Compare two distributions with different sizes using Python
<p>I want to compare two different distributions, where one has 100 data points, the other 150 data points.</p>

<p>In <code>seaborn</code> I am able to do it using <code>lmplot</code> in this way:</p>

<pre><code>import pandas as pd
import seaborn as sns

df = pd.DataFrame(data)
sns.lmplot(x="dist1", y="dist2", data=df)
</code></pre>

<p>considering the input <code>pandas</code> DataFrame as composed by two columns <code>dist1</code> and <code>dist2</code>, each one having the same number of data points.</p>

<p>However, this only works with distribution of the same size. Therefore I was thinking about taking percentiles of each distribution. 
Is there already an implementation of such plot (e.g. in matplotlib, seaborn, statsmodels, plotly..)?</p>

<h3>Edit</h3>

<p>About closing votes: this question does not belong to <a href="http://stats.stackexchange.com/">CrossValidated</a> SE because I am clearly asking about code or libraries API to compare two distributions, not theoretical questions about distributions or statistical methodologies to analyse them. Here for distribution I only meant: set of data points.</p>

<p>Assuming that want the two data sets on the same axis, see <a href="http://stackoverflow.com/questions/4270301/matplotlib-multiple-datasets-on-the-same-scatter-plot">this</a>. You need a reference to the axis to which you want to draw.</p>

<p>sample:</p>

<pre><code>a = [1.1, 2.8, 14, 21, 23]
b = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

fig, ax1 = plt.subplots()
ax1.scatter(range(len(a)), a)
ax1.scatter(range(len(b)), b)
</code></pre>

python matplotlib distribution seaborn percentile