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VisualizaçõesPlot bar chart in multiple subplot rows with Pandas
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.
·
Santiago Trujillo
1 Respostas
Responde à pergunta0
1. seaborn.catplot
The simplest option for a long-form dataframe is the seaborn.catplot wrapper, as Johan said:
import seaborn as sns
sns.catplot(data=df, x='Month_diff', y='data', row='Year',
kind='bar', height=2, aspect=4)
2. pivot + DataFrame.plot
Without seaborn:
pivotfrom long-form to wide-form (1 year per column)- use
DataFrame.plotwithsubplots=Trueto put each year into its own subplot (and optionallysharey=True)
(df.pivot(index='Month_diff', columns='Year', values='data')
.plot.bar(subplots=True, sharey=True, legend=False))
plt.tight_layout()
Note that if you prefer a single grouped bar chart (which you alluded to at the end), you can just leave out the subplots param:
df.pivot(index='Month_diff', columns='Year', values='data').plot.bar()
3. DataFrame.groupby + subplots
You can also iterate the df.groupby('Year') object:
- Create a
subplotsgrid of axes based on the number of groups (years) - Plot each group (year) onto its own subplot row
groups = df.groupby('Year')
fig, axs = plt.subplots(nrows=len(groups), ncols=1, sharex=True, sharey=True)
for (name, group), ax in zip(groups, axs):
group.plot.bar(x='Month_diff', y='data', legend=False, ax=ax)
ax.set_title(name)
fig.supylabel('data')
plt.tight_layout()
·
Santiago Trujillo
Relatório
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