Plotly Express is a new advanced Python visualization library, which is plotly Py, which provides simple syntax for complex charts. The most important thing is that plotly can be perfectly combined with Pandas data type DataFrame. It is too convenient for data analysis and visualization, and it is completely free, which is very worth trying

Next, we use several built-in data sets of ployy to demonstrate the drawing of relevant charts

data set

All data sets built in plot are in DataFrame format, which is the embodiment of the depth fit with Pandas

GDP income and life expectancy in different countries over the years

Including fields: country, continent, year, life expectancy, population, GDP, country abbreviation and country number

gap = px.data.gapminder()
gap2007 = gap.query("year==2007")
gap2007

Output

Restaurant order flow

Including fields: general ledger sheet, tip, gender, smoking or not, day of week, dining time and number of people

tips = px.data.tips()
tips

Output

Iris

Include fields: sepal length, sepal width, petal length, petal width, species, species number

iris = px.data.iris()  
iris

Output

Wind data

Contains fields: direction, strength, and value

wind = px.data.wind()  
wind

Output

Voting results of 2013 Montreal mayoral election

Including fields: region, Coderre votes, Bergeron votes, Joly votes, total votes, winner and result (proportion classification)

election = px.data.election() 
election

Output

Availability of car sharing services near a regional center in Montreal

Including fields: latitude, longitude, car hours and peak hours

carshare = px.data.carshare()
carshare

Output

Built in palette

Plotly also has many advanced color palettes, which makes us no longer worry about color matching when drawing charts

Color and sequence of cartoons

px.colors.carto.swatches() 

Output

Color scale of CMOcean project

px.colors.cmocean.swatches() 

Output

There are many other color palettes to choose from. I won't show them one by one. Only the code is given below. You can run the code to view the specific color style

Color scale of ColorBrewer2 project

px.colors.colorbrewer

Periodic color code, suitable for continuous data with natural periodic structure

px.colors.cyclical 

Disperse color scale, suitable for continuous data with natural end points

px.colors.diverging     

Qualitative color code, applicable to data without natural sequence

px.colors.qualitative

Sequential color code, applicable to most continuous data

px.colors.sequential        

Plot express basic drawing

Scatter diagram

Plotting a scatter plot is very easy and can be done in one line of code

px.scatter(gap2007, x="gdpPercap", y="lifeExp")

Output

Different data categories can also be distinguished by the parameter color

px.scatter(gap2007, x="gdpPercap", y="lifeExp", color="continent")

Output

Each dot here represents a country, and different colors represent different continents

You can use the parameter size to reflect the size of the data

px.scatter(gap2007, x="gdpPercap", y="lifeExp", color="continent", size="pop", size_max=60)

Output

You can also use the parameter hover_name to specify the information displayed when the mouse hovers

You can also split charts according to different data types in the dataset

px.scatter(gap2007, x="gdpPercap", y="lifeExp", color="continent", size="pop", 
           size_max=60, hover_name="country", facet_col="continent", log_x=True)

Output

Of course, we can also view the data of different years and generate dynamic charts for automatic switching

px.scatter(gap, x="gdpPercap", y="lifeExp", color="continent", size="pop", 
           size_max=60, hover_name="country", animation_frame="year", animation_group="country", log_x=True,
          range_x=[100, 100000], range_y=[25, 90], labels=dict(pop="Population", gdpPercap="GDP per Capa", lifeExp="Life Expectancy"))

Output

Geographic Information Map

Plotly is also very convenient to draw dynamic geographic information charts. In the form of this map, we can clearly see that the data set lacks relevant data of the former Soviet Union

px.choropleth(gap, locations="iso_alpha", color="lifeExp", hover_name="country", animation_frame="year", 
              color_continuous_scale=px.colors.sequential.Plasma, projection="natural earth")

Output

Matrix scatter diagram

px.scatter_matrix(iris, dimensions=['sepal_width', 'sepal_length', 'petal_width', 'petal_length'], color='species', symbol='species')

Output

Parallel coordinate diagram

px.parallel_coordinates(tips, color='size', color_continuous_scale=px.colors.sequential.Inferno)

Output

Ternary scatter diagram

px.scatter_ternary(election, a="Joly", b="Coderre", c="Bergeron", color="winner", size="total", hover_name="district",
                   size_max=15, color_discrete_map = {"Joly": "blue", 
                   "Bergeron": "green", "Coderre":"red"} )

Output

Polar line drawing

px.line_polar(wind, r="frequency", theta="direction", color="strength", 
            line_close=True,color_discrete_sequence=px.colors.sequential.Plotly3[-2::-1])

Output

Violin picture

px.violin(tips, y="tip", x="sex", color="smoker", facet_col="day", facet_row="time",box=True, points="all", 
          category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]},
          hover_data=tips.columns)

Output

Polar bar chart

px.bar_polar(wind, r="frequency", theta="direction", color="strength",
            color_discrete_sequence= px.colors.sequential.Plotly3[-2::-1])

Output

Parallel category graph

px.parallel_categories(tips, color="size", color_continuous_scale=px.
            colors.sequential.Inferno)

Output

histogram

px.histogram(tips, x="total_bill", color="smoker",facet_row="day", facet_col="time")

Output

3D scatter diagram

px.scatter_3d(election, x="Joly", y="Coderre", z="Bergeron", color="winner", 
              size="total", hover_name="district",symbol="result", 
              color_discrete_map = {"Joly": "blue", "Bergeron": "green", 
              "Coderre":"red"})

Output

Density contour map

px.density_contour(iris, x="sepal_width", y="sepal_length", color="species")

Output

Box diagram

px.box(tips, x="sex", y="tip", color="smoker", notched=True)

Output

Geographic coordinate line drawing

px.line_geo(gap.query("year==2007"), locations="iso_alpha", 
            color="continent", projection="orthographic")

Output

Bar graph

px.line(gap, x="year", y="lifeExp", color="continent", 
        line_group="country", hover_name="country",
        line_shape="spline", render_mode="svg")

Output

Area map

px.area(gap, x="year", y="pop", color="continent", 
        line_group="country")

Output

Thermodynamic diagram

px.density_heatmap(iris, x="sepal_width", y="sepal_length", 
                   marginal_x="rug", marginal_y="histogram")

Output

Bar chart

px.bar(tips, x="sex", y="total_bill", color="smoker", barmode="group")

Output

In general, plot / plot express is still a very powerful drawing tool, which is worth studying in detail~