[FIXED] Plot bar chart in multiple subplot rows with Pandas

Issue

I have a simple long-form dataset I would like to generate bar charts from. The dataframe looks like this:

data = {'Year':[2019,2019,2019,2020,2020,2020,2021,2021,2021],
        'Month_diff':[0,1,2,0,1,2,0,1,2],
        'data': [12,10,13,16,12,18,19,45,34]}
df = pd.DataFrame(data)

I would like to plot a bar chart that has 3 rows, each for 2019, 2020 and 2021. X axis being month_diff and data goes on Y axis. How do I do this?

If the data was in different columns, then I could have just used this code:

df.plot(x="X", y=["A", "B", "C"], kind="bar")

But my data is in a single column and ideally, I'd like to have different row for each year.

Solution

seaborn.catplot

The simplest way is using the seaborn.catplot wrapper, as Johan said:

import seaborn as sns
sns.catplot(kind='bar', height=2, aspect=4,
    data=df, x='Month_diff', y='data', row='Year')

---

DataFrame.groupby

Without seaborn, you can iterate df.groupby('Year'):

fig, axs = plt.subplots(3, 1, sharex=True, sharey=True)
for (name, data), ax in zip(df.groupby('Year'), axs):
    data.plot.bar(ax=ax, x='Month_diff', y='data', legend=False, rot=0)
    ax.set_title(name)

---

pivot + DataFrame.plot

Note that if you prefer a single grouped bar chart (which you alluded to at the end), you can pivot into DataFrame.plot:

df.pivot(index='Month_diff', columns='Year', values='data').plot.bar()

Answered By - tdy