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I need help creating subplots in matplotlib dynamically from a pandas dataframe. The data I am using is from data.word.

I have already created the viz but the plots have been created manually.

enter image description here

The reason why I need it dynamically is because I am going to apply a filter dynamically (in Power BI) and i need the graph to adjust to the filter.

This is what i have so far: I imported the data and got it in the shape i need:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as 

# read file from makeover monday year 2018 week 48
df = pd.read_csv(r'C:\Users\Ruth Pozuelo\Documents\python_examples\data\2018w48.csv',  usecols=["city", "category","item", "cost"], index_col=False, decimal=",")
df.head()

this is the table: enter image description here

I then apply the filter that will come from Power BI dynamically:

df = df[df.category=='Party night']

and then I count the number of plots based on the number of items I get after I apply the filter:

itemCount = df['item'].nunique()         #number of plots

If I then plot the subplots:

fig, ax = plt.subplots( nrows=1, ncols=itemCount ,figsize=(30,10), sharey=True)

I get the skeleton: enter image description here

So far so good! But now i am suck on how to feed the x axis to the loop to generate the subcategories. I am trying something like below, but nothing works.

#for i, ax in enumerate(axes.flatten()):
 #   ax.plot(??,cityValues, marker='o',markersize=25, lw=0, color="green") # The top-left axes

As I already have the code for the look and feel of the chart, annotations,ect, I would love to be able to use the plt.subplots method and I prefer not use seaborn if possible.

Any ideas on how to get his working?

Thanks in advance!

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  • 1
    The list of columns and subplots to be processed can be looped through to organize the processing of the graph. Sorry if I am wrong as I don't want to register as a member to get the data. fig, axes = plt.subplots(1,5, figsize=(30,10), sharey=True) colors = ['green','blue','red','black','brown'] for i, (ax,column,c) in enumerate(zip(axes.flatten(), df.columns[1:], colors)): ax.plot(df[column], df['City'], marker='o', markersize=25, lw=0, color=c) ax.set_title(col) for i,j in zip(df[column], City): ax.annotate('$'+str(i), xy=(i, j),xytext =(i-4,j),...) Commented Oct 18, 2022 at 10:01
  • I forgot you need to register. I uploaded the file here: github.com/Curbal-Data-Labs/Matplotlib-Labs/blob/master/… Tried your code but not working :( Commented Oct 18, 2022 at 10:30

2 Answers 2

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The data was presented to us and we used it as the basis for our code. I prepared a list of columns and a list of coloring and looped through them. axes.rabel() is more memory efficient than axes.fatten(). This is because the list contains an object for each subplot, allowing for centralized configuration.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

url='https://raw.githubusercontent.com/Curbal-Data-Labs/Matplotlib-Labs/master/2018w48.csv'
dataset = pd.read_csv(url)

dataset.drop_duplicates(['city','item'], inplace=True)
dataset.pivot_table(index='city', columns='item', values='cost', aggfunc='sum', margins = True).sort_values('All', ascending=True).drop('All', axis=1)

df = dataset.pivot_table(index='city', columns='item', values='cost', aggfunc='sum', margins = True).sort_values('All', ascending=True).drop('All', axis=1).sort_values('All', ascending=False, axis=1).drop('All').reset_index()

# comma replace
for c in df.columns[1:]:
    df[c] = df[c].str.replace(',','.').astype(float)

fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(30,10), sharey=True)

colors = ['green','blue','red','black','brown'] 
col_names = ['Dinner','Drinks at Dinner','2 Longdrinks','Club entry','Cinema entry']

for i, (ax,col,c) in enumerate(zip(axes.ravel(), col_names, colors)):
    ax.plot(df.loc[:,col], df['city'], marker='o', markersize=25, lw=0, color=c)
    ax.set_title(col)
    for i,j in zip(df[col], df['city']):
        ax.annotate('$'+str(i), xy=(i, j), xytext=(i-4,j), color="white", fontsize=8)
    ax.set_xticks([])
    ax.spines[['top', 'right', 'left', 'bottom']].set_visible(False)
    ax.grid(True, axis='y', linestyle='solid', linewidth=2)
    ax.grid(True, axis='x', linestyle='solid', linewidth=0.2)
    ax.xaxis.tick_top() 
    ax.xaxis.set_label_position('top')
    ax.set_xlim(xmin=0, xmax=160)
    ax.xaxis.set_major_formatter('${x:1.0f}')
    ax.tick_params(labelsize=8, top=False, left=False)

plt.show()

enter image description here

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7 Comments

oh, i see what you did there, of course I can pivot the table after filtering it. Question: is there a way to do the plots without pivoting the table?
If it does not pivot, the item column is filtered and processed. For example. items = ['Dinner','Drinks at Dinner','2 Longdrinks','Club entry','Cinema entry'] for i in items: df = dataset.query('item == @i') print(df.head())
You pivoted items, right? I was hoping to pick the items from the column without pivoting them to avoid that step and understand if it could be done. In this step: df = dataset.pivot_table(index='city', columns='item', values='cost', aggfunc='sum', margins = True).sort_values('All', ascending=True).drop('All', axis=1).sort_values('All', ascending=False, axis=1).drop('All').reset_index()
I understood by your comment that there is a way to not convert wide format data frames, so I answered that we can just loop through the items we want to extract in long format data frames. Am I not understanding your comment?
I am not explaining myself, apologies for that. I am trying to add the item column to the loop without unpivoting it first. Do you understand what I mean? I can edit the original question to explain myself better if needed.
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Working Example below. I used seaborn to plot the bars but the idea is the same you can loop through the facets and increase a count. Starting from -1 so that your first count = 0, and use this as the axis label.

import seaborn as sns 

fig, ax = plt.subplots( nrows=1, ncols=itemCount ,figsize=(30,10), sharey=True)  

df['Cost'] = df['Cost'].astype(float) 
count = -1 
variables = df['Item'].unique() 

fig, axs = plt.subplots(1,itemCount , figsize=(25,70), sharex=False, sharey= False) 

for var in variables:
        count += 1 
        sns.barplot(ax=axs[count],data=df, x='Cost', y='City')
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3 Comments

Trying to stick to matplotlib for learning purposes. Do you know how to do it without seaborn?
Sorry about that :( . Above solution does the trick
It does, thanks for showing the sns solution. It will come handy in the future.

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