Python
  • Introduction
  • Chapter 1.Notes from research
    • 1.Introduction of Python
    • 2. Build developer environment
      • 2.1.Sublime Text3
      • 2.2.Jupyter(IPython notebook)
        • 2.2.1.Introduction
        • 2.2.2.Basic usage
        • 2.2.3.some common operations
      • 2.3.Github
        • 2.3.1.Create Github account
        • 2.3.2.Create a new repository
        • 2.3.3.Basic operations: config, clone, push
      • 2.4.Install Python 3.4 in Windows
    • 3. Write Python code
      • 3.1.Hello Python
      • 3.2.Basic knowledges
      • 3.3.撰寫獨立python程式
      • 3.4.Arguments parser
      • 3.5.Class
      • 3.6.Sequence
    • 4. Web crawler
      • 4.1.Introduction
      • 4.2.requests
      • 4.3.beautifulSoup4
      • 3.4.a little web crawler
    • 5. Software testing
      • 5.1. Robot Framework
        • 1.1.Introduction
        • 1.2.What is test-automation framework?
        • 1.3.Robot Framework Architecture
        • 1.4.Robot Framework Library
        • 1.5.Reference
    • 6. encode/ decode
      • 6.1.編碼/解碼器的基本概念
      • 6.2.常見的編碼/ 解碼錯誤訊息與其意義
      • 6.3 .處理文字檔案
    • 7. module
      • 7.1.Write a module
      • 7.2.Common module
        • 7.2.1.sched
        • 7.2.2.threading
    • 8. Integrate IIS with django
      • 8.1.Integrate IIS with django
  • Chapter 2.Courses
    • 2.1.Python for Data Science and Machine Learning Bootcamp
      • 2.1.1.Virtual Environment
      • 2.1.2.Python crash course
      • 2.1.3.Python for Data Analysis - NumPy
        • 2.1.3.1.Numpy arrays
        • 2.1.3.2.Numpy Array Indexing
        • 2.1.3.3.Numpy Operations
      • 2.1.4.Python for Data Analysis - Pandas
        • 2.1.4.1.Introduction
        • 2.1.4.2.Series
        • 2.1.4.3.DataFrames
        • 2.1.4.4.Missing Data
        • 2.1.4.5.GroupBy
        • 2.1.4.6.Merging joining and Concatenating
        • 2.1.4.7.Data input and output
      • 2.1.5.Python for Data Visual Visualization - Pandas Built-in Data Visualization
      • 2.1.6.Python for Data Visualization - Matplotlib
        • 2.1.6.1.Introduction of Matplotlib
        • 2.1.6.2.Matplotlib
      • 2.1.7.Python for Data Visualization - Seaborn
        • 2.1.7.1.Introduction to Seaborn
        • 2.1.7.2.Distribution Plots
        • 2.1.7.3.Categorical Plots
        • 2.1.7.4.Matrix Plots
        • 2.1.7.5.Grids
        • 2.1.7.6.Regression Plots
      • 2.1.8. Python for Data Visualization - Plotly and Cufflinks
        • 2.1.8.1.Introduction to Plotly and Cufflinks
        • 2.1.8.2.Plotly and Cufflinks
      • 2.1.9. Python for Data Visualization - Geographical plotting
        • 2.1.9.1.Choropleth Maps - USA
        • 2.1.9.2.Choropleth Maps - World
      • 2.1.10.Combine data analysis and visualization to tackle real world data sets
        • 911 calls capstone project
      • 2.1.11.Linear regression
        • 2.1.11.1.Introduction to Scikit-learn
        • 2.1.11.2.Linear regression with Python
      • 2.1.12.Logistic regression
        • 2.1.12.1.Logistic regression Theory
        • 2.1.12.2.Logistic regression with Python
      • 2.1.13.K Nearest Neighbors
        • 2.1.13.1.KNN Theory
        • 2.1.13.2.KNN with Python
      • 2.1.14.Decision trees and random forests
        • 2.1.14.1.Introduction of tree methods
        • 2.1.14.2.Decision trees and Random Forests with Python
      • 2.1.15.Support Vector Machines
      • 2.1.16.K means clustering
      • 2.1.17.Principal Component Analysis
    • 2.2. Machine Learning Crash Course Jam
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  • 2.1.8.2.Plotly and Cufflinks
  • 1. 使用library
  • 2. 畫圖的基本操作
  • 3. Scatter plot
  • 4. Bar plot
  • 5. Box plot
  • 6.3D surface plot
  • 7. Histogram plot
  • 8. Spread plot
  • 9. Bubble plot
  • 10. Scatter matrix plot

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  1. Chapter 2.Courses
  2. 2.1.Python for Data Science and Machine Learning Bootcamp
  3. 2.1.8. Python for Data Visualization - Plotly and Cufflinks

2.1.8.2.Plotly and Cufflinks

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Last updated 5 years ago

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2.1.8.2.Plotly and Cufflinks

1. 使用library

import pandas as pd
import numpy as np
from plotly import __version__

  • 確認版本

from plotly import __version__
print(__version__)

  • 將圖表直接嵌入到Notebook之中

%matplotlib inline

  • 使用Cufflinks

import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
cf.go_offline()

  • 產生DataFrame資料

    • 產生(100, 4)的隨機DataFrame

      df = pd.DataFrame(np.random.randn(100, 4), columns='A B C D'.split())
df.head()

    • 產生Category, Values的DataFrame

      df2 = pd.DataFrame({'Category':['A', 'B', 'C'], 'Values':[32, 43, 50]})
df2.head()

2. 畫圖的基本操作

df.iplot()

3. Scatter plot

df.iplot(kind = 'scatter', x = 'A', y = 'B')

  • 點狀

df.iplot(kind = 'scatter', x = 'A', y = 'B', mode = 'markers', size = 20)

4. Bar plot

  • 畫出特定資料的bar

df2.iplot(kind='bar', x='Category', y='Values')

  • 各種類數量的bar

df.count().iplot(kind='bar')

5. Box plot

df.iplot(kind='box')

6.3D surface plot

  • 產生三維資料

df3 = pd.DataFrame({'x':[1,2,3,4,5], 'y':[10,20,30,40,50], 'z':[500,400,300,200,100]})

  • 基本用法

df3.iplot(kind='surface')

  • 改變色調

df3.iplot(kind='surface', colorscale = 'rdylbu')

7. Histogram plot

df.iplot(kind='hist')

8. Spread plot

df[['A', 'B']].iplot(kind='spread')

9. Bubble plot

df.iplot(kind='bubble', x='A', y='B',size='C')

10. Scatter matrix plot

df.scatter_matrix()