Using pyecharts for data visualization

osc_ ezb5qfcz 2021-01-23 12:41:49
using pyecharts data visualization


Use pyecharts 1.5 Data visualization

install  pip install pyecharts
Use this command to install the latest version 1.5. The syntax is very different from the previous version , Therefore, this paper only aims at 1.5 And later versions . To use the previous version, use the command pip install pyecharts == 0.1.5.19
notes : It is suggested that jupyter notebook in coding, convenient debug.

Start using

The basic routine is to create an empty layer you need first , And then use .set_global_opts Modify global items and then .set_series_opts Modify the specific configuration . Of course, the best learning address must be the official document , But it's too complicated , Here are just a few examples to illustrate the routine .

Map

pyecharts The better thing is to draw a map , Here we use 2019-nCov In the project, the map drawing of Anhui Province is taken as an example .

First import the required package

from pyecharts.charts import Pie ,Grid,Bar,Line
from pyecharts.faker import Faker # Data packets from pyecharts.charts import Map,Geo
from pyecharts import options as opts
from pyecharts.globals import ThemeType



OK, I now have a set of data for a province , It's like this

locate =[' Hefei ', ' Fuyang City ', ' Bozhou City ', ' Anqing City ', ' Ma'anshan City ', ' tongling ', ' Lu'an City ', ' Chuzhou ', ' Chizhou City ',' Bengbu City ',' Wuhu City ',' Suzhou City ',' Xuancheng City ',' Huaibei City ',' Huainan City ',' Huangshan City ']`
data =['115','105','72','66','30','22','41','11','11','88','27','27','5','22','14','9']

This is also the data format that you need to draw a map , Two list, One is place names , One is the data for each city , Now execute the following code to get the epidemic map of Anhui Province .

list1 = [[locate[i],data[i]] for i in range(len(locate))] # First create the data 
map_1 = Map(init_opts=opts.InitOpts(width="400px", height="460px")) # Create map , The brackets can be resized , You can also change the theme color .
map_1.add(" Anhui epidemic situation ", list1, maptype=" anhui ") # Add a map of Anhui
map_1.set_global_opts( # Set global configuration item #title_opts=opts.TitleOpts(title=" Anhui epidemic situation "), Add the title
   visualmap_opts=opts.VisualMapOpts(max_=120, is_piecewise=True),# Maximum data range And use segmentation
   legend_opts=opts.LegendOpts(is_show=False), # Show legend or not
   )
map_1.render_notebook() # Directly in notebook It shows that # map_1.render('map1.html') Map to html The form is stored in the working directory






Of course, maps also have many customizable configuration items , Select the required configuration item and add it to the corresponding function .

# Data item ( Coordinate point name , Coordinate point values )
   data_pair: Sequence,

   # Map type , Specific reference pyecharts.datasets.map_filenames.json file
   maptype: str = "china",

   # Check legend
   is_selected: bool = True,

   # Whether to turn on mouse zoom and pan roaming .
   is_roam: bool = True,

   # The center of the current perspective , Expressed in latitude and longitude
   center: Optional[Sequence] = None,

   # The zoom of the current view .
   zoom: Optional[Numeric] = 1,

   # Name mapping of custom regions
   name_map: Optional[dict] = None,

   # Mark graphic shapes
   symbol: Optional[str] = None,

   # Whether to display the mark graph
   is_map_symbol_show: bool = True,

   # Tag configuration item , Reference resources `series_options.LabelOpts`
   label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),

   # Prompt box component configuration item , Reference resources `series_options.TooltipOpts`
   tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,

   # Element style configuration item , Reference resources `series_options.ItemStyleOpts`
   itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,

   # Highlight tag configuration items , Reference resources `series_options.LabelOpts`
   emphasis_label_opts: Union[opts.LabelOpts, dict, None] = None,

   # Highlight element style configuration , Reference resources `series_options.ItemStyleOpts`
   emphasis_itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,








































The pie chart

The simplest pie chart

 More configuration items for pie charts 
# Series name , be used for tooltip Display of ,legend The legend selection of .
   series_name: str,

   # Series data items , The format is [(key1, value1), (key2, value2)]
   data_pair: Sequence,

   # series label Color
   color: Optional[str] = None,

   # The radius of the pie , The first item in the array is the inner radius , The second term is the outer radius # The default setting is percentage , Relative to half of the smaller item in the height and width of the container
   radius: Optional[Sequence] = None,

   # The center of the pie chart ( center of a circle ) coordinate , The first item in the array is the abscissa , The second is the ordinate # The default setting is percentage , When set as a percentage, the first item is relative to the container width , The second is relative to the height of the container
   center: Optional[Sequence] = None,

   # Is it shown as a nightingale map , Size data by radius , Yes 'radius' and 'area' Two modes .# radius: The percentage of data presented by the sector center angle , Radius shows the size of the data # area: All sectors have the same center angle , Show data size by radius only
   rosetype: Optional[str] = None,

   # Whether the sectors of the pie chart are arranged clockwise .
   is_clockwise: bool = True,

   # Tag configuration item , Reference resources `series_options.LabelOpts`
   label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),

   # Prompt box component configuration item , Reference resources `series_options.TooltipOpts`
   tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,

   # Element style configuration item , Reference resources `series_options.ItemStyleOpts`
   itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,




























Continue to use the data in the map to draw a pie chart , Now I want to see the distribution ratio of the epidemic situation in Anhui Province , Consider using pie charts ( Rose chart ). Detailed code

map_2 = (
   Pie(init_opts=opts.InitOpts(width="600px", height="500px")) Create a pie chart
   .add(
       "", # Title
       [[locate[i],data[i]] for i in range(len(locate))], # Add data
       radius=["40%", "75%"], # Adjust the radius
   )
   .set_global_opts(
       legend_opts=opts.LegendOpts(
           orient="vertical", pos_top="10%", pos_left="88%"# Legend settings
       ),
   )
   .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) # Set the label
)
map_2.render_notebook() # Directly in notebook It shows that #map_2.render('map2.html') # Save to local













Histogram

demo

[object Object] You can adjust the title by adding configuration items 、 legend 、 thickness 、 Location 、 Background, etc 
# Series data 
   yaxis_data: Sequence[Numeric, opts.BarItem, dict],

   # Check legend
   is_selected: bool = True,

   # The use of x The shaft index, There are multiple... In a single chart instance x It's useful when it comes to the shaft .
   xaxis_index: Optional[Numeric] = None,

   # The use of y The shaft index, There are multiple... In a single chart instance y It's useful when it comes to the shaft .
   yaxis_index: Optional[Numeric] = None,

   # series label Color
   color: Optional[str] = None,

   # Data stack , The series on the same category axis are configured with the same stack Values can be stacked .
   stack: Optional[str] = None,

   # Distance between columns in the same series , Default to category spacing 20%, Fixed value can be set
   category_gap: Union[Numeric, str] = "20%",

   # Distance between columns of different series , As a percentage ( Such as '30%', The width of a column 30%).# If you want two series of columns to overlap , You can set gap by '-100%'. This is useful when you use columns as background .
   gap: Optional[str] = None,

   # Tag configuration item , Reference resources `series_options.LabelOpts`
   label_opts: Union[opts.LabelOpts, dict] = opts.LabelOpts(),

   # Mark point configuration item , Reference resources `series_options.MarkPointOpts`
   markpoint_opts: Union[opts.MarkPointOpts, dict, None] = None,

   # Tag line configuration item , Reference resources `series_options.MarkLineOpts`
   markline_opts: Union[opts.MarkLineOpts, dict, None] = None,

   # Prompt box component configuration item , Reference resources `series_options.TooltipOpts`
   tooltip_opts: Union[opts.TooltipOpts, dict, None] = None,

   # Element style configuration item , Reference resources `series_options.ItemStyleOpts`
   itemstyle_opts: Union[opts.ItemStyleOpts, dict, None] = None,





































Draw multiple maps on the same layer

If you want to overlay and draw graphics at the same time, you can refer to the following methods

[object Object]

Use new version pyecharts It is not difficult to , The basic routine is the same as above , Just learn how to draw first , Just read more official documents .


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