I have a function that plots a graph. I can call this graph with different variables to alter the graph. I'd like to call this function multiple times and plot the graphs along side each other but not sure how to do so
def plt_graph(x, graph_title, horiz_label):
df[x].plot(kind='barh')
plt.title(graph_title)
plt.ylabel("")
plt.xlabel(horiz_label)
plt_graph('gross','Total value','Gross (in millions)')
In case you know the number of plots you want to produce beforehands, you can first create as many subplots as you need
fig, axes = plt.subplots(nrows=1, ncols=5)
(in this case 5) and then provide the axes to the function
def plt_graph(x, graph_title, horiz_label, ax):
df[x].plot(kind='barh', ax=ax)
Finally, call every plot like this
plt_graph("framekey", "Some title", "some label", axes[4])
(where 4 stands for the fifth and last plot)
df is a pandas Dataframe with at least one column named "framekey". You may refer to the other answer for a more complete code. And in general, if you cannot solve your problem, ask a new question, with a complete problem description.ax argument in plot. It simply refuses, by giving a very long error. But I seemed to solve it by using it as a sort of conditional argument and then doing argumentaxis.plot( etc etc) inside the function.df[x].plot is a pandas function, while ax.plot is a matplotlib function.I have created a tool to do this really easily. I use it all the time in jupyter notebooks and find it so much neater than a big column of charts. Copy the Gridplot class from this file:
https://github.com/simonm3/analysis/blob/master/analysis/plot.py
Usage:
gridplot = Gridplot()
plt.plot(x)
plt.plot(y)
It shows each new plot in a grid with 4 plots to a row. You can change the size of the charts or the number per row. It works for plt.plot, plt.bar, plt.hist and plt.scatter. However it does require you use matplot directly rather than pandas.
If you want to turn it off:
gridplot.off()
If you want to reset the grid to position 1:
gridplot.on()
Here is a way that you can do it. First you create the figure which will contain the axes object. With those axes you have something like a canvas, which is where every graph will be drawn.
fig, ax = plt.subplots(1,2)
Here I have created one figure with two axes. This is a one row and two columns figure. If you inspect the ax variable you will see two objects. This is what we'll use for all the plotting. Now, going back to the function, let's start with a simple dataset and define the function.
df = pd.DataFrame({"a": np.random.random(10), "b": np.random.random(10)})
def plt_graph(x, graph_title, horiz_label, ax):
df[x].plot(kind = 'barh', ax = ax)
ax.set_xlabel(horiz_label)
ax.set_title(graph_title)
Then, to call the function you will simply do this:
plt_graph("a", "a", "a", ax=ax[0])
plt_graph("b", "b", "b", ax=ax[1])
Note that you pass each graph that you want to create to any of the axes you have. In this case, as you have two, you pass the first to the first axes and so on. Note that if you include seaborn in your import (import seaborn as sns), automatically the seaborn style will be applied to your graphs.
This is how your graphs will look like.
When you are creating plotting functions, you want to look at matplotlib's object oriented interface.
