Visualizing API Response Times with Python Plotly Distplot

Overview

During API testing the author switched from violin plots to a distplot, which shows integer‑based histograms with an overlaid smooth curve to illustrate response‑time distributions.

Implementation

The Python script uses plotly.plotly, plotly.figure_factory (aliased as fff) and numpy. A Distplots class defines an initializer that prints a confirmation message and a makeDistplot method that creates the chart with fff.create_distplot and saves it as an HTML file via plotly.offline.plot.

Data preparation

In the __main__ block three synthetic data series are generated with np.random.randn, scaled, and combined with real‑world data read from a log file ( /Users/Vicky/Documents/workspace/fission/long.log). Each line is converted to float, filtered to keep values ≤ 1, multiplied by 100, and collected into a list xy. This list becomes data1 with a single group label "test1". An instance of Distplots then calls makeDistplot to produce 3333.html.

Key code

#!/usr/bin/python
# coding=utf-8
import plotly.plotly
import plotly.figure_factory as fff
import numpy as np

class Distplots:
    def __init__(self):
        print("distplots图标生成!")

    def makeDistplot(self, data, group):
        fig = fff.create_distplot(data, group)
        plotly.offline.plot(fig, filename="3333.html")

if __name__ == "__main__":
    x = np.random.randn(1000) * 10
    y = np.random.randn(1000) * 10 + 50
    z = np.random.randn(1000) * 10 + 100
    data = [x, y, z]
    group = ["one", "two", "three"]

    xy = []
    with open("/Users/Vicky/Documents/workspace/fission/long.log", "r") as f:
        for line in f.readlines():
            t = float(line)
            if t > 1:
                continue
            xy.append(t)

    xy = [v * 100 for v in xy]
    data1 = [xy]
    group1 = ["test1"]
    drive = Distplots()
    drive.makeDistplot(data1, group1)

Result

The generated HTML file contains a distplot that visualizes the distribution of API response times, allowing engineers to assess performance characteristics quickly.