气泡图-数据可视化包
bubble-plot的Python项目详细描述
气泡图
大家好
我喜欢数据可视化!如果你也爱他们,我想你会发现这个泡泡情节非常好和有用。
How to install
Very simple - just write in your command line:
^{pr 1}$Motivation & Usage
The goal for the bubble plot is to help us visualize linear and non-linear connections between numerical/categorical features in our data in an easy and simple way. The bubble plot is a kind of a 2-dimensional histogram using bubbles. It suits every combination of categorical and numerical features.
The bubble size is proportional to the frequency of the data points in this point.
Function signature:
^{pr 2}$For numerical features the values will be presented in buckets (ten equally spaced bins will be used as default, you can provide the specific bins / bins number through the ^{
For categorical features the features will be presented according to their categories. If you would like a specific order for the categories presentation please supply a list of the values by order using the ^{
You can plot a numerical feature vs. another numerical feature or vs. a categorical feature or a categorical feature vs another categorical feature or numerical feature. All options are possible.
Setting the parameter normalization_by_all to False defines that we would like to plot P(y/x), meaning, plot the distribution of y given x. Each column in this plot is an independent (1D) histogram of the values of the y given x. Setting the parameter normalization_by_all to True would plot the joint distribution of x and y, P(x,y), this is in fact a 2D histogram with bubbles.
Setting the ^{
Setting the ^{
Usage Example
^{pr 3}$The resulting bubble plot will look like this:
Usage Example 2
Census income dataset - plot the age vs. hours per week vs. the income level. How is that even possible? Can we visualize three dimensions of information in a two dimensional plot?
^{pr 4}$The resulting bubble plot will look like this:
P(x,y), x: age, y: working hours, color — proportional to the rate of high income people within each bucket
In this bubble plot, we see the joint distribution of the hours-per-week vs. the age (p(x,y)), but here the color is proportional to the rate of high income people — (#>50K/((#>50K)+(#≤50K)) - within all the people in this bucket . By supplying the z_boolean variable, we added additional dimension to the plot using the color of the bubble.
The pinker the color, the higher the ratio for the given boolean feature/target Z. See colormap in the image.
Cool colormap — Pink would stand for the higher ratios in our case, cyan would stand for the lower ratios
This plot shows us clearly that the higher income is much more common within people of age higher than 30 which work more than 40 hours a week.
Dependencies
- pandas
- numpy
- matplotlib
Contact
More usage examples and explanations can be found at: https://medium.com/@DataLady/exploring-the-census-income-dataset-using-bubble-plot-cfa1b366313b
如果你有任何问题,请告诉我我的电子邮件是meir.shir86@gmail.com。
享受吧, shir