如何创建 Python iplot 图形，颜色随值变化？答案

【问题标题】：How can create Python iplot graph, colors changes with value?如何创建 Python iplot 图形，颜色随值变化？
【发布时间】：2017-03-26 23:03:19
【问题描述】：

这里你是我数据的一部分。

我统计我的数据

count_interests = interests.count()

然后做了一个图表

count_interests.iplot(kind = 'bar', xTitle='Interests', yTitle='Number of Person', colors='Red')

我尝试了很多次找到一个函数来改变列的颜色值，所以更大和更小的列看起来不同的颜色。

我知道有colorscale 和color 函数，我试了很多次都找不到。有谁知道函数吗？

【问题讨论】：

标签： python plot plotly

【解决方案1】：

您可以定义一个函数，该函数为每个值返回一种颜色，然后在 list 中传递每个条形的颜色。

import pandas as pd
import plotly

def color(val, median, std):
    if val > median + std:
            return 'darkgreen'
    if val < median - std:
            return 'darkred'
    return 'orange'

df = pd.DataFrame({'cinema': [1, 2, 5, 3, 3, None],
                   'theatre': [3, 0, 8, 4, 0, 4],
                   'wine': [3, 2, 5, None, 1, None],
                   'beer': [4, 8, 2, None, None, None]})

med = df.count().median()
std = df.count().std()

colors = [color(i, med, std) for i in df.count()]

fig = plotly.graph_objs.Bar(x=df.columns,
                            y=df.count(),
                            marker=dict(color=colors))

plotly.offline.plot([fig])

【讨论】：

【解决方案2】：

条形也可以通过pd.pivot_table() 行到列或通过为条形创建单独的跟踪列表来着色。此处，以sum() 为例对每一列进行聚合。代码如下：

# Import libraries
import datetime
from datetime import date
import pandas as pd
import numpy as np
from plotly import __version__
%matplotlib inline

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

init_notebook_mode(connected=True)
cf.go_offline()

import plotly.graph_objs as go
import plotly.offline as pyo


# Create dataframe
INT_M_PUB = [0,0,0,0,0,1,0,0,0,0]
INT_M_CINEMA = [1,1,1,0,0,0,0,0,0,1]
INT_M_THEATRE = [1,0,1,0,0,1,0,1,0,1]
INT_M_GYM = [0,0,0,0,0,1,0,0,0,1]
INT_M_ENTERTAIN = [0,0,1,1,0,1,0,1,0,1]
INT_M_EATOUT = [0,1,1,0,0,1,0,0,1,1]
INT_M_WINE = [0,0,0,0,0,1,0,0,0,1]


interests = pd.DataFrame({'INT_M_PUB':INT_M_PUB, 'INT_M_CINEMA':INT_M_CINEMA, 'INT_M_THEATRE':INT_M_THEATRE,
                   'INT_M_GYM':INT_M_GYM, 'INT_M_ENTERTAIN':INT_M_ENTERTAIN, 'INT_M_EATOUT':INT_M_EATOUT,
                   'INT_M_WINE':INT_M_WINE
                  })
interests.head(2)


dfm = interests.sum().reset_index().rename(columns={'index':'interests', 0:'value'})
dfm

# Re-creating the plot similar to that in question (note: y-axis scales are different)
df = dfm.copy()
col_list = df.columns

df.iplot(kind = 'bar', x='interests', y='value', xTitle='Interests', yTitle='Number of Person', title='These bars need to be colored', color='red')

# Color plots by creating traces
# Initialize empty list named data to collect traces for each bar
data = []
for col_name in col_list:
    trace = go.Bar(
        x=[col_name],
        y=df[col_name],
        name=col_name
    )
    data.append(trace)

data = data
layout = go.Layout(
    barmode='group',
    title='Interests',
    xaxis=dict(title='Interests'),
    yaxis=dict(title='Number of Person')
)

fig = go.Figure(data=data, layout=layout)
pyo.iplot(fig, filename='grouped-bar')

# Creating plot by pivoting the table
df = pd.pivot_table(dfm, values='value', columns='interests')
df.iplot(kind = 'bar',xTitle='Interests', yTitle='Number of Person')

【讨论】：