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pandasdataframematplotlibplotbinning

Plotting data binned in a pandas dataframe in a scatterplot

I've got a large amount of astronomical data that I need to plot in a scatterplot. I've binned the data according to distance, and I want to plot 4 scatterplots, side by side.

For the purposes of asking this question, I've constructed a MWE based, obviously with different data, on what I've got so far:

import pandas as pd
import matplotlib.pyplot as plt

data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky', 'Jim', 'Lee', 'Rob', 'Dave',
                'Jane', 'Bronwyn', 'Karen', 'Liz', 'Claire', 'Chris', 'Jan', 'Ruby'],
         'Age':[28,34,29,42,14,16,75,68,
                27,3,2,19,17,32,71,45],
         'Weight':[60,75,73,82,54,55,98,82,45,9,8,47,54,62,67,67]}

stages = ['Toddler', 'Teen', ' Young Adult', 'Adult']
ages = [0,4,20,40,100]
df = pd.DataFrame(data)

df['binned'] = pd.cut(df['Age'], bins=ages, labels=stages)

fig=plt.figure()
fig.subplots_adjust(hspace=0) 
fig.subplots_adjust(wspace=0) 
gridsize = 1,4

ax1 = plt.subplot2grid(gridsize, (0,0))
ax1.scatter(df['Name'], df['Weight'], alpha = 0.5)
ax1.set_ylabel('Weight, kg', fontsize=20)
ax1.set_xlabel('Name', fontsize=20)

ax2 = plt.subplot2grid(gridsize, (0,1), sharey=ax1, sharex = ax1)
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.scatter(df['Name'], df['Weight'], alpha = 0.5)
ax2.set_xlabel('Name', fontsize=20)

ax3 = plt.subplot2grid(gridsize, (0,2), sharey=ax1, sharex = ax1)
plt.setp(ax3.get_yticklabels(), visible=False)
ax3.scatter(df['Name'], df['Weight'], alpha = 0.5)
ax3.set_xlabel('Name', fontsize=20)

ax4 = plt.subplot2grid(gridsize, (0,3), sharey=ax1, sharex = ax1)
plt.setp(ax4.get_yticklabels(), visible=False)
ax4.scatter(df['Name'], df['Weight'], alpha = 0.5)
ax4.set_xlabel('Name', fontsize=20)

This plots four graphs as expected: enter image description here but how do I get each graph to plot only the data from one of each of the bins? In other words, how do I plot just one of the bins?

I'm not worried about the scrunching up of the names on the x axis, those are just for this MWE. They'll be numbers in my actual plots.

Just for clarification, my actual data is binned like

sources['z bins']=pd.cut(sources['z'], [0,1,2,3, max(z)],
                   labels = ['z < 1', '1 < z < 2', '2 < z < 3', 'z > 3'])
Solution

  • What if you grouped the dataframe by binned and then plotted each group?

    For example:

    fig=plt.figure()
fig.subplots_adjust(hspace=0) 
fig.subplots_adjust(wspace=0) 
gridsize = 1,4

for i, (name, frame) in enumerate(df.groupby('binned')):
    ax = plt.subplot2grid(gridsize, (0,i))
    ax.scatter(frame['Name'], frame['Weight'], alpha = 0.5)
    ax.set_xlabel(name, fontsize=20)

    enter image description here

    I realize you will likely want to clean up the labels a bit, but this at least puts the different bins on a different axes object.

    You can iterate over a groupby object and return the name of the group and the dataframe of that group. Here I am using enumerate in order to increment the axes object

    Alternatively if you do not want to use a for loop you can access each group with the get_group method of a groupby object.

    grouped = df.groupby('binned')

ax1 = plt.subplot2grid(gridsize, (0,0))

ax1.scatter(grouped.get_group('Toddler')['Name'],
            grouped.get_group('Toddler')['Weight'],
            alpha = 0.5)
ax1.set_ylabel('Weight, kg', fontsize=20)
ax1.set_xlabel('Name', fontsize=20)