So I've spent some time managing to plot data using time on the x-axis, and the way I've found to do that is to use matplotlib.plot_date after converting datetime objects to pltdates objects.
X_d = pltdates.date2num(X) # X is an array containing datetime objects
(...)
plt.plot_date(X_d, Y)
It works great, all my data is plotted properly.
Plot with dates appearing on x-axis
However, all the measures I want to plot were made the same day (17/12/2021), the only difference is the time.
As shown on the image, matplotlib still displays the number of the the day (17th) although it is the same within the whole plot.
Anyone has a clue how to keep only the time, still using matplotlib.plot_date?
Use this example:
import matplotlib
import matplotlib.pyplot as plt
from datetime import datetime
origin = ['2020-02-05 04:11:55',
'2020-02-05 05:01:51',
'2020-02-05 07:44:49']
a = [datetime.strptime(d, '%Y-%m-%d %H:%M:%S') for d in origin]
b = ['35.764299', '20.3008', '36.94704']
x = matplotlib.dates.date2num(a)
formatter = matplotlib.dates.DateFormatter('%H:%M')
figure = plt.figure()
axes = figure.add_subplot(1, 1, 1)
axes.xaxis.set_major_formatter(formatter)
plt.setp(axes.get_xticklabels(), rotation=15)
axes.plot(x, b)
plt.show()
Related
If I run the following, it appears to work as expected, but the y-axis is limited to the earliest and latest times in the data. I want it to show midnight to midnight. I thought I could do that with the code that's commented out. But when I uncomment it, I get the correct y-axis, yet nothing plots. Where am I going wrong?
from datetime import datetime
import matplotlib.pyplot as plt
data = ['2018-01-01 09:28:52', '2018-01-03 13:02:44', '2018-01-03 15:30:27', '2018-01-04 11:55:09']
x = []
y = []
for i in range(0, len(data)):
t = datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
x.append(t.strftime('%Y-%m-%d')) # X-axis = date
y.append(t.strftime('%H:%M:%S')) # Y-axis = time
plt.plot(x, y, '.')
# begin = datetime.strptime('00:00:00', '%H:%M:%S').strftime('%H:%M:%S')
# end = datetime.strptime('23:59:59', '%H:%M:%S').strftime('%H:%M:%S')
# plt.ylim(begin, end)
plt.show()
Edit: I also noticed that the x-axis isn't right either. The data skips Jan 2, but I want that on the axis so the data is to scale.
This is a dramatically simplified version of code dealing with over a year's worth of data with over 2,500 entries.
If Pandas is available to you, consider this approach:
import pandas as pd
data = pd.to_datetime(data, yearfirst=True)
plt.plot(data.date, data.time)
_=plt.ylim(["00:00:00", "23:59:59"])
Update per comments
X-axis date formatting can be adjusted using the Locator and Formatter methods of the matplotlib.dates module. Locator finds the tick positions, and Formatter specifies how you want the labels to appear.
Sometimes Matplotlib/Pandas just gets it right, other times you need to call out exactly what you want using these extra methods. In this case, I'm not sure why those numbers are showing up, but this code will remove them.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
f, ax = plt.subplots()
data = pd.to_datetime(data, yearfirst=True)
ax.plot(data.date, data.time)
ax.set_ylim(["00:00:00", "23:59:59"])
days = mdates.DayLocator()
d_fmt = mdates.DateFormatter('%m-%d')
ax.xaxis.set_major_locator(days)
ax.xaxis.set_major_formatter(d_fmt)
I'm trying to have the tick labels of my Graph displayed fully, but I'm not getting the desired result, despite my efforts.
If I merely use autofmt_xdate(), the dates are correctly shown, but not for every data point plotted; however, if I force my x tick labels to be displayed by passing x by datetime objects to xtick(), It only seems to display the year.
fig1 = plt.figure(1)
# x is a list of datetime objects
plt.title('Portfolio Instruments')
plt.subplot(111)
plt.plot(x, y)
plt.xticks(fontsize='small')
plt.yticks([i * 5 for i in range(0, 15)])
fig1.autofmt_xdate()
plt.show()
Graph passing x to plt.xticks():
Graph without passing x to plt.xticks()
Where's my mistake? I can't find it.
Question
How do I plot all of my data points of x and format it to show the entire datetime object I'm passing the graph using autofmt_xdate()?
I have a list of datetime objects which I want to pass as the x values of my plot.
Pass the dates you want ticks at to xticks, and then set the major formatter for the x axis, using plt.gca().xaxis.set_major_formatter:
You can then use the DateFormatter from matplotlib.dates, and use a strftime format string to get the format in your question:
import matplotlib.dates as dates
fig1 = plt.figure(1)
# x is a list of datetime objects
plt.title('Portfolio Instruments')
plt.subplot(111)
plt.plot(x, y)
plt.xticks(x,fontsize='small')
plt.gca().xaxis.set_major_formatter(dates.DateFormatter('%b %d %Y'))
plt.yticks([i * 5 for i in range(0, 15)])
fig1.autofmt_xdate()
plt.show()
Note: I created the data for the above plot using the code below, so x is just a list of datetime objects for each weekday in a month (i.e. without weekends).
import numpy as np
from datetime import datetime,timedelta
start = datetime(2016, 1, 1)
end = datetime(2016, 2, 1)
delta = timedelta(days=1)
d = start
weekend = set([5, 6])
x = []
while d <= end:
if d.weekday() not in weekend:
x.append(d)
d += delta
y = np.random.rand(len(x))*70
I'm pretty sure I had a similar problem, and the way I solved it was to use the following code:
def formatFig():
date_formatter = DateFormatter('%H:%M:%S') #change the format here to whatever you like
plt.gcf().autofmt_xdate()
ax = plt.gca()
ax.xaxis.set_major_formatter(date_formatter)
max_xticks = 10 # sets the number of x ticks shown. Change this to number of data points you have
xloc = plt.MaxNLocator(max_xticks)
ax.xaxis.set_major_locator(xloc)
def makeFig():
plt.plot(xList,yList,color='blue')
formatFig()
makeFig()
plt.show(block=True)
It is a pretty simple example but you should be able to transfer the formatfig() part to use in your code.
I have an array of timestamps in the format (HH:MM:SS.mmmmmm) and another array of floating point numbers, each corresponding to a value in the timestamp array.
Can I plot time on the x axis and the numbers on the y-axis using Matplotlib?
I was trying to, but somehow it was only accepting arrays of floats. How can I get it to plot the time? Do I have to modify the format in any way?
Update:
This answer is outdated since matplotlib version 3.5. The plot function now handles datetime data directly. See https://matplotlib.org/3.5.1/api/_as_gen/matplotlib.pyplot.plot_date.html
The use of plot_date is discouraged. This method exists for historic
reasons and may be deprecated in the future.
datetime-like data should directly be plotted using plot.
If you need to plot plain numeric data as Matplotlib date format or
need to set a timezone, call ax.xaxis.axis_date / ax.yaxis.axis_date
before plot. See Axis.axis_date.
Old, outdated answer:
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.
I have written code which plots the past seven day stock value for a user-determined stock market over time.
The problem I have is that I want to format the x axis in a YYMMDD format.
I also don't understand what 2.014041e7 means at the end of the x axis.
Values for x are:
20140421.0, 20140417.0, 20140416.0, 20140415.0, 20140414.0, 20140411.0, 20140410.0
Values for y are:
531.17, 524.94, 519.01, 517.96, 521.68, 519.61, 523.48
My code is as follows:
mini = min(y)
maxi = max(y)
minimum = mini - 75
maximum = maxi + 75
mini2 = int(min(x))
maxi2 = int(max(x))
plt.close('all')
fig, ax = plt.subplots(1)
pylab.ylim([minimum,maximum])
pylab.xlim([mini2,maxi2])
ax.plot(x, y)
ax.plot(x, y,'ro')
ax.plot(x, m*x + c)
ax.grid()
ax.plot()
When plotting your data using your method you are simply plotting your y data against numbers (floats) in x such as 20140421.0 (which I assume you wish to mean the date 21/04/2014).
You need to convert your data from these floats into an appropriate format for matplotlib to understand. The code below takes your two lists (x, y) and converts them.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime as dt
# Original data
raw_x = [20140421.0, 20140417.0, 20140416.0, 20140415.0, 20140414.0, 20140411.0, 20140410.0]
y = [531.17, 524.94, 519.01, 517.96, 521.68, 519.61, 523.48]
# Convert your x-data into an appropriate format.
# date_fmt is a string giving the correct format for your data. In this case
# we are using 'YYYYMMDD.0' as your dates are actually floats.
date_fmt = '%Y%m%d.0'
# Use a list comprehension to convert your dates into datetime objects.
# In the list comp. strptime is used to convert from a string to a datetime
# object.
dt_x = [dt.datetime.strptime(str(i), date_fmt) for i in raw_x]
# Finally we convert the datetime objects into the format used by matplotlib
# in plotting using matplotlib.dates.date2num
x = [mdates.date2num(i) for i in dt_x]
# Now to actually plot your data.
fig, ax = plt.subplots()
# Use plot_date rather than plot when dealing with time data.
ax.plot_date(x, y, 'bo-')
# Create a DateFormatter object which will format your tick labels properly.
# As given in your question I have chosen "YYMMDD"
date_formatter = mdates.DateFormatter('%y%m%d')
# Set the major tick formatter to use your date formatter.
ax.xaxis.set_major_formatter(date_formatter)
# This simply rotates the x-axis tick labels slightly so they fit nicely.
fig.autofmt_xdate()
plt.show()
The code is commented throughout so should be easily self explanatory. Details on the various modules can be found below:
datetime
matplotlib.dates
I have a trouble plotting data, I only want plot HH:MM:SS but the plot shows HH:MM:SS.sssss or HH:MM:SS.%f. Below i gonna detail what I did (matplotlib, numpy are already imported )
Method I
Loading files to plot
import datetime as dt
data=genfromtxt('27JAN12.K7O', delimiter=2*[4]+5*[2]+8*[7])
f245 = data[:, 7]
Generating array for time (1 data per second)
base = dt.datetime(2014,1,27,11,07,59)
time = array([base + dt.timedelta(seconds=i) for i in range(len(data))])
plot(time,f245)
When i did this, i got this plot (with innecesary precision)
here i got time like 18:15:00.000000 (i just like 18:15:00)
Method II
The same way to load data, in this case only I took the time of the data and coverted in time string
t = data[:,1] #in decimals e.g. 18,5 represents 18:30:00
tstr = map(str, [dt.timedelta(seconds=x) for x in t])
time = []
for i in tstr:
try:
time.append(dt.datetime.strptime(i, "%H:%M:%S"))
except ValueError:
time.append(dt.datetime.strptime(i, "%H:%M:%S.%f"))
plot(time,f245)
In this case, i got time like 18:15:00.%f
So, how i could repair this?
You should use plot_date to plot datetime objects using matplotlib.
You can use matplotlib.dates.date2num to convert the datetime objects into the matplotlib format.
Furthermore you can use DateFormatter objects to set the formatting of the x-axis tick labels.
I have now included a small example using generated data, hopefully this will explain everything for you.
import numpy as numpy
import matplotlib.pyplot as plt
from matplotlib.dates import date2num, DateFormatter
import datetime as dt
base = dt.datetime(2014, 1, 27, 11, 7, 59)
x = [base + dt.timedelta(seconds=i) for i in range(10)]
y = [i**2 for i in range(10)]
x = date2num(x) # Convert datetime objects to the correct format for matplotlib.
fig, ax = plt.subplots()
ax.plot_date(x, y) # Use plot_date rather than plot
# Set the xaxis major formatter as a DateFormatter object
# The string argument shows what format you want (HH:MM:SS)
ax.xaxis.set_major_formatter(DateFormatter('%H:%M:%S'))
# This simply makes them look pretty by setting them diagonal.
fig.autofmt_xdate()
plt.show()