python – How to automatically annotate maximum value in pyplot – Code Utility

I’m trying to figure out how I can automatically annotate the maximum value in a figure window. I know you can do this by manually entering in x,y coordinates to annotate whatever point you want using the .annotate() method, but I want the annotation to be automatic, or to find the maximum point by itself.

Here’s my code so far:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import Series, DataFrame

df = pd.read_csv('macrodata.csv') #Read csv file into dataframe
years = df'year' #Get years column
infl = df'infl' #Get inflation rate column

fig10 = plt.figure()
win = fig10.add_subplot(1,1,1)
fig10 = plt.plot(years, infl, lw = 2)

fig10 = plt.xlabel("Years")
fig10 = plt.ylabel("Inflation")
fig10 = plt.title("Inflation with Annotations")

If x and y are the arrays to plot, you get the coordinates of the maximum via

xmax = xnumpy.argmax(y)
ymax = y.max()

This can be incorporated into a function that you may simply call with your data.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2,8, num=301)
y = np.sinc((x-2.21)*3)


fig, ax = plt.subplots()
ax.plot(x,y)

def annot_max(x,y, ax=None):
    xmax = xnp.argmax(y)
    ymax = y.max()
    text= "x={:.3f}, y={:.3f}".format(xmax, ymax)
    if not ax:
        ax=plt.gca()
    bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
    arrowprops=dict(arrowstyle="->",connectionstyle="angle,angleA=0,angleB=60")
    kw = dict(xycoords='data',textcoords="axes fraction",
              arrowprops=arrowprops, bbox=bbox_props, ha="right", va="top")
    ax.annotate(text, xy=(xmax, ymax), xytext=(0.94,0.96), **kw)

annot_max(x,y)


ax.set_ylim(-0.3,1.5)
plt.show()

I don’t have data of macrodata.csv to go with. However, generically, assuming you have x and y axis data as an list, you can use following method to get auto positioning of max.

Working Code:

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)

x=1,2,3,4,5,6,7,8,9,10
y=1,1,1,2,10,2,1,1,1,1
line, = ax.plot(x, y)

ymax = max(y)
xpos = y.index(ymax)
xmax = xxpos

ax.annotate('local max', xy=(xmax, ymax), xytext=(xmax, ymax+5),
            arrowprops=dict(facecolor='black', shrink=0.05),
            )

ax.set_ylim(0,20)
plt.show()

Plot :

The method proposed by @ImportanceOfBeingErnest in his response is really neat, but it doesn’t work if the data is within a panda data-frame whose index isn’t a zero based uniform index (0,1,2,..,N), and it is desired to plot against the index -whose values are the x’s-.

I took the liberty to adapt the aforementioned solution and use it with pandas plot function. I also wrote the symmetric min function.

def annot_max(x,y, ax=None):
    maxIxVal = np.argmax(y);
    zeroBasedIx = np.argwhere(y.index==maxIxVal).flatten()0;
    xmax = xzeroBasedIx;
    ymax = y.max()
    text= "k={:d}, measure={:.3f}".format(xmax, ymax)
    if not ax:
        ax=plt.gca()
    bbox_props = dict(boxstyle="round,pad=0.3", fc="w", ec="k", lw=0.72)
    arrowprops=dict(arrowstyle="-",connectionstyle="arc3,rad=0.1")
    kw = dict(xycoords='data',textcoords="axes fraction",
              arrowprops=arrowprops, bbox=bbox_props, ha="right", va="top")
    ax.annotate(text, xy=(xmax, ymax), xytext=(0.94,0.90), **kw)

def annot_min(x,y, ax=None):
    minIxVal = np.argmin(y);
    zeroBasedIx = np.argwhere(y.index==minIxVal).flatten()0;
    xmin = xzeroBasedIx;
    ymin = y.min()
    text= "k={:d}, measure={:.3f}".format(xmin, ymin)
    if not ax:
        ax=plt.gca()
    bbox_props = dict(boxstyle="round,pad=0.3", fc="w", ec="k", lw=0.72)
    arrowprops=dict(arrowstyle="-",connectionstyle="arc3,rad=0.1")
    kw = dict(xycoords='data',textcoords="axes fraction",
              arrowprops=arrowprops, bbox=bbox_props, ha="right", va="top")
    ax.annotate(text, xy=(xmin, ymin), xytext=(0.94,0.90), **kw)

Usage is straightforward, for example:

ax = dfSeries0.plot(grid=True, use_index=True, \
                  title=None);
annot_max(dfSeries0.index,dfSeries0,ax);
plt.show();

I hope this would be of any help to anyone.

Something like this would work:

infl_max_index = np.where(infl == max(infl)) #get the index of the maximum inflation
infl_max = inflinfl_max_index # get the inflation corresponding to this index
year_max = yearinfl_max_index # get the year corresponding to this index

plt.annotate('max inflation', xy=(year_max, infl_max))