graphs

Guest Writer
| July 22, 2018

The dplyr package in R is a powerful tool to do data munging and data manipulation, perhaps more so than many people would initially realize, making it extremely useful in data science.
Shortly after I embarked on the data science journey earlier this year, I came to increasingly appreciate the handy utilities of dplyr, particularly the mighty combo functions of group_by() and summarize (). Below, I will go through the first project I completed as a budding data scientist using the package along with ggplot. I will demonstrate some convenient features of both.

I obtained my dataset from Kaggle. It has 150,930 observations containing wine ratings from across the world. The data had been scraped from Wine Enthusiast during the week of June 15th, 2017. Right off the bat, we should recognize one caveat when deriving any insight from this data: the magazine only posted reviews on wines receiving a grade of 80 or more (out of 100).

As a best practice, any data analysis should be done with limitations and constraints of the data in mind. The analyst should bear in mind the conclusions he or she draws from the data will be impacted by the inherent limitations in breadth and depth of the data at hand.

After reading the dataset in RStudio and naming it “wine,” we’ll get started by installing and loading the packages.

Install and load packages (dplyr, ggplot)

# Please do install.packages() for these two libraries if you don't have them
library(dplyr)

library(ggplot2)

Data preparation

First, we want to clean the data. As I will leave textual data out of this analysis and not touch on NLP techniques in this post, I will drop the “description” column using the select () function from dplyr that lets us select columns by name. As you would’ve probably guessed, the minus sign in front of it indicates we want to exclude this column.

As select() is a non-mutating function, don’t forget to reassign the data frame to overwrite it (or you could create a new name for the new data frame if you want to keep the original one for reference). A convenient way to pass functions with dplyr is the pipe operator, %>%, which allows us to call multiple functions on an object sequentially and will take the immediately preceding output as the object of each function.

wine = wine %>% select(-c(description))

There is quite a range of producer countries in the list, and I want to find out which countries are most represented in the dataset. This is the first instance where we encounter one of my favorites uses in R: the group-by aggregation using “group_by” followed by “summarize”:

wine %>% group_by(country) %>% summarize(count=n()) %>% arrange(desc(count))
## # A tibble: 49 x 2

## country count

##

## 1 US 62397

## 2 Italy 23478

## 3 France 21098

## 4 Spain 8268

## 5 Chile 5816

## 6 Argentina 5631

## 7 Portugal 5322

## 8 Australia 4957

## 9 New Zealand 3320

## 10 Austria 3057

## # ... with 39 more rows

We want to only focus our attention on the top producers; say we want to select only the top ten countries. We’ll again turn to the powerful group_by()
and summarize() functions for group-by aggregation, followed by another select() command to choose the column we want from the newly created data frame.

Note* that after the group-by aggregation, we only retain the relevant portion of the original data frame. In this case, since we grouped by country and summarized the count per country, the result will only be a two-column data frame consisting of “country” and the newly named variable “count.” All other variables in the original set, such as “designation” and “points” were removed.

Furthermore, the new data frame only has as many rows as there were unique values in the variable grouped by – in our case, “country.” There were 49 unique countries in this column when we started out, so this new data frame has 49 rows and 2 columns. From there, we use arrange () to sort the entries by count. Passing desc(count) as an argument ensures we’re sorting from the largest to the smallest value, as the default is the opposite.

The next step top_n(10) selects the top ten producers. Finally, select () retains only the “country” column and our final object “selected_countries” becomes a one-column data frame. We transform it into a character vector using as.character() as it will become handy later on.

selected_countries = wine %>% group_by(country) %>% summarize(count=n ()) %>% arrange(desc(count)) %>% top_n(10) %>% select(country)
selected_countries = as.character(selected_countries$country)

So far we’ve already learned one of the most powerful tools from dplyr, group-by aggregation, and a method to select columns. Now we’ll see how we can select rows.

# creating a country and points data frame containing only the 10 selected countries' data select_points=wine %>% filter (country %in% selected_countries) %>% select(country, points) %>% arrange(country)

In the above code, filter(country %in% selected_countries) ensures we’re only selecting rows where the “country” variable has a value that’s in the “selected_countries” vector we created just a moment ago. After subsetting these rows, we use select() them to select the two columns we want to keep and arrange to sort the values. Not that the argument passed into the latter ensures we’re sorting by the “country” variable, as the function by default sorts by the last column in the data frame – which would be “points” in our case since we selected that column after “country.”

Data exploration and visualization

At a high level, we want to know if higher-priced wines are really better, or at least as judged by Wine Enthusiast. To achieve this goal we create a scatterplot of “points” and “price” and add a smoothed line to see the general trajectory.

ggplot(wine, aes(points,price)) + geom_point() + geom_smooth()

Data exploration of Wine enthusiasts

It seems overall expensive wines tend to be rated higher, and the most expensive wines tend to be among the highest-rated as well.

Let’s further explore possible visualizations with ggplot, and create a panel of boxplots sorted by the national median point received. Passing x=reorder(country,points,median) creates a reordered vector for the x-axis, ranked by the median “points” value by country. aes(fill=country) fills each boxplot with a distinct color for the country represented. xlab() and ylab() give labels to the axes, and ggtitle()gives the whole plot a title.

Finally, passing element_text(hjust = 0.5) to the theme() function essentially moves the plot title to horizontally centered, as “hjust”controls horizontal justification of the text’s positioning on the graph.

gplot(select_points, aes(x=reorder(country,points,median),y=points)) + geom_boxplot(aes(fill=country)) + xlab("Country") +

ylab(“Points”) + ggtitle(“Distribution of Top 10 Wine Producing Countries”) + theme(plot.title = element_text(hjust = 0.5))

Distribution | Data Science Dojo
When we ask the question “which countries may be hidden dream destinations for an oenophile?” we can subset rows of countries that aren’t in the top ten producer list. When we pass a new parameter into summarize() and assign it a new value based on a function of another variable, we create a new feature – “median” in our case. Using arrange(desc()) ensures we’re sorting by descending order of this new feature.

As we grouped by country and created one new variable, we end up with a new data frame containing two columns and however many rows there were that had values for “country” not listed in “selected_countries.”

wine %>% filter(!(country %in% selected_countries)) %>% group_by(country) %>% summarize(median=median(points))
%>% arrange(desc(median))

## # A tibble: 39 x 2
## country median
##
## 1 England 94.0
## 2 India 89.5
## 3 Germany 89.0
## 4 Slovenia 89.0
## 5 Canada 88.5
## 6 Morocco 88.5
## 7 Albania 88.0
## 8 Serbia 88.0
## 9 Switzerland 88.0
## 10 Turkey 88.0
## # ... with 29 more rows

We find England, India, Germany, Slovenia, and Canada as top-quality producers, despite not being the most prolific ones. If you’re an oenophile like me, this may shed light on some ideas for hidden treasures when we think about where to find our next favorite wines. Beyond the usual suspects like France and Italy, maybe our next bottle will come from Slovenia or even India.

Which countries produce a large quantity of wine but also offer high-quality wines? We’ll create a new data frame called “top” that contains the countries with the highest median “points” values. Using the intersect() function and subsetting the observations that appear in both the “selected_countries” and “top” data frames, we can find out the answer to that question.

top=wine %>% group_by(country) %>% summarize(median=median(points)) %>% arrange(desc(median))
top=as.character(top$country)
both=intersect(top,selected_countries)
both
##  [1] "Austria"     "France"      "Australia"   "Italy"       "Portugal"
## [6] "US" "New Zealand" "Spain" "Argentina" "Chile"

We see there are ten countries that appear in both lists. These are the real deals not highly represented just because of their mass production. Note that we transformed “top” from a data frame structure to a vector one, just like we had done for “selected_countries,” prior to intersecting the two.

Next, let’s turn from the country to the grape, and find the top ten most represented grape varietals in this set:

topwine = wine %>% group_by(variety) %>% summarize(number=n()) %>% arrange(desc(number)) %>% top_n(10)
topwine=as.character(topwine$variety)
topwine
##  [1] "Chardonnay"               "Pinot Noir"
## [3] "Cabernet Sauvignon" "Red Blend"
## [5] "Bordeaux-style Red Blend" "Sauvignon Blanc"
## [7] "Syrah" "Riesling"
## [9] "Merlot" "Zinfandel"

The pipe operator doesn’t work just with dplyr functions. Below we’ll examine graphs with ggplot functions that work seamlessly with dplyr syntax.

wine %>% filter(variety %in% topwine) %>% group_by(variety)%>% summarize(median=median(points)) %>% ggplot(aes(reorder(variety,median),median))
+ geom_col(aes(fill=variety)) + xlab('Variety') + ylab('Median Point') + scale_x_discrete(labels=abbreviate)

dplyr functions with ggplot

Finally, we’d be interested in learning which wines provide the best value, meaning priced toward the bottom rung but ranked in the top rung:

top15percent=wine %>% arrange(desc(points)) %>% filter(points > quantile(points, prob = 0.85))
cheapest15percent=wine %>% arrange(price) %>% head(nrow(top15percent))
goodvalue = intersect(top15percent,cheapest15percent)
goodvalue
## 2  Portugal Picos do Couto Reserva     92    11     Dão
## 3        US                            92    11       Washington
## 4        US                            92    11       Washington
## 5    France                            92    12         Bordeaux
## 6        US                            92    12           Oregon
## 7    France        Aydie l'Origine     93    12 Southwest France
## 8        US       Moscato d'Andrea     92    12       California
## 9        US                            92    12       California
## 10       US                            93    12       Washington
## 11    Italy             Villachigi     92    13          Tuscany
## 12 Portugal            Dona Sophia     92    13             Tejo
## 13   France       Château Labrande     92    13 Southwest France
## 14 Portugal              Alvarinho     92    13            Minho
## 15  Austria                  Andau     92    13       Burgenland
## 16 Portugal             Grand'Arte     92    13           Lisboa
##                region_1          region_2                  variety
## 1                                                   Portuguese Red
## 2                                                   Portuguese Red
## 3  Columbia Valley (WA)   Columbia Valley                 Riesling
## 4  Columbia Valley (WA)   Columbia Valley                 Riesling
## 5            Haut-Médoc                   Bordeaux-style Red Blend
## 6     Willamette Valley Willamette Valley               Pinot Gris
## 7               Madiran                      Tannat-Cabernet Franc
## 8           Napa Valley              Napa           Muscat Canelli
## 9           Napa Valley              Napa          Sauvignon Blanc
## 10 Columbia Valley (WA)   Columbia Valley    Johannisberg Riesling
## 11              Chianti                                 Sangiovese
## 12                                                  Portuguese Red
## 13               Cahors                                     Malbec
## 14                                                       Alvarinho
## 15                                                        Zweigelt
## 16                                                Touriga Nacional
##                       winery
## 1              Pedra Cancela
## 2          Quinta do Serrado
## 3                Pacific Rim
## 4                   Bridgman
## 5  Château Devise d'Ardilley
## 6                      Lujon
## 7            Château d'Aydie
## 8              Robert Pecota
## 9               Honker Blanc
## 10             J. Bookwalter
## 11            Chigi Saracini
## 12    Quinta do Casal Branco
## 13           Jean-Luc Baldès
## 14                   Aveleda
## 15              Scheiblhofer
## 16                DFJ Vinhos

Now that you’ve learned some handy tools you can use with dplyr, I hope you can go off into the world and explore something of interest to you. Feel free to make a comment below and share what other dplyr features you find helpful or interesting.

Watch the video below

Contributor: Ningxi Xu

Ningxi holds a MS in Finance with honors from Georgetown McDonough School of Business, and graduated magna cum laude with a BA from the George Washington University.

Graphs play a very important role in the data science workflow. Learn how to create dynamic professional-looking plots with Plotly.py.

We use plots to understand the distribution and nature of variables in the data and use visualizations to describe our findings in reports or presentations to both colleagues and clients. The importance of plotting in a data scientist’s work cannot be overstated.

Learn more about visualizing your data at Data Science Dojo’s Introduction to Python for Data Science!

Plotting with Matplotlib

If you have worked on any kind of data analysis problem in Python you will probably have encountered matplotlib, the default (sort of) plotting library. I personally have a love-hate relationship with it — the simplest plots require quite a bit of extra code but the library does offer flexibility once you get used to its quirks. The library is also used by pandas for its built-in plotting feature. So even if you haven’t heard of matplotlib, if you’ve used df.plot(), then you’ve unknowingly used matplotlib.

Plotting with Seaborn

Another popular library is seaborn, which is essentially a high-level wrapper around matplotlib and provides functions for some custom visualizations, these require quite a bit of code to create in the standard matplotlib. Another nice feature seaborn provides is sensible defaults for most options like axis labels, color schemes, and sizes of shapes.

Introducing Plotly

Plotly might sound like the new kid on the block, but in reality, it’s nothing like that. Plotly originally provided functionality in the form of a JavaScript library built on top of D3.js and later branched out into frontends for other languages like R, MATLAB and, of course, Python. plotly.py is the Python interface to the library.

As for usability, in my experience Plotly falls in between matplotlib and seaborn. It provides a lot of the same high-level plots as seaborn but also has extra options right there for you to tweak, such as matplotlib. It also has generally much better defaults than matplotlib.

Plotly’s interactivity

The most fascinating feature of Plotly is the interactivity. Plotly is fundamentally different from both matplotlib and seaborn because plots are rendered as static images by both of them while Plotly uses the full power of JavaScript to provide interactive controls like zooming in and panning out of the visual panel. This functionality can also be extended to create powerful dashboards and responsive visualizations that could convey so much more information than a static picture ever could.

First, let’s see how the three libraries differ in their output and complexity of code. I’ll use common statistical plots as examples.

To have a relatively even playing field, I’ll use the built-in seaborn theme that matplotlib comes with so that we don’t have to deduct points because of the plot’s looks.

fig, ax = plt.subplots(figsize=(8,6))

for species, species_df in iris.groupby('species'):
    ax.scatter(species_df['sepal_length'], species_df['sepal_width'], label=species);

ax.set(xlabel='Sepal Length', ylabel='Sepal Width', title='A Wild Scatterplot appears');
ax.legend();

 

Wild Scatterplot

 

fig, ax = plt.subplots(figsize=(8,6))

sns.scatterplot(data=iris, x='sepal_length', y='sepal_width', hue='species', ax=ax);

ax.set(xlabel='Sepal Length', ylabel='Sepal Width', title='A Wild Scatterplot appears');

 

statistical plot

 

fig = go.FigureWidget()

for species, species_df in iris.groupby('species'):
    fig.add_scatter(x=species_df['sepal_length'], y=species_df['sepal_width'],
                    mode='markers', name=species);

fig.layout.hovermode = 'closest'
fig.layout.xaxis.title = 'Sepal Length'
fig.layout.yaxis.title = 'Sepal Width'
fig.layout.title = 'A Wild Scatterplot appears'
fig
scatterplot

 

Looking at the plots, the matplotlib and seaborn plots are basically identical, the only difference is in the amount of code. The seaborn library has a nice interface to generate a colored scatter plot based on the hue argument, but in matplotlib we are basically creating three scatter plots on the same axis. The different colors are automatically assigned in both (default color cycle but can also be specified for customization). Other relatively minor differences are in the labels and legend, where seaborn creates these automatically. This, in my experience, is less useful than it seems because very rarely do datasets have nicely formatted column names. Usually they contain abbreviations or symbols so you still have to assign ‘proper’ labels.

But we really want to see what Plotly has done, don’t we? This time I’ll start with the code. It’s eerily similar to matplotlib, apart from not sharing the exact syntax of course and the hovermode option. Hovering? Does that mean…? Yes, yes it does. Moving the cursor over a point reveals a tooltip showing the coordinates of the point and the class label. The tooltip can also be customized to show other information about the particular point. To the top right of the panel, there are controls to zoom, select and pan across the plot. The legend is also interactive, it acts sort of like checkboxes. You can click on a class to hide/show all the points of that class.

Since the amount or complexity of code isn’t that drastically different from the other two options and we get all these interactivity options, I’d argue this is basically free benefits.

fig, ax = plt.subplots(figsize=(8,6))

grouped_df = iris.groupby('species').mean()
ax.bar(grouped_df.index.values, 
       grouped_df['sepal_length'].values);

ax.set(xlabel='Species', ylabel='Average Sepal Length', title='A Wild Barchart appears');

 

wild bar chart

 

fig, ax = plt.subplots(figsize=(8,6))

sns.barplot(data=iris, x='species', y='sepal_length', estimator=np.mean, ax=ax);

ax.set(xlabel='Species', ylabel='Average Sepal Length', title='A Wild Barchart appears');

 

bar chart - Python plots

 

fig = go.FigureWidget()

grouped_df = iris.groupby('species').mean()
fig.add_bar(x=grouped_df.index, y=grouped_df['sepal_length']);

fig.layout.xaxis.title = 'Species'
fig.layout.yaxis.title = 'Average Sepal Length'
fig.layout.title = 'A Wild Barchart appears'
fig

 

bar chart - python plot

 

The bar chart story is similar to the scatter plots. In this case, again, seaborn provides the option within the function call to specify the metric to be shown on the y axis using the x variable as the grouping variable. For the other two, we have to do this ourselves using pandasPlotly still provides interactivity out of the box.

Now that we’ve seen that Plotly can hold its own against our usual plotting options, let’s see what other benefits it can bring to the table. I will showcase some trace types in Plotly that are useful in a data science workflow, and how interactivity can make them more informative.

Heatmaps

fig = go.FigureWidget()

cor_mat = car_crashes.corr()
fig.add_heatmap(z=cor_mat, 
                x=cor_mat.columns,
                y=cor_mat.columns,
                showscale=True)

fig.layout.width = 500
fig.layout.height = 500
fig.layout.yaxis.automargin = True
fig.layout.title = 'A Wild Heatmap appears'
fig

 

heatmap - python

Heatmaps are commonly used to plot correlation or confusion matrices. As expected, we can hover over the squares to get more information about the variables. I’ll paint a picture for you. Suppose you have trained a linear regression model to predict something from this dataset. You can then show the appropriate coefficients in the hover tooltips to get a better idea of which correlations in the data the model has captured.

Parallel coordinates plot

fig = go.FigureWidget()

parcords = fig.add_parcoords(dimensions=[{'label':n.title(),
                                          'values':iris[n],
                                          'range':[0,8]} for n in iris.columns[:-2]])

fig.data[0].dimensions[0].constraintrange = [4,8]
parcords.line.color = iris['species_id']
parcords.line.colorscale = make_plotly(cl.scales['3']['qual']['Set2'], repeat=True)

parcords.line.colorbar.title = ''
parcords.line.colorbar.tickvals = np.unique(iris['species_id']).tolist()
parcords.line.colorbar.ticktext = np.unique(iris['species']).tolist()
fig.layout.title = 'A Wild Parallel Coordinates Plot appears'
fig

 

parralel coordinates plot .gif

 

I suspect some of you might not yet be familiar with this visualization, as I wasn’t a few months ago. This is a parallel coordinates plot of four variables. Each variable is shown on a separate vertical axis. Each line corresponds to a row in the dataset and the color obviously shows which class that row belongs to. A thing that should jump out at you is that the class separation in each variable axis is clearly visible. For instance, the Petal_Length variable can be used to classify all the Setosa flowers very well.

Since the plot is interactive, the axes can be reordered by dragging to explore interconnectedness between the classes and how it affects the class separations. Another interesting interaction is the constrained range widget (the bright pink object on the Sepal_Length axis). It can be dragged up or down to decolor the plot. Imagine having these on all axes and finding a sweet spot where only one class is visible. As a side note, the decolored plot has a transparency effect on the lines so the density of values can be seen.

A version of this type of visualization also exists for categorical variables in Plotly. It is called Parallel Categories.

Choropleth plot

fig = go.FigureWidget()

choro = fig.add_choropleth(locations=gdp['CODE'],
                           z=gdp['GDP (BILLIONS)'],
                           text = gdp['COUNTRY'])

choro.marker.line.width = 0.1
choro.colorbar.tickprefix = '$'
choro.colorbar.title = 'GDP<br>Billions US$'
fig.layout.geo.showframe = False
fig.layout.geo.showcoastlines = False
fig.layout.title = 'A Wild Choropleth appears<br>Source:\
                    <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
                    CIA World Factbook</a>'
fig

 

Choropleth | Data Science Dojo

 

A choropleth is a very commonly used geographical plot. The benefit of the interactivity should be clear in this one. We can only show a single variable using the color but the tooltip can be used for extra information. Zooming in is also very useful in this case, allowing us to look at the smaller countries. The plot title contains HTML which is being rendered properly. This can be used to create fancier labels.

Interactive scatter plot

fig = go.FigureWidget()

scatter_trace = fig.add_scattergl(x=diamonds['carat'], y=diamonds['price'],
                                  mode='markers', marker={'opacity':0.2});

fig.layout.hovermode = 'closest'
fig.layout.xaxis.title = 'Carat'
fig.layout.yaxis.title = 'Price'
fig.layout.title = 'A Wild Scatterplot appears'
fig

 

scatterplot

 

I’m using the scattergl trace type here. This is a version of the scatter plot which uses WebGL in the background so that the interactions don’t get laggy even with larger datasets.

There is quite a bit of over-plotting here even with the aggressive transparency, so let’s zoom into the densest part to take a closer look. Zooming in reveals that the carat variable is quantized and there are clean vertical lines.

def selection_handler(trace, points, selector):
    data_mean = np.mean(points.ys)
    fig.data[0].figure.layout.title.text = f'A Wild Scatterplot appears - mean price: ${data_mean:.1f}'
fig.data[0].on_selection(selection_handler)

fig

 

scatter plot

 

Selecting a bunch of points in this scatter plot will change the title of the plot to show the mean price of the selected points. This could prove to be very useful in a plot where there are groups and you want to visually see some statistics of a cluster.

This behavior is easily implemented using callback functions attached to predefined event handlers for each trace.

More interactivity

Let’s do something fancier now.

fig1 = go.FigureWidget()
fig1.add_scattergl(x=exports['beef'], y=exports['total exports'],
                   text=exports['state'],
                   mode='markers');
fig1.layout.hovermode = 'closest'
fig1.layout.xaxis.title = 'Beef Exports in Million US$'
fig1.layout.yaxis.title = 'Total Exports in Million US$'
fig1.layout.title = 'A Wild Scatterplot appears'

fig2 = go.FigureWidget()
fig2.add_choropleth(locations=exports['code'],
                    z=exports['total exports'].astype('float64'),
                    text=exports['state'],
                    locationmode='USA-states')
fig2.data[0].marker.line.width = 0.1
fig2.data[0].marker.line.color = 'white'
fig2.data[0].marker.line.width = 2
fig2.data[0].colorbar.title = 'Exports Millions USD'
fig2.layout.geo.showframe = False
fig2.layout.geo.scope = 'usa'
fig2.layout.geo.showcoastlines = False
fig2.layout.title = 'A Wild Choropleth appears'

def do_selection(trace, points, selector):
    if trace is fig2.data[0]:
        fig1.data[0].selectedpoints = points.point_inds
    else:
        fig2.data[0].selectedpoints = points.point_inds
fig1.data[0].on_selection(do_selection)
fig2.data[0].on_selection(do_selection)

HBox([fig1, fig2])

 

scatterplot choropleth linked

 

We have already seen how to make scatter and choropleth plots so let’s put them to use and plot the same data-frame. Then, using the event handlers we also saw before, we can link both plots together and interactively explore which states produce which kinds of goods.

This kind of interactive exploration of different slices of the dataset is far more intuitive and natural than transforming the data in pandas and then plotting it again.

fig = go.FigureWidget()
fig.add_histogram(x=iris['sepal_length'],
                  histnorm='probability density');
fig.layout.xaxis.title = 'Sepal Length'
fig.layout.yaxis.title = 'Probability Density'
fig.layout.title = 'A Wild Histogram appears'

def change_binsize(s):
    fig.data[0].xbins.size = s
slider = interactive(change_binsize, s=(0.1,1,0.1))
label = Label('Bin Size: ')

VBox([HBox([label, slider]),
      fig])

 

histogram

 

Using the ipywidgets module’s interactive controls different aspects of the plot can be changed to gain a better understanding of the data. Here the bin size of the histogram is being controlled.

fig = go.FigureWidget()

scatter_trace = fig.add_scattergl(x=diamonds['carat'], y=diamonds['price'],
                                  mode='markers', marker={'opacity':0.2});

fig.layout.hovermode = 'closest'
fig.layout.xaxis.title = 'Carat'
fig.layout.yaxis.title = 'Price'
fig.layout.title = 'A Wild Scatterplot appears'

def change_opacity(x):
    fig.data[0].marker.opacity = x
slider = interactive(change_opacity, x=(0.1,1,0.1))
label = Label('Marker Opacity: ')

VBox([HBox([label, slider]),
      fig])

 

scatter opacity

 

The opacity of the markers in this scatter plot is controlled by the slider. These examples only control the visual or layout aspects of the plot. We can also change the actual data which is being shown using dropdowns. I’ll leave you to explore that on your own.

What have we learned about Python plots

Let’s take a step back and sum up what we have learned. We saw that Plotly can reveal more information about our data using interactive controls, which we get for free and with no extra code. We saw a few interesting, slightly more complex visualizations available to us. We then combined the plots with custom widgets to create custom interactive workflows.

All this is just scratching the surface of what Plotly is capable of. There are many more trace types, an animations framework, and integration with Dash to create professional dashboards and probably a few other things that I don’t even know of.

 

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