Edit: I changed Data Type to Pandas DataFrame that looks like this (datetime.datetime,int) in order to make the problem more simple.
Original Post:
I have a numpy array of data reports that looks like this (datetime.datetime,int,int) and I can't seem to plot it right. I need the X axes to be a 24 hours and this array
np.array([datetime.datetime.time(x) for x in DataArr])
the Y should be the days(monday,tuesday and so on) from the datetime
and the int should give me different colors for different events but I can't find an example
in matplotlib's web site.
An example of what I'm looking for:
It sounds like you want something like this?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# I'm using pandas here just to easily create a series of dates.
time = pd.date_range('01/01/2013', '05/20/2013', freq='2H')
z = np.random.random(time.size)
# There are other ways to do this, but we'll exploit how matplotlib internally
# handles dates. They're floats where a difference of 1.0 corresponds to 1 day.
# Therefore, modulo 1 results in the time of day. The +1000 yields a valid date.
t = mdates.date2num(time) % 1 + 1000
# Pandas makes getting the day of the week trivial...
day = time.dayofweek
fig, ax = plt.subplots()
scat = ax.scatter(t, day, c=z, s=100, edgecolor='none')
ax.xaxis_date()
ax.set(yticks=range(7),
yticklabels=['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])
# Optional formatting tweaks
ax.xaxis.set_major_formatter(mdates.DateFormatter('%l%p'))
ax.margins(0.05)
fig.colorbar(scat)
plt.show()
Related
I am trying to create a plot with an amount (int) in the y-axis and days in the x-axis.
I want the plot to always have the whole month in the x-axis although I dont have data for all days.
This is the code I tryed:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
import datetime as dt
df=get_pandas_data(datab) #Taking data from database in pandas DataFrame
fig = plt.figure(figsize=(10,10)) #Initialize plot
ax1 = fig.add_subplot(1,1,1)
dates=[dt.datetime.strptime(d,'%Y-%m-%d').date() for d in df['date']]
dates=list(set(dates)) #Takes all the dates from de Dataframe and sets to avoid repeated dates
s=df.resample('D', on='date')['amount'].sum() #Takes the total amount for the same date
ax1.bar(dates,s) #Bar plot for dates and amount
ax1.set(xlabel="Date",
ylabel="Balance (€)",
title="Total Monthly balance") # Plot information
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
#this is soposed to set all days of the month in the x-axis
ax1.xaxis.set_major_locator(mdates.DayLocator(interval=1))
fig.autofmt_xdate()
plt.show()
The result I get from this is a plot but only with those days that have data.
How can I make the plot to have all days in the month and plot the bar on those who have data?
This works fine with bare datetimes and matplotlib so you must be malforming your data somehow when doing your pandas manipulations. But we can't really help because we don't have your dataframe. Its always preferable to create a standalone example with dummy data, and as little code as possible to recreate the issue. a) 90% of the time you will realize your problem b) if not, we can help...
import numpy as np
import matplotlib.pyplot as plt
import datetime
x = np.array([1, 3, 7, 8, 10])
y = x * 2
dates = [datetime.datetime(2000, 2, xx) for xx in x]
fig, ax = plt.subplots()
ax.bar(dates, y)
fig.autofmt_xdate()
plt.show()
In the timeline plot I’m making, I want date tickers to show only specified dates. (In my example I show tickers for events ‘A’, but it can be any list on tickers). I found how to do it when x-axis data is numeric (upper subplot in my example), but this won’t work with timestamp date type (bottom plot).
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as ticker
myData = pd.DataFrame({'date':['2019-01-15','2019-02-10','2019-03-20','2019-04-17','2019-05-23','2019-06-11'],'cnt':range(6),'event':['a','b','a','b','a','b']})
myData['date'] = [pd.Timestamp(j) for j in myData['date']]
start = pd.Timestamp('2019-01-01')
stop = pd.Timestamp('2019-07-01')
inxa = myData.loc[myData['event'] == 'a'].index
inxb = myData.loc[myData['event'] == 'b'].index
# create two plots, one with 'cnt' as x-axis, the other 'dates' on x-axis.
fig, ax = plt.subplots(2,1,figsize=(16,9))
ax[0].plot((0,6),(0,0), 'k')
ax[1].plot((start, stop),(0,0))
for g in inxa:
ax[0].plot((myData.loc[g,'cnt'],myData.loc[g,'cnt']),(0,1),c='r')
ax[1].plot((myData.loc[g,'date'],myData.loc[g,'date']),(0,1),c='r')
for g in inxb:
ax[0].plot((myData.loc[g,'cnt'],myData.loc[g,'cnt']),(0,2),c='b')
ax[1].plot((myData.loc[g,'date'],myData.loc[g,'date']),(0,2),c='b')
xlist0 = myData.loc[myData['event']=='a','cnt']
xlist1 = myData.loc[myData['event']=='a','date']
ax[0].xaxis.set_major_locator(ticker.FixedLocator(xlist0))
# ax[1].xaxis.set_major_locator(**???**)
Couldn't find a sufficient duplicate, maybe I didn't look hard enough. There are a number of ways to do this:
Converting to numbers first or using the underlying values of a Pandas DateTime Series
xticks = [mdates.date2num(z) for z in xlist1]
# or
xticks = xlist1.values
and at least a couple ways to use it/them
ax[1].xaxis.set_major_locator(ticker.FixedLocator(xticks))
ax[1].xaxis.set_ticks(xticks)
Date tick labels
How to set the xticklabels for date in matplotlib
how to get ticks every hour?
...
I want to use Python's plt.scatter or ax.scatter to show a car finishing times as scatterplot chart. So my x axis contains an array:
'car001','car002','car003', ...
The y axes should contain the finish time in datetime format like:
'2019-01-01 23:32:01','2019-01-01 23:32:01','2019-01-01 23:32:01', ...
Why it is so difficult to use datetime values as pandas dataframe with a scatterplot?
I don't want to use plt.plot() with linestyle 'o'.
Thank you very much!
Did you try something like this ?
import pandas as pd
import matplotlib.pyplot as plt
dates = ['2017-01-01 23:32:01','2018-01-01 23:32:01','2019-01-01 23:32:01']
PM_25 = ['car001','car002','car003']
dates = [pd.to_datetime(d) for d in dates]
plt.scatter(dates, PM_25)
plt.show()
I am plotting such data:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
a = pd.DatetimeIndex(start='2010-01-01',end='2011-06-01' , freq='M')
b = pd.Series(np.random.randn(len(a)), index=a)
I would like the plot to be in the format of bars, so I use this:
b.plot(kind='bar')
This is what I get:
As you can see, the dates are formatted in full, which is very ugly and unreadable. I happened to test this command which creates a very nice Date format:
b.plot()
As you can see:
I like this format very much, it includes the months, marks the beginning of the year and is easily readable.
After doing some search, the closest I could get to that format is using this:
fig, ax = plt.subplots()
ax.plot(b.index, b)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
However the output looks like this:
I am able to have month names on x axis this way, but I like the first formatting much more. That is much more elegant. Does anyone know how I can get the same exact xticks for my bar plot?
Here's a solution that will get you the format you're looking for. You can edit the tick labels directly, and use set_major_formatter() method:
fig, ax = plt.subplots()
ax.bar(b.index, b)
ticklabels = [item.strftime('%b') for item in b.index] #['']*len(b.index)
ticklabels[::12] = [item.strftime('%b\n%Y') for item in b.index[::12]]
ax.xaxis.set_major_formatter(matplotlib.ticker.FixedFormatter(ticklabels))
ax.set_xticks(b.index)
plt.gcf().autofmt_xdate()
Output:
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()