We Keep Coding

Matplotlib pyplot - Plot graph with framelines [duplicate]

I want to get both horizontal and vertical grid lines on my plot but only the horizontal grid lines are appearing by default. I am using a pandas.DataFrame from an sql query in python to generate a line plot with dates on the x-axis. I'm not sure why they do not appear on the dates and I have tried to search for an answer to this but couldn't find one.
All I have used to plot the graph is the simple code below.
data.plot()
grid('on')
data is the DataFrame which contains the dates and the data from the sql query.
I have also tried adding the code below but I still get the same output with no vertical grid lines.
ax = plt.axes()
ax.yaxis.grid() # horizontal lines
ax.xaxis.grid() # vertical lines
Any suggestions?

You may need to give boolean arg in your calls, e.g. use ax.yaxis.grid(True) instead of ax.yaxis.grid(). Additionally, since you are using both of them you can combine into ax.grid, which works on both, rather than doing it once for each dimension.
ax = plt.gca()
ax.grid(True)
That should sort you out.

plt.gca().xaxis.grid(True) proved to be the solution for me

According to matplotlib documentation, The signature of the Axes class grid() method is as follows:
Axes.grid(b=None, which='major', axis='both', **kwargs)
Turn the axes grids on or off.
which can be ‘major’ (default), ‘minor’, or ‘both’ to control whether
major tick grids, minor tick grids, or both are affected.
axis can be ‘both’ (default), ‘x’, or ‘y’ to control which set of
gridlines are drawn.
So in order to show grid lines for both the x axis and y axis, we can use the the following code:
ax = plt.gca()
ax.grid(which='major', axis='both', linestyle='--')
This method gives us finer control over what to show for grid lines.

Short answer (read below for more info):
ax.grid(axis='both', which='both')
What you do is correct and it should work.
However, since the X axis in your example is a DateTime axis the Major tick-marks (most probably) are appearing only at the both ends of the X axis. The other visible tick-marks are Minor tick-marks.
The ax.grid() method, by default, draws grid lines on Major tick-marks.
Therefore, nothing appears in your plot.
Use the code below to highlight the tick-marks. Majors will be Blue while Minors are Red.
ax.tick_params(which='both', width=3)
ax.tick_params(which='major', length=20, color='b')
ax.tick_params(which='minor', length=10, color='r')
Now to force the grid lines to be appear also on the Minor tick-marks, pass the which='minor' to the method:
ax.grid(b=True, which='minor', axis='x', color='#000000', linestyle='--')
or simply use which='both' to draw both Major and Minor grid lines.
And this a more elegant grid line:
ax.grid(b=True, which='minor', axis='both', color='#888888', linestyle='--')
ax.grid(b=True, which='major', axis='both', color='#000000', linestyle='-')

maybe this can solve the problem:
matplotlib, define size of a grid on a plot
ax.grid(True, which='both')
The truth is that the grid is working, but there's only one v-grid in 00:00 and no grid in others. I meet the same problem that there's only one grid in Nov 1 among many days.

For only horizontal lines
ax = plt.axes()
ax.yaxis.grid() # horizontal lines
This worked

Try:
plt.grid(True)
This turns on both horizontal and vertical grids for date series with major tick marks in the right place.
Using Python3 / MatPlotLib 3.4.3

Is there any way to give matplotlib pie chart a zorder?

I am using the matplotlib pie chart: https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.pie.html.
I am generating a network diagram that uses these piecharts. I am drawing a line down the middle of the pie chart to delineate two different processes. My problem is that when I draw this line down the middle it will overlay ontop of all piecharts, so if they overlap, the lines will not be layered correctly:
I realize that there is no zorder for the matplotlib pie chart, but is there a way to get it to emulate a zorder? That way I can use the zorder for the line, and then layer a pie chart on top of that line to overlap it.

pie() returns a list of patches. These individual patches have a zorder property, so you could loop over them and adjust their zorder
fig,ax = plt.subplots()
ax.set_aspect('equal')
p1,t1 = plt.pie([50,50], center=(0,0))
p2,t2 = plt.pie([1,1,1,1], center=(1.2,0)) # this pie-chart is over the first one
[p.set_zorder(-1) for p in p2] # change the z-order of the patches so that the
# 2nd pie-chart ends up below the first one

Python - Legend overlaps with the pie chart

Using matplotlib in python. The legend overlaps with my pie chart. Tried various options for "loc" such as "best" ,1,2,3... but to no avail. Any Suggestions as to how to either exactly mention the legend position (such as giving padding from the pie chart boundaries) or at least make sure that it does not overlap?

The short answer is: You may use plt.legend's arguments loc, bbox_to_anchor and additionally bbox_transform and mode, to position the legend in an axes or figure.
The long version:
Step 1: Making sure a legend is needed.
In many cases no legend is needed at all and the information can be inferred by the context or the color directly:
If indeed the plot cannot live without a legend, proceed to step 2.
Step 2: Making sure, a pie chart is needed.
In many cases pie charts are not the best way to convey information.
If the need for a pie chart is unambiguously determined, let's proceed to place the legend.
Placing the legend
plt.legend() has two main arguments to determine the position of the legend. The most important and in itself sufficient is the loc argument.
E.g. plt.legend(loc="upper left") placed the legend such that it sits in the upper left corner of its bounding box. If no further argument is specified, this bounding box will be the entire axes.
However, we may specify our own bounding box using the bbox_to_anchor argument. If bbox_to_anchor is given a 2-tuple e.g. bbox_to_anchor=(1,1) it means that the bounding box is located at the upper right corner of the axes and has no extent. It then acts as a point relative to which the legend will be placed according to the loc argument. It will then expand out of the zero-size bounding box. E.g. if loc is "upper left", the upper left corner of the legend is at position (1,1) and the legend will expand to the right and downwards.
This concept is used for the above plot, which tells us the shocking truth about the bias in Miss Universe elections.
import matplotlib.pyplot as plt
import matplotlib.patches
total = [100]
labels = ["Earth", "Mercury", "Venus", "Mars", "Jupiter", "Saturn",
"Uranus", "Neptune", "Pluto *"]
plt.title('Origin of Miss Universe since 1952')
plt.gca().axis("equal")
pie = plt.pie(total, startangle=90, colors=[plt.cm.Set3(0)],
wedgeprops = { 'linewidth': 2, "edgecolor" :"k" })
handles = []
for i, l in enumerate(labels):
handles.append(matplotlib.patches.Patch(color=plt.cm.Set3((i)/8.), label=l))
plt.legend(handles,labels, bbox_to_anchor=(0.85,1.025), loc="upper left")
plt.gcf().text(0.93,0.04,"* out of competition since 2006", ha="right")
plt.subplots_adjust(left=0.1, bottom=0.1, right=0.75)
In order for the legend not to exceed the figure, we use plt.subplots_adjust to obtain more space between the figure edge and the axis, which can then be taken up by the legend.
There is also the option to use a 4-tuple to bbox_to_anchor. How to use or interprete this is detailed in this question: What does a 4-element tuple argument for 'bbox_to_anchor' mean in matplotlib?
and one may then use the mode="expand" argument to make the legend fit into the specified bounding box.
There are some useful alternatives to this approach:
Using figure coordinates
Instead of specifying the legend position in axes coordinates, one may use figure coordinates. The advantage is that this will allow to simply place the legend in one corner of the figure without adjusting much of the rest. To this end, one would use the bbox_transform argument and supply the figure transformation to it. The coordinates given to bbox_to_anchor are then interpreted as figure coordinates.
plt.legend(pie[0],labels, bbox_to_anchor=(1,0), loc="lower right",
bbox_transform=plt.gcf().transFigure)
Here (1,0) is the lower right corner of the figure. Because of the default spacings between axes and figure edge, this suffices to place the legend such that it does not overlap with the pie.
In other cases, one might still need to adapt those spacings such that no overlap is seen, e.g.
title = plt.title('What slows down my computer')
title.set_ha("left")
plt.gca().axis("equal")
pie = plt.pie(total, startangle=0)
labels=["Trojans", "Viruses", "Too many open tabs", "The anti-virus software"]
plt.legend(pie[0],labels, bbox_to_anchor=(1,0.5), loc="center right", fontsize=10,
bbox_transform=plt.gcf().transFigure)
plt.subplots_adjust(left=0.0, bottom=0.1, right=0.45)
Saving the file with bbox_inches="tight"
Now there may be cases where we are more interested in the saved figure than at what is shown on the screen. We may then simply position the legend at the edge of the figure, like so
but then save it using the bbox_inches="tight" to savefig,
plt.savefig("output.png", bbox_inches="tight")
This will create a larger figure, which sits tight around the contents of the canvas:
A sophisticated approach, which allows to place the legend tightly inside the figure, without changing the figure size is presented here:
Creating figure with exact size and no padding (and legend outside the axes)
Using Subplots
An alternative is to use subplots to reserve space for the legend. In this case one subplot could take the pie chart, another subplot would contain the legend. This is shown below.
fig = plt.figure(4, figsize=(3,3))
ax = fig.add_subplot(211)
total = [4,3,2,81]
labels = ["tough working conditions", "high risk of accident",
"harsh weather", "it's not allowed to watch DVDs"]
ax.set_title('What people know about oil rigs')
ax.axis("equal")
pie = ax.pie(total, startangle=0)
ax2 = fig.add_subplot(212)
ax2.axis("off")
ax2.legend(pie[0],labels, loc="center")

Matplotlib control which plot is on top

I am wondering if there is a way to control which plot lies on top of other plots if one makes multiple plots on one axis. An example:
As you can see, the green series is on top of the blue series, and both series are on top of the black dots (which I made with a scatter plot). I would like the black dots to be on top of both series (lines).
I first did the above with the following code
plt.plot(series1_x, series1_y)
plt.plot(series2_x, series2_y)
plt.scatter(series2_x, series2_y)
Then I tried the following
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(series1_x, series1_y)
ax2 = fig.add_subplot(111)
ax2.plot(series2_x, series2_y)
ax3 = fig.add_subplot(111)
ax3.scatter(series2_x, series2_y)
And some variations on that, but no luck.
Swapping around the plot functions has an effect on which plot is on top, but no matter where I put the scatter function, the lines are on top of the dots.
NOTE:
I am using Python 3.5 on Windows 10 (this example), but mostly Python 3.4 on Ubuntu.
NOTE 2:
I know this may seem like a trivial issue, but I have a case where the series on top of the dots are so dense that the colour of the dots get obscured, and in those cases I need my readers to clearly see which dots are what colour, hence why I need the dots to be on top.

Use the zorder kwarg where the lower the zorder the further back the plot, e.g.
plt.plot(series1_x, series1_y, zorder=1)
plt.plot(series2_x, series2_y, zorder=2)
plt.scatter(series2_x, series2_y, zorder=3)

Yes, you can. Just use zorder parameter. The higher the value, more on top the plot shall be.
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(series1_x, series1_y, zorder=3)
ax2 = fig.add_subplot(111)
ax2.plot(series2_x, series2_y, zorder=4)
ax3 = fig.add_subplot(111)
ax3.scatter(series2_x, series2_y, zorder=5)
Alternatively, you can do line and marker plot at the same time. You can even set different colors for line and marker face.
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(series1_x, series1_y)
ax2 = fig.add_subplot(111)
ax2.plot(series2_x, series2_y, '-o', color='b', mfc='k')
The '-o' sets plot style to line and circle markers, color='b' sets line color to blue and mfc='k' sets the marker face color to black.

Another solution besides using zorder, and worth knowing: You can simply plot a scatter of points using the plot command. Something like plot(series2_x, series2_y, ' o'). Note the ' o' with a space means no lines but circle points. This way the order of plotting them on the axes does put them on top.

python axhline label not showing up in plot

I have the following little function:
def plotresults(freqs,power,prob,title,sigP):
pl.suptitle(title)
ax1 = pl.subplot(2,1,1)
ax1.axhline(y=sigP, color='r', ls='--',label='p=0.05')
pl.plot(freqs,power)
ax1.set_ylabel('Spectral Power')
ax2 = pl.subplot(2,1,2)
ax2.axhline(y=0.05, color='r', ls='--', label='p=0.05')
pl.semilogy(freqs,prob)
ax2.set_xlabel(r'Frequency (years$^{-1}$)')
ax2.set_ylabel('p-value')
pl.savefig('lsfast/figs/'+title+'.png')
pl.close()
It plots fine and draws the lines where they should be, but the line labels don't appear anywhere. What am I doing wrong? AN example of the output is attached:

The label kwarg for plot sets the label that's used by legend. To display it you can add a legend to your plot. Alternately, you might want to use annotate instead.

I don't think attaching a label to a line is meant to draw this label to the plot, it just associates this label with the line and can be used to create a legend.