How to plot 1D array using python to get 25th, 50th and 75th percentiles

Question

Hello I have an array

x = [9.904725629894529e-06, 1.2634955055546016e-05, 1.5201896530925296e-05, 2.9845547032891773e-05, 3.49247275153175e-05, 3.4970289561897516e-05, 4.780302697326988e-05, 4.802399416803382e-05, 4.810681639355607e-05, 4.843798524234444e-05, 4.859635737375356e-05, 5.9152505855308846e-05, 5.9193022025283426e-05, 5.9363908803788945e-05, 5.9468671679496765e-05, 6.630286952713504e-05, 7.005851512076333e-05, 7.014916627667844e-05, 8.021695248316973e-05, 8.989680645754561e-05, 9.008277265820652e-05, 9.028125350596383e-05, 9.037737618200481e-05, 9.04681728570722e-05, 9.083149052457884e-05, 0.00021005164308007807, 0.00021028853370808065, 0.00021039179409854114, 0.00021039319108240306, 0.00021107416250742972, 0.00021134318376425654, 0.00021135005226824433, 0.00021196113084442914, 0.00021199103503022343, 0.0002120915160048753, 0.0002126666222466156, 0.000213045728742145, 0.00021316968195606023, 0.00021321520034689456, 0.00021339277736842632, 0.00021368247689679265, 0.0002137374976882711, 0.00021400606783572584, 0.00021451167413033545, 0.00021501131413970143]

I want to plot this in python to find 25th, 50th and 75th percentile value.

I used plt.plot(x) plt.show() But the distribution doesn't look in a way like we can distinguish the numbers.

Answer 1

You can use numpy to get percentiles for data. This following is from the documentation for percentile.

import numpy as np

# 1D array
arr = [20, 2, 7, 1, 34]
print("arr : ", arr)
print("50th percentile of arr : ",
      np.percentile(arr, 50))
print("25th percentile of arr : ",
      np.percentile(arr, 25))
print("75th percentile of arr : ",
      np.percentile(arr, 75))

Output:

arr :  [20, 2, 7, 1, 34]
50th percentile of arr :  7.0
25th percentile of arr :  2.0
75th percentile of arr :  20.0

to apply this to your code this should show you the percentiles nicely

import matplotlib.pyplot as plt
import numpy as np

x = [
    9.904725629894529e-06,
    1.2634955055546016e-05,
    1.5201896530925296e-05,
    2.9845547032891773e-05,
    3.49247275153175e-05,
    3.4970289561897516e-05,
    4.780302697326988e-05,
    4.802399416803382e-05,
    4.810681639355607e-05,
    4.843798524234444e-05,
    4.859635737375356e-05,
    5.9152505855308846e-05,
    5.9193022025283426e-05,
    5.9363908803788945e-05,
    5.9468671679496765e-05,
    6.630286952713504e-05,
    7.005851512076333e-05,
    7.014916627667844e-05,
    8.021695248316973e-05,
    8.989680645754561e-05,
    9.008277265820652e-05,
    9.028125350596383e-05,
    9.037737618200481e-05,
    9.04681728570722e-05,
    9.083149052457884e-05,
    0.00021005164308007807,
    0.00021028853370808065,
    0.00021039179409854114,
    0.00021039319108240306,
    0.00021107416250742972,
    0.00021134318376425654,
    0.00021135005226824433,
    0.00021196113084442914,
    0.00021199103503022343,
    0.0002120915160048753,
    0.0002126666222466156,
    0.000213045728742145,
    0.00021316968195606023,
    0.00021321520034689456,
    0.00021339277736842632,
    0.00021368247689679265,
    0.0002137374976882711,
    0.00021400606783572584,
    0.00021451167413033545,
    0.00021501131413970143,
]

percentile_25 = np.percentile(x, 25)
percentile_50 = np.percentile(x, 50)
percentile_75 = np.percentile(x, 75)

plt.figure("plot1")
plt.plot(x)
plt.axhline(percentile_25, color="C1", label="25")
plt.axhline(percentile_50, color="C2", label="50")
plt.axhline(percentile_75, color="C3", label="75")
plt.legend(loc="best", title="percentiles")
plt.xlabel("x_axis_label")
plt.ylabel("y_axis_label")
plt.title("stack title")
plt.show()

output graph: