Pandas is a hugely popular Python data manipulation library. Learn how
to use its API to plot data.
![Two pandas sitting in bamboo][1]
In this series of articles on Python-based plotting libraries, we're going to have a conceptual look atplots using pandas, the hugely popular Python data manipulation library. Pandas is a standard tool in Python for scalably transforming data, and it has also become a popular way to [import and export from CSV and Excel formats][2].
On top of all that, it also contains a very nice plotting API. This is extremely convenient—you already have your data in a pandas DataFrame, so why not use the same library to plot it?
In this series, we'll be making the same multi-bar plot in each library so we can compare how they work. The data we'll use is UK election results from 1966 to 2020:
![Matplotlib UK election results][3]
### Data that plots itself
Before we go further,note that you may need to tune your Python environment to get this code to run, including the following.
* Running a recent version of Python (instructions for[Linux][4],[Mac][5], and[Windows][6])
* Verify you're running a version of Python that works with these libraries
The data is available online and can be imported using pandas:
Now we're ready to go. We've seen some impressively simple APIs in this series of articles, but pandas has to take the crown.
To plot a bar plot with a group for each party and `year` on the x-axis, I simply need to do this:
```
import matplotlib.pyplot as plt
ax = df.plot.bar(x='year')
plt.show()
```
Four lines—definitely the tersest multi-bar plot we've created in this series.
I’m using my data in [wide form][7], meaning there’s one column per political party:
```
year conservative labour liberal others
0 1966 253 364 12 1
1 1970 330 287 6 7
2 Feb 1974 297 301 14 18
.. ... ... ... ... ...
12 2015 330 232 8 80
13 2017 317 262 12 59
14 2019 365 202 11 72
```
This means pandas automatically knows how I want my bars grouped, and if I wanted them grouped differently, pandas makes it easy to [restructure my DataFrame][8].
As with [Seaborn][9], pandas' plotting feature is an abstraction on top of Matplotlib, which is why you call Matplotlib's `plt.show()` function to actually produce the plot.
Here's what it looks like:
![pandas unstyled data plot][10]
Looks great, especially considering how easy it was! Let's style it to look just like the [Matplotlib][11] example.
#### Styling it
We can easily tweak the styling by accessing the underlying Matplotlib methods.
Firstly, we can color our bars by passing a Matplotlib colormap into the plotting function:
And we can set up axis labels and titles using the return value of the plotting function—it's simply a [Matplotlib `Axis` object][12].
```
ax.set_xlabel(None)
ax.set_ylabel('Seats')
ax.set_title('UK election results')
```
Here's what it looks like now:
![pandas styled plot][13]
That's pretty much identical to the Matplotlib version shown above but in 8 lines of code rather than 16! My inner [code golfer][14] is very pleased.
### Abstractions must be escapable
As with Seaborn, the ability to drop down and access Matplotlib APIs to do the detailed tweaking was really helpful. This is a great example of giving an abstraction [escape hatches][15] to make it powerful as well as simple.
* * *
_This article is based on[How to make plots using Pandas][16]on Anvil's blog and is reused with permission._
