How to groupby and plot the aggregated values

Question

This is the dataframe that I'm using: https://www.kaggle.com/spscientist/students-performance-in-exams

It contains the following columns (That I want to use):

1. Race/Ethnicity (String: GROUP A, GROUP B, GROUP C...)
2. Math Score (Numeric: 60, 70, 80...)
3. Reading Score (Numeric: 60, 70, 80...)
4. Writing Score (Numeric: 60, 70, 80...)

Basically, I want a barplot that takes Race/Ethnicity to X-Axis with each containing 3 Bars, those will be Math Score, Reading Score, and Writing Score. Every Race/Ethnicity must be in the same graph each with its own Scores.

It should somewhat look like this:

I used the group_by function to get the mean for each lesson in each group:

df3 = df.groupby('race/ethnicity')[['math score', 'writing score', 'reading score']].mean().reset_index()

And it gave me this (I translated "group" to "grupo"):

So now all I need to do is find a way to make the graphic I told you, but that's what I can't figure out. Got any ideas?

I would prefer to use seaborn, but if you want to use matplotlib instead there's no problem.

Answer 1

• Addressing I would prefer to use seaborn: seaborn is just a high-level API for matplotlib.
• There are two easy ways to generate the desired grouped plot
  1. Groupby and plot the grouped dataframe
     • The OP already has grouped the dataframe, but should not have reset the index
  2. Stack the dataframe from a wide to a long format and use seaborn.barplot

Imports and Create DataFrame

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

# load the Kaggle dataset
df = pd.read_csv('StudentsPerformance.csv')

Groupby Plot

• For the example in the OP, this is the simplest implementation

# groupby without resetting the dataframe
df_groupby = df.groupby('race/ethnicity')[['math score', 'writing score', 'reading score']].mean()

# plot df_groupby
ax = df_groupby.plot.bar(figsize=(8, 6), width=0.7)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.xticks(rotation=0)
plt.show()

Stack and use seaborn.barplot

• This implementation requires converting the dataframe from a wide to long format, using .stack().
• The default estimator for seaborn.barplot is mean.

# stack the dataframe into a long format
df_long = df.set_index(keys=df.columns[:5].tolist()).stack().reset_index().rename(columns={'level_5': 'subject', 0: 'score'}).sort_values('race/ethnicity')

# plot with seaborn
fig, ax = plt.subplots(figsize=(8, 7))
sns.barplot(data=df_long, x='race/ethnicity', y='score', hue='subject', ci=False, ax=ax)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

Plot Result of Both

Reproducible DataFrame

• Use this if the file is no longer available

import pandas as pd

data = {'gender': ['female', 'female', 'female', 'male', 'male', 'female', 'female', 'male', 'male', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'male', 'male', 'female', 'male', 'female', 'female', 'female', 'female', 'male', 'male', 'male', 'female', 'female', 'female', 'male', 'male', 'female', 'female', 'male', 'female', 'male', 'female', 'female', 'female', 'male', 'male', 'male', 'male', 'male', 'female', 'female', 'female', 'male', 'male', 'female', 'male', 'male', 'male', 'female', 'female', 'male', 'male', 'female', 'male', 'female', 'female', 'male', 'female', 'male', 'male', 'male', 'male', 'male', 'female', 'female', 'female', 'male', 'male', 'male', 'male', 'female', 'female', 'female', 'female', 'female', 'female', 'male', 'male', 'male', 'female', 'male', 'male', 'female', 'female', 'female'],
        'race/ethnicity': ['group B', 'group C', 'group B', 'group A', 'group C', 'group B', 'group B', 'group B', 'group D', 'group B', 'group C', 'group D', 'group B', 'group A', 'group A', 'group C', 'group C', 'group B', 'group C', 'group C', 'group D', 'group B', 'group D', 'group C', 'group D', 'group A', 'group B', 'group C', 'group C', 'group D', 'group D', 'group B', 'group E', 'group D', 'group E', 'group E', 'group D', 'group D', 'group D', 'group B', 'group C', 'group C', 'group B', 'group B', 'group E', 'group B', 'group A', 'group C', 'group D', 'group C', 'group E', 'group E', 'group C', 'group D', 'group C', 'group C', 'group E', 'group D', 'group D', 'group C', 'group E', 'group A', 'group A', 'group C', 'group D', 'group B', 'group D', 'group C', 'group B', 'group C', 'group D', 'group D', 'group A', 'group C', 'group C', 'group B', 'group E', 'group A', 'group D', 'group E', 'group B', 'group B', 'group A', 'group E', 'group D', 'group C', 'group C', 'group D', 'group A', 'group D', 'group C', 'group C', 'group C', 'group C', 'group B', 'group C', 'group B', 'group E', 'group D', 'group D'],
        'parental level of education': ["bachelor's degree", 'some college', "master's degree", "associate's degree", 'some college', "associate's degree", 'some college', 'some college', 'high school', 'high school', "associate's degree", "associate's degree", 'high school', 'some college', "master's degree", 'some high school', 'high school', 'some high school', "master's degree", "associate's degree", 'high school', 'some college', 'some college', 'some high school', "bachelor's degree", "master's degree", 'some college', "bachelor's degree", 'high school', "master's degree", 'some college', 'some college', "master's degree", 'some college', 'some college', "associate's degree", "associate's degree", 'some high school', "associate's degree", "associate's degree", "associate's degree", "associate's degree", "associate's degree", 'some college', "associate's degree", "associate's degree", "associate's degree", 'high school', "associate's degree", 'high school', 'some college', "associate's degree", 'some college', 'high school', 'some high school', 'high school', "associate's degree", "associate's degree", 'some college', 'some high school', "bachelor's degree", 'some high school', "associate's degree", "associate's degree", 'some high school', 'some high school', 'some high school', 'some college', "associate's degree", "associate's degree", 'some college', 'some college', "associate's degree", 'some high school', 'some high school', "associate's degree", 'some high school', "bachelor's degree", 'some high school', "master's degree", "associate's degree", 'high school', 'some college', "associate's degree", 'high school', 'some college', 'some college', "associate's degree", 'some college', 'some high school', "bachelor's degree", 'high school', 'high school', "associate's degree", 'some college', "associate's degree", 'some high school', 'some college', 'some college', "bachelor's degree"], 'lunch': ['standard', 'standard', 'standard', 'free/reduced', 'standard', 'standard', 'standard', 'free/reduced', 'free/reduced', 'free/reduced', 'standard', 'standard', 'standard', 'standard', 'standard', 'standard', 'standard', 'free/reduced', 'free/reduced', 'free/reduced', 'standard', 'free/reduced', 'standard', 'standard', 'free/reduced', 'free/reduced', 'standard', 'standard', 'standard', 'standard', 'standard', 'standard', 'free/reduced', 'standard', 'standard', 'standard', 'standard', 'free/reduced', 'free/reduced', 'free/reduced', 'free/reduced', 'standard', 'standard', 'free/reduced', 'free/reduced', 'standard', 'standard', 'standard', 'free/reduced', 'standard', 'standard', 'free/reduced', 'standard', 'standard', 'free/reduced', 'free/reduced', 'standard', 'standard', 'standard', 'free/reduced', 'free/reduced', 'free/reduced', 'free/reduced', 'standard', 'standard', 'standard', 'free/reduced', 'standard', 'free/reduced', 'standard', 'free/reduced', 'standard', 'free/reduced', 'free/reduced', 'standard', 'free/reduced', 'standard', 'standard', 'standard', 'standard', 'standard', 'free/reduced', 'free/reduced', 'standard', 'free/reduced', 'standard', 'free/reduced', 'standard', 'standard', 'standard', 'standard', 'free/reduced', 'standard', 'free/reduced', 'standard', 'free/reduced', 'standard', 'standard', 'free/reduced', 'standard'], 'test preparation course': ['none', 'completed', 'none', 'none', 'none', 'none', 'completed', 'none', 'completed', 'none', 'none', 'none', 'none', 'completed', 'none', 'none', 'none', 'none', 'completed', 'none', 'none', 'completed', 'none', 'none', 'completed', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'completed', 'none', 'none', 'completed', 'none', 'none', 'none', 'none', 'completed', 'none', 'none', 'completed', 'none', 'completed', 'completed', 'none', 'completed', 'none', 'none', 'completed', 'none', 'completed', 'none', 'completed', 'none', 'completed', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'completed', 'completed', 'none', 'none', 'none', 'none', 'none', 'completed', 'completed', 'none', 'none', 'none', 'completed', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'none', 'completed', 'none', 'completed', 'completed', 'completed', 'none', 'none'],
        'math score': [72, 69, 90, 47, 76, 71, 88, 40, 64, 38, 58, 40, 65, 78, 50, 69, 88, 18, 46, 54, 66, 65, 44, 69, 74, 73, 69, 67, 70, 62, 69, 63, 56, 40, 97, 81, 74, 50, 75, 57, 55, 58, 53, 59, 50, 65, 55, 66, 57, 82, 53, 77, 53, 88, 71, 33, 82, 52, 58, 0, 79, 39, 62, 69, 59, 67, 45, 60, 61, 39, 58, 63, 41, 61, 49, 44, 30, 80, 61, 62, 47, 49, 50, 72, 42, 73, 76, 71, 58, 73, 65, 27, 71, 43, 79, 78, 65, 63, 58, 65], 'reading score': [72, 90, 95, 57, 78, 83, 95, 43, 64, 60, 54, 52, 81, 72, 53, 75, 89, 32, 42, 58, 69, 75, 54, 73, 71, 74, 54, 69, 70, 70, 74, 65, 72, 42, 87, 81, 81, 64, 90, 56, 61, 73, 58, 65, 56, 54, 65, 71, 74, 84, 55, 69, 44, 78, 84, 41, 85, 55, 59, 17, 74, 39, 61, 80, 58, 64, 37, 72, 58, 64, 63, 55, 51, 57, 49, 41, 26, 78, 74, 68, 49, 45, 47, 64, 39, 80, 83, 71, 70, 86, 72, 34, 79, 45, 86, 81, 66, 72, 67, 67], 'writing score': [74, 88, 93, 44, 75, 78, 92, 39, 67, 50, 52, 43, 73, 70, 58, 78, 86, 28, 46, 61, 63, 70, 53, 73, 80, 72, 55, 75, 65, 75, 74, 61, 65, 38, 82, 79, 83, 59, 88, 57, 54, 68, 65, 66, 54, 57, 62, 76, 76, 82, 48, 68, 42, 75, 87, 43, 86, 49, 58, 10, 72, 34, 55, 71, 59, 61, 37, 74, 56, 57, 73, 63, 48, 56, 41, 38, 22, 81, 72, 68, 50, 45, 54, 63, 34, 82, 88, 74, 67, 82, 74, 36, 71, 50, 92, 82, 62, 70, 62, 62]}
df = pd.DataFrame(data)

Answer 2

I would reform your data so it works easier with catplot:

df3 = df.groupby('race/ethnicity')[['math score', 'writing score', 'reading score']].mean().reset_index()
df_scores = pd.DataFrame(columns=["Score", "Subject", "Race/Ethnicity"])

for subject in ["math", "reading", "writing"]:
    df_tmp1 = pd.DataFrame({"Score":df3[subject+" score"], "Race/Ethnicity":df3["race/ethnicity"]})
    df_tmp1["Subject"] = subject
    df_scores = df_scores.append(df_tmp1)

This gives you a df_scores table like this:

Score	Subject	Race/Ethnicity
61.6292	math	group A
63.4526	math	group B
64.4639	math	group C
67.3626	math	group D
73.8214	math	group E
64.6742	reading	group A
67.3526	reading	group B
69.1034	reading	group C
70.0305	reading	group D
73.0286	reading	group E
62.6742	writing	group A
65.6	writing	group B
67.8276	writing	group C
70.145	writing	group D
71.4071	writing	group E

Then plot it:

g = sns.catplot(data=df_scores, kind="bar",
    x="Race/Ethnicity", 
    y="Score",
    hue="Subject")