[#]: collector: (lujun9972)
[#]: translator: (geekpi)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Using pandas to plot data in Python)
[#]: via: (https://opensource.com/article/20/6/pandas-python)
[#]: author: (Shaun Taylor-Morgan https://opensource.com/users/shaun-taylor-morgan)

Using pandas to plot data in Python
======
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 at plots 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:


```
import pandas as pd
df = pd.read_csv('<https://anvil.works/blog/img/plotting-in-python/uk-election-results.csv>')
```

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:


```
from matplotlib.colors import ListedColormap
cmap = ListedColormap(['#0343df', '#e50000', '#ffff14', '#929591'])
ax = df.plot.bar(x='year', colormap=cmap)
```

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._

--------------------------------------------------------------------------------

via: https://opensource.com/article/20/6/pandas-python

作者：[Shaun Taylor-Morgan][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)

本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出

[a]: https://opensource.com/users/shaun-taylor-morgan
[b]: https://github.com/lujun9972
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/panda.png?itok=0lJlct7O (Two pandas sitting in bamboo)
[2]: https://anvil.works/docs/data-tables/csv-and-excel
[3]: https://opensource.com/sites/default/files/uploads/matplotlib_2.png (Matplotlib UK election results)
[4]: https://opensource.com/article/20/4/install-python-linux
[5]: https://opensource.com/article/19/5/python-3-default-mac
[6]: https://opensource.com/article/19/8/how-install-python-windows
[7]: https://anvil.works/blog/tidy-data
[8]: https://anvil.works/blog/tidy-data#converting-between-long-and-wide-data-in-pandas
[9]: https://anvil.works/blog/plotting-in-seaborn
[10]: https://opensource.com/sites/default/files/uploads/pandas-unstyled.png (pandas unstyled data plot)
[11]: https://opensource.com/article/20/5/matplotlib-python
[12]: https://matplotlib.org/api/axis_api.html#axis-objects
[13]: https://opensource.com/sites/default/files/uploads/pandas_3.png (pandas styled plot)
[14]: https://en.wikipedia.org/wiki/Code_golf
[15]: https://anvil.works/blog/escape-hatches-and-ejector-seats
[16]: https://anvil.works/blog/plotting-in-pandas