如何使用按钮将排序功能添加到 Matplotlib 栏 plot 和行 plot

Question

I am just starting out to experiment with visualization in Python. With the following code, I am trying to add sort functionality to a Matplotlib bar plot which is drawn from a data frame. I would like to add a button on the graph like sort , so that when it's click it would display a new plot in the order from the highest sales figure to lowest sales figure, currently the button can be display yet the sort function cannot be triggered. Any idea or pointer would be appreciated.

[Updated attempt]

import matplotlib.pyplot as plt
from matplotlib.widgets import Button

def sort(data_frame):
    sorted = data_frame.sort_values('Sales')
    return data_frame2

def original():
   
    return data_frame

data_frame.plot.bar(x="Product", y="Sales", rot=70, title="Sales Report");
plot.xlabel('Product')
plot.ylabel('Sales')

axcut = plt.axes([0.9, 0.0, 0.1, 0.075])
bsort = Button(axcut,'Sort')
bsort.on_clicked(sort)
axcut2 = plt.axes([1.0, 0.0, 0.1, 0.075])
binit = Button(axcut2,'Original')
binit.on_clicked(original)
plt.show()

Expected graph output

Integration

import matplotlib.pyplot as plt
from matplotlib.widgets import Button
import seaborn as sns
%matplotlib notebook

class Index(object):
        ind = 0
        global funcs
    
        def next(self, event):
            self.ind += 1
            i = self.ind %(len(funcs))
            x,y,name = funcs[i]() # unpack tuple data
            for r1, r2 in zip(l,y):
                r1.set_height(r2)
            ax.set_xticklabels(x)
            ax.title.set_text(name) # set title of graph
            plt.draw()        

class Show():
        
        def trigger(self):
            number_button = tk.Button(button_frame2, text='Trigger', command= self.sort)
        
    
        def sort(self,df_frame):
    
            fig, ax = plt.subplots()
            plt.subplots_adjust(bottom=0.2)
            
            ######intial dataframe
            df_frame
            ######sorted dataframe
            dfsorted = df_frame.sort_values('Sales')
           
    
            x, y = df_frame['Product'], df_frame['Sales']
            x1, y1 = df_frame['Product'], df_frame['Sales']
            x2, y2 = dfsorted['Product'], dfsorted['Sales']
    
            l = plt.bar(x,y)
            plt.title('Sorted - Class')
            l2 = plt.bar(x2,y1)
            l2.remove()
            
            def plot1():
                x = x1
                y = y1
                name = 'ORginal'
                return (x,y,name)
    
            def plot2():
                x = x2
                y = y2
                name = 'Sorteds'
                return (x,y,name)
            
            funcs = [plot1, plot2]        
            callback = Index()
            button = plt.axes([0.81, 0.05, 0.1, 0.075])
            bnext = Button(button, 'Sort', color='green')
            bnext.on_clicked(callback.next)
    
            plt.show()

Answer 1

I have included two reproducible examples using the famous titanic dataset to a basic comparison of class vs. # of survivors for interactive sorting for both matplotlib bar and plot (ie line) sorting on the x-axis below:

With bar plots you have to loop through the rectangles using set_height , eg for r1, r2 in zip(l,y): r1.set_height(r2) and for line plots, you use set_ydata , eg l.set_ydata(y) .

Make sure to use %matplotlib notebook if using a jupyter notebook.

BAR

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
import seaborn as sns
%matplotlib notebook

fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)

df = sns.load_dataset('titanic')
df1 = df.groupby('class', as_index=False)['survived'].sum().sort_values('class')
df2 = df1.sort_values('survived', ascending=False)
x, y = df1['class'], df1['survived']
x1, y1 = df1['class'], df1['survived']
x2, y2 = df2['class'], df2['survived']

l = plt.bar(x,y)
plt.title('Sorted - Class')
l2 = plt.bar(x2,y1)
l2.remove()

class Index(object):
    ind = 0
    global funcs

    def next(self, event):
        self.ind += 1
        i = self.ind %(len(funcs))
        x,y,name = funcs[i]() # unpack tuple data
        for r1, r2 in zip(l,y):
            r1.set_height(r2)
        ax.set_xticklabels(x)
        ax.title.set_text(name) # set title of graph
        plt.draw()


def plot1():
    x = x1
    y = y1
    name = 'Sorted - Class'
    return (x,y,name)


def plot2():
    x = x2
    y = y2
    name = 'Sorted - Highest # Survivors'
    return (x,y,name)


funcs = [plot1, plot2]        
callback = Index()
button = plt.axes([0.81, 0.05, 0.1, 0.075])
bnext = Button(button, 'Sort', color='green')
bnext.on_clicked(callback.next)

plt.show()

LINE

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
import seaborn as sns
%matplotlib notebook

fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)

df = sns.load_dataset('titanic')
df1 = df.groupby('class', as_index=False)['survived'].sum().sort_values('class')
df2 = df1.sort_values('survived', ascending=False)
x, y = df1['class'].to_numpy(), df1['survived'].to_numpy()
x1, y1 = df1['class'].to_numpy(), df1['survived'].to_numpy()
x2, y2 = df2['class'].to_numpy(), df2['survived'].to_numpy()
l, = plt.plot(x,y)
plt.title('Sorted - Class')

class Index(object):
    ind = 0
    global funcs

    def next(self, event):
        self.ind += 1
        i = self.ind %(len(funcs))
        x,y,name = funcs[i]() # unpack tuple data
        l.set_ydata(y) #set y value data
        ax.set_xticklabels(x)
        ax.title.set_text(name) # set title of graph
        plt.draw()


def plot1():
    x = x1
    y = y1
    name = 'Sorted - Class'
    return (x,y,name)


def plot2():
    x = x2
    y = y2
    name = 'Sorted - Highest # Survivors'
    return (x,y,name)


funcs = [plot1, plot2]        
callback = Index()
button = plt.axes([0.81, 0.05, 0.1, 0.075])
bnext = Button(button, 'Sort', color='green')
bnext.on_clicked(callback.next)

plt.show()