I want to plot some timestamps (Year-month-day Hour-Minute-Second format). I am using the following code, however it doesn't show any hour-minute-second information, it shows them as 00-00-00. I double checked my date array, and as you can see from the snippet below, they are not zero.
Do you have any idea about why I am getting 00-00-00's?
import matplotlib.pyplot as plt
import matplotlib.dates as md
import dateutil
dates = [dateutil.parser.parse(s) for s in datestrings]
# datestrings = ['2012-02-21 11:28:17.980000', '2012-02-21 12:15:32.453000', '2012-02-21 23:26:23.734000', '2012-02-26 17:42:15.804000']
plt.subplots_adjust(bottom=0.2)
plt.xticks( rotation= 80 )
ax=plt.gca()
xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dates[0:10],plt_data[0:10], "o-")
plt.show()
Try zooming in on your graph, you will see the datetimes expand as your x axis scale changes.
plotting unix timestamps in matplotlib
I had a similarly annoying problem when trying to plot heatmaps of positive selection on chromosomes. If I zoomed out too far things would disappear entirely!
edit: This code plots your dates exactly as you give them, but doesn't add ticks in between.
import matplotlib.pyplot as plt
import matplotlib.dates as md
import dateutil
datestrings = ['2012-02-21 11:28:17.980000', '2012-02-21 12:15:32.453000', '2012-02-21 23:26:23.734000', '2012-02-26 17:42:15.804000']
dates = [dateutil.parser.parse(s) for s in datestrings]
plt_data = range(5,9)
plt.subplots_adjust(bottom=0.2)
plt.xticks( rotation=25 )
ax=plt.gca()
ax.set_xticks(dates)
xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dates,plt_data, "o-")
plt.show()
I can tell you why it shows the 00:00:00. It's because that's the start time of that particular day. For example, one tick is at 2012-02-22 00:00:00 (12 midnight of 2012-02-22) and another is at 2012-02-23 00:00:00 (12 midnight of 2012-02-23).
Ticks for the timestamps in between these two times are not shown.
I myself am trying to figure out how to show ticks for in between these times.
Related
I have pulled in a dataset that I want to use, with columns named Date and Adjusted. Adjusted is just the adjusted percentage growth on the base month.
The code I currently have is:
x = data['Date']
y = data['Adjusted']
fig = plt.figure(dpi=128, figsize=(7,3))
plt.plot(x,y)
plt.title("FTSE 100 Growth", fontsize=25)
plt.xlabel("Date", fontsize=14)
plt.ylabel("Adjusted %", fontsize=14)
plt.show()
However, when I run it I get essentially a solid black line across the bottom where all of the dates are covering each other up. It is trying to show every single date, when obviously I only want to show major ones. That dates are in the format Apr-19, and the data runs from Oct-03 to May-20.
How do I limit the number of date ticks and labels to one per year, or any amount I choose? If you do have a solution, if you could respond with the edits made to the code itself that would be great. I've tried other solutions I've found on here but I haven't been able to get it to work.
dates module of matplotlib will do the job. You can control the interval by modifying the MonthLocator (It's currently set to 6 months). Here's how:
import pandas as pd
from datetime import date, datetime, timedelta
import matplotlib.pyplot as plt
import matplotlib.dates as md
import numpy as np
import matplotlib.ticker as ticker
x = data['Date']
y = data['Adjusted']
#converts differently formatted date to a datetime object
def convert_date(df):
return datetime.strptime(df['Date'], '%b-%y')
data['Formatted_Date'] = data.apply(convert_date, axis=1)
# plot
fig, ax = plt.subplots(1, 1)
ax.plot(data['Formatted_Date'], y,'ok')
## Set time format and the interval of ticks (every 6 months)
xformatter = md.DateFormatter('%Y-%m') # format as year, month
xlocator = md.MonthLocator(interval = 6)
## Set xtick labels to appear every 6 months
ax.xaxis.set_major_locator(xlocator)
## Format xtick labels as YYYY:mm
plt.gcf().axes[0].xaxis.set_major_formatter(xformatter)
plt.title("FTSE 100 Growth", fontsize=25)
plt.xlabel("Date", fontsize=14)
plt.ylabel("Adjusted %", fontsize=14)
plt.show()
Example output:
I am plotting over a period of seconds and have time as the labels on the x-axis. Here is the only way I could get the correct time stamps. However, there are a bunch of zeros on the end. Any idea how to get rid of them??
plt.style.use('seaborn-whitegrid')
df['timestamp'] = pd.to_datetime(df['timestamp'])
fig, ax = plt.subplots(figsize=(8,4))
seconds=MicrosecondLocator(interval=500000)
myFmt = DateFormatter("%S:%f")
ax.plot(df['timestamp'], df['vibration(g)_0'], c='blue')
ax.xaxis.set_major_locator(seconds)
ax.xaxis.set_major_formatter(myFmt)
plt.gcf().autofmt_xdate()
plt.show()
This produces this image. Everything looks perfect except for all of the extra zeros. How can I get rid of them while still keeping the 5?
I guess you would want to simply cut the last 5 digits out of the string. That's also what answers to python datetime: Round/trim number of digits in microseconds suggest.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.dates import MicrosecondLocator, DateFormatter
from matplotlib.ticker import FuncFormatter
x = np.datetime64("2018-11-30T00:00") + np.arange(1,4, dtype="timedelta64[s]")
fig, ax = plt.subplots(figsize=(8,4))
seconds=MicrosecondLocator(interval=500000)
myFmt = DateFormatter("%S:%f")
ax.plot(x,[2,1,3])
def trunc_ms_fmt(x, pos=None):
return myFmt(x,pos)[:-5]
ax.xaxis.set_major_locator(seconds)
ax.xaxis.set_major_formatter(FuncFormatter(trunc_ms_fmt))
plt.gcf().autofmt_xdate()
plt.show()
Note that this format is quite unusual; so make sure the reader of the plot understands it.
I am new to matplotlib (1.3.1-2) and I cannot find a decent place to start.
I want to plot the distribution of points over time in a histogram with matplotlib.
Basically I want to plot the cumulative sum of the occurrence of a date.
date
2011-12-13
2011-12-13
2013-11-01
2013-11-01
2013-06-04
2013-06-04
2014-01-01
...
That would make
2011-12-13 -> 2 times
2013-11-01 -> 3 times
2013-06-04 -> 2 times
2014-01-01 -> once
Since there will be many points over many years, I want to set the start date on my x-Axis and the end date, and then mark n-time steps(i.e. 1 year steps) and finally decide how many bins there will be.
How would I achieve that?
Matplotlib uses its own format for dates/times, but also provides simple functions to convert which are provided in the dates module. It also provides various Locators and Formatters that take care of placing the ticks on the axis and formatting the corresponding labels. This should get you started:
import random
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# generate some random data (approximately over 5 years)
data = [float(random.randint(1271517521, 1429197513)) for _ in range(1000)]
# convert the epoch format to matplotlib date format
mpl_data = mdates.epoch2num(data)
# plot it
fig, ax = plt.subplots(1,1)
ax.hist(mpl_data, bins=50, color='lightblue')
ax.xaxis.set_major_locator(mdates.YearLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d.%m.%y'))
plt.show()
Result:
To add to hitzg's answer, you can use AutoDateLocator and AutoDateFormatter to have matplotlib do the location and formatting for you:
locator = mdates.AutoDateLocator()
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(locator))
Here is a more modern solution for matplotlib version 3.5.3.
Also, it explicitly specifies the min/max date instead of relying on min/max values derived from the data.
import random
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
days = 365*3
start_date = datetime.now()
random_dates = [
start_date + timedelta(days=int(random.random()*days))
for _ in range(100)
]
end_date = start_date + timedelta(days=days)
fig, ax = plt.subplots(figsize=(5,3))
n, bins, patches = ax.hist(random_dates, bins=52, range=(start_date, end_date))
fig.autofmt_xdate()
plt.show()
I have data that shows some values collected on three different dates: 2015-01-08, 2015-01-09 and 2015-01-12. For each date there are several data points that have timestamps.
Date/times are in a list and it looks as follows:
['2015-01-08-09:00:00', '2015-01-08-10:00:00', '2015-01-08-11:00:00', '2015-01-08-12:00:00', '2015-01-08-13:00:00', '2015-01-09-14:00:00', '2015-01-09-15:00:00', '2015-01-09-16:00:00', '2015-01-12-09:00:00', '2015-01-12-10:00:00', '2015-01-12-11:00:00']
On the other hand I have corresponding values (floats) in another list:
[12210.0, 12210.0, 12180.0, 12240.0, 12250.0, 12420.0, 12390.0, 12400.0, 12380.0, 12450.0, 12460.0]
To put all this together and plot a graph I use following code:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.dates as md
import dateutil
from matplotlib.font_manager import FontProperties
timestamps = ['2015-01-08-09:00:00', '2015-01-08-10:00:00', '2015-01-08-11:00:00', '2015-01-08-12:00:00', '2015-01-08-13:00:00', '2015-01-09-14:00:00', '2015-01-09-15:00:00', '2015-01-09-16:00:00', '2015-01-12-09:00:00', '2015-01-12-10:00:00', '2015-01-12-11:00:00']
ticks = [12210.0, 12210.0, 12180.0, 12240.0, 12250.0, 12420.0, 12390.0, 12400.0, 12380.0, 12450.0, 12460.0]
plt.subplots_adjust(bottom=0.2)
plt.xticks( rotation=90 )
dates = [dateutil.parser.parse(s) for s in timestamps]
ax=plt.gca()
ax.set_xticks(dates)
ax.tick_params(axis='x', labelsize=8)
xfmt = md.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
plt.plot(dates, ticks, label="Price")
plt.xlabel("Date and time", fontsize=12)
plt.ylabel("Price", fontsize=12)
plt.suptitle("Price during last three days", fontsize=12)
plt.legend(loc=0,prop={'size':8})
plt.savefig("figure.pdf")
When I try to plot these datetimes and values I get a messy graph with the line going back and forth.
It looks like the dates are being ignored and only timestamps are taken in account which is the reason for the messy chart. I tried to edit the datetimes to have the same date and consecutive timestamps and it fixed the chart. However, I must have dates as well..
What am I doing wrong?
When I try to plot these datetimes and values I get a messy graph with the line going back and forth.
Your plots are going all over the place because plt.plot connects the dots in the order you give it. If this order is not monotonically increasing in x, then it looks "messy". You can sort the points by x first to fix this. Here is a minimal example:
import numpy as np
import pylab as plt
X = np.random.random(20)
Y = 2*X+np.random.random(20)
idx = np.argsort(X)
X2 = X[idx]
Y2 = Y[idx]
fig,ax = plt.subplots(2,1)
ax[0].plot(X,Y)
ax[1].plot(X2,Y2)
plt.show()
When I plots the complete data works fine and displays the date on the x-axis:
.
When I zoom into particular portion to view:
the plot shows only the time rather than date, I do understand with less points can't display different set of date but how to show date or set date format even if the graph is zoomed?
dataToPlot = pd.read_csv(fileName, names=['time','1','2','3','4','plotValue','6','7','8','9','10','11','12','13','14','15','16'],
sep=',', index_col=0, parse_dates=True, dayfirst=True)
dataToPlot.drop(dataToPlot.index[0])
startTime = dataToPlot.head(1).index[0]
endTime = dataToPlot.tail(1).index[0]
ax = pd.rolling_mean(dataToPlot_plot[startTime:endTime][['plotValue']],mar).plot(linestyle='-', linewidth=3, markersize=9, color='#FECB00')
Thanks in advance!
I have a solution to make the labels look consistent, though bear in mind that it will also include the time on the "larger scale" time plot.
The code below uses the matplotlib.dates functionality to choose a date format for the x-axis. Note that as we're using the matplotlib formatting you can't simple use df.plot but must instead use plt.plot_date and convert your index to the correct format.
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates
# Generate some random data and plot it
time = pd.date_range('07/11/2014', periods=1000, freq='5min')
ts = pd.Series(pd.np.random.randn(len(time)), index=time)
fig, ax = plt.subplots()
ax.plot_date(ts.index.to_pydatetime(), ts.data)
# Create your formatter object and change the xaxis formatting.
date_fmt = '%d/%m/%y %H:%M:%S'
formatter = dates.DateFormatter(date_fmt)
ax.xaxis.set_major_formatter(formatter)
plt.gcf().autofmt_xdate()
plt.show()
An example showing the fully zoomed out plot
An example showing the plot zoomed in.