Plotting time in Python with Matplotlib

Question

You must first convert your timestamps to Python datetime objects (use datetime.strptime). Then use date2num to convert the dates to matplotlib format.

Plot the dates and values using plot_date:

import matplotlib.pyplot
import matplotlib.dates

from datetime import datetime

x_values = [datetime(2021, 11, 18, 12), datetime(2021, 11, 18, 14), datetime(2021, 11, 18, 16)]
y_values = [1.0, 3.0, 2.0]

dates = matplotlib.dates.date2num(x_values)
matplotlib.pyplot.plot_date(dates, y_values)

You can also plot the timestamp, value pairs using pyplot.plot (after parsing them from their string representation). (Tested with matplotlib versions 1.2.0 and 1.3.1.)

Example:

import datetime
import random
import matplotlib.pyplot as plt

# make up some data
x = [datetime.datetime.now() + datetime.timedelta(hours=i) for i in range(12)]
y = [i+random.gauss(0,1) for i,_ in enumerate(x)]

# plot
plt.plot(x,y)
# beautify the x-labels
plt.gcf().autofmt_xdate()

plt.show()

Resulting image:

---

Here's the same as a scatter plot:

import datetime
import random
import matplotlib.pyplot as plt

# make up some data
x = [datetime.datetime.now() + datetime.timedelta(hours=i) for i in range(12)]
y = [i+random.gauss(0,1) for i,_ in enumerate(x)]

# plot
plt.scatter(x,y)
# beautify the x-labels
plt.gcf().autofmt_xdate()

plt.show()

Produces an image similar to this:

7 years later and this code has helped me. However, my times still were not showing up correctly.

Using Matplotlib 2.0.0 and I had to add the following bit of code from Editing the date formatting of x-axis tick labels in matplotlib by Paul H.

import matplotlib.dates as mdates
myFmt = mdates.DateFormatter('%d')
ax.xaxis.set_major_formatter(myFmt)

I changed the format to (%H:%M) and the time displayed correctly.

All thanks to the community.

I had trouble with this using matplotlib version: 2.0.2. Running the example from above I got a centered stacked set of bubbles.

I "fixed" the problem by adding another line:

plt.plot([],[])

The entire code snippet becomes:

import datetime
import random
import matplotlib.pyplot as plt
import matplotlib.dates as mdates


# make up some data
x = [datetime.datetime.now() + datetime.timedelta(minutes=i) for i in range(12)]
y = [i+random.gauss(0,1) for i,_ in enumerate(x)]

# plot
plt.plot([],[])
plt.scatter(x,y)

# beautify the x-labels
plt.gcf().autofmt_xdate()
myFmt = mdates.DateFormatter('%H:%M')
plt.gca().xaxis.set_major_formatter(myFmt)

plt.show()
plt.close()

This produces an image with the bubbles distributed as desired.

Pandas dataframes haven't been mentioned yet. I wanted to show how these solved my datetime problem. I have datetime to the milisecond 2021-04-01 16:05:37. I am pulling linux/haproxy throughput from /proc so I can really format it however I like. This is nice for feeding data into a live graph animation.

Here's a look at the csv. (Ignore the packets per second column I'm using that in another graph)

head -2 ~/data
date,mbps,pps
2021-04-01 16:05:37,113,9342.00
...

By using print(dataframe.dtype) I can see how the data was read in:

(base) ➜  graphs ./throughput.py
date      object
mbps      int64
pps       float64
dtype:    object

Pandas pulls the date string in as "object", which is just type char. Using this as-is in a script:

import matplotlib.pyplot as plt
import pandas as pd

dataframe = pd.read_csv("~/data")

dates = dataframe["date"]
mbps = dataframe["mbps"]

plt.plot(dates, mbps, label="mbps")
plt.title("throughput")
plt.xlabel("time")
plt.ylabel("mbps")
plt.legend()
plt.xticks(rotation=45)

plt.show()

Matplotlib renders all the milisecond time data. I've added plt.xticks(rotation=45) to tilt the dates but it's not what I want. I can convert the date "object" to a datetime64[ns]. Which matplotlib does know how to render.

dataframe["date"] = pd.to_datetime(dataframe["date"]) 

This time my date is type datetime64[ns]

(base) ➜  graphs ./throughput.py
date    datetime64[ns]
mbps             int64
pps            float64
dtype:          object

Same script with 1 line difference.

#!/usr/bin/env python
import matplotlib.pyplot as plt
import pandas as pd

dataframe = pd.read_csv("~/data")

# convert object to datetime64[ns]
dataframe["date"] = pd.to_datetime(dataframe["date"]) 

dates = dataframe["date"]
mbps = dataframe["mbps"]

plt.plot(dates, mbps, label="mbps")
plt.title("throughput")
plt.xlabel("time")
plt.ylabel("mbps")
plt.legend()
plt.xticks(rotation=45)

plt.show()

This might not have been ideal for your usecase but it might help someone else.