Use a dictionary!!

You can also use dictionaries that allows you to have more control over the plots:

import matplotlib.pyplot as plt
#   plot 0     plot 1    plot 2   plot 3
x=[[1,2,3,4],[1,4,3,4],[1,2,3,4],[9,8,7,4]]
y=[[3,2,3,4],[3,6,3,4],[6,7,8,9],[3,2,2,4]]

plots = zip(x,y)
def loop_plot(plots):
    figs={}
    axs={}
    for idx,plot in enumerate(plots):
        figs[idx]=plt.figure()
        axs[idx]=figs[idx].add_subplot(111)
        axs[idx].plot(plot[0],plot[1])
    return figs, axs  
        
figs, axs = loop_plot(plots)

Now you can select the plot that you want to modify easily:

axs[0].set_title("Now I can control it!")

Of course, is up to you to decide what to do with the plots. You can either save them to disk figs[idx].savefig("plot_%s.png" %idx) or show them plt.show(). Use the argument block=False only if you want to pop up all the plots together (this could be quite messy if you have a lot of plots). You can do this inside the loop_plot function or in a separate loop using the dictionaries that the function provided.

Just to add returning figs and axs is not mandatory to execute plt.show().

Here are two examples of how to generate graphs in separate windows (frames), and, an example of how to generate graphs and save them into separate graphics files.

Okay, first the on-screen example. Notice that we use a separate instance of plt.figure(), for each graph, with plt.plot(). At the end, we have to call plt.show() to put it all on the screen.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace( 0,10 )

for n in range(3):
    y = np.sin( x+n )
    plt.figure()
    plt.plot( x, y )

plt.show()

Another way to do this, is to use plt.show(block=False) inside the loop:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace( 0,10 )

for n in range(3):
    y = np.sin( x+n )
    plt.figure()
    plt.plot( x, y )
    plt.show( block=False )

Now, let's generate the graphs and instead, write them each to a file. Here we replace plt.show(), with plt.savefig( filename ). The difference from the previous example is that we don't have to account for ''blocking'' at each graph. Note also, that we number the file names. Here we use %03d so that we can conveniently have them in number order afterwards.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace( 0,10 )

for n in range(3):
    y = np.sin( x+n )
    plt.figure()
    plt.plot( x, y )
    plt.savefig('myfilename%03d.png'%(n))

If your requirement is to plot against one column, then feel free to use this (First import data into a pandas DF) (plots a matrix of plots with 5 columns and as many rows required):

import math
i,j=0,0
PLOTS_PER_ROW = 5
fig, axs = plt.subplots(math.ceil(len(df.columns)/PLOTS_PER_ROW),PLOTS_PER_ROW, figsize=(20, 60))
for col in df.columns:
    axs[i][j].scatter(df['target_col'], df[col], s=3)
    axs[i][j].set_ylabel(col)
    j+=1
    if j%PLOTS_PER_ROW==0:
        i+=1
        j=0
plt.show()

A simple way of plotting on different frames would be like:

import matplotlib.pyplot as plt  
for grp in list_groups:
        plt.figure()
        plt.plot(grp)
        plt.show()

Then python will plot multiple frames for each iteration.

We can create a for loop and pass all the numeric columns into it. The loop will plot the graphs one by one in separate pane as we are including plt.figure() into it.

import pandas as pd
import seaborn as sns
import numpy as np

numeric_features=[x for x in data.columns if data[x].dtype!="object"]
#taking only the numeric columns from the dataframe.

for i in data[numeric_features].columns:
    plt.figure(figsize=(12,5))
    plt.title(i)
    sns.boxplot(data=data[i])