Creating a stacked area plot in python with a Pandas DataFrame

Question

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

dates = np.arange(1990,2061, 1)
dates = dates.astype('str').astype('datetime64')

df = pd.DataFrame(np.random.randint(0, dates.size, size=(dates.size,3)), columns=list('ABC'))
df['year'] = dates

cols = df.columns.tolist()
cols = [cols[-1]] + cols[:-1]
df = df[cols]

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.stackplot(df['year'], df.drop('year',axis=1))

Based on this code, I'm getting an error "TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"

I'm trying to figure out how to plot a DataFrame object with years in the first column, and then stacked area from the subsequent columns (A, B, C)..

Also, since I'm a complete beginner here... feel free to comment on my code as to make it cleaner / better. I understand that if I use Matplotlib instead of the Pandas integrated plot method, that I have more functionality to adjust things later on?

Thanks!

A. Entuluva · Accepted Answer · 2017-10-13 21:38:13Z

17

I run into two problems running your code.

First, stackplot seems to dislike using string representations of dates. Datetime data types are very finicky sometimes. Either use integers for your 'year' column, or use .values to convert from pandas to numpy datatypes as described in this question

Secondly, according to the documentation for stackplot, when you call stackplot(x, y) if x is a Nx1 array, then y must be MxN, where M is the number of columns. Your df.drop('year',axis=1)) will end up as NxM and throw another error at you. If you take the transpose, however, you can make it work.

If I just replace your final line with

ax.stackplot(df['year'].values, df.drop('year',axis=1).T)

I get a plot that looks like this:

answered Oct 13, 2017 at 21:38

A. Entuluva

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2 Comments

bpdronkers Over a year ago

Wow. Good reading and also appreciate your insights to the problem!

boardkeystown Over a year ago

OMG. THANK YOU! You saved me a lot of confusion. Pandas and dataframes are wonderful.

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