Make a series of events 1 day apart
x = mpl.dates.drange(dt.datetime(2009,10,1),
dt.datetime(2010,1,15),
dt.timedelta(days=1))
# Vary the datetimes so that they occur at random times
# Remember, 1.0 is equivalent to 1 day in this case...
x += np.random.random(x.size)
# We can extract the time by using a modulo 1, and adding an arbitrary base date
times = x % 1 + int(x[0]) # (The int is so the y-axis starts at midnight...)
# I'm just plotting points here, but you could just as easily use a bar.
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot_date(x, times, 'ro')
ax.yaxis_date()
fig.autofmt_xdate()
plt.show()
```

Hopefully that helps a bit!

Follow Joe Kingston's response, but I'd add that you need to convert your date strings into datetime objects, which can easily be done with the datetime module, and then convert them to matplotlib dates represented as floats (which can then be formatted as desired).

```
import datetime as dt
import matplotlib as mpl
some_time_str = '2010-12-20 05:00:00'
some_time_dt = dt.datetime.strptime(some_time_str, '%Y-%m-%d %H:%M:%S')
some_time_num = mpl.dates.date2num(some_time_dt) # 734126.20833333337
```

If you start with an array filled with time strings, you can do something like:

```
time_str_list = ['2010-12-20 05:00:00','2010-12-20 05:30:00']
time_num_list = map(
lambda x: mpl.dates.date2num(dt.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')),
time_str_list) #[734126.20833333337, 734126.22916666663]
```

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