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How can I return a matplotlib figure from a function?

I need to plot changing molecule numbers against time. But I'm also trying to investigate the effects of parallel processing so I'm trying to avoid writing to global variables. At the moment I have the following two numpy arrays tao_all, contains all the time points to be plotted on the x-axis and popul_num_all which contains the changing molecule numbers to be plotted on the y-axis.
The current code I've got for plotting is as follows:
for i, label in enumerate(['Enzyme', 'Substrate', 'Enzyme-Substrate complex', 'Product']):
figure1 = plt.plot(tao_all, popul_num_all[:, i], label=label)
plt.legend()
plt.tight_layout()
plt.show()
I need to encapsulate this in a function that takes the above arrays as the input and returns the graph. I've read a couple of other posts on here that say I should write my results to an axis and return the axis? But I can't quite get my head around applying that to my problem?
Cheers

def plot_func(x, y):
fig,ax = plt.subplots()
ax.plot(x, y)
return fig
Usage:
fig = plot_func([1,2], [3,4])
Alternatively you may want to return ax. For details about Figure and Axes see the docs. You can get the axes array from the figure by fig.axes and the figure from the axes by ax.get_figure().

In addition to above answer, I can suggest you to use matplotlib animation.FuncAnimation method if you are working with the time series and want to make your visualization better.
You can find the details here https://matplotlib.org/api/_as_gen/matplotlib.animation.FuncAnimation.html

Making an odd number of subplots in Python

I'm very new to Python, and I want to plot 13 different figures all in one plot. To do this nicely, I would like to plot the first 12 figures in a 6x2 grid (this works just fine), and then plot the 13th figure below it; either the same size as the other figures and centered, or larger than the rest so that its width is equal to twice the width of the other figures and all the edges are aligned. What would be the best way to specify axes of this kind using subplots? (So far, I've just used nrows=6, ncols=2, but I think something like that won't work with an odd number of figures to plot.) The code I have so far for plotting the first 12 plots looks like this (with simple test data):
fig, axes = plt.subplots(nrows=6, ncols=2, figsize=(45,10))
for ax in axes.flat:
ax.plot([1,2,3,4])
fig.subplots_adjust(right=0.5)
How can I add a 13th figure below the others?

You can use GridSpec (link to documentation) to generate flexible axes layout.
The following code creates the desired layout and puts all Axes objects in a list for easy access.
gs00 = matplotlib.gridspec.GridSpec(7, 2)
fig = plt.figure()
axs = []
for i in range(6):
for j in range(2):
ax = fig.add_subplot(gs00[i,j])
axs.append(ax)
ax = fig.add_subplot(gs00[6,:])
axs.append(ax)

Plot more than data frame on the same figure pandas

I have a list of many aggregated data frames with identical structure.
I would like to plot two columns from each dataframe on the same graph.
I used this code snippet but it gives me a separate plot for each dataframe:
# iterate through a list
for df in frames:
df.plot(x='Time', y='G1', figsize=(16, 10))
plt.hold(True)
plt.show()

If you have each set indexed, you can just concatenate all of them and plot them at once without having to iterate.
# If not indexed:
# frames = [df.assign(sample=i) for i, df in enumerate(frames)]
df = pd.concat(frames).pivot(index='Time', columns='sample', values='G1')
df.plot(figsize=(16, 10));
This helps make sure that your data is aligned and plt.hold is deprecated in matplotlib 2.0.

As you noticed, pandas.DataFrame.plot is not affected by matplotlib's hold parameter because it creates a new figure every time. The way to get around this is to pass in the ax parameter explicitly. If ax is not None, it tells the DataFrame to plot on a specific set of axes instead of making a new figure on its own.
You can prepare a set of axes ahead of time, or use the return value of the first call to df.plot. I show the latter approach here:
ax = None
for df in frames:
ax = df.plot(x='Time', y='G1', figsize=(16, 10), ax=ax)
plt.hold(True)
plt.show()

matplotlib ylabels disappear upon creating second subplot

In the MWE below, the y-label and ticks disappear. It seems to happen only with a few specific combinations of twiny and pandas for the other plot. The MWE is easily solved by changing the order in which the two subplots are created, but in my full script this is not as straightforward.
Any ideas on why this is happening and how it can be solved?
df = pd.DataFrame(np.random.randn(100))
def plot_twin(ax):
ax.plot([0,1])
ax.set_ylabel('test')
# Add return time axis to plot
other_ax = ax.twiny()
return
def plot_df(df,ax):
df.plot(kind='box',ax=ax)
return
fig,(ax1,ax2) = plt.subplots(1,2)
plot_twin(ax2)
plot_df(df,ax1)

matplotlib: Creating two (stacked) subplots with SHARED X axis but SEPARATE Y axis values

I am using matplotlib 1.2.x and Python 2.6.5 on Ubuntu 10.0.4. I am trying to create a SINGLE plot that consists of a top plot and a bottom plot.
The X axis is the date of the time series. The top plot contains a candlestick plot of the data, and the bottom plot should consist of a bar type plot - with its own Y axis (also on the left - same as the top plot). These two plots should NOT OVERLAP.
Here is a snippet of what I have done so far.
datafile = r'/var/tmp/trz12.csv'
r = mlab.csv2rec(datafile, delimiter=',', names=('dt', 'op', 'hi', 'lo', 'cl', 'vol', 'oi'))
mask = (r["dt"] >= datetime.date(startdate)) & (r["dt"] <= datetime.date(enddate))
selected = r[mask]
plotdata = zip(date2num(selected['dt']), selected['op'], selected['cl'], selected['hi'], selected['lo'], selected['vol'], selected['oi'])
# Setup charting
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # Eg, Jan 12
dayFormatter = DateFormatter('%d') # Eg, 12
monthFormatter = DateFormatter('%b %y')
# every Nth month
months = MonthLocator(range(1,13), bymonthday=1, interval=1)
fig = pylab.figure()
fig.subplots_adjust(bottom=0.1)
ax = fig.add_subplot(111)
ax.xaxis.set_major_locator(months)#mondays
ax.xaxis.set_major_formatter(monthFormatter) #weekFormatter
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.format_ydata = price
ax.grid(True)
candlestick(ax, plotdata, width=0.5, colorup='g', colordown='r', alpha=0.85)
ax.xaxis_date()
ax.autoscale_view()
pylab.setp( pylab.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
# Add volume data
# Note: the code below OVERWRITES the bottom part of the first plot
# it should be plotted UNDERNEATH the first plot - but somehow, that's not happening
fig.subplots_adjust(hspace=0.15)
ay = fig.add_subplot(212)
volumes = [ x[-2] for x in plotdata]
ay.bar(range(len(plotdata)), volumes, 0.05)
pylab.show()
I have managed to display the two plots using the code above, however, there are two problems with the bottom plot:
It COMPLETELY OVERWRITES the bottom part of the first (top) plot - almost as though the second plot was drawing on the same 'canvas' as the first plot - I can't see where/why that is happening.
It OVERWRITES the existing X axis with its own indice, the X axis values (dates) should be SHARED between the two plots.
What am I doing wrong in my code?. Can someone spot what is causing the 2nd (bottom) plot to overwrite the first (top) plot - and how can I fix this?
Here is a screenshot of the plot created by the code above:
[[Edit]]
After modifying the code as suggested by hwlau, this is the new plot. It is better than the first in that the two plots are separate, however the following issues remain:
The X axis should be SHARED by the two plots (i.e. the X axis should be shown only for the 2nd [bottom] plot)
The Y values for the 2nd plot seem to be formmated incorrectly
I think these issues should be quite easy to resolve however, my matplotlib fu is not great at the moment, as I have only recently started programming with matplotlib. any help will be much appreciated.

There seem to be a couple of problems with your code:
If you were using figure.add_subplots with the full
signature of subplot(nrows, ncols, plotNum) it may have
been more apparent that your first plot asking for 1 row
and 1 column and the second plot was asking for 2 rows and
1 column. Hence your first plot is filling the whole figure.
Rather than fig.add_subplot(111) followed by fig.add_subplot(212)
use fig.add_subplot(211) followed by fig.add_subplot(212).
Sharing an axis should be done in the add_subplot command using sharex=first_axis_instance
I have put together an example which you should be able to run:
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
import datetime as dt
n_pts = 10
dates = [dt.datetime.now() + dt.timedelta(days=i) for i in range(n_pts)]
ax1 = plt.subplot(2, 1, 1)
ax1.plot(dates, range(10))
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
ax2.bar(dates, range(10, 20))
# Now format the x axis. This *MUST* be done after all sharex commands are run.
# put no more than 10 ticks on the date axis.
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
# format the date in our own way.
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
# rotate the labels on both date axes
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(30)
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(30)
# tweak the subplot spacing to fit the rotated labels correctly
plt.subplots_adjust(hspace=0.35, bottom=0.125)
plt.show()
Hope that helps.

You should change this line:
ax = fig.add_subplot(111)
to
ax = fig.add_subplot(211)
The original command means that there is one row and one column so it occupies the whole graph. So your second graph fig.add_subplot(212) cover the lower part of the first graph.
Edit
If you dont want the gap between two plots, use subplots_adjust() to change the size of the subplots margin.

The example from #Pelson, simplified.
import matplotlib.pyplot as plt
import datetime as dt
#Two subplots that share one x axis
fig,ax=plt.subplots(2,sharex=True)
#plot data
n_pts = 10
dates = [dt.datetime.now() + dt.timedelta(days=i) for i in range(n_pts)]
ax[0].bar(dates, range(10, 20))
ax[1].plot(dates, range(10))
#rotate and format the dates on the x axis
fig.autofmt_xdate()
The subplots sharing an x-axis are created in one line, which is convenient when you want more than two subplots:
fig, ax = plt.subplots(number_of_subplots, sharex=True)
To format the date correctly on the x axis, we can simply use fig.autofmt_xdate()
For additional informations, see shared axis demo and date demo from the pylab examples.
This example ran on Python3, matplotlib 1.5.1