In the rapidly growing world of today, when technology is expanding at a rate like never before, big data is swiftly walking into people’s lives. Though people may have ways to obtain data, when it comes to drawing insights or conclusions with it, there exists a scratching on the head because the data were too many.

在当今快速发展的世界中，当技术以前所未有的速度发展时，大数据正Swift进入人们的生活。 尽管人们可能有获取数据的方法，但是当涉及到对数据的见解或结论时，由于数据太多，头上有些抓挠。

As they said a picture is better than a thousand words, executives and non-tech decision-makers heavily rely on and prefer visualizations and infographics to understand the underlying business insights and convey/pitch to the stakeholder. Data-driven decision making is making its way to the top and we need to make sure to stay upbeat with the tools for the art.

正如他们所说的，一幅图胜于千言万语，高管和非技术决策者在很大程度上依赖于并且更喜欢可视化和图表来理解潜在的业务洞察力并向利益相关者传达/宣传。 数据驱动的决策方法正朝着最前沿发展，我们需要确保对最新工具保持乐观。

Having said that, this week I have compiled the top libraries that I have used, discovered, or heard about for great data visualizations and a bonus! Let’s get right into knowing these libraries, in no particular order.

话虽如此，本周我整理了一些我曾使用，发现或听说过的顶级库，以实现出色的数据可视化和奖励！ 让我们直接了解这些库， 而无特定顺序。

1. matplotlib (1. matplotlib)

The OG Python library, to create stories with data. Another library from the SciPy Stack, Matplotlib plots 2D figures mainly.

OG Python库，用于创建带有数据的故事。 Matplotlib是SciPy Stack中的另一个库，主要绘制2D图形。

When to use? Matplotlib is the quintessential plotting library for Python that provides an object-oriented API for embedding plots into project files or applications. I find a close resemblance of Matplotlib as MATLAB embedded in Python programming language.

什么时候使用？ Matplotlib是Python的典型绘图库，它提供了一个面向对象的API，用于将绘图嵌入到项目文件或应用程序中。 我发现Matplotlib非常像嵌入在Python编程语言中的MATLAB。

Matplotlib可以做什么？ (What can you do with Matplotlib?)

Histogram, bar plots, scatter plots, area plot to pie plot, Matplotlib can depict a wide range of 2D visualizations. A bit of effort and tint of visualization capabilities, with Matplotlib, you can create just any visualizations:

直方图，条形图，散点图，面积图到饼图，Matplotlib可以描绘各种2D可视化效果。 借助Matplotlib，您可以花一点精力和可视化功能来创建任何可视化：

1. Line plots
   线图
2. Scatter plots
   散点图
3. Area plots
   面积图
4. Bar charts and Histograms
   条形图和直方图
5. Pie charts
   饼状图
6. Stem plots
   干图
7. Contour plots
   等高线图
8. Quiver plots
   箭袋图
9. Spectrograms
   频谱图

Matplotlib also facilitates labels, grids, legends, and some more formatting entities with Matplotlib. Basically, everything that can be drawn!

Matplotlib还通过Matplotlib简化了标签，网格，图例以及其他一些格式设置实体。 基本上，所有可以绘制的东西！

Created by: John D. HunterWhere to learn more: matplotlib.org

创建者： 约翰·亨特(John D. Hunter)了解更多信息： matplotlib.org

2. Seaborn (2. Seaborn)

When you read the official documentation on Seaborn, it is defined as the data visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. Putting it simply, seaborn is an extension of Matplotlib with advanced features.

当您阅读有关Seaborn的官方文档时，它被定义为基于Matplotlib的数据可视化库，该库提供了用于绘制引人入胜且内容丰富的统计图形的高级界面。 简单来说， seaborn是Matplotlib的扩展，具有高级功能。

Then, what is the difference between Matplotlib and Seaborn? Matplotlib is used for basic plotting; bars, pies, lines, scatter plots and stuff whereas, seaborn provides a variety of advanced visualization patterns with less complex and fewer syntax.

那么，Matplotlib和Seaborn有什么区别？ Matplotlib用于基本绘图； 条形，饼形， 线形 ，散点图和其他东西，而seaborn提供了各种高级可视化模式，语法更简单，语法更少。

你可以用Seaborn做什么？ (What can you do with Seaborn?)

1. Determine relationships between multiple variables (correlation)
   确定多个变量之间的关系(相关性)
2. Observe categorical variables for aggregate statistics
   观察分类变量以获取汇总统计信息
3. Analyze uni-variate or bi-variate distributions and compare them between different data subsets
   分析单变量或双变量分布，并比较不同数据子集之间的分布
4. Plot linear regression models for dependent variables
   绘制因变量的线性回归模型
5. Provide high-level abstractions, multi-plot grids
   提供高级抽象，多图网格

Created by: Michael Waskom, available in ModeWhere to learn more: http://web.stanford.edu/~mwaskom/software/seaborn/index.html

创建者： Michael Waskom (可在Mode下获得)了解更多信息： http : //web.stanford.edu/~mwaskom/software/seaborn/index.html

3.密谋 (3. Plotly)

Plotly is again a graph plotting library for Python. Users can import, copy, paste, or stream data that is to be analyzed and visualized.

Plotly还是用于Python的图形绘图库。 用户可以导入，复制，粘贴或流式传输要分析和可视化的数据。

Each data viz library in Python varies from one another while also overlaps on some features. Plotly has many overlaps with matplotlib in terms of the range of viz available and seaborn for the advanced features.

Python中的每个数据视图库都互不相同，但在某些功能上也有所重叠。 Plotly与matplotlib有许多重叠之处 ，就高级功能而言，可用范围很广。

When to use Plotly? You can use Plotly if you want to create and display figures, update figures, hover over text for details. Plotly also has an additional feature of sending data to cloud servers. That’s interesting!

何时使用Plotly？ 如果要创建和显示图形，更新图形，将鼠标悬停在文本上以获取详细信息，可以使用Plotly。 Plotly还具有将数据发送到云服务器的附加功能。 那很有意思！

Plotly可以做什么？ (What can you do with Plotly?)

The Plotly graph library has a wide range of graphs that you can plot:

Plotly图形库具有多种图形，可以绘制：

1. Basic Charts: Line, Pie, Scatter, Bubble, Dot, Gantt, Sunburst, Treemap, Filled Area Charts

   基本图表：折线图，饼图，散点图，气泡图，圆点图，甘特图，朝阳图，树形图，填充图

2. Statistical and Seaborn Styles: Error, Box, Histograms, Facet and Trellis Plots, Tree plots, Violin Plots, Trend Lines

   统计样式和Seaborn样式 ：错误，方框，直方图，刻面和网格图，树图，小提琴图，趋势线

3. Scientific charts: Contour, Ternary, Log, Quiver, Carpet, Radar, Heat maps Windrose and Polar Plots

   科学图表：轮廓图，三元图，对数图，颤动图，地毯，雷达，热图Windrose和极地图

4. Financial Charts
   财务图表
5. Maps
   地图
6. Subplots
   子图
7. Transforms
   变身
8. Jupyter Widgets Interaction
   Jupyter小部件交互

Think of visualization and Plotly can do it!

想想可视化，Plotly可以做到！

Created by: Plotly, available in ModeWhere to learn more: https://plot.ly/python/

创建者： Plotly ，在Mode下可用了解更多信息： https : //plot.ly/python/

4.散景 (4. Bokeh)

One more from the Python family as I would like to term it as the library for interactive data visualization — Bokeh allows building complex statistical plots with simple commands real quick.

我想将它称为Python家族中的另一个产品，它是用于交互式数据可视化的库— Bokeh允许使用真正快捷的简单命令构建复杂的统计图。

The reason of Bokeh’s popularity and its place on my list is because Bokeh makes the data visually appealing, forcing the focus on a particular area of the visualization. Bokeh also works well with D3.js to create interactive visualization with high interactivity quotient for very large or streaming datasets.

Bokeh之所以受欢迎并在我的列表中占位是因为Bokeh使数据具有视觉吸引力，从而迫使人们将注意力集中在可视化的特定区域。 Bokeh还可以与D3.js很好地配合使用，以为大型或流数据集创建具有高交互商的交互式可视化。

Fun fact: The word Bokeh (also “boke”) comes from the Japanese language, which translates to blur.

有趣的事实： Bokeh(也称为“ boke”)一词来自日语，其翻译为模糊 。

您可以用Bokeh做什么？ (What can you do with Bokeh?)

1. Elegant, concise construction of versatile graphics
   优雅，简洁的通用图形构造
2. Extend high-performance interactivity over large or real-time datasets
   在大型或实时数据集上扩展高性能的交互性
3. Quickly and easily create interactive plots, dashboards, and data applications
   快速轻松地创建交互式绘图，仪表板和数据应用程序
4. Leverage HTML, notebook or a server output
   利用HTML，笔记本或服务器输出
5. Integrate Bokeh visualization to Flask and Django apps or visualizations written in other libraries like matplotlib, seaborn, ggplot.
   将Bokeh可视化集成到Flask和Django应用程序或用其他库(如matplotlib，seaborn，ggplot)编写的可视化。

Created by: Continuum AnalyticsWhere to learn more: http://bokeh.pydata.org/en/latest/

创建人： Continuum Analytics在哪里了解更多信息： http : //bokeh.pydata.org/en/latest/

5. geoplotlib (5. geoplotlib)

geoplotlib, a toolbox primarily for creating maps and plotting geographical data can be used to create a variety of map-types, like choropleths, heatmaps, or dot-density maps. Since most Python data visualization libraries don’t offer maps, it’s nice to have a library dedicated solely to them and not look up to Tableau for external viz.

geoplotlib是一个主要用于创建地图和绘制地理数据的工具箱，可用于创建各种地图类型，例如，节流图，热图或点密度图。 由于大多数Python数据可视化库均不提供地图，因此拥有一个专用于它们的库而无需查找Tableau以获得外部可视效果是一件很不错的事情。

One feature in particular that I most liked was the colormap — that converted real values to colors.

我最喜欢的功能之一就是颜色图 -将实数转换为颜色。

I do not have what can you do with geoplotlib because it is essentially a map plotting library.

我没有geoplotlib可以做什么，因为它本质上是一个地图绘制库。

Created by: Andrea CuttoneWhere to learn more: https://github.com/andrea-cuttone/geoplotlib

创建人： Andrea Cuttone在哪里了解更多信息： https : //github.com/andrea-cuttone/geoplotlib

6. D3.js (6. D3.js)

When talking about data visualization, how can we not talk about D3.js, the dominant tool for SVG vector graphics! With SVG, no matter how deep you zoom, the graphics never look pixelated — the best sell for a report out.

在谈论数据可视化时，我们怎能不谈论SVG矢量图形的主要工具D3 .js！ 使用SVG，无论您缩放多深，图形都永远不会显得像素化-最好的出售报告。

D3.js empowers a data wiz with a variety of graphics. D3 is a framework to load information into the browser and generate reports based on data elements. D3.js does not suggest a particular type of graphic but rather a way on how to create visualizations (per my best understanding).

D3.js通过各种图形来支持数据向导。 D3是一个框架，用于将信息加载到浏览器中并基于数据元素生成报告。 D3.js不建议使用特定类型的图形，而是建议如何创建可视化效果的方法(以我的最佳理解)。

您可以用D3.js做什么？ (What can you do with D3.js?)

1. Distribution charts — violin, density, histogram, box plot, ridgeline
   分布图-小提琴，密度，直方图，箱形图，山脊线
2. Correlation — scatter, heat map, correlogram, bubble, density
   相关性—散点图，热图，相关图，气泡，密度
3. Ranking — barplot, spider, word cloud, parallel, lollipop, circular
   排名-条形图，蜘蛛，词云，平行，棒棒糖，圆形
4. Part of the whole — Treemap, donut, pie, dendrogram, circular packing
   整体的一部分—树形图，甜甜圈，派，树形图，圆形包装
5. Evolution — Line, area, stacked area, stream chart
   演变—折线，面积，堆积面积，流图
6. Map — map, choropleth, hex bin, cartogram, connection
   地图—地图，十字线，十六进制垃圾箱，地图，连接
7. Flow — chord, sankey, arc diagram
   流-和弦，sankey，弧形图
8. Animations
   动画制作

D3.js offers an extensive range of visualizations, more of which can be explored here.

D3.js提供了广泛的可视化效果，可在此处进行更多探索。

7. ggplot2 (7. ggplot2)

R’s rich ecosystem has numerous famous packages for making beautiful graphics but one of the majorly popular and commonly used visualization packages is ggplot2.

R的丰富生态系统有许多著名的软件包可以制作漂亮的图形，但是ggplot2是最受欢迎和常用的可视化软件包之一 。

ggplot2 is the Python implementation of the Grammar of Graphics of R programming language to build layered, customization plots. I do not find ggplot2 of the Tidyverse with as extensive plot collection as seaborn, but for R, ggplot2 makes the most robust data viz libraries (atleast to what I’ve had experience with). If you know more, feel free to add in the comments!

ggplot2是R编程语言图形语法的Python实现，用于构建分层的自定义图。 我找不到Tidyverse的ggplot2具有像seaborn一样广泛的地块集合，但是对于R来说，ggplot2可以提供最可靠的数据可视化库(与我所经历的一样)。 如果您了解更多，请随时添加评论！

ggplot2可以做什么？ (What can you do with ggplot2?)

The Plotly graph library has a wide range of graphs that you can plot:

Plotly图形库具有多种图形，可以绘制：

1. Basic Charts: Line, contour, density, jitter, bar, point, raster, polygon, tile, rug, Pie, Scatter, Bubble

   基本图表：线，轮廓，密度，抖动，条，点，栅格，多边形，瓷砖，地毯，饼图，散点图，气泡

2. Statistical Charts: Box plot, Histograms, Violin Plots, Ribbon, Quantile charts

   统计图 ：箱形图，直方图，小提琴图，功能区，分位数图

3. Animation charts
   动画图表
4. Marginal charts
   边际图

Created by: ŷhatWhere to learn more: http://ggplot.yhathq.com/

创建者： ŷhat在哪里了解更多信息： http : //ggplot.yhathq.com/

8.为R情节 (8. Plotly for R)

Plotly as we went over for Python is also available in R. Plotly in R creates interactive web-based plots using the plotly.js library as a base. The advantage here is that it can build contour plots, candlestick charts, maps, and 3D charts, which cannot be created using most packages in R. In addition, I found around 30 repositories available! Plotly also gives an opportunity to interact with graphs, change their scale, and point out the necessary record — show it to the executive in the eye. The library also supports graph hovering, somewhat similar to Tableau. Moreover, it is easy to add Plotly in knitr, R Markdown, or Shiny apps.

R上也提供了我们在Python上使用的Plotly。R中的Plotly使用plotly.js库作为基础来创建基于Web的交互式图。 这样做的好处是它可以构建等高线图，烛台图，地图和3D图，这是无法使用R中的大多数软件包创建的。此外，我发现大约有30个存储库可用！ Plotly还提供了与图表进行交互，更改其比例并指出必要记录的机会-将其显示给执行人员。 该库还支持图形悬停，有点类似于Tableau。 此外，很容易在针织衫，R Markdown或Shiny应用程序中添加Plotly。

您可以在R中使用Plotly做什么？ (What can you do with Plotly in R?)

1. Contour plot
   等高线图
2. Candlestick chart
   烛台图
3. 3D scatterplot (like the one in Andrew Ng’s Coursera ML class :p)
   3D散点图(如吴安德教授的Coursera ML类中的一个：p)

9. pygal (9. pygal)

pygal is again a Python module that creates SVG graphs/charts. I haven’t used Pygal but have read it to be a highly customizable, low code module yet also extremely simplistic.

pygal还是一个可创建SVG图形/图表的Python模块。 我没有使用过Pygal，但已将其阅读为一个高度可定制的，低代码量的模块，但同时也非常简化。

Like Bokeh and Plotly, pygal also offers interactive plots that can be embedded in a web browser. The key difference among them all is its ability to output charts as SVGs. As long as you’re working with smaller datasets, SVGs will do you just fine. The con here though creeps in when working with large datasets with thousands of data points, they’ll have trouble rendering and become sluggish.

像Bokeh和Plotly一样，pygal还提供可以嵌入到Web浏览器中的交互式图。 它们之间的主要区别在于其将图表输出为SVG的能力。 只要您使用较小的数据集，SVG都可以满足您的要求。 尽管在处理具有数千个数据点的大型数据集时，这里的缺点会逐渐蔓延，但它们将难以渲染并且变得迟钝。

pygal你能做什么？ (What can you do with pygal?)

1. Line graphs
   折线图
2. Bar charts
   条形图
3. Pie charts
   饼状图
4. Maps
   地图
5. Customized charts
   定制图表

Created by: Florian MounierWhere to learn more: http://www.pygal.org/en/latest/index.html

创建者： Florian Mounier在哪里了解更多信息： http : //www.pygal.org/en/latest/index.html

That’s it from my end for this blog. Thank you for reading! I hope this helped. Do let me know which libraries have you used or are looking up to learn or explore. If you any more libraries, feel free to drop them in the comments!

从本篇博客的结尾我就是这样。 感谢您的阅读！ 希望对您有所帮助。 让我知道您使用过哪些图书馆，或者正在寻找学习或探索的图书馆。 如果您还有其他库，请随时在评论中添加它们！

Disclaimer: The views expressed in this article are my own and do not represent a strict outlook.

免责声明：本文中表达的观点仅属于我个人观点，并不代表严格的观点。

认识你的作者 (Know your author)

Rashi is a graduate student at the University of Illinois, Chicago. She loves to visualize data and create insightful stories. When not rushing to meet school deadlines, she adores writing about technology, UX, and more with a good cup of hot chocolate.

Rashi是芝加哥伊利诺伊大学的研究生。 她喜欢可视化数据并创建有洞察力的故事。 当不急于按时上课时，她会喝一杯热巧克力，喜欢写有关技术，用户体验等的文章。