Python Data Science Handbook

Introduktion

IPython: Beyond Normal Python8 Ämnen

Introduction to NumPy9 Ämnen

Understanding Data Types in Python

The Basics of NumPy Arrays

Computation on NumPy Arrays: Universal Functions

Aggregations: Min, Max, and Everything In Between

Computation on Arrays: Broadcasting

Comparisons, Masks, and Boolean Logic

Fancy Indexing

Sorting Arrays

Structured Data: NumPy's Structured Arrays

Understanding Data Types in Python

Data Manipulation with Pandas13 Ämnen

Introducing Pandas Objects

Data Indexing and Selection

Operating on Data in Pandas

Handling Missing Data

Hierarchical Indexing

Combining Datasets: Concat and Append

Combining Datasets: Merge and Join

Aggregation and Grouping

Pivot Tables

Vectorized String Operations

Working with Time Series

HighPerformance Pandas: eval() and query()

Further Resources

Introducing Pandas Objects

Visualization with Matplotlib15 Ämnen

Simple Line Plots

Simple Scatter Plots

Visualizing Errors

Density and Contour Plots

Histograms, Binnings, and Density

Customizing Plot Legends

Customizing Colorbars

Multiple Subplots

Text and Annotation

Customizing Ticks

Customizing Matplotlib: Configurations and Stylesheets

ThreeDimensional Plotting in Matplotlib

Geographic Data with Basemap

Visualization with Seaborn

Further Resources

Simple Line Plots

Machine Learning15 Ämnen

What Is Machine Learning?

Introducing ScikitLearn

Hyperparameters and Model Validation

Feature Engineering

In Depth: Naive Bayes Classification

In Depth: Linear Regression

InDepth: Support Vector Machines

InDepth: Decision Trees and Random Forests

In Depth: Principal Component Analysis

InDepth: Manifold Learning

In Depth: kMeans Clustering

In Depth: Gaussian Mixture Models

InDepth: Kernel Density Estimation

Application: A Face Detection Pipeline

Further Machine Learning Resources

What Is Machine Learning?

Appendix: Figure Code
Visualizing Errors
juli 27, 2021
For any scientific measurement, accurate accounting for errors is nearly as important, if not more important, than accurate reporting of the number itself. For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe. I know that the current literature suggests a value of around 71 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.
Suppose I augment this information with reported uncertainties: the current literature suggests a value of around 71 ±± 2.5 (km/s)/Mpc, and my method has measured a value of 74 ±± 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.
In visualization of data and results, showing these errors effectively can make a plot convey much more complete information.
Basic Errorbars
A basic errorbar can be created with a single Matplotlib function call:In [1]:
%matplotlib inline import matplotlib.pyplot as plt plt.style.use('seabornwhitegrid') import numpy as np
In [2]:
x = np.linspace(0, 10, 50) dy = 0.8 y = np.sin(x) + dy * np.random.randn(50) plt.errorbar(x, y, yerr=dy, fmt='.k');
Here the fmt
is a format code controlling the appearance of lines and points, and has the same syntax as the shorthand used in plt.plot
, outlined in Simple Line Plots and Simple Scatter Plots.
In addition to these basic options, the errorbar
function has many options to finetune the outputs. Using these additional options you can easily customize the aesthetics of your errorbar plot. I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves:In [3]:
plt.errorbar(x, y, yerr=dy, fmt='o', color='black', ecolor='lightgray', elinewidth=3, capsize=0);
In addition to these options, you can also specify horizontal errorbars (xerr
), onesided errorbars, and many other variants. For more information on the options available, refer to the docstring of plt.errorbar
.
Continuous Errors
In some situations it is desirable to show errorbars on continuous quantities. Though Matplotlib does not have a builtin convenience routine for this type of application, it’s relatively easy to combine primitives like plt.plot
and plt.fill_between
for a useful result.
Here we’ll perform a simple Gaussian process regression, using the ScikitLearn API (see Introducing ScikitLearn for details). This is a method of fitting a very flexible nonparametric function to data with a continuous measure of the uncertainty. We won’t delve into the details of Gaussian process regression at this point, but will focus instead on how you might visualize such a continuous error measurement:In [4]:
from sklearn.gaussian_process import GaussianProcess # define the model and draw some data model = lambda x: x * np.sin(x) xdata = np.array([1, 3, 5, 6, 8]) ydata = model(xdata) # Compute the Gaussian process fit gp = GaussianProcess(corr='cubic', theta0=1e2, thetaL=1e4, thetaU=1E1, random_start=100) gp.fit(xdata[:, np.newaxis], ydata) xfit = np.linspace(0, 10, 1000) yfit, MSE = gp.predict(xfit[:, np.newaxis], eval_MSE=True) dyfit = 2 * np.sqrt(MSE) # 2*sigma ~ 95% confidence region
We now have xfit
, yfit
, and dyfit
, which sample the continuous fit to our data. We could pass these to the plt.errorbar
function as above, but we don’t really want to plot 1,000 points with 1,000 errorbars. Instead, we can use the plt.fill_between
function with a light color to visualize this continuous error:In [5]:
# Visualize the result plt.plot(xdata, ydata, 'or') plt.plot(xfit, yfit, '', color='gray') plt.fill_between(xfit, yfit  dyfit, yfit + dyfit, color='gray', alpha=0.2) plt.xlim(0, 10);
Note what we’ve done here with the fill_between
function: we pass an x value, then the lower ybound, then the upper ybound, and the result is that the area between these regions is filled.
The resulting figure gives a very intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained and this is reflected in the small model errors. In regions far from a measured data point, the model is not strongly constrained, and the model errors increase.
For more information on the options available in plt.fill_between()
(and the closely related plt.fill()
function), see the function docstring or the Matplotlib documentation.
Finally, if this seems a bit too low level for your taste, refer to Visualization With Seaborn, where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar.