Examples with code

The basic idea for all of these plots is that you should be able to do ppl.{plot_command}(ax, x, y) instead of plt.{plot_command}(x, y). I haven't figured yet how to completely inherit Axes and such from matplotlib, because it is a huge, very complicated piece of code. But hopefully that functionality will be in future releases :)

plot (lines, e.g. time series) with a legend

For data where the x and y are ordered, like by time or distance, use ppl.plot, similar to how you'd use plt.plot or ax.plot from matplotlib. Except instead of plt.plot(x, y) or ax.plot(x, y), do ppl.plot(ax, x, y).

import prettyplotlib as ppl

# prettyplotlib imports 
from prettyplotlib import plt
from prettyplotlib import mpl
from prettyplotlib import brewer2mpl

# Set the random seed for consistency
np.random.seed(12)

fig, ax = plt.subplots(1)

# Show the whole color range
for i in range(8):
    y = np.random.normal(size=1000).cumsum()
    x = np.arange(1000)

    # For now, you need to specify both x and y :(
    # Still figuring out how to specify just one
    ppl.plot(ax, x, y, label=str(i), linewidth=0.75)
    
ppl.legend(ax)

fig.savefig('plot_prettyplotlib_default.png')

legend parameters

If you want to move the legend to somewhere else, use the arguments you'd normally use for legend(), e.g. loc='lower right', ncol=4:

ppl.legend(ax, loc='lower left', ncol=4)

The full code is here:

import prettyplotlib as ppl

# prettyplotlib imports 
from prettyplotlib import plt
from prettyplotlib import mpl
from prettyplotlib import brewer2mpl

# Set the random seed for consistency
np.random.seed(12)

fig, ax = plt.subplots(1)

# Show the whole color range
for i in range(8):
    y = np.random.normal(size=1000).cumsum()
    x = np.arange(1000)

    # For now, you need to specify both x and y :(
    # Still figuring out how to specify just one
    ppl.plot(ax, x, y, label=str(i))
    
ppl.legend(ax, loc='lower left', ncol=4)

fig.savefig('plot_prettyplotlib_legend_lower_left.png')

scatter (points)

This is for less structured data than plot, like if you have two samples X and Y, and gene expression for each one, and you want to see how the expression of the same gene is for the two samples. :)

import prettyplotlib as ppl

# This is "import matplotlib.pyplot as plt" from the prettyplotlib library
from prettyplotlib import plt

# This is "import matplotlib as mpl" from the prettyplotlib library
from prettyplotlib import mpl

# Set the random seed for consistency
np.random.seed(12)

fig, ax = plt.subplots(1)

# Show the whole color range
for i in range(8):
    x = np.random.normal(loc=i, size=1000)
    y = np.random.normal(loc=i, size=1000)
    ppl.scatter(ax, x, y, label=str(i))
    
ppl.legend(ax)
    
ax.set_title('prettyplotlib `scatter` example\nshowing default color cycle and scatter params')
fig.savefig('scatter_prettyplotlib_default.png')

Back to matplotlib-style scatterplots

If you really want to change all the parameters I worked so hard on, you can:

import prettyplotlib as ppl
from prettyplotlib import plt
from prettyplotlib import mpl
from prettyplotlib import brewer2mpl

# Set the random seed for consistency
np.random.seed(12)

fig, ax = plt.subplots(1)

#mpl.rcParams['axis.color_cycle'] = ['blue']

# Show the whole color range
for i in range(8):
    x = np.random.normal(loc=i, size=1000)
    y = np.random.normal(loc=i, size=1000)
    ax.scatter(x, y, label=str(i), facecolor='blue', edgecolor='black', linewidth=1)
    
# Get back the top and right axes lines ("spines")
spines_to_remove = ['top', 'right']
for spine in spines_to_remove:
    ax.spines[spine].set_visible(True)
    
# Get back the ticks. The position of the numbers is informative enough of
# the position of the value.
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')

# For all the spines, make their line thicker and return them to be black
all_spines = ['top', 'left', 'bottom', 'right']
for spine in all_spines:
    ax.spines[spine].set_linewidth(1.0)
    ax.spines[spine].set_color('black')

# Change the labels back to black
ax.xaxis.label.set_color('black')
ax.yaxis.label.set_color('black')

# Change the axis title also back to black
ax.title.set_color('black')

# Remove the line around the legend box, and instead fill it with a light grey
# Also only use one point for the scatterplot legend because the user will 
# get the idea after just one, they don't need three.
ax.legend()
    
ax.set_title('prettyplotlib `scatter` example\nshowing default color cycle and scatter params')
fig.savefig('scatter_prettyplotlib_back_to_matplotlib_default.png')

bar

This plot is great for categorical data, unrelated quantities such as number of cats, oranges, and computers per household.

import prettyplotlib as ppl
from prettyplotlib import plt

fig, ax = plt.subplots(1)

np.random.seed(14)

ppl.bar(ax, np.arange(10), np.abs(np.random.randn(10)))
fig.savefig('bar_prettyplotlib_default.png')

bar with a white grid to "erase" and yet add information!

If you supply the command grid="y", where the "y" indicates I want the grid coming out of the y-axis at certain y=... values, aka perpendicular to the y-axis, you will get a white grid drawn over your data. It's almost like adding ticks, but since it's "erasing", it increases the data-ink ratio.

import prettyplotlib as ppl
from prettyplotlib import plt

fig, ax = plt.subplots(1)

np.random.seed(14)

# 'y' for make a grid based on where the major ticks are on the y-axis
ppl.bar(ax, np.arange(10), np.abs(np.random.randn(10)), grid='y')
fig.savefig('bar_prettyplotlib_grid.png')

bar with value annotation on each bar

If you say annotate=True in the ppl.bar command, then the value of the number on top of the bar plot will be added.

import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np
import string

fig, ax = plt.subplots(1)
np.random.seed(14)
n = 10
ppl.bar(ax, np.arange(n), np.abs(np.random.randn(n)), annotate=True, grid='y')
fig.savefig('bar_prettyplotlib_grid_annotated.png')

bar correctly annotates negative values

If you happen to have negative values in your bar plot, the annotation on them will correctly appear underneath them. Also, the x-axis line will be moved up to the y=0 mark, instead of staying at the bottom.

import prettyplotlib as ppl
from prettyplotlib import plt

fig, axes = plt.subplots(nrows=4, figsize=(6,8))

# This is the order we want to plot them, too
nucleotides = ['C', 'G', 'A', 'T']

colors = {nucleotide: ppl.set2[i] for i, nucleotide in enumerate(nucleotides)}

entire_strand = {'C':427419, 'G':413241, 'A':491488, 'T':491363}
reverse_half = {'C': 219518, 'G':201634, 'A':243963, 'T':246641}
forward_half = {'C': 207901, 'G':211607, 'A':247525, 'T':244722}
skew = {nucleotide: forward_half[nucleotide] - reverse_half[nucleotide] for nucleotide in nucleotides}

strands = {'Entire strand':entire_strand, 
            'Reverse half-strand':reverse_half, 
            'Forward half-strand':forward_half,
            'Forward - Reverse':skew}

# The order that we want to plot the strand data in:
strand_names_ordered = ['Entire strand', 'Forward half-strand', 'Reverse half-strand', 'Forward - Reverse']

left = range(len(nucleotides))
for ax, strand_name in zip(axes, strand_names_ordered):
    strand_data = strands[strand_name]
    ppl.bar(ax, left=left, 
            height=[strand_data[nucleotide] for nucleotide in nucleotides], 
            annotate=True, 
            xticklabels=nucleotides, 
            grid='y', 
            color=[colors[nucleotide] for nucleotide in nucleotides])
    ax.set_title(strand_name)
    
# Tell matplotlib to smartly lay out our figure
fig.tight_layout()

bar with arbitrary annotation

If you don't like the way prettyplotlib formats the numbers on top of the bars, or you just want to label with something else, you can supply your own numbers or strings by giving an iterable (like a list of strings or numbers) to annotate:

import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np
import string

fig, ax = plt.subplots(1)
np.random.seed(14)
n = 10
ppl.bar(ax, np.arange(n), np.abs(np.random.randn(n)),
        annotate=range(n,2*n), grid='y', xticklabels=string.uppercase[:n])
fig.savefig('bar_prettyplotlib_grid_annotated_user_labeled.png')

bar with each bar labeled on x-axis

If you supply xticklabels as an argument to plt.bar, this will label each bar with this xlabel.

import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np
import string

fig, ax = plt.subplots(1)
np.random.seed(14)
n = 10
ppl.bar(ax, np.arange(n), np.abs(np.random.randn(n)), annotate=True, xticklabels=string.uppercase[:n], grid='y')
fig.savefig('bar_prettyplotlib_grid_annotated_labeled.png')

hist

If you have a large number of values and you're curious about the distribution of values within the data, hist is the function for you!

Right now, sideways histograms (with orientation=horizontal) aren't working in my matplotlib so they are untested.

import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np

np.random.seed(12)

fig, ax = plt.subplots(1)

ppl.hist(ax, np.random.randn(1000))
fig.savefig('hist_prettyplotlib_default.png')

hist with a white grid to "erase" and yet add information!

Like with bar, you can add a white grid with grid="y", where the "y" indicates I want the grid coming out of the y-axis at certain y=... values, aka perpendicular to the y-axis.

import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np

np.random.seed(12)

fig, ax = plt.subplots(1)

# 'y' for the 'y' axis. Could also add a grid over the 'x' axis.
ppl.hist(ax, np.random.randn(1000), grid='y')
fig.savefig('hist_prettyplotlib_grid.png')

boxplot

If you have several things you'd like to compare distributions with, the boxplot can be very nice.

import prettyplotlib as ppl
from prettyplotlib import mpl

np.random.seed(10)

data = np.random.randn(8, 4)
labels = ['A', 'B', 'C', 'D']

fig, ax = plt.subplots()
ppl.boxplot(ax, data, xticklabels=labels)
fig.savefig('boxplot_prettyplotlib_default.png')

pcolormesh (heatmaps)

Because matplotlib took the color scheme from MATLAB, it inherited the ubiquitous and every-distorting rainbow color map:

How can you tell what values are positive and negative, at a glance? it's impossible without heavy scrutiny and switching back and forth between the figure and the colorbar. Indeed, there have been studies stating that "the rainbow colormap is universally inferior to all other colormaps"

So prettyplotlib makes a beautiful default colormap

import prettyplotlib as ppl
from prettyplotlib import plt
import numpy as np

fig, ax = plt.subplots(1)

np.random.seed(10)

ppl.pcolormesh(fig, ax, np.random.randn(10,10))
fig.savefig('pcolormesh_prettyplotlib_default.png')

pcolormesh with labels

pcolormesh: auto-detect positive/negative/both data