# Grouped Bar Charts in Matplotlib

## A few examples of how to create grouped bar charts in Matplotlib

A bar chart is a great way to compare categorical data across one or two dimensions. More often than not, it's more interesting to compare values across two dimensions and for that, a grouped bar chart is needed.

Matplotlib does not make this super easy, but with a bit of repetition, you'll be coding up grouped bar charts from scratch in no time.

## Get our data

```# Load Matplotlib and data wrangling libraries.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Load jobs dataset from Vega's dataset library.
from vega_datasets import data

# Let's use the jobs dataset for this since
# it has two dimensions we can compare across:
# job type and gender.
df = data.jobs()
```
count job perc sex year
0 708 Accountant / Auditor 0.000131 men 1850
1 1805 Accountant / Auditor 0.000214 men 1860
2 1310 Accountant / Auditor 0.000100 men 1870
3 2295 Accountant / Auditor 0.000125 men 1880
4 11753 Accountant / Auditor 0.000396 men 1900

Let's filter the data down a bit to get a more practical dataset for this example.

```# Let's just look at the most recent data from 2000.
df = df.loc[df['year'] == 2000]

# Let's also just look at a sample of jobs since there
# are a bunch!
jobs = ['Actor', 'Bartender', 'Dentist', 'Engineer', 'Sailor', 'Scientist']
df = df.loc[df['job'].isin(jobs)]

# Note, the data is in "long" or "tidy" format, but wide can work too.
```
count job perc sex year
44 29975 Actor 0.000178 men 2000
59 25931 Actor 0.000154 women 2000
734 185678 Bartender 0.001104 men 2000
749 232073 Bartender 0.001380 women 2000
1844 141323 Dentist 0.000841 men 2000

## A simple (but wrong) bar chart

Let's look at the number of people in each job, split out by gender.

With the grouped bar chart we need to use a numeric axis (you'll see why further below), so we create a simple range of numbers using `np.arange` to use as our `x` values.

We then use `ax.bar()` to add bars for the two series we want to plot: jobs for men and jobs for women.

```fig, ax = plt.subplots(figsize=(12, 8))

# Our x-axis. We basically just want a list
# of numbers from zero with a value for each
# of our jobs.
x = np.arange(len(df.job.unique()))

b1 = ax.bar(x, df.loc[df['sex'] == 'men', 'count'])
b2 = ax.bar(x, df.loc[df['sex'] == 'women', 'count'])
``` That doesn't look right. Matplotlib is just plotting the two bars on top of each other. Let's fix it!

## A basic grouped bar chart

We do that by first setting `bar_width`. You can play around with the value here to make your chart look the way you want it to; the important thing is to set it and then use it when you are generating each bar: `ax.bar(..., width=bar_width)`.

The most important thing however is to offset the `x` value of the second bar by `bar_width`. We do this simply by adding them together: `x + bar_width`. This is why it's important to use the numeric x-axis instead of a categorical one.

```fig, ax = plt.subplots(figsize=(12, 8))
x = np.arange(len(df.job.unique()))

# Define bar width. We'll use this to offset the second bar.
bar_width = 0.4

# Note we add the `width` parameter now which sets the width of each bar.
b1 = ax.bar(x, df.loc[df['sex'] == 'men', 'count'],
width=bar_width)
# Same thing, but offset the x by the width of the bar.
b2 = ax.bar(x + bar_width, df.loc[df['sex'] == 'women', 'count'],
width=bar_width)
``` Things are looking pretty good but the x-axis labels are a bit messed up. They are numbers instead of job labels and they're not really centered.

## Fixing the x-axis

To move the ticks to be centered, we just have to shift them by half the width of a bar, or `bar_width / 2`. We also just assign our labels (be a bit careful here to make sure you're assigning labels in the right order).

```fig, ax = plt.subplots(figsize=(12, 8))
x = np.arange(len(df.job.unique()))
bar_width = 0.4
b1 = ax.bar(x, df.loc[df['sex'] == 'men', 'count'],
width=bar_width)
b2 = ax.bar(x + bar_width, df.loc[df['sex'] == 'women', 'count'],
width=bar_width)

# Fix the x-axes.
ax.set_xticks(x + bar_width / 2)
ax.set_xticklabels(df.job.unique())
``` ## Styling the chart

Lastly, let's just add some labels and styles and put it all together.

```# Use Seaborn's context settings to make fonts larger.
import seaborn as sns
sns.set_context('talk')

# Create a grouped bar chart, with job as the x-axis
# and gender as the variable we're grouping on so there
# are two bars per job.
fig, ax = plt.subplots(figsize=(12, 8))

# Our x-axis. We basically just want a list
# of numbers from zero with a value for each
# of our jobs.
x = np.arange(len(df.job.unique()))

# Define bar width. We need this to offset the second bar.
bar_width = 0.4

b1 = ax.bar(x, df.loc[df['sex'] == 'men', 'count'],
width=bar_width, label='Men')
# Same thing, but offset the x.
b2 = ax.bar(x + bar_width, df.loc[df['sex'] == 'women', 'count'],
width=bar_width, label='Women')

# Fix the x-axes.
ax.set_xticks(x + bar_width / 2)
ax.set_xticklabels(df.job.unique())

ax.legend()

# Axis styling.
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_color('#DDDDDD')
ax.tick_params(bottom=False, left=False)
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)

# Add axis and chart labels. 