Plotly简易教程

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from IPython.display import IFrame

IFrame(width="853",height="480",src = "https://www.youtube.com/embed/87jyeklhTH8")

什么是Plotly?

Plotly 是一个非常强大的开源数据可视化框架，它通过构建基于 HTML 的交互式图表来显示信息，可创建各种形式的精美图表。本文所说的 Plotly 指的是 Plotly.js 的 Python 封装，plotly本身是个生态非常复杂的绘图工具，它对很多编程语言提供接口。交互式和美观易用应该是 Plotly 最大的优势，而 Matplotlib 的特点则是可定制化程度高，但语法也相对难学，各有优缺点。

安装Plotly

pip install plotly

# pip install chart_studio
# pip install cufflinks

import pandas as pd
import numpy as np
import chart_studio.plotly as py
import cufflinks as cf
import seaborn as sns
import plotly.express as px
%matplotlib inline

# Make Plotly work in your Jupyter Notebook
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
# Use Plotly locally
cf.go_offline()

init_notebook_mode()

Basics

# Create a dataframe using a NumPy array that is 50 by 4
arr_1 = np.random.randn(50, 4)
df_1 = pd.DataFrame(arr_1, columns=['A','B','C','D'])
df_1.head()

# Compare old plots to a Plotly interactive plot
# You can save as PNG, Zoom, Pan, Turn off & on Data and more
df_1.plot()
df_1.iplot()

Line Plots

# Allows us to create graph objects for making more customized plots
import plotly.graph_objects as go

# Use included Google price data to make one plot
df_stocks = px.data.stocks()
px.line(df_stocks, x='date', y='GOOG', labels={'x':'Date', 'y':'Price'})

# Make multiple line plots
px.line(df_stocks, x='date', y=['GOOG','AAPL'], labels={'x':'Date', 'y':'Price'},
       title='Apple Vs. Google')

# Create a figure to which I'll add plots
fig = go.Figure()
# You can pull individual columns of data from the dataset and use markers or not
fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AAPL, 
                        mode='lines', name='Apple'))
fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.AMZN, 
                        mode='lines+markers', name='Amazon'))
# You can create custom lines (Dashes : dash, dot, dashdot)
fig.add_trace(go.Scatter(x=df_stocks.date, y=df_stocks.GOOG, 
                        mode='lines+markers', name='Google',
                        line=dict(color='firebrick', width=2, dash='dashdot')))
# Further style the figure
# fig.update_layout(title='Stock Price Data 2018 - 2020',
#                    xaxis_title='Price', yaxis_title='Date')

# Go crazy styling the figure
fig.update_layout(
    # Shows gray line without grid, styling fonts, linewidths and more
    xaxis=dict(
        showline=True,
        showgrid=False,
        showticklabels=True,
        linecolor='rgb(204, 204, 204)',
        linewidth=2,
        ticks='outside',
        tickfont=dict(
            family='Arial',
            size=12,
            color='rgb(82, 82, 82)',
        ),
    ),
    # Turn off everything on y axis
    yaxis=dict(
        showgrid=False,
        zeroline=False,
        showline=False,
        showticklabels=False,
    ),
    autosize=False,
    margin=dict(
        autoexpand=False,
        l=100,
        r=20,
        t=110,
    ),
    showlegend=False,
    plot_bgcolor='white'
)

Bar Charts

# Get population change in US by querying for US data
df_us = px.data.gapminder().query("country == 'United States'")
px.bar(df_us, x='year', y='pop')

# Create a stacked bar with more customization
df_tips = px.data.tips()
px.bar(df_tips, x='day', y='tip', color='sex', title='Tips by Sex on Each Day',
      labels={'tip': 'Tip Amount', 'day': 'Day of the Week'})

# Place bars next to each other
px.bar(df_tips, x="sex", y="total_bill",
             color='smoker', barmode='group')

# Display pop data for countries in Europe in 2007 greater than 2000000
df_europe = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop > 2.e6")
fig = px.bar(df_europe, y='pop', x='country', text='pop', color='country')
# Put bar total value above bars with 2 values of precision
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
# Set fontsize and uniformtext_mode='hide' says to hide the text if it won't fit
fig.update_layout(uniformtext_minsize=8)
# Rotate labels 45 degrees
fig.update_layout(xaxis_tickangle=-45)

Scatter Plot

# Use included Iris data set
df_iris = px.data.iris()
# Create a scatter plot by defining x, y, different color for count of provided
# column, size based on supplied column and additional data to display on hover
px.scatter(df_iris, x="sepal_width", y="sepal_length", color="species",
                 size='petal_length', hover_data=['petal_width'])

# Create a customized scatter with black marker edges with line width 2, opaque
# and colored based on width. Also show a scale on the right
fig = go.Figure()
fig.add_trace(go.Scatter(
    x=df_iris.sepal_width, y=df_iris.sepal_length,
    mode='markers',
    marker_color=df_iris.sepal_width,
    text=df_iris.species,
    marker=dict(showscale=True)
))
fig.update_traces(marker_line_width=2, marker_size=10)

# Working with a lot of data use Scattergl
fig = go.Figure(data=go.Scattergl(
    x = np.random.randn(100000),
    y = np.random.randn(100000),
    mode='markers',
    marker=dict(
        color=np.random.randn(100000),
        colorscale='Viridis',
        line_width=1
    )
))
fig

Pie Charts

# Create Pie chart of the largest nations in Asia
# Color maps here plotly.com/python/builtin-colorscales/
df_samer = px.data.gapminder().query("year == 2007").query("continent == 'Asia'")
px.pie(df_samer, values='pop', names='country', 
       title='Population of Asian continent', 
       color_discrete_sequence=px.colors.sequential.RdBu)

# Customize pie chart
colors = ['blue', 'green', 'black', 'purple', 'red', 'brown']
fig = go.Figure(data=[go.Pie(labels=['Water','Grass','Normal','Psychic', 'Fire', 'Ground'], 
                       values=[110,90,80,80,70,60])])
# Define hover info, text size, pull amount for each pie slice, and stroke
fig.update_traces(hoverinfo='label+percent', textfont_size=20,
                  textinfo='label+percent', pull=[0.1, 0, 0.2, 0, 0, 0],
                  marker=dict(colors=colors, line=dict(color='#FFFFFF', width=2)))

Histograms

# Plot histogram based on rolling 2 dice
dice_1 = np.random.randint(1,7,5000)
dice_2 = np.random.randint(1,7,5000)
dice_sum = dice_1 + dice_2
# bins represent the number of bars to make
# Can define x label, color, title
# marginal creates another plot (violin, box, rug)
fig = px.histogram(dice_sum, nbins=11, labels={'value':'Dice Roll'},
             title='5000 Dice Roll Histogram', marginal='violin',
            color_discrete_sequence=['green'])

fig.update_layout(
    xaxis_title_text='Dice Roll',
    yaxis_title_text='Dice Sum',
    bargap=0.2, showlegend=False
)

# Stack histograms based on different column data
df_tips = px.data.tips()
px.histogram(df_tips, x="total_bill", color="sex")

Box Plots

# A box plot allows you to compare different variables
# The box shows the quartiles of the data. The bar in the middle is the median 
# The whiskers extend to all the other data aside from the points that are considered
# to be outliers
df_tips = px.data.tips()
# We can see which sex tips the most, points displays all the data points
px.box(df_tips, x='sex', y='tip', points='all')

# Display tip sex data by day
px.box(df_tips, x='day', y='tip', color='sex')

# Adding standard deviation and mean
fig = go.Figure()
fig.add_trace(go.Box(x=df_tips.sex, y=df_tips.tip, marker_color='blue',
                    boxmean='sd'))

# Complex Styling
df_stocks = px.data.stocks()
fig = go.Figure()
# Show all points, spread them so they don't overlap and change whisker width
fig.add_trace(go.Box(y=df_stocks.GOOG, boxpoints='all', name='Google',
                    fillcolor='blue', jitter=0.5, whiskerwidth=0.2))
fig.add_trace(go.Box(y=df_stocks.AAPL, boxpoints='all', name='Apple',
                    fillcolor='red', jitter=0.5, whiskerwidth=0.2))
# Change background / grid colors
fig.update_layout(title='Google vs. Apple', 
                  yaxis=dict(gridcolor='rgb(255, 255, 255)',
                 gridwidth=3),
                 paper_bgcolor='rgb(243, 243, 243)',
                 plot_bgcolor='rgb(243, 243, 243)')

Violin Plot

# Violin Plot is a combination of the boxplot and KDE
# While a box plot corresponds to data points, the violin plot uses the KDE estimation
# of the data points
df_tips = px.data.tips()
px.violin(df_tips, y="total_bill", box=True, points='all')

# Multiple plots
px.violin(df_tips, y="tip", x="smoker", color="sex", box=True, points="all",
          hover_data=df_tips.columns)

# Morph left and right sides based on if the customer smokes
fig = go.Figure()
fig.add_trace(go.Violin(x=df_tips['day'][ df_tips['smoker'] == 'Yes' ],
                        y=df_tips['total_bill'][ df_tips['smoker'] == 'Yes' ],
                        legendgroup='Yes', scalegroup='Yes', name='Yes',
                        side='negative',
                        line_color='blue'))
fig.add_trace(go.Violin(x=df_tips['day'][ df_tips['smoker'] == 'No' ],
                        y=df_tips['total_bill'][ df_tips['smoker'] == 'No' ],
                        legendgroup='Yes', scalegroup='Yes', name='No',
                        side='positive',
                        line_color='red'))

Density Heatmap

# Create a heatmap using Seaborn data
flights = sns.load_dataset("flights")
flights

# You can set bins with nbinsx and nbinsy
fig = px.density_heatmap(flights, x='year', y='month', z='passengers', 
                         color_continuous_scale="Viridis")
fig

# You can add histograms
fig = px.density_heatmap(flights, x='year', y='month', z='passengers', 
                         marginal_x="histogram", marginal_y="histogram")
fig

3D Scatter Plots

# Create a 3D scatter plot using flight data
fig = px.scatter_3d(flights, x='year', y='month', z='passengers', color='year',
                   opacity=0.7, width=800, height=400)
fig

3D Line Plots

fig = px.line_3d(flights, x='year', y='month', z='passengers', color='year')
fig

Scatter Matrix

# With a scatter matrix we can compare changes when comparing column data
fig = px.scatter_matrix(flights, color='month')
fig

Map Scatter Plots

# There are many interesting ways of working with maps
# plotly.com/python-api-reference/generated/plotly.express.scatter_geo.html
df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations="iso_alpha",
                     color="continent", # which column to use to set the color of markers
                     hover_name="country", # column added to hover information
                     size="pop", # size of markers
                     projection="orthographic")
fig

Choropleth Maps

# You can color complex maps like we do here representing unemployment data

# Allows us to grab data from a supplied URL
from urllib.request import urlopen
# Used to decode JSON data
import json
# Grab US county geometry data
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

# Grab unemployment data based on each counties Federal Information Processing number
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

# Draw map using the county JSON data, color using unemployment values on a range of 12
fig = px.choropleth(df, geojson=counties, locations='fips', color='unemp',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemp':'unemployment rate'}
                          )
fig

Polar Chart

# Polar charts display data radially 
# Let's plot wind data based on direction and frequency
# You can change size and auto-generate different symbols as well
df_wind = px.data.wind()
px.scatter_polar(df_wind, r="frequency", theta="direction", color="strength",
                size="frequency", symbol="strength")

# Data can also be plotted using lines radially
# A template makes the data easier to see
px.line_polar(df_wind, r="frequency", theta="direction", color="strength",
                line_close=True, template="plotly_dark", width=800, height=400)

Ternary Plot

# Used to represent ratios of 3 variables
df_exp = px.data.experiment()
px.scatter_ternary(df_exp, a="experiment_1", b="experiment_2", 
                   c='experiment_3', hover_name="group", color="gender")

Facets

# You can create numerous subplots
df_tips = px.data.tips()
px.scatter(df_tips, x="total_bill", y="tip", color="smoker", facet_col="sex")

# We can line up data in rows and columns
px.histogram(df_tips, x="total_bill", y="tip", color="sex", facet_row="time", facet_col="day",
       category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})

# This dataframe provides scores for different students based on the level
# of attention they could provide during testing
att_df = sns.load_dataset("attention")
fig = px.line(att_df, x='solutions', y='score', facet_col='subject',
             facet_col_wrap=5, title='Scores Based on Attention')
fig

Animated Plots

# Create an animated plot that you can use to cycle through continent
# GDP & life expectancy changes
df_cnt = px.data.gapminder()
px.scatter(df_cnt, x="gdpPercap", y="lifeExp", animation_frame="year", 
           animation_group="country",
           size="pop", color="continent", hover_name="country",
           log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])

# Watch as bars chart population changes
px.bar(df_cnt, x="continent", y="pop", color="continent",
  animation_frame="year", animation_group="country", range_y=[0,4000000000])