Wylie lives for ideas. He speaks 19 to the dozen for hours at a time. He had a theory to prove. And at the time, this was a purely intellectual problem. Politics was like fashion, he told Bannon. He believes in the whole Andrew Breitbart doctrine that politics is downstream from culture, so to change politics you need to change culture. And fashion trends are a useful proxy for that.
Trump is like a pair of Uggs, or Crocs, basically. That was the inflection point he was looking for. He had recently been exposed to a new discipline: They do cyberwarfare for elections. It was Bannon who took this idea to the Mercers: Robert Mercer — the co-CEO of the hedge fund Renaissance Technologies , who used his billions to pursue a rightwing agenda, donating to Republican causes and supporting Republican candidates — and his daughter Rebekah. She loved the gays. So did Steve [Bannon]. He saw us as early adopters. He figured, if you can get the gays on board, everyone else will follow.
Robert Mercer was a pioneer in AI and machine translation. He wanted to understand the science. And he wanted proof that it worked. How Cambridge Analytica acquired the data has been the subject of internal reviews at Cambridge University, of many news articles and much speculation and rumour. They did some research for us back in That research proved to be fruitless and so the answer is no.
He has the bank records and wire transfers.
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Emails reveal Wylie first negotiated with Michal Kosinski, one of the co-authors of the original myPersonality research paper, to use the myPersonality database. But when negotiations broke down, another psychologist, Aleksandr Kogan, offered a solution that many of his colleagues considered unethical. For Wylie it seemed a perfect solution.
Kogan then set up GSR to do the work, and proposed to Wylie they use the data to set up an interdisciplinary institute working across the social sciences.
Kogan was able to throw money at the hard problem of acquiring personal data: What the email correspondence between Cambridge Analytica employees and Kogan shows is that Kogan had collected millions of profiles in a matter of weeks. But neither Wylie nor anyone else at Cambridge Analytica had checked that it was legal. Kogan did have permission to pull Facebook data, but for academic purposes only. Cambridge Analytica had its data. The models, the algorithm. And then, all they did was write a letter. They said it must be deleted immediately.
Dr Kogan — who later changed his name to Dr Spectre, but has subsequently changed it back to Dr Kogan — is still a faculty member at Cambridge University, a senior research associate. The opportunity came about on a trip to the city to visit friends and family, he said. Why would a Russian oil company want to target information on American voters?
And it was in late summer of the same year that Cambridge Analytica presented the Russian oil company with an outline of its datasets, capabilities and methodology. Instead, documents show it focused on election disruption techniques. When I asked Bill Browder — an Anglo-American businessman who is leading a global campaign for a Magnitsky Act to enforce sanctions against Russian individuals — what he made of it, he said: One should be highly suspicious of any Russian company pitching anything outside its normal business activities.
Last month, Nix told MPs on the parliamentary committee investigating fake news: We do not have any relationship with Russia or Russian individuals. Facebook, microtargeting, data, election disruption. Only once has Wylie made the case of pointing out that he was 24 at the time. He thrilled to the intellectual possibilities of it. Because the past few months have been like watching a tornado gathering force.
And when Wylie turns the full force of his attention to something — his strategic brain, his attention to detail, his ability to plan 12 moves ahead — it is sometimes slightly terrifying to behold. Dealing with someone trained in information warfare has its own particular challenges, and his suite of extraordinary talents include the kind of high-level political skills that makes House of Cards look like The Great British Bake Off.
Another described the screaming matches he and Nix would have. And how fearless he is. After many months, I learn the terrible, dark backstory that throws some light on his determination, and which he discusses candidly. At six, while at school, Wylie was abused by a mentally unstable person. The school tried to cover it up, blaming his parents, and a long court battle followed. His parents — his father is a doctor and his mother is a psychiatrist — were wonderful, he says. He says he grew up listening to psychologists discuss him in the third person, and, aged 14, he successfully sued the British Columbia Ministry of Education and forced it to change its inclusion policies around bullying.
What I observe now is how much he loves the law, lawyers, precision, order. I come to think of his pink hair as a false-flag operation. What he cannot tolerate is bullying. At least bullying respects the agency of people because they know. And fundamentally, information warfare is not conducive to democracy.
Every one of these threads runs through Cambridge Analytica. Facebook denies that the data transfer was a breach. In addition, a spokesperson said: Who would pitch to Russian oil companies. Would they subvert elections abroad on behalf of foreign governments? It also encapsulates all of the problems of outsourcing — at a global scale, with added cyberweapons. The Facebook data is out in the wild. Russia has been so explicit about this, paying for the ads in roubles and so on. This article was amended on 18 March to clarify the full title of the British Columbia Ministry of Education.
The Observer is the world's oldest Sunday newspaper, founded in The Cambridge Analytica Files. Now, year-old Christopher Wylie goes on the record to discuss his role in hijacking the profiles of millions of Facebook users in order to target the US electorate by Carole Cadwalladr.
Antonio Olmos for the Observer Sun 18 Mar A better way to handle this is to use the plt. This must be changed when showing gridded data. Finally, it can sometimes be useful to combine contour plots and image plots. The combination of these three functions— plt. For more information on the options available in these functions, refer to their docstrings. A simple histogram can be a great first step in understanding a dataset. If you would like to simply compute the histogram that is, count the number of points in a given bin and not display it, the np. Just as we create histograms in one dimension by dividing the number line into bins, we can also create histograms in two dimensions by dividing points among two-dimensional bins.
Just as with plt. Further, just as plt. For the generalization of this histogram binning in dimensions higher than two, see the np. The two-dimensional histogram creates a tessellation of squares across the axes. Another natural shape for such a tessellation is the regular hexagon.
For this purpose, Matplotlib provides the plt. Another common method of evaluating densities in multiple dimensions is kernel density estimation KDE. One extremely quick and simple KDE implementation exists in the scipy. KDE has a smoothing length that effectively slides the knob between detail and smoothness one example of the ubiquitous bias—variance trade-off.
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The literature on choosing an appropriate smoothing length is vast: Other KDE implementations are available within the SciPy ecosystem, each with its own various strengths and weaknesses; see, for example, sklearn. For visualizations based on KDE, using Matplotlib tends to be overly verbose. Plot legends give meaning to a visualization, assigning labels to the various plot elements. The simplest legend can be created with the plt. But there are many ways we might want to customize such a legend. If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.
Passing any of these to plt. Notice that by default, the legend ignores all elements without a label attribute set. Sometimes the legend defaults are not sufficient for the given visualization. In this case, the objects we want gray circles are not on the plot, so we fake them by plotting empty lists. Notice too that the legend only lists plot elements that have a label specified. By plotting empty lists, we create labeled plot objects that are picked up by the legend, and now our legend tells us some useful information. This strategy can be useful for creating more sophisticated visualizations.
Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements. Unfortunately, Matplotlib does not make this easy: If you try to create a second legend using plt. We can work around this by creating a new legend artist from scratch, and then using the lower-level ax. This is a peek into the low-level artist objects that compose any Matplotlib plot. If you examine the source code of ax.
Plot legends identify discrete labels of discrete points. For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool. In Matplotlib, a colorbar is a separate axes that can provide a key for the meaning of colors in a plot. Because the book is printed in black and white, this section has an accompanying online appendix where you can view the figures in full color https: As we have seen several times throughout this section, the simplest colorbar can be created with the plt.
All the available colormaps are in the plt. But being able to choose a colormap is just the first step: The choice turns out to be much more subtle than you might initially expect. These consist of one continuous sequence of colors e. These usually contain two distinct colors, which show positive and negative deviations from a mean e.
The jet colormap, which was the default in Matplotlib prior to version 2. Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data. Among the problems is the fact that qualitative maps usually do not display any uniform progression in brightness as the scale increases. Notice the bright stripes in the grayscale image. Even in full color, this uneven brightness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset.
For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as RdBu short for Red-Blue can be useful. There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the plt. Matplotlib allows for a large range of colorbar customization. The colorbar itself is simply an instance of plt. The colorbar has some interesting flexibility; for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the extend property.
Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes out the pattern we are interested in. In the right panel, we manually set the color limits, and add extensions to indicate values that are above or below those limits. The result is a much more useful visualization of our data. The easiest way to do this is to use the plt. Because each digit is defined by the hue of its 64 pixels, we can consider each digit to be a point lying in dimensional space: But visualizing relationships in such high-dimensional spaces can be extremely difficult.
One way to approach this is to use a dimensionality reduction technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest. The projection also gives us some interesting insights on the relationships within the dataset: Other values, like 0 and 1, are more distantly separated, and therefore much less likely to be confused. This observation agrees with our intuition, because 5 and 3 look much more similar than do 0 and 1.
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Sometimes it is helpful to compare different views of data side by side. To this end, Matplotlib has the concept of subplots: These subplots might be insets, grids of plots, or other more complicated layouts. The most basic method of creating an axes is to use the plt. These numbers represent [ bottom , left , width , height ] in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.
For example, we might create an inset axes at the top-right corner of another axes by setting the x and y position to 0. The equivalent of this command within the object-oriented interface is fig. We now have two axes the top with no tick labels that are just touching: Aligned columns or rows of subplots are a common enough need that Matplotlib has several convenience routines that make them easy to create.
The lowest level of these is plt. For this purpose, plt. Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array. The arguments are the number of rows and number of columns, along with optional keywords sharex and sharey , which allow you to specify the relationships between different axes. In comparison to plt. To go beyond a regular grid to subplots that span multiple rows and columns, plt. GridSpec is the best tool. GridSpec object does not create a plot by itself; it is simply a convenient interface that is recognized by the plt.
For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:. This type of flexible grid alignment has a wide range of uses. Creating a good visualization involves guiding the reader so that the figure tells a story. In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary.
Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this. This can be done manually with the plt. See the docstring of plt. Text for more information on available options. In the previous example, we anchored our text annotations to data locations. In Matplotlib, we do this by modifying the transform. Any graphics display framework needs some scheme for translating between coordinate systems.
For example, a data point at needs to somehow be represented at a certain location on the figure, which in turn needs to be represented in pixels on the screen. Mathematically, such coordinate transformations are relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform them the tools can be explored in the matplotlib. The average user rarely needs to worry about the details of these transforms, but it is helpful knowledge to have when considering the placement of text on a figure.
There are three predefined transforms that can be useful in this situation:. The transData coordinates give the usual data coordinates associated with the x- and y-axis labels. The transAxes coordinates give the location from the bottom-left corner of the axes here the white box as a fraction of the axes size.
The transFigure coordinates are similar, but specify the position from the bottom left of the figure here the gray box as a fraction of the figure size. Along with tick marks and text, another useful annotation mark is the simple arrow. Drawing arrows in Matplotlib is often much harder than you might hope. While there is a plt. This function creates some text and an arrow, and the arrows can be very flexibly specified. The arrow style is controlled through the arrowprops dictionary, which has numerous options available.
Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time-consuming when one is producing publication-quality graphics! More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery , in particular http: Before we go into examples, it will be best for us to understand further the object hierarchy of Matplotlib plots.
Matplotlib aims to have a Python object representing everything that appears on the plot: Each Matplotlib object can also act as a container of sub-objects; for example, each figure can contain one or more axes objects, each of which in turn contain other objects representing plot contents. The tick marks are no exception. Each axes has attributes xaxis and yaxis , which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes. Within each axis, there is the concept of a major tick mark and a minor tick mark. As the names would imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller.
We see here that each major tick shows a large tick mark and a label, while each minor tick shows a smaller tick mark with no label. We can customize these tick properties—that is, locations and labels—by setting the formatter and locator objects of each axis. We see that both major and minor tick labels have their locations specified by a LogLocator which makes sense for a logarithmic plot.
Minor ticks, though, have their labels formatted by a NullFormatter ; this says that no labels will be shown. We can do this using plt. Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images. One common problem with the default settings is that smaller subplots can end up with crowded labels. Particularly for the x ticks, the numbers nearly overlap, making them quite difficult to decipher. We can fix this with the plt. MaxNLocator , which allows us to specify the maximum number of ticks that will be displayed.
This makes things much cleaner. If you want even more control over the locations of regularly spaced ticks, you might also use plt. There are a couple changes we might like to make. We can do this by setting a MultipleLocator , which locates ticks at a multiple of the number you provide. But now these tick labels look a little bit silly: To fix this, we can change the tick formatter.
This is much better! For more information on any of these, refer to the docstrings or to the Matplotlib online documentation. Each of the following is available in the plt namespace:. While much is slated to change in the 2. But this took a whole lot of effort! We definitely do not want to have to do all that tweaking each time we create a plot. Fortunately, there is a way to adjust these defaults once in a way that will work for all plots. Each time Matplotlib loads, it defines a runtime configuration rc containing the default styles for every plot element you create.
You can adjust this configuration at any time using the plt. I find this much more aesthetically pleasing than the default styling. If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes! These settings can be saved in a. That said, I prefer to customize Matplotlib using its stylesheets instead. These stylesheets are formatted similarly to the.
The available styles are listed in plt. But keep in mind that this will change the style for the rest of the session! Alternatively, you can use the style context manager, which sets a style temporarily:. The ggplot package in the R language is a very popular visualization tool. There is a very nice short online book called Probabilistic Programming and Bayesian Methods for Hackers ; it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually appealing style throughout the book. For figures used within presentations, it is often useful to have a dark rather than light background.
Sometimes you might find yourself preparing figures for a print publication that does not accept color figures. As we will see, these styles are loaded automatically when Seaborn is imported into a notebook. With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication. Throughout this book, I will generally use one or more of these style conventions when creating plots. Matplotlib was initially designed with only two-dimensional plotting in mind.
Around the time of the 1. With this 3D axes enabled, we can now plot a variety of three-dimensional plot types. The most basic three-dimensional plot is a line or scatter plot created from sets of x, y, z triples. In analogy with the more common two-dimensional plots discussed earlier, we can create these using the ax. Notice that by default, the scatter points have their transparency adjusted to give a sense of depth on the page. While the three-dimensional effect is sometimes difficult to see within a static image, an interactive view can lead to some nice intuition about the layout of the points.
Two other types of three-dimensional plots that work on gridded data are wireframes and surface plots.
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These take a grid of values and project it onto the specified three-dimensional surface, and can make the resulting three-dimensional forms quite easy to visualize. A surface plot is like a wireframe plot, but each face of the wireframe is a filled polygon. Note that though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear. For some applications, the evenly sampled grids required by the preceding routines are overly restrictive and inconvenient.
In these situations, the triangulation-based plots can be very useful. What if rather than an even draw from a Cartesian or a polar grid, we instead have a set of random draws? This leaves a lot to be desired. The function that will help us in this case is ax. The result is certainly not as clean as when it is plotted with a grid, but the flexibility of such a triangulation allows for some really interesting three-dimensional plots.
Now from this parameterization, we must determine the x, y, z positions of the embedded strip. Thinking about it, we might realize that there are two rotations happening: Now we use our recollection of trigonometry to derive the three-dimensional embedding. Finally, to plot the object, we must make sure the triangulation is correct. Combining all of these techniques, it is possible to create and display a wide variety of three-dimensional objects and patterns in Matplotlib.
One common type of visualization in data science is that of geographic data. Admittedly, Basemap feels a bit clunky to use, and often even simple visualizations take much longer to render than you might hope. More modern solutions, such as leaflet or the Google Maps API, may be a better choice for more intensive map visualizations. Still, Basemap is a useful tool for Python users to have in their virtual toolbelts. The useful thing is that the globe shown here is not a mere image; it is a fully functioning Matplotlib axes that understands spherical coordinates and allows us to easily over-plot data on the map!
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For example, we can use a different map projection, zoom in to North America, and plot the location of Seattle. This gives you a brief glimpse into the sort of geographic visualizations that are possible with just a few lines of Python. Using these brief examples as building blocks, you should be able to create nearly any map visualization that you desire. The first thing to decide when you are using maps is which projection to use. These projections have been developed over the course of human history, and there are a lot of choices! Depending on the intended use of the map projection, there are certain map features e.
The Basemap package implements several dozen such projections, all referenced by a short format code. The simplest of map projections are cylindrical projections, in which lines of constant latitude and longitude are mapped to horizontal and vertical lines, respectively. This type of mapping represents equatorial regions quite well, but results in extreme distortions near the poles. The spacing of latitude lines varies between different cylindrical projections, leading to different conservation properties, and different distortion near the poles. The additional arguments to Basemap for this view specify the latitude lat and longitude lon of the lower-left corner llcrnr and upper-right corner urcrnr for the desired map, in units of degrees.
Pseudo-cylindrical projections relax the requirement that meridians lines of constant longitude remain vertical; this can give better properties near the poles of the projection. It is constructed so as to preserve area across the map: Perspective projections are constructed using a particular choice of perspective point, similar to if you photographed the Earth from a particular point in space a point which, for some projections, technically lies within the Earth!
Thus, it can show only half the globe at a time. These are often the most useful for showing small portions of the map. A conic projection projects the map onto a single cone, which is then unrolled. This can lead to very good local properties, but regions far from the focus point of the cone may become very distorted. Conic projections, like perspective projections, tend to be good choices for representing small to medium patches of the globe. Most likely, they are available in the Basemap package. Earlier we saw the bluemarble and shadedrelief methods for projecting global images on the map, as well as the drawparallels and drawmeridians methods for drawing lines of constant latitude and longitude.
The Basemap package contains a range of useful functions for drawing borders of physical features like continents, oceans, lakes, and rivers, as well as political boundaries such as countries and US states and counties. For the boundary-based features, you must set the desired resolution when creating a Basemap image. The resolution argument of the Basemap class sets the level of detail in boundaries, either 'c' crude , 'l' low , 'i' intermediate , 'h' high , 'f' full , or None if no boundaries will be used.
This choice is important: Notice that the low-resolution coastlines are not suitable for this level of zoom, while high-resolution works just fine. The low level would work just fine for a global view, however, and would be much faster than loading the high-resolution border data for the entire globe! It might require some experimentation to find the correct resolution parameter for a given view; the best route is to start with a fast, low-resolution plot and increase the resolution as needed. Perhaps the most useful piece of the Basemap toolkit is the ability to over-plot a variety of data onto a map background.
For simple plotting and text, any plt function works on the map; you can use the Basemap instance to project latitude and longitude coordinates to x, y coordinates for plotting with plt , as we saw earlier in the Seattle example. In addition to this, there are many map-specific functions available as methods of the Basemap instance. These work very similarly to their standard Matplotlib counterparts, but have an additional Boolean argument latlon , which if set to True allows you to pass raw latitudes and longitudes to the method, rather than projected x, y coordinates.
For more information on these functions, including several example plots, see the online Basemap documentation. This shows us roughly where larger populations of people have settled in California: You can install this library as shown here:. Now we can load the latitude and longitude data, as well as the temperature anomaly for this index:. The data paints a picture of the localized, extreme temperature anomalies that happened during that month. The eastern half of the United States was much colder than normal, while the western half and Alaska were much warmer.
Regions with no recorded temperature show the map background. Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired. There are several valid complaints about Matplotlib that often come up:. Prior to version 2. Doing sophisticated statistical visualization is possible, but often requires a lot of boilerplate code.
Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas DataFrame s. In order to visualize data from a Pandas DataFrame , you must extract each Series and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the DataFrame labels in a plot. An answer to these problems is Seaborn. Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas DataFrame s.
To be fair, the Matplotlib team is addressing this: But for all the reasons just discussed, Seaborn remains an extremely useful add-on. Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors. We start with the typical imports:.
By convention, Seaborn is imported as sns:. The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting. Note that all of the following could be done using raw Matplotlib commands this is, in fact, what Seaborn does under the hood , but the Seaborn API is much more convenient. Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.
Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with sns. We can see the joint distribution and the marginal distributions together using sns. When you generalize joint plots to datasets of larger dimensions, you end up with pair plots. Visualizing the multidimensional relationships among the samples is as easy as calling sns. Sometimes the best way to view data is via histograms of subsets. Factor plots can be useful for this kind of visualization as well.
Similar to the pair plot we saw earlier, we can use sns. Time series can be plotted with sns. For more information on plotting with Seaborn, see the Seaborn documentation , a tutorial , and the Seaborn gallery. We will start by downloading the data from the Web, and loading it into Pandas:. By default, Pandas loaded the time columns as Python strings type object ; we can see this by looking at the dtypes attribute of the DataFrame:.
That looks much better. The fact that the distribution lies above this indicates as you might expect that most people slow down over the course of the marathon. Where this split difference is less than zero, the person negative-split the race by that fraction. It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: The difference between men and women here is interesting. The interesting thing here is that there are many more men than women who are running close to an even split! This almost looks like some kind of bimodal distribution among the men and women.
Looking at this, we can see where the distributions of men and women differ: Also surprisingly, the year-old women seem to outperform everyone in terms of their split time. Back to the men with negative splits: Does this split fraction correlate with finishing quickly? We can plot this very easily. People slower than that are much less likely to have a fast second split. A single chapter in a book can never hope to cover all the available features and plot types available in Matplotlib. See in particular the Matplotlib gallery linked on that page: In this way, you can visually inspect and learn about a wide range of different plotting styles and visualization techniques.
Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well. Plotly is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free. Vispy is an actively developed project focused on dynamic visualizations of very large datasets.
Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations. Vega and Vega-Lite are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization.
The visualization space in the Python community is very dynamic, and I fully expect this list to be out of date as soon as it is published. Stay ahead with the world's most comprehensive technology and business learning platform. With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more. Start Free Trial No credit card required.
General Matplotlib Tips Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package. Importing matplotlib Just as we use the np shorthand for NumPy and the pd shorthand for Pandas, we will use some standard shorthands for Matplotlib imports: Setting Styles We will use the plt. Here we will set the classic style, which ensures that the plots we create use the classic Matplotlib style: