Pandas plot density plot from frequency table

Question

Let's say I have a DataFrame that looks (simplified) like this

where the index column represents a value, and the freq column represents the frequency of occurance of that value, as in a frequency table.

I'd like to plot a density plot for this table like one obtained from plot kind kde. However, this kind is apparently only meant for pd.Series. My df is too large to flatten out to a 1D Series, i.e. df = [2, 2, 3, 3, 3, ..,, 1, 1]. How can I plot such a density plot under these circumstances?

Which version of Pandas?

Alexander
– Alexander

2015-12-09 21:11:30 +00:00
Commented Dec 9, 2015 at 21:11 — Alexander
– Alexander, Commented Dec 9, 2015 at 21:11

thebigspin · Accepted Answer · 2019-08-29 13:27:42Z

1

I know you have asked for the case where df is too large to flatten out, but the following answer works where this isn't the case:

pd.Series(df.index.repeat(df.freq)).plot.kde()

Or more generally, when the values are in a column called val and not the index:

df.val.repeat(df.freq).plot.kde()

answered Aug 29, 2019 at 13:27

thebigspin

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Stig Johan B. · Accepted Answer · 2019-08-29 13:49:02Z

1

You can plot a density distribution using a bar plot if you normalize the y values by the product of the size of the population. This will make the area covered by the bars equal to 1.

plt.bar(
    df.index,
    df.freq / df.freq.sum(),
    width=-1,
    align='edge'
)

The width and align parameters are to make sure each bar covers the interval (k-1, k].

Somebody with better knowledge of statistics should answer whether kernel density estimation actually makes sense for discrete distributions.

answered Aug 29, 2019 at 13:49

Stig Johan B.

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gabra · Accepted Answer · 2015-12-09 21:07:45Z

0

Maybe this will work:

import matplotlib.pyplot as plt

plt.plot(df.index, df['freq'])

plt.show()

answered Dec 9, 2015 at 21:07

gabra

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m13op22 · Accepted Answer · 2019-08-29 15:59:39Z

0

Seaborn was built to do this on top of Matplotlib and automatically calculates kernel density estimates if you want.

import seaborn as sns

x = pd.Series(np.random.randint(0, 20, size = 10000), name = 'freq')

sns.distplot(x, kde = True)

answered Aug 29, 2019 at 15:59

m13op22

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