继续在for循环中向图形添加子图

Question

I believe that my problem is really straightforward and there must be a really easy way to solve this issue, however as I am quite new with Python, I could not sort it out. I saw multiple answer for cases in which the number of subplots is already known at first, but none apply to my case.

I am doing a foor loop for each unique value in a column, and for every unique value in this specific column, I will make a scatter plot of two of it's features. What I am looking for is the following: The number of subplot will depend on the number of unique values of a certain column on my dataframe, which may vary depending on which file I load with the script. Hence, I want for each loop, add a subplot to the figure. May sound confusing, but with the code that I will post here things will get clearer.

This is naturally a way simpler example than the case in which I am trying to work on:

import pandas as pd
data = {'Column A': [100,200,300,400,500,500,500,300],
'Column B': [1,1,2,2,3,3,0,2], 
'Column C': ["Value_1", "Value_2", "Value_3", "Value_4", "Value_1", 
"Value_2", "Value_3", "Value_4"]}
df = pd.DataFrame(data, columns=['Column A','Column B', 'Column C'])

fig = plt.figure()
for val in df['Column C'].unique():
    sdf = df.loc[df['Column C']==val]
    sdf.plot(x='Column A', y='Column B', label='C = {}'.format(val))

So, the idea is to add all the created plots, whose quantity may vary depending on the loaded data, to the figure.

Thanks in advance. Wish you the best.

Answer 1

You can use subplots .

import pandas as pd
import matplotlib.pyplot as plt

data = {'Column A': [100,200,300,400,500,500,500,300],
'Column B': [1,1,2,2,3,3,0,2], 
'Column C': ["Value_1", "Value_2", "Value_3", "Value_4", "Value_1", 
"Value_2", "Value_3", "Value_4"]}
df = pd.DataFrame(data, columns=['Column A','Column B', 'Column C'])

# The first two argument are the 2 dimension of the matrix of plot
# The figsize param was only to make them bigger in my jupyter notebook

fig, ax = plt.subplots(1, len(df['Column C'].unique()), figsize=(17,5))

for i, val in enumerate(df['Column C'].unique()):
    sdf = df.loc[df['Column C']==val]
    sdf.plot(x='Column A', y='Column B', label='C = {}'.format(val), ax=ax[i])

And this was my result: