我需要创建一个简单的图表线图,该图表由分类的数据列着色。数据是需要按类别着色的时间序列数据。有没有人知道如何使用python plotly api在一个简单的折线图或时间序列图中设置颜色类别?
x_axes - 时间数据 y_axes - 深度数据从0'到5000' 类别 - on_bottom,off_bottom,钻井等
输出示例如下图所示,由上面列出的类别列着色?
答案 0 :(得分:2)
您需要对数据进行分组,并在图表中以不同的轨迹显示它们。您可以使用DataFrame Subsetting
执行此操作。进行子集化的主线就是这样。
df[df['direction'] == 'Increasing']['AAPL.Open']
在df[df['direction'] == 'Increasing']
部分中,我们会检查数据框的direction
列是否等于Increasing
值/类别,如果为true,则数据框是子集化的只有那些值存在,那么我们可以通过使用零件['AAPL.Open']
选择列来选择要绘制的特定列
请参阅以下示例,如果您的问题已解决,请与我们联系!
<强>代码:强>
import plotly.offline as py
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot, plot
from plotly import tools
import pandas as pd
import numpy as np
init_notebook_mode(connected=True)
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
opening_increasing = go.Scatter(
x=df.Date,
y=df[df['direction'] == 'Increasing']['AAPL.Open'],
name = "AAPL Opening Price - Increasing",
line = dict(color = '#17BECF'),
opacity = 0.8)
opening_decreasing = go.Scatter(
x=df.Date,
y=df[df['direction'] == 'Decreasing']['AAPL.Open'],
name = "AAPL Opening Price - Decreasing",
line = dict(color = '#7F7F7F'),
opacity = 0.8)
data = [opening_increasing, opening_decreasing]
layout = dict(
title = "Apple Opening Price by Increasing/Decreasing Categories of Direction"
)
fig = dict(data=data, layout=layout)
py.iplot(fig, filename = "Manually Set Range")
<强>输出:强>