# 2.1.9.2.Choropleth Maps - World

## 1. 使用library

```
import plotly.plotly as py
import plotly.graph_objs as go 
from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot
init_notebook_mode(connected = True)
```

## 2.從實際資料中繪製Choropleth Map

* 讀取資料
  * 可在資料中放入想要顯示的country code, z值 (數量), label

    ```
    import pandas as pd
    df = pd.read_csv('2014_World_GDP')
    df.head()
    ```

    ![](/files/-M4M0SoXopAfx35v8csP)
* Choropleth Map的兩個要素 Data, Layout
  * Data的locationmode預設為country code, 因此若locations是country code則不需指定locationmode

    ```
    data = dict(
      type = 'choropleth',
      locations = df['CODE'],
      z = df['GDP (BILLIONS)'],
      text = df['COUNTRY'],
      colorbar = {'title' : 'GDP Billions US'},
    ) 

    layout = dict(
    title = '2014 Global GDP',
    geo = dict(
      showframe = False,
      projection = {'type':'Mercator'}
    )
    )
    ```
  * 若資料來源是country name, 則必須在指定locationmode中指定: locationmode = "country names"

    ```
    data = dict(
      type = 'choropleth',
      locations = df['COUNTRY'],
      locationmode = "country names",
      z = df['GDP (BILLIONS)'],
      text = df['COUNTRY'],
      colorbar = {'title' : 'GDP Billions US'},
    )

    layout = dict(
    title = '2014 Global GDP',
    geo = dict(
      showframe = False,
      projection = {'type':'Mercator'}
    )
    )
    ```
* 製作Choropleth Map

```
choromap = go.Figure(data = [data], layout = layout)
iplot(choromap)
```

![](/files/-M4M0So_cEUUXcxL9T_i)


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