Barplot per each ax in matplotlib

Question

I have the following dataset, ratings in stars for two fictitious places:

import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'id':['A','A','A','A','A','A','A','B','B','B','B','B','B'], 
                   'rating':[1,2,4,5,5,5,3,1,3,3,3,5,2]})

Since the rating is a category (is not a continuous data) I convert it to a category:

df['rating_cat'] = pd.Categorical(df['rating'])

What I want is to create a bar plot per each fictitious place ('A or B'), and the count per each rating. This is the intended plot:

I guess using a for per each value in id could work, but I have some trouble to decide the size:

fig, ax = plt.subplots(1,2,figsize=(6,6))
axs = ax.flatten()
cats = df['rating_cat'].cat.categories.tolist()
ids_uniques = df.id.unique()
for i in range(len(ids_uniques)):
    ax[i].bar(df[df['id']==ids_uniques[i]], df['rating'].size())

But it returns me an error TypeError: 'int' object is not callable

Perhaps it's something complicated what I am doing, please, could you guide me with this code

The "int" error you're getting is because df['rating'].size() should be df['rating'].size — mitoRibo
– mitoRibo, Commented Aug 19, 2022 at 23:39

Code Different · Accepted Answer · 2022-08-19 23:54:57Z

2

The pure matplotlib way:

from math import ceil

# Prepare the data for plotting
df_plot = df.groupby(["id", "rating"]).size()
unique_ids = df_plot.index.get_level_values("id").unique()

# Calculate the grid spec. This will be a n x 2 grid
# to fit one chart by id
ncols = 2
nrows = ceil(len(unique_ids) / ncols)
fig = plt.figure(figsize=(6,6))

for i, id_ in enumerate(unique_ids):
    # In a figure grid spanning nrows x ncols, plot into the
    # axes at position i + 1
    ax = fig.add_subplot(nrows, ncols, i+1)
    df_plot.xs(id_).plot(axes=ax, kind="bar")

You can simplify things a lot with Seaborn:

import seaborn as sns

sns.catplot(data=df, x="rating", col="id", col_wrap=2, kind="count")

answered Aug 19, 2022 at 23:54

Code Different

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1 Comment

Alexis Over a year ago

Thanks @Code Different, both solutions work fine!, catplot as you mention simplifies a lot, and col_wrap let's me add more levels on the category

mitoRibo · Accepted Answer · 2022-08-19 23:53:23Z

2

If you're ok with installing a new library, seaborn has a very helpful countplot. Seaborn uses matplotlib under the hood and makes certain plots easier.

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

df = pd.DataFrame({'id':['A','A','A','A','A','A','A','B','B','B','B','B','B'], 
                   'rating':[1,2,4,5,5,5,3,1,3,3,3,5,2]})

sns.countplot(
    data = df,
    x = 'rating',
    hue = 'id',
)
plt.show()
plt.close()

answered Aug 19, 2022 at 23:53

mitoRibo

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1 Comment

Alexis Over a year ago

Thanks @mitoRibo, I was looking for a solution to plot apart each level in the category, but your solution works fine. Have a great day.

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