Raymond Raymond

Pandas DataFrame Plot - Area Chart

event 2020-04-04 visibility 1,675 comment 0 insights toc
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Pandas DataFrame Plot - Area Chart

This article provides examples about plotting area chart using pandas.DataFrame.plot or pandas.core.groupby.DataFrameGroupBy.plot function.

Prerequisites

The data I'm going to use is the same as the other article Pandas DataFrame Plot - Bar Chart. I'm also using Jupyter Notebook to plot them.

The DataFrame has 9 records:

DATETYPESALES
02020-01-01TypeA1000
12020-01-01TypeB200
22020-01-01TypeC300
32020-02-01TypeA700
42020-02-01TypeB400
52020-02-01TypeC500
62020-03-01TypeA300
72020-03-01TypeB900
82020-03-01TypeC100


Stacked area chart

import matplotlib
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter

df_unstack = df.groupby(['DATE','TYPE']).sum().unstack()
plt =df_unstack.plot(kind='area',y='SALES', stacked = True)

date_form = DateFormatter("%Y-%m")
plt.xaxis.set_major_formatter(date_form)
plt.xaxis.set_major_locator(mdates.MonthLocator(interval=1))

The above code snippet plots a stacked area chart as the following screenshot shows:

2020040441542-image.png

Similar as the bar or line chart examples, major locator and formatter are used to control the formats.

Cumulative stacked area chart

import matplotlib
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter

df_unstack = df.groupby(['DATE','TYPE']).sum().groupby(level=[1]).cumsum().unstack()
plt =df_unstack.plot(kind='area',y='SALES', stacked = True, figsize=[10,5])

date_form = DateFormatter("%Y-%m")
plt.xaxis.set_major_formatter(date_form)
plt.xaxis.set_major_locator(mdates.MonthLocator(interval=1))

The chart looks like the following:

2020040441751-image.png

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