There are 50 Visualizations which can finish 7 different purposes of data analysis.

一共有50组图，可以实现7种不同的数据分析的目的。

来源 |Source From Machine Learning Plus

notice: matplotlib:version #> 3.0.0 seaborn:version #> 0.9.0

Scatteplot is a classic and fundamental plot used to study the relationship between two variables. If you have multiple groups in your data you may want to visualise each group in a different color. In matplotlib, you can conveniently do this using plt.scatterplot().

当研究两个变量之间的关系时，散点图是经典和基本的图表。 如果你的数据中有多个组，你可能需要以不同颜色可视化每个组。 在 matplotlib 中，你可以使用 plt.scatterplot（） 方便地执行此操作。

Code (代码): Scatter plot.py

Sometimes you want to show a group of points within a boundary to emphasize their importance. In this example, you get the records from the dataframe that should be encircled and pass it to the encircle() described in the code below.

有时，你希望展示一组在边界内的点以强调其重要性。 在这个例子中，你从数据框中获取记录，并用下面代码中描述的 encircle（） 来使边界显示出来。

Code(代码)：Bubble plot with Encircling.py

If you want to understand how two variables change with respect to each other, the line of best fit is the way to go. The below plot shows how the line of best fit differs amongst various groups in the data. To disable the groupings and to just draw one line-of-best-fit for the entire dataset, remove the hue='cyl' parameter from the sns.lmplot() call below.

如果你想了解两个变量如何相互改变，那么最佳拟合线就是常用的方法。 下图显示了数据中各组之间最佳拟合线的差异。 要禁用分组并仅为整个数据集绘制一条最佳拟合线，请从下面的 sns.lmplot（）调用中删除 hue ='cyl'参数。

Code(代码)：Scatter plot with line of best fit.py

Alternately, you can show the best fit line for each group in its own column. You cando this by setting the col=groupingcolumn parameter inside the sns.lmplot().

或者，可以在其每列中显示每个组的最佳拟合线。 可以通过在 sns.lmplot() 中设置 col=groupingcolumn 参数来实现。

Code(代码)：Each regression line in its own column.py

Often multiple datapoints have exactly the same X and Y values. As a result, multiple points get plotted over each other and hide. To avoid this, jitter the points slightly so you can visually see them. This is convenient to do using seaborn’s stripplot().

通常，多个数据点具有完全相同的 X 和 Y 值。 结果，多个点绘制会重叠并隐藏。 为避免这种情况，请将数据点稍微抖动，以便您可以直观地看到它们。 使用 seaborn 的 stripplot（） 很方便实现这个功能。

Code(代码)：Jittering with stripplot

Another option to avoid the problem of points overlap is the increase the size of the dot depending on how many points lie in that spot. So, larger the size of the point more is the concentration of points around that.

避免点重叠问题的另一个选择是增加点的大小，这取决于该点中有多少点。 因此，点的大小越大，其周围的点的集中度越高。

Code(代码)：Counts Plot.py

Marginal histograms have a histogram along the X and Y axis variables. This is used to visualize the relationship between the X and Y along with the univariate distribution of the X and the Y individually. This plot if often used in exploratory data analysis (EDA).

边缘直方图具有沿 X 和 Y 轴变量的直方图。 这用于可视化 X 和 Y 之间的关系以及单独的 X 和 Y 的单变量分布。 这种图经常用于探索性数据分析（EDA）。

Code(代码)：Marginal Histogram.py

Marginal boxplot serves a similar purpose as marginal histogram. However, the boxplot helps to pinpoint the median, 25th and 75th percentiles of the X and the Y.

边缘箱图与边缘直方图具有相似的用途。 然而，箱线图有助于精确定位 X 和 Y 的中位数、第25和第75百分位数。

Code (代码):Marginal Boxplot.py

Correlogram is used to visually see the correlation metric between all possible pairs of numeric variables in a given dataframe (or 2D array).

相关图用于直观地查看给定数据框（或二维数组）中所有可能的数值变量对之间的相关度量。

Code(代码)：Correlogram.py

Pairwise plot is a favorite in exploratory analysis to understand the relationship between all possible pairs of numeric variables. It is a must have tool for bivariate analysis.

矩阵图是探索性分析中的最爱，用于理解所有可能的数值变量对之间的关系。 它是双变量分析的必备工具。

Code(代码)：Pairwise Plot.py

If you want to see how the items are varying based on a single metric and visualize the order and amount of this variance, the diverging bars is a great tool. It helps to quickly differentiate the performance of groups in your data and is quite intuitive and instantly conveys the point.

如果您想根据单个指标查看项目的变化情况，并可视化此差异的顺序和数量，那么散型条形图 （Diverging Bars） 是一个很好的工具。 它有助于快速区分数据中组的性能，并且非常直观，并且可以立即传达这一点。

Code (代码): Diverging Bars.py

Diverging texts is similar to diverging bars and it preferred if you want to show the value of each items within the chart in a nice and presentable way.

发散型文本 （Diverging Texts）与发散型条形图 （Diverging Bars）相似，如果你想以一种漂亮和可呈现的方式显示图表中每个项目的价值，就可以使用这种方法。

Code (代码): Diverging Texts.py

Divering dot plot is also similar to the diverging bars. However compared to diverging bars, the absence of bars reduces the amount of contrast and disparity between the groups.

发散型包点图 （Diverging Dot Plot）也类似于发散型条形图 （Diverging Bars）。 然而，与发散型条形图 （Diverging Bars）相比，条的缺失减少了组之间的对比度和差异。

Code (代码): Diverging Dot Plot.py

Lollipop with markers provides a flexible way of visualizing the divergence by laying emphasis on any significant datapoints you want to bring attention to and give reasoning within the chart appropriately.

带标记的棒棒糖图通过强调您想要引起注意的任何重要数据点并在图表中适当地给出推理，提供了一种对差异进行可视化的灵活方式。

Code (代码): Diverging Lollipop Chart with Markers.py

By coloring the area between the axis and the lines, the area chart throws more emphasis not just on the peaks and troughs but also the duration of the highs and lows. The longer the duration of the highs, the larger is the area under the line.

通过对轴和线之间的区域进行着色，面积图不仅强调峰和谷，而且还强调高点和低点的持续时间。 高点持续时间越长，线下面积越大。

Code (代码): Area Chart.py

Ordered bar chart conveys the rank order of the items effectively. But adding the value of the metric above the chart, the user gets the precise information from the chart itself.

有序条形图有效地传达了项目的排名顺序。 但是，在图表上方添加度量标准的值，用户可以从图表本身获取精确信息。

Code(代码)：Ordered Bar Chart.py

Lollipop chart serves a similar purpose as a ordered bar chart in a visually pleasing way.

棒棒糖图表以一种视觉上令人愉悦的方式提供与有序条形图类似的目的。

Code（代码）:Lollipop Chart.py

The dot plot conveys the rank order of the items. And since it is aligned along the horizontal axis, you can visualize how far the points are from each other more easily.

包点图表传达了项目的排名顺序，并且由于它沿水平轴对齐，因此您可以更容易地看到点彼此之间的距离。

Code（代码）: Dot Plot.py

Slope chart is most suitable for comparing the ‘Before’ and ‘After’ positions of a given person/item.

坡度图最适合比较给定人/项目的“前”和“后”位置。

Code（代码）: Slope Chart.py

Dumbbell plot conveys the ‘before’ and ‘after’ positions of various items along with the rank ordering of the items. Its very useful if you want to visualize the effect of a particular project / initiative on different objects.

哑铃图表传达了各种项目的“前”和“后”位置以及项目的等级排序。 如果您想要将特定项目/计划对不同对象的影响可视化，那么它非常有用。

Code（代码）: Dumbbell Plot.py

Histogram shows the frequency distribution of a given variable. The below representation groups the frequency bars based on a categorical variable giving a greater insight about the continuous variable and the categorical variable in tandem.

直方图显示给定变量的频率分布。 下面的图表示基于类型变量对频率条进行分组，从而更好地了解连续变量和类型变量。

Code（代码）: Histogram for Continuous Variable.py

The histogram of a categorical variable shows the frequency distribution of a that variable. By coloring the bars, you can visualize the distribution in connection with another categorical variable representing the colors.

类型变量的直方图显示该变量的频率分布。 通过对条形图进行着色，可以将分布与表示颜色的另一个类型变量相关联。

Code（代码）: Histogram for Categorical Variable.py

Density plots are a commonly used tool visualise the distribution of a continuous variable. By grouping them by the ‘response’ variable, you can inspect the relationship between the X and the Y. The below case if for representational purpose to describe how the distribution of city mileage varies with respect the number of cylinders.

密度图是一种常用工具，用于可视化连续变量的分布。 通过“响应”变量对它们进行分组，您可以检查 X 和 Y 之间的关系。以下情况用于表示目的，以描述城市里程的分布如何随着汽缸数的变化而变化。

Code（代码）: Density Plot.py

Density curve with histogram brings together the collective information conveyed by the two plots so you can have them both in a single figure instead of two.

带有直方图的密度曲线汇集了两个图所传达的集体信息，因此您可以将它们放在一个图中而不是两个图中。

Code（代码）: Density Curves with Histogram.py

Joy Plot allows the density curves of different groups to overlap, it is a great way to visualize the distribution of a larger number of groups in relation to each other. It looks pleasing to the eye and conveys just the right information clearly. It can be easily built using the joypy package which is based on matplotlib.

Joy Plot允许不同组的密度曲线重叠，这是一种可视化大量分组数据的彼此关系分布的好方法。 它看起来很悦目，并清楚地传达了正确的信息。 它可以使用基于 matplotlib 的 joypy 包轻松构建。 （注：需要安装 joypy 库）

Code（代码）: Joy Plot.py

Distributed dot plot shows the univariate distribution of points segmented by groups. The darker the points, more is the concentration of data points in that region. By coloring the median differently, the real positioning of the groups becomes apparent instantly.

分布式包点图显示按组分割的点的单变量分布。 点数越暗，该区域的数据点集中度越高。 通过对中位数进行不同着色，组的真实定位立即变得明显。

Code（代码）: Distributed Dot Plot.py

Box plots are a great way to visualize the distribution, keeping the median, 25th 75th quartiles and the outliers in mind. However, you need to be careful about interpreting the size the boxes which can potentially distort the number of points contained within that group. So, manually providing the number of observations in each box can help overcome this drawback.For example, the first two boxes on the left have boxes of the same size even though they have 5 and 47 obs respectively. So writing the number of observations in that group becomes necessary.

箱形图是一种可视化分布的好方法，记住中位数、第25个第45个四分位数和异常值。 但是，您需要注意解释可能会扭曲该组中包含的点数的框的大小。 因此，手动提供每个框中的观察数量可以帮助克服这个缺点。例如，左边的前两个框具有相同大小的框，即使它们的值分别是5和47。 因此，写入该组中的观察数量是必要的。

Code（代码）: Box Plot.py

Dot + Box plot Conveys similar information as a boxplot split in groups. The dots, in addition, gives a sense of how many data points lie within each group.

包点+箱形图 （Dot and Box Plot）传达类似于分组的箱形图信息。 此外，这些点可以了解每组中有多少数据点。

Code（代码）: Dot and Box Plot.py

Violin plot is a visually pleasing alternative to box plots. The shape or area of the violin depends on the number of observations it holds. However, the violin plots can be harder to read and it not commonly used in professional settings.

小提琴图是箱形图在视觉上令人愉悦的替代品。 小提琴的形状或面积取决于它所持有的观察次数。 但是，小提琴图可能更难以阅读，并且在专业设置中不常用。

Code（代码）: Violin Plot.py

Population pyramid can be used to show either the distribution of the groups ordered by the volumne. Or it can also be used to show the stage-by-stage filtering of the population as it is used below to show how many people pass through each stage of a marketing funnel.

人口金字塔可用于显示由数量排序的组的分布。 或者它也可以用于显示人口的逐级过滤，因为它在下面用于显示有多少人通过营销渠道的每个阶段。

Code（代码）:Population Pyramid.py

Categorical plots provided by the seaborn library can be used to visualize the counts distribution of 2 ore more categorical variables in relation to each other.

由 seaborn库 提供的分类图可用于可视化彼此相关的2个或更多分类变量的计数分布。

Code（代码）:Categorical Plots.py

Code（代码）:Hist Frequency Plots.py

The waffle chart can be created using the pywaffle package and is used to show the compositions of groups in a larger population.

可以使用 pywaffle包 创建华夫饼图，并用于显示更大群体中的组的组成。（注：需要安装 pywaffle 库）

Code（代码）:Waffle Chart One.py

Code（代码）:Waffle Chart Two.py

Pie chart is a classic way to show the composition of groups. However, its not generally advisable to use nowadays because the area of the pie portions can sometimes become misleading. So, if you are to use pie chart, its highly recommended to explicitly write down the percentage or numbers for each portion of the pie.

饼图是显示组成的经典方式。 然而，现在通常不建议使用它，因为馅饼部分的面积有时会变得误导。 因此，如果您要使用饼图，强烈建议明确记下饼图每个部分的百分比或数字。

Code（代码）:Pie Chart One.py

Code（代码）:Pie Chart Two.py

Tree map is similar to a pie chart and it does a better work without misleading the contributions by each group.

树形图类似于饼图，它可以更好地完成工作而不会误导每个组的贡献。（注：需要安装 squarify 库）

Code（代码）:Treemap.py

Bar chart is a classic way of visualizing items based on counts or any given metric. In below chart, I have used a different color for each item, but you might typically want to pick one color for all items unless you to color them by groups. The color names get stored inside all_colors in the code below. You can change the color of the bars by setting the color parameter in plt.plot().

条形图是基于计数或任何给定指标可视化项目的经典方式。 在下面的图表中，我为每个项目使用了不同的颜色，但您通常可能希望为所有项目选择一种颜色，除非您按组对其进行着色。 颜色名称存储在下面代码中的all_colors中。 您可以通过在 plt.plot（）中设置颜色参数来更改条的颜色。

Code（代码）:Bar Chart.py

Time series plot is used to visualise how a given metric changes over time. Here you can see how the Air Passenger traffic changed between 1949 and 1969.

时间序列图用于显示给定度量随时间变化的方式。 在这里，您可以看到 1949年 至 1969年间航空客运量的变化情况。

Code（代码）: Time Series Plott.py

The below time series plots all the the peaks and troughs and annotates the occurence of selected special events.

下面的时间序列绘制了所有峰值和低谷，并注释了所选特殊事件的发生。

Code（代码）: Time Series with Peaks and Troughs Annotated.py

The ACF plot shows the correlation of the time series with its own lags. Each vertical line (on the autocorrelation plot) represents the correlation between the series and its lag starting from lag 0. The blue shaded region in the plot is the significance level. Those lags that lie above the blue line are the significant lags.

So how to interpret this?

For AirPassengers, we see upto 14 lags have crossed the blue line and so are significant. This means, the Air Passengers traffic seen upto 14 years back has an influence on the traffic seen today.

PACF on the other had shows the autocorrelation of any given lag (of time series) against the current series, but with the contributions of the lags-inbetween removed.

自相关图（ACF图）显示时间序列与其自身滞后的相关性。 每条垂直线（在自相关图上）表示系列与滞后0之间的滞后之间的相关性。图中的蓝色阴影区域是显着性水平。 那些位于蓝线之上的滞后是显着的滞后。那么如何解读呢？对于空乘旅客，我们看到多达14个滞后跨越蓝线，因此非常重要。 这意味着，14年前的航空旅客交通量对今天的交通状况有影响。PACF在另一方面显示了任何给定滞后（时间序列）与当前序列的自相关，但是删除了滞后的贡献。

Code（代码）: ACF and PACF Plot.py

Cross correlation plot shows the lags of two time series with each other.

交叉相关图显示了两个时间序列相互之间的滞后。

Code（代码）:Cross Correlation plot.py

Time series decomposition plot shows the break down of the time series into trend, seasonal and residual components.

时间序列分解图显示时间序列分解为趋势，季节和残差分量。

Code（代码）:Time Series Decomposition Plot.py

You can plot multiple time series that measures the same value on the same chart as shown below.

您可以绘制多个时间序列，在同一图表上测量相同的值，如下所示。

Code（代码）:Multiple Time Series.py

If you want to show two time series that measures two different quantities at the same point in time, you can plot the second series againt the secondary Y axis on the right.

如果要显示在同一时间点测量两个不同数量的两个时间序列，则可以在右侧的辅助Y轴上再绘制第二个系列。

Code（代码）:Plotting with different scales using secondary Y axis.py

Time series with error bands can be constructed if you have a time series dataset with multiple observations for each time point (date / timestamp). Below you can see a couple of examples based on the orders coming in at various times of the day. And another example on the number of orders arriving over a duration of 45 days.

In this approach, the mean of the number of orders is denoted by the white line. And a 95% confidence bands are computed and drawn around the mean.

如果您有一个时间序列数据集，每个时间点（日期/时间戳）有多个观测值，则可以构建带有误差带的时间序列。 您可以在下面看到一些基于每天不同时间订单的示例。 另一个关于45天持续到达的订单数量的例子。在该方法中，订单数量的平均值由白线表示。 并且计算95％置信区间并围绕均值绘制。

Code（代码）:Time Series with Error Bands One.py

Code（代码）:Time Series with Error Bands Two.py

Stacked area chart gives an visual representation of the extent of contribution from multiple time series so that it is easy to compare against each other.

堆积面积图可以直观地显示多个时间序列的贡献程度，因此很容易相互比较。

Code（代码）:Stacked Area Chart.py

An unstacked area chart is used to visualize the progress (ups and downs) of two or more series with respect to each other. In the chart below, you can clearly see how the personal savings rate comes down as the median duration of unemployment increases. The unstacked area chart brings out this phenomenon nicely.

未堆积面积图用于可视化两个或更多个系列相对于彼此的进度（起伏）。 在下面的图表中，您可以清楚地看到随着失业中位数持续时间的增加，个人储蓄率会下降。 未堆积面积图表很好地展示了这种现象。

Code（代码）:Area Chart UnStacked.py

Calendar map is an alternate and a less preferred option to visualise time based data compared to a time series. Though can be visually appealing, the numeric values are not quite evident. It is however effective in picturising the extreme values and holiday effects nicely.

与时间序列相比，日历地图是可视化基于时间的数据的备选和不太优选的选项。 虽然可以在视觉上吸引人，但数值并不十分明显。 然而，它可以很好地描绘极端值和假日效果。（注：需要安装 calmap 库）

Code（代码）:Calendar Heat Map.py

The seasonal plot can be used to compare how the time series performed at same day in the previous season (year / month / week etc).

季节图可用于比较上一季中同一天（年/月/周等）的时间序列。

Code（代码）:Seasonal Plot.py

A Dendrogram groups similar points together based on a given distance metric and organizes them in tree like links based on the point’s similarity.

树形图基于给定的距离度量将相似的点组合在一起，并基于点的相似性将它们组织在树状链接中。

Code（代码）:Dendrogram.py

Cluster Plot canbe used to demarcate points that belong to the same cluster. Below is a representational example to group the US states into 5 groups based on the USArrests dataset. This cluster plot uses the ‘murder’ and ‘assault’ columns as X and Y axis. Alternately you can use the first to principal components as rthe X and Y axis.

簇状图可用于划分属于同一群集的点。 下面是根据USArrests数据集将美国各州分为5组的代表性示例。 此图使用“谋杀”和“攻击”列作为X和Y轴。 或者，您可以将第一个到主要组件用作X轴和Y轴。

Code（代码）:Cluster Plot.py

Andrews Curve helps visualize if there are inherent groupings of the numerical features based on a given grouping. If the features (columns in the dataset) doesn’t help discriminate the group (cyl), then the lines will not be well segregated as you see below.

安德鲁斯曲线有助于可视化是否存在基于给定分组的数字特征的固有分组。 如果要素（数据集中的列）无法区分组（cyl），那么这些线将不会很好地隔离，如下所示。

Code（代码）:Andrews Curve.py

Parallel coordinates helps to visualize if a feature helps to segregate the groups effectively. If a segregation is effected, that feature is likely going to be very useful in predicting that group.

平行坐标有助于可视化特征是否有助于有效地隔离组。 如果实现隔离，则该特征可能在预测该组时非常有用。

Code（代码）:Parallel Coordinates.py