Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>I'm not entirely clear on what you want, so I'm going to guess, here...</p> <p>You want the "Probability/Percentile" values to be a cumulative histogram?</p> <p>So for a single plot, you'd have something like this? (Plotting it with markers as you've shown above, instead of the more traditional step plot...)</p> <pre><code>import scipy.stats import numpy as np import matplotlib.pyplot as plt # 100 values from a normal distribution with a std of 3 and a mean of 0.5 data = 3.0 * np.random.randn(100) + 0.5 counts, start, dx, _ = scipy.stats.cumfreq(data, numbins=20) x = np.arange(counts.size) * dx + start plt.plot(x, counts, 'ro') plt.xlabel('Value') plt.ylabel('Cumulative Frequency') plt.show() </code></pre> <p><img src="https://i.stack.imgur.com/39tkO.png" alt="enter image description here"></p> <p>If that's roughly what you want for a single plot, there are multiple ways of making multiple plots on a figure. The easiest is just to use subplots. </p> <p>Here, we'll generate some datasets and plot them on different subplots with different symbols...</p> <pre><code>import itertools import scipy.stats import numpy as np import matplotlib.pyplot as plt # Generate some data... (Using a list to hold it so that the datasets don't # have to be the same length...) numdatasets = 4 stds = np.random.randint(1, 10, size=numdatasets) means = np.random.randint(-5, 5, size=numdatasets) values = [std * np.random.randn(100) + mean for std, mean in zip(stds, means)] # Set up several subplots fig, axes = plt.subplots(nrows=1, ncols=numdatasets, figsize=(12,6)) # Set up some colors and markers to cycle through... colors = itertools.cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k']) markers = itertools.cycle(['o', '^', 's', r'$\Phi$', 'h']) # Now let's actually plot our data... for ax, data, color, marker in zip(axes, values, colors, markers): counts, start, dx, _ = scipy.stats.cumfreq(data, numbins=20) x = np.arange(counts.size) * dx + start ax.plot(x, counts, color=color, marker=marker, markersize=10, linestyle='none') # Next we'll set the various labels... axes[0].set_ylabel('Cumulative Frequency') labels = ['This', 'That', 'The Other', 'And Another'] for ax, label in zip(axes, labels): ax.set_xlabel(label) plt.show() </code></pre> <p><img src="https://i.stack.imgur.com/7ZhT4.png" alt="enter image description here"></p> <p>If we want this to look like one continuous plot, we can just squeeze the subplots together and turn off some of the boundaries. Just add the following in before calling <code>plt.show()</code></p> <pre><code># Because we want this to look like a continuous plot, we need to hide the # boundaries (a.k.a. "spines") and yticks on most of the subplots for ax in axes[1:]: ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.yaxis.set_ticks([]) axes[0].spines['right'].set_color('none') # To reduce clutter, let's leave off the first and last x-ticks. for ax in axes: xticks = ax.get_xticks() ax.set_xticks(xticks[1:-1]) # Now, we'll "scrunch" all of the subplots together, so that they look like one fig.subplots_adjust(wspace=0) </code></pre> <p><img src="https://i.stack.imgur.com/Xllnj.png" alt="enter image description here"></p> <p>Hopefully that helps a bit, at any rate!</p> <p>Edit: If you want percentile values, instead a cumulative histogram (I really shouldn't have used 100 as the sample size!), it's easy to do.</p> <p>Just do something like this (using <code>numpy.percentile</code> instead of normalizing things by hand):</p> <pre><code># Replacing the for loop from before... plot_percentiles = range(0, 110, 10) for ax, data, color, marker in zip(axes, values, colors, markers): x = np.percentile(data, plot_percentiles) ax.plot(x, plot_percentiles, color=color, marker=marker, markersize=10, linestyle='none') </code></pre> <p><img src="https://i.stack.imgur.com/hcPxv.png" alt="enter image description here"></p>
    singulars
    1. This table or related slice is empty.
    1. POPython: Matplotlib - probability plot for several data set
      singulars
      1. PTQuestion
    1. PTAnswer
    1. USJoe Kington
    1. USJoe Kington
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. POPython: Matplotlib - probability plot for several data set
      singulars
      1. PTQuestion
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    3. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTAcceptedByOriginator
    1. CONice! Incidentally, have you considered sending some of these to the gallery? Half the time I find the fastest way to figure out how to do something in matplotlib is to skim the gallery for something that looks like it.
      singulars
      1. PO
      1. USDSM
    2. CO@Joe: Are cumulative frequency as same as percentiles? I need to check on that. You almost solved my issue, I'm tweaking here and there to process the data table.
      singulars
      1. PO
      1. USsiva
    3. CO@siva - No, they're not. I shouldn't have used 100 as the sample size! That makes it very misleading! (Sorry about that!) However, it's fairly simple to express the cumulative frequency values as percentiles. You just need to normalize by the number of samples in the dataset.
      singulars
      1. PO
      1. USJoe Kington
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload