joferkington

import matplotlib.pyplot as plt

def main():
    resized = (8, 3)

    basic_example()
    basic_example(resized)

    fixed_aspect()
    fixed_aspect(resized)

joferkington

import numpy as np
import pandas as pd

#-- Generate some data similar to yours
idx = np.arange(20)

np.random.shuffle(idx)
idx1 = idx[:15]
np.random.shuffle(idx)
idx2 = idx[:10]

joferkington

import random

class BadIdea(object):
    def __getattr__(self, key):
        return random.randint(0, 1000)

    def __setattr__(self, key, value):
        pass

x = BadIdea()

joferkington

import matplotlib.pyplot as plt
from matplotlib.mlab import csv2rec
from matplotlib.cbook import get_sample_data

#fname = get_sample_data('percent_bachelors_degrees_women_usa.csv')
fname = 'percent_bachelors_degrees_women_usa.csv'
gender_degree_data = csv2rec(fname)

# These are the colors that will be used in the plot

joferkington

import random
import matplotlib.pyplot as plt
from mpldatacursor import datacursor

def main():
    accounts = generate_accounts()
    lookup = plot(accounts)
    datacursor(formatter=Formatter(lookup), bbox=dict(alpha=1))
    plt.show()

joferkington

import random
import matplotlib.pyplot as plt
from mpldatacursor import datacursor

def main():
    accounts = generate_accounts()
    plot(accounts)
    datacursor(formatter='Account #\n{label}'.format, bbox=dict(alpha=1))
    plt.show()

joferkington

import numpy as np
import matplotlib.pyplot as plt

x, y = np.random.normal(0, 1, (2, 1000))

counts, ybins, xbins = np.histogram2d(y, x, bins=30)
counts = np.ma.masked_equal(counts, 0)

fig, ax = plt.subplots()
ax.pcolormesh(xbins, ybins, counts)

joferkington

import numpy as np
import matplotlib.pyplot as plt

class DrawDragPoints(object):
    """
    Demonstrates a basic example of the "scaffolding" you need to efficiently
    blit drawable/draggable/deleteable artists on top of a background.
    """
    def __init__(self):
        self.fig, self.ax = self.setup_axes()

joferkington

import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
import mpl_toolkits.axisartist.floating_axes as floating_axes

fig = plt.figure()

plot_extents = 0, 10, 0, 10
transform = Affine2D().rotate_deg(45)
helper = floating_axes.GridHelperCurveLinear(transform, plot_extents)
ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=helper)

joferkington

import itertools
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection

import numpy as np
np.random.seed(1977)

def main():
    azi = np.random.normal(20, 40, 1000)