v0.6 released!
A Python Plotting Analgesic
Install the latest version:
pip install fivecentplots
Read the docs at: https://endangeredoxen.github.io/fivecentplots
There is no shortage of quality plotting libraries in Python. Basic plots with default styling are often easy, but acheiving complex figures with subplots, legends, overlays, and custom styling can require a mastery of a daunting API and many lines of code. This complexity is discouraging to the new/casual Python user and can lead them to abandon Python in favor of more comfortable, albeit inferior, plotting tools like Excel.
fivecentplots exists to drastically simplify the API required to generate complex plots, specifically for data within pandas DataFrames.
- Plots are generated from a single function call
- Plot contents and design defined by optional keyword arguments with defaults in a simple "theme" file
- Simple data access using DataFrame column names and text-based filtering
- Colors, sizes, marker styles, subplot grouping, you name it--everything is defined by keyword arguments
- Keyword names follow a basic naming convention (ex:
legend_title_font_sizedefines the font size of the legend title) - Behind the scenes, keywords translated into the complex code / API of the underlying plotting package (like
matplotlib)
-
pandas enables statistical analysis in Python comparable to that of commercial software packages like JMP. However, JMP offers easy creation of many useful charts that are tedious to create in Python. fivencentplots solves this, making it easy to create:
- JMP-style variability gauge charts
- Grouped overlay plots
- Normal quantile plots
- Need a high-quality static image in
matplotlibstyle? No problem! Prefer an interactive web plot fromplotly? No problem! fivecentplots can wrap any plotting "engine" (or package) - Most importantly, fivecentplots maintains the same API regardless of plotting library. The same function call invoked for
matplotlibcan be used forplotly--no need to learn a new syntax and API
And even more...read the docs
Consider the following plot of some fake current vs voltage data contained in a dummy DataFrame, df:
Using fivecentplots, we need a single function call with the appropriate keyword arguments:
fcp.plot(df, x='Voltage', y='I [A]', legend='Die', col='Boost Level', ax_size=[225, 225], share_y=False,
filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25',
ref_line=df['Voltage'], ref_line_legend_text='y==x', ref_line_style='--',
xmin=0, xmax=1.7, ymin=[0, 0, 0], ymax=[1.3, 1.7, 5.2])Consider one possible approach to generate a very similar plot using matplotlib only:
import matplotlib.pylab as plt
import matplotlib
import natsort
# Filter the DataFrame to get the subset of interest
df_sub = df[(df.Substrate=="Si")&(df['Target Wavelength']==450)&(df['Temperature [C]']==25)]
# Set some defaults
markers = ['o', '+', 's']
colors = ['#4b72b0', '#c34e52', '#54a767']
ymax = [1.3, 1.7, 5.2]
lines = []
# Create the figure and axes
f, axes = plt.subplots(1, 3, sharex=False, sharey=False, figsize=[9.82, 3.46])
# Plot the data and style the axes
for iboost, boost in enumerate(df_sub['Boost Level'].unique()):
df_boost = df_sub[df_sub['Boost Level']==boost]
for idie, die in enumerate(natsort.natsorted(df_boost.Die.unique())):
df_die = df_boost[df_boost.Die==die]
axes[iboost].set_facecolor('#eaeaea')
axes[iboost].grid(which='major', axis='both', linestyle='-', color='#ffffff', linewidth=1.3)
lines += axes[iboost].plot(df_die['Voltage'], df_die['I [A]'], '-', color=colors[idie],
marker=markers[idie], markeredgecolor=colors[idie], markerfacecolor='none',
markeredgewidth=1.5, markersize=6)
axes[iboost].set_axisbelow(True)
axes[iboost].spines['bottom'].set_color('#aaaaaa')
axes[iboost].spines['top'].set_color('#aaaaaa')
axes[iboost].spines['right'].set_color('#aaaaaa')
axes[iboost].spines['left'].set_color('#aaaaaa')
if iboost==0:
axes[iboost].set_ylabel('I [A]', fontsize=14, fontweight='bold', fontstyle='italic')
axes[iboost].set_xlabel('Voltage', fontsize=14, fontweight='bold', fontstyle='italic')
axes[iboost].set_xlim(left=0, right=1.6)
axes[iboost].set_ylim(bottom=0, top=ymax[iboost])
# Add the column labels
rect = matplotlib.patches.Rectangle((0, 1.044), 1, 30/225, fill=True, transform=axes[iboost].transAxes,
facecolor='#8c8c8c', edgecolor='#8c8c8c', clip_on=False)
axes[iboost].add_patch(rect)
text = 'Boost Level = {}'.format(boost)
axes[iboost].text(0.5, 1.111, text, transform=axes[iboost].transAxes,
horizontalalignment='center', verticalalignment='center',
rotation=0, color='#ffffff', weight='bold', size=16)
# Customize ticks
axes[iboost].tick_params(axis='both', which='major', pad=5, colors='#ffffff',
labelsize=13, labelcolor='#000000', width=2.2)
# Add reference line
ref_line = df_die['Voltage']
ref = axes[iboost].plot(df_die['Voltage'], ref_line, '-', color='#000000', linestyle='--')
if iboost == 0 :
lines = ref + lines
# Style the figure
f.set_facecolor('#ffffff')
f.subplots_adjust(left=0.077, right=0.882, top=0.827, bottom=0.176, hspace=0.133, wspace=0.313)
# Add legend
leg = f.legend(lines[0:4], ['y==x'] + list(df_boost.Die.unique()), title='Die', numpoints=1,
bbox_to_anchor=(1, 0.85), prop={'size': 12})
leg.get_frame().set_edgecolor('#ffffff')
# Show the plot
plt.show()This example is obviously a bit contrived as you could simplify things by modifying rc_params or eliminating some of the specific style elments used here, but the general idea should be clear: fivecentplots can reduce the barrier to generate complex plots.
What if we wanted to do the same plot using code for plotly or bokeh? Well, we'd need to learn an entirely different API! But with fivecentplots we can just change the kwarg defining the plotting engine (engine) and we are all set:
fcp.plot(df, x='Voltage', y='I [A]', legend='Die', col='Boost Level', ax_size=[225, 225], share_y=False,
filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25',
ref_line=df['Voltage'], ref_line_legend_text='y==x', ref_line_style='--',
xmin=0, xmax=1.7, ymin=[0, 0, 0], ymax=[1.3, 1.7, 5.2], engine='bokeh')