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This article mainly shows you "Python how to achieve cool dynamic diagram", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn "how to achieve cool dynamic graph in Python" this article.

Start

If you have not installed Plotly, just run the following command on your terminal to complete the installation:

Pip install plotly

After the installation is complete, start using it!

Animation

When studying the evolution of this or that indicator, we often refer to time data. The Plotly animation tool allows people to watch how the data changes over time with just one line of code, as shown in the following figure:

The code is as follows:

Import plotly.express as pxfrom vega_datasets import datadf = data.disasters () df = df [df.Year > 1990] fig = px.bar (df, y = "Entity", x = "Deaths", animation_frame= "Year", orientation='h', range_x= [0, df.Deaths.max ()], color= "Entity") # improve aesthetics (size, grids etc.) fig.update_layout (width=1000 Height=800, xaxis_showgrid=False, yaxis_showgrid=False, paper_bgcolor='rgba', plot_bgcolor='rgba', title_text='Evolution of Natural Disasters' Showlegend=False) fig.update_xaxes (title_text='Number of Deaths') fig.update_yaxes (title_text='') fig.show ()

As long as you have a time variable to filter, almost any chart can be animated. Here is an example of animating a scatter chart:

Import plotly.express as pxdf = px.data.gapminder () fig = px.scatter (df, x = "gdpPercap", y = "lifeExp", animation_frame= "year", size= "pop", color= "continent", hover_name= "country", log_x=True, size_max=55, range_x= [100,100], range_y= [25,90], # color_continuous_scale=px.colors.sequential.Emrld) fig.update_layout (width=1000) Height=800, xaxis_showgrid=False, yaxis_showgrid=False, paper_bgcolor='rgba', plot_bgcolor='rgba') Sun Map

The sun map (sunburst chart) is a good way to visualize group by statements. If you want to decompose a given quantity by one or more category variables, use the sun chart.

Assuming that we want to break down the average tip data according to gender and time of day, this double group by statement can be visualized more effectively than tables.

This chart is interactive, allowing you to click and explore the categories yourself. You just need to define all your categories and declare the hierarchy between them (see the parents parameter in the following code) and assign the corresponding values, which in our case is the output of the group by statement.

Import plotly.graph_objects as goimport plotly.express as pximport numpy as npimport pandas as pddf = px.data.tips () fig = go.Figure (go.Sunburst (labels= ["Female", "Male", "Dinner", "Lunch", 'Dinner', 'Lunch'], parents= [",", "Female", "Female", 'Male',' Male'], values=np.append (df.groupby ('sex'). Tip.mean (). Values Df.groupby (['sex',' time']). Tip.mean () .values), marker=dict (colors=px.colors.sequential.Emrld)), layout=go.Layout (paper_bgcolor='rgba)', plot_bgcolor='rgba')) fig.update_layout (margin=dict (tweak 0, lame 0, rang 0, bang 0), title_text='Tipping Habbits Per Gender Time and Day') fig.show ()

Now let's add another layer to this hierarchy:

To do this, we add the value of another group by statement involving three category variables.

Import plotly.graph_objects as goimport plotly.express as pximport pandas as pdimport numpy as npdf = px.data.tips () fig = go.Figure (go.Sunburst (labels= ["Female", "Male", "Dinner", "Lunch", 'Dinner', 'Lunch', 'Fri',' Sat', 'Sun',' Thu', 'Fri',' Thu', 'Fri',' Sat', 'Sun',' Fri', 'Thu'] Parents= [",", "Female", "Female", 'Male',' Male', 'Dinner',' Lunch', 'Lunch',' Dinner', 'Dinner' 'Dinner', 'Lunch', 'Lunch'], values=np.append (np.append (df.groupby ('sex'). Tip.mean (). Values Df.groupby (['sex',' time']). Tip.mean (). Values,), df.groupby (['sex',' time'') ) .tip.mean () .values), marker=dict (colors=px.colors.sequential.Emrld)), layout=go.Layout (paper_bgcolor='rgba), plot_bgcolor='rgba (day')), fig.update_layout (margin=dict (tweak 0, lame 0, rang 0) ), title_text='Tipping Habbits Per Gender, Time and Day') fig.show () parallel category

Another way to explore the relationship between category variables is the following flowchart. You can drag and drop, highlight and browse values at any time, which is very suitable for demonstration.

The code is as follows:

Import plotly.express as pxfrom vega_datasets import dataimport pandas as pddf = data.movies () df = df.dropna () df ['Genre_id'] = df.Major_Genre.factorize () [0] fig = px.parallel_categories (df, dimensions= [' MPAA_Rating', 'Creative_Type',' Major_Genre'], color= "Genre_id", color_continuous_scale=px.colors.sequential.Emrld,) fig.show () parallel coordinate diagram

The parallel coordinate diagram is a continuous version of the chart above. Here, each string represents a single observation. This is a useful tool for identifying outliers (a single line away from other data), clustering, trends, and redundant variables (for example, if two variables have similar values on each observation, they will be on the same horizontal line). Indicates redundancy).

The code is as follows:

Import plotly.express as pxfrom vega_datasets import dataimport pandas as pddf = data.movies () df = df.dropna () df ['Genre_id'] = df.Major_Genre.factorize () [0] fig = px.parallel_coordinates (df, dimensions= [' IMDB_Rating', 'IMDB_Votes',' Production_Budget', 'Running_Time_min',' US_Gross', 'Worldwide_Gross',' US_DVD_Sales'] Color='IMDB_Rating', color_continuous_scale=px.colors.sequential.Emrld) fig.show () gauge chart and indicator

The measuring chart is just to look good. You can use this chart when reporting success metrics such as KPI and showing their distance from your goal.

Indicators are very useful in business and consulting. They can complement visual effects with text markings, attract the audience's attention and show your growth indicators.

Import plotly.graph_objects as gofig = go.Figure (go.Indicator (domain = {'x: [0,1]), value = 4.3, mode = "gauge+number+delta", title = {'text': "Success Metric"}, delta = {' reference': 3.9}, gauge = {'bar': {' color': "lightgreen"}, 'axis': {' range': [None]) 5]}, 'steps': [{' range': [0,2.5], 'color': "lightgray"}, {' range': [2.5,4], 'color': "gray"}],}) fig.show () above is all the content of the article "how to make Python Cool dynamic Graph" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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