Take a look at this example:
import datetime as dt
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
x = []
d = dt.datetime(2013, 7, 4)
for i in range(30):
d = d+dt.timedelta(days=1)
x.append(d)
y = range(len(x))
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator())
plt.gcf().autofmt_xdate()
plt.bar(x,y)
plt.show()
The code writes out dates on the x-axis in the plot, see the picture below. The problem is that the dates get clogged up, as seen in the picture. How to make matplotlib to only write out every fifth or every tenth coordinate?
You can specify an interval argument to the DateLocator as in the following. With e.g. interval=5 the locator places ticks at every 5th date. Also, place the autofmt_xdate() after the bar method to get the desired output.
import datetime as dt
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
x = []
d = dt.datetime(2013, 7, 4)
for i in range(30):
d = d+dt.timedelta(days=1)
x.append(d)
y = range(len(x))
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=5))
plt.bar(x, y, align='center') # center the bars on their x-values
plt.title('DateLocator with interval=5')
plt.gcf().autofmt_xdate()
plt.show()
With interval=3 you will get a tick for every 3rd date:
Related
I have a dataframe and I want to show them on graph. When I start my code, the x and y axis are non-sequential. How can I solve it? Also I give a example graph on picture. First image is mine, the second one is what I want.
This is my code:
from datetime import timedelta, date
import datetime as dt #date analyse
import matplotlib.pyplot as plt
import pandas as pd #read file
def daterange(date1, date2):
for n in range(int ((date2 - date1).days)+1):
yield date1 + timedelta(n)
tarih="01-01-2021"
tarih2="20-06-2021"
start=dt.datetime.strptime(tarih, '%d-%m-%Y')
end=dt.datetime.strptime(tarih2, '%d-%m-%Y')
fg=pd.DataFrame()
liste=[]
tarih=[]
for dt in daterange(start, end):
dates=dt.strftime("%d-%m-%Y")
with open("fng_value.txt", "r") as filestream:
for line in filestream:
date = line.split(",")[0]
if dates == date:
fng_value=line.split(",")[1]
liste.append(fng_value)
tarih.append(dates)
fg['date']=tarih
fg['fg_value']=liste
print(fg.head())
plt.subplots(figsize=(20, 10))
plt.plot(fg.date,fg.fg_value)
plt.title('Fear&Greed Index')
plt.ylabel('Fear&Greed Data')
plt.xlabel('Date')
plt.show()
This is my graph:
This is the graph that I want:
Line plot with datetime x axis
So it appears this code is opening a text file, adding values to either a list of dates or a list of values, and then making a pandas dataframe with those lists. Finally, it plots the date vs values with a line plot.
A few changes should help your graph look a lot better. A lot of this is very basic, and I'd recommend reviewing some matplotlib tutorials. The Real Python tutorial is a good starting place in my opinion.
Fix the y axis limit:
plt.set_ylim(0, 100)
Use a x axis locator from mdates to find better spaced x label locations, it depends on your time range, but I made some data and used day locator.
import matplotlib.dates as mdates
plt.xaxis.set_major_locator(mdates.DayLocator())
Use a scatter plot to add data points as on the linked graph
plt.scatter(x, y ... )
Add a grid
plt.grid(axis='both', color='gray', alpha=0.5)
Rotate the x tick labels
plt.tick_params(axis='x', rotation=45)
I simulated some data and plotted it to look like the plot you linked, this may be helpful for you to work from.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.dates as mdates
fig, ax = plt.subplots(figsize=(15,5))
x = pd.date_range(start='june 26th 2021', end='july 25th 2021')
rng = np.random.default_rng()
y = rng.integers(low=15, high=25, size=len(x))
ax.plot(x, y, color='gray', linewidth=2)
ax.scatter(x, y, color='gray')
ax.set_ylim(0,100)
ax.grid(axis='both', color='gray', alpha=0.5)
ax.set_yticks(np.arange(0,101, 10))
ax.xaxis.set_major_locator(mdates.DayLocator())
ax.tick_params(axis='x', rotation=45)
ax.set_xlim(min(x), max(x))
I have an x-axis labels of timedelta64[ns] coming out as:
0 days 12:01:13.165040
How can I achieve the following format if there's only 1 day?:
12:01:13
If there's more than one day, I need the following format:
2018.11.27
I've been successful in modifyng the labels by making a function and then calling it with:
ax.xaxis.set_major_formatter(plt.FuncFormatter(xaxisFormat))
But I don't know how to exactly go about formatting them.
You could set the respective formatter in dependence of the limits of the plot. This could look as follows.
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, HourLocator, DayLocator
def plot_something(h, ax=None):
td = np.arange(0,h, np.timedelta64(1, "h"))
y = np.sin(np.linspace(0,h,len(td)))
t = np.datetime64("2018-11-27") + td
(ax or plt.gca()).plot(t, y)
fig, axes = plt.subplots(nrows=4)
plot_something(16, ax=axes[0])
plot_something(24, ax=axes[1])
plot_something(40, ax=axes[2])
plot_something(72, ax=axes[3])
def ticking(ax):
d = np.diff(ax.get_xlim())
if d <= 1:
ax.xaxis.set_major_formatter(DateFormatter("%H:%M:%S"))
elif d <= 2:
ax.xaxis.set_major_locator(HourLocator(byhour=(0,6,12,18)))
ax.xaxis.set_major_formatter(DateFormatter("%H:%M:%S"))
else:
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_major_formatter(DateFormatter("%Y.%m.%d"))
for ax in axes.flat:
ticking(ax)
fig.tight_layout()
plt.show()
I am trying to plot information against dates. I have a list of dates in the format "01/02/1991".
I converted them by doing the following:
x = parser.parse(date).strftime('%Y%m%d'))
which gives 19910102
Then I tried to use num2date
import matplotlib.dates as dates
new_x = dates.num2date(x)
Plotting:
plt.plot_date(new_x, other_data, fmt="bo", tz=None, xdate=True)
But I get an error. It says "ValueError: year is out of range". Any solutions?
You can do this more simply using plot() instead of plot_date().
First, convert your strings to instances of Python datetime.date:
import datetime as dt
dates = ['01/02/1991','01/03/1991','01/04/1991']
x = [dt.datetime.strptime(d,'%m/%d/%Y').date() for d in dates]
y = range(len(x)) # many thanks to Kyss Tao for setting me straight here
Then plot:
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator())
plt.plot(x,y)
plt.gcf().autofmt_xdate()
Result:
I have too low reputation to add comment to #bernie response, with response to #user1506145. I have run in to same issue.
The answer to it is an interval parameter which fixes things up
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import datetime as dt
np.random.seed(1)
N = 100
y = np.random.rand(N)
now = dt.datetime.now()
then = now + dt.timedelta(days=100)
days = mdates.drange(now,then,dt.timedelta(days=1))
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=5))
plt.plot(days,y)
plt.gcf().autofmt_xdate()
plt.show()
As #KyssTao has been saying, help(dates.num2date) says that the x has to be a float giving the number of days since 0001-01-01 plus one. Hence, 19910102 is not 2/Jan/1991, because if you counted 19910101 days from 0001-01-01 you'd get something in the year 54513 or similar (divide by 365.25, number of days in a year).
Use datestr2num instead (see help(dates.datestr2num)):
new_x = dates.datestr2num(date) # where date is '01/02/1991'
Adapting #Jacek Szałęga's answer for the use of a figure fig and corresponding axes object ax:
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
import datetime as dt
np.random.seed(1)
N = 100
y = np.random.rand(N)
now = dt.datetime.now()
then = now + dt.timedelta(days=100)
days = mdates.drange(now,then,dt.timedelta(days=1))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(days,y)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax.xaxis.set_major_locator(mdates.DayLocator(interval=5))
ax.tick_params(axis='x', labelrotation=45)
plt.show()
I want to set the x tick density by specifying how many ticks to skip each time. For example, if the x axis is labelled by 100 consecutive dates, and I want to skip every 10 dates, then I will do something like
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
ts = pd.period_range("20060101", periods=100).strftime("%Y%m%d")
y = np.random.randn(100)
ax = plt.subplot(1, 1, 1)
ax.plot(ts, y)
xticks = ax.get_xticks()
ax.set_xticks(xticks[::10])
plt.xticks(rotation="vertical")
plt.show()
However the output is out of place. Pyplot only picks the first few ticks and place them all in the wrong positions, although the spacing is correct:
What can I do to get the desired output? Namely the ticks should be instead:
['20060101' '20060111' '20060121' '20060131' '20060210' '20060220'
'20060302' '20060312' '20060322' '20060401']
#klim's answer seems to put the correct marks on the axis, but the labels still won't show. An example where the date axis is correctly marked yet without labels:
Set xticklabels also. Like this.
xticks = ax.get_xticks()
xticklabels = ax.get_xticklabels()
ax.set_xticks(xticks[::10])
ax.set_xticklabels(xticklabels[::10], rotation=90)
Forget the above, which doesn't work.
How about this?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
ts = pd.period_range("20060101", periods=100).strftime("%Y%m%d")
x = np.arange(len(ts))
y = np.random.randn(100)
ax = plt.subplot(1, 1, 1)
ax.plot(x, y)
ax.set_xticks(x[::10])
ax.set_xticklabels(ts[::10], rotation="vertical")
plt.show()
This works on my machine.
I'm trying to make a plot where the x-axis is time and the y-axis is a bar chart that will have the bars covering a certain time period like this:
______________
|_____________|
_____________________
|___________________|
----------------------------------------------------->
time
I have 2 lists of datetime values for the start and end of these times I'd like to have covered. So far I have
x = np.array([dt.datetime(2010, 1, 8, i,0) for i in range(24)])
to cover a 24-hour period. My question is then how do I set and plot my y-values to look like this?
You could use plt.barh:
import datetime as DT
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
start = [DT.datetime(2000,1,1)+DT.timedelta(days=i) for i in (2,0,3)]
end = [s+DT.timedelta(days=i) for s,i in zip(start, [15,7,10])]
start = mdates.date2num(start)
end = mdates.date2num(end)
yval = [1,2,3]
width = end-start
fig, ax = plt.subplots()
ax.barh(bottom=yval, width=width, left=start, height=0.3)
xfmt = mdates.DateFormatter('%Y-%m-%d')
ax.xaxis.set_major_formatter(xfmt)
# autorotate the dates
fig.autofmt_xdate()
plt.show()
yields