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geekpi
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[#]: collector: (lujun9972)
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[#]: translator: (geekpi)
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[#]: reviewer: ( )
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[#]: url: ( )
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[#]: subject: (Using pandas to plot data in Python)
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[#]: via: (https://opensource.com/article/20/6/pandas-python)
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[#]: author: (Shaun Taylor-Morgan https://opensource.com/users/shaun-taylor-morgan)
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Using pandas to plot data in Python
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======
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Pandas is a hugely popular Python data manipulation library. Learn how
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to use its API to plot data.
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![Two pandas sitting in bamboo][1]
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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].
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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?
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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:
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![Matplotlib UK election results][3]
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### Data that plots itself
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Before we go further, note that you may need to tune your Python environment to get this code to run, including the following.
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* Running a recent version of Python (instructions for [Linux][4], [Mac][5], and [Windows][6])
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* Verify you're running a version of Python that works with these libraries
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The data is available online and can be imported using pandas:
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```
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import pandas as pd
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df = pd.read_csv('<https://anvil.works/blog/img/plotting-in-python/uk-election-results.csv>')
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```
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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.
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To plot a bar plot with a group for each party and `year` on the x-axis, I simply need to do this:
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```
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import matplotlib.pyplot as plt
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ax = df.plot.bar(x='year')
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plt.show()
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```
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Four lines—definitely the tersest multi-bar plot we've created in this series.
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I’m using my data in [wide form][7], meaning there’s one column per political party:
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```
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year conservative labour liberal others
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0 1966 253 364 12 1
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1 1970 330 287 6 7
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2 Feb 1974 297 301 14 18
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.. ... ... ... ... ...
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12 2015 330 232 8 80
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13 2017 317 262 12 59
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14 2019 365 202 11 72
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```
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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].
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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.
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Here's what it looks like:
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![pandas unstyled data plot][10]
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Looks great, especially considering how easy it was! Let's style it to look just like the [Matplotlib][11] example.
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#### Styling it
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We can easily tweak the styling by accessing the underlying Matplotlib methods.
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Firstly, we can color our bars by passing a Matplotlib colormap into the plotting function:
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```
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from matplotlib.colors import ListedColormap
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cmap = ListedColormap(['#0343df', '#e50000', '#ffff14', '#929591'])
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ax = df.plot.bar(x='year', colormap=cmap)
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```
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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].
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```
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ax.set_xlabel(None)
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ax.set_ylabel('Seats')
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ax.set_title('UK election results')
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```
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Here's what it looks like now:
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![pandas styled plot][13]
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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.
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### Abstractions must be escapable
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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.
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* * *
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_This article is based on [How to make plots using Pandas][16] on Anvil's blog and is reused with permission._
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/20/6/pandas-python
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作者：[Shaun Taylor-Morgan][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/shaun-taylor-morgan
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/panda.png?itok=0lJlct7O (Two pandas sitting in bamboo)
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[2]: https://anvil.works/docs/data-tables/csv-and-excel
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[3]: https://opensource.com/sites/default/files/uploads/matplotlib_2.png (Matplotlib UK election results)
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[4]: https://opensource.com/article/20/4/install-python-linux
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[5]: https://opensource.com/article/19/5/python-3-default-mac
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[6]: https://opensource.com/article/19/8/how-install-python-windows
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[7]: https://anvil.works/blog/tidy-data
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[8]: https://anvil.works/blog/tidy-data#converting-between-long-and-wide-data-in-pandas
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[9]: https://anvil.works/blog/plotting-in-seaborn
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[10]: https://opensource.com/sites/default/files/uploads/pandas-unstyled.png (pandas unstyled data plot)
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[11]: https://opensource.com/article/20/5/matplotlib-python
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[12]: https://matplotlib.org/api/axis_api.html#axis-objects
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[13]: https://opensource.com/sites/default/files/uploads/pandas_3.png (pandas styled plot)
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[14]: https://en.wikipedia.org/wiki/Code_golf
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[15]: https://anvil.works/blog/escape-hatches-and-ejector-seats
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[16]: https://anvil.works/blog/plotting-in-pandas
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[#]: collector: (lujun9972)
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[#]: translator: (geekpi)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Using pandas to plot data in Python)
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[#]: via: (https://opensource.com/article/20/6/pandas-python)
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[#]: author: (Shaun Taylor-Morgan https://opensource.com/users/shaun-taylor-morgan)
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使用 pandas 在 Python 中绘制数据
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======
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Pandas 是一个非常流行的 Python 数据操作库。学习怎样使用它的 API 绘制数据。
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![Two pandas sitting in bamboo][1]
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在有关基于 Python 的绘图库的系列文章中，我们将对使用 pandas（一种非常流行的 Python 数据操作库）绘图进行概念性研究。Pandas 是 Python 中用于可扩展转换数据的标准工具，它也已成为[从 CSV 和 Excel 格式导入和导出数据][2]的流行方法。
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最重要的是，它还包含一个非常好的绘图 API。这非常方便，你已将数据存储在 pandas DataFrame 中，那么为什么不使用相同的库进行绘制呢？
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在本系列中，我们将在每个库中制作相同的多柱状图，以便我们可以比较它们的工作方式。我们将使用的数据是 1966 年至 2020 年的英国大选结果：
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![Matplotlib UK election results][3]
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### 自行绘制的数据
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在继续之前，请注意你可能需要调整 Python 环境来运行此代码，包括：
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* 运行最新版本的 Python（[Linux][4]、[Mac][5] 和 [Windows][6] 的说明）
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* 确认你运行的是与这些库兼容的 Python 版本
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数据可在线获得，并可使用 pandas 导入：
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```
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import pandas as pd
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df = pd.read_csv('<https://anvil.works/blog/img/plotting-in-python/uk-election-results.csv>')
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```
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完成了。在本系列文章中，我们已经看到了一些令人印象深刻的简单 API，但是 pandas 一定能夺冠。
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要在 x 轴上绘制按`年`和每个党派分组的柱状图，我只需要这样做：
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```
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import matplotlib.pyplot as plt
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ax = df.plot.bar(x='year')
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plt.show()
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```
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只有四行，这绝对是我们在本系列中创建的最棒的多柱状图。
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我以[宽格式][7]使用数据，这意味着每个党派都有一列：
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```
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0 1966 253 364 12 1
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1 1970 330 287 6 7
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2 Feb 1974 297 301 14 18
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.. ... ... ... ... ...
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12 2015 330 232 8 80
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13 2017 317 262 12 59
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14 2019 365 202 11 72
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```
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这意味着 pandas 会自动知道我希望如何分组，如果我希望进行不同的分组，pandas 可以很容易地[重组 DataFrame][8]。
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与 [Seaborn][9] 一样，pandas 的绘图功能是 Matplotlib 之上的抽象，这就是为什么要调用 Matplotlib 的 `plt.show()` 函数来实际生成绘图的原因。
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看起来是这样的：
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![pandas unstyled data plot][10]
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看起来很棒，特别是它又这么简单！让我们对它进行样式设置，使其看起来像 [Matplotlib][11] 的例子。
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#### 调整样式
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我们可以通过访问底层的 Matplotlib 方法轻松地调整样式。
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首先，我们可以通过将 Matplotlib 颜色表传递到绘图函数来为柱状图着色：
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```
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from matplotlib.colors import ListedColormap
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cmap = ListedColormap(['#0343df', '#e50000', '#ffff14', '#929591'])
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ax = df.plot.bar(x='year', colormap=cmap)
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```
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我们可以使用绘图函数的返回值设置坐标轴标签和标题，它只是一个 [Matplotlib `Axis` 对象][12]。
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```
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ax.set_xlabel(None)
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ax.set_ylabel('Seats')
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ax.set_title('UK election results')
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```
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这是现在的样子：
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![pandas styled plot][13]
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这与上面的 Matplotlib 版本几乎相同，但是只用了 8 行代码而不是 16 行！我内心的[代码高尔夫选手][14]非常高兴。
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### 抽象必须是可转义的
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与 Seaborn 一样，向下访问 Matplotlib API 进行细节调整的能力确实很有帮助。这是给出抽象[紧急出口][15]使其既强大又简单的一个很好的例子。
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* * *
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_本文基于 Anvil 博客上的[如何使用 Pandas 绘图][16]，并获许可以重复使用。_
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/20/6/pandas-python
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作者：[Shaun Taylor-Morgan][a]
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选题：[lujun9972][b]
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译者：[geekpi](https://github.com/geekpi)
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校对：[校对者ID](https://github.com/校对者ID)
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|
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/shaun-taylor-morgan
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/panda.png?itok=0lJlct7O (Two pandas sitting in bamboo)
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[2]: https://anvil.works/docs/data-tables/csv-and-excel
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[3]: https://opensource.com/sites/default/files/uploads/matplotlib_2.png (Matplotlib UK election results)
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[4]: https://opensource.com/article/20/4/install-python-linux
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[5]: https://opensource.com/article/19/5/python-3-default-mac
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[6]: https://opensource.com/article/19/8/how-install-python-windows
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[7]: https://anvil.works/blog/tidy-data
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[8]: https://anvil.works/blog/tidy-data#converting-between-long-and-wide-data-in-pandas
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[9]: https://anvil.works/blog/plotting-in-seaborn
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[10]: https://opensource.com/sites/default/files/uploads/pandas-unstyled.png (pandas unstyled data plot)
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[11]: https://opensource.com/article/20/5/matplotlib-python
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[12]: https://matplotlib.org/api/axis_api.html#axis-objects
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[13]: https://opensource.com/sites/default/files/uploads/pandas_3.png (pandas styled plot)
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[14]: https://en.wikipedia.org/wiki/Code_golf
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[15]: https://anvil.works/blog/escape-hatches-and-ejector-seats
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[16]: https://anvil.works/blog/plotting-in-pandas
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