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+[#]: collector: (lujun9972)
+[#]: translator: ( )
+[#]: 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