由网友(怀里藏娇)分享简介：(这是一篇自我回答的帖子，通过不必解释 plotly 如何最好地处理长格式和宽格式数据来帮助其他人缩短他们对 plotly 问题的答案)我想在尽可能少的行中基于 pandas 数据框构建一个绘图图.我知道你可以使用 plotly.express 来做到这一点，但这对于我称之为标准 pandas 数据框的方法来说是失败的...

(这是一篇自我回答的帖子，通过不必解释 plotly 如何最好地处理长格式和宽格式数据来帮助其他人缩短他们对 plotly 问题的答案)

我想在尽可能少的行中基于 pandas 数据框构建一个绘图图.我知道你可以使用 plotly.express 来做到这一点，但这对于我称之为标准 pandas 数据框的方法来说是失败的；描述行顺序的索引，以及描述数据框中值名称的列名:

示例数据框:

 a b c0 100.000000 100.000000 100.0000001 98.493705 99.421400 101.6514372 96.067026 98.992487 102.9173733 95.200286 98.313601 102.8226644 96.691675 97.674699 102.378682

尝试:

fig=px.line(x=df.index, y = df.columns)

这会引发错误:

ValueError:所有参数的长度应该相同.参数 y 的长度是 3，而前面的参数 ['x'] 的长度是 100`

解决方案

在这里，您尝试使用宽格式的 pandas 数据框作为 px.line 的源.plotly.express 旨在与

如何使用px绘制长数据?

fig = px.line(df, x='id', y='value', color='variable')

如何使用 go 绘制宽数据?

colors = px.colors.qualitative.Plotlyfig = go.Figure()fig.add_traces(go.Scatter(x=df['id'], y = df['a'], mode = 'lines', line=dict(color=colors[0])))fig.add_traces(go.Scatter(x=df['id'], y = df['b'], mode = 'lines', line=dict(color=colors[1])))fig.add_traces(go.Scatter(x=df['id'], y = df['c'], mode = 'lines', line=dict(color=colors[2])))图.show()

从表面上看，go 更复杂，或许更灵活?嗯，是.和不.您可以使用 px 轻松构建图形并添加您想要的任何 go 对象！

完整的 go 代码段:

将 numpy 导入为 np将熊猫导入为 pd将 plotly.express 导入为 px导入 plotly.graph_objects# 宽格式数据帧np.random.seed(123)X = np.random.randn(100,3)df=pd.DataFrame(X, columns=['a','b','c'])df=df.cumsum()df['id']=df.index# plotly.graph_objects颜色 = px.colors.qualitative.Plotlyfig = go.Figure()fig.add_traces(go.Scatter(x=df['id'], y = df['a'], mode = 'lines', line=dict(color=colors[0])))fig.add_traces(go.Scatter(x=df['id'], y = df['b'], mode = 'lines', line=dict(color=colors[1])))fig.add_traces(go.Scatter(x=df['id'], y = df['c'], mode = 'lines', line=dict(color=colors[2])))图.show()

完整的像素片段:

将 numpy 导入为 np将熊猫导入为 pd将 plotly.express 导入为 px从 plotly.offline 导入 iplot# 宽格式数据帧np.random.seed(123)X = np.random.randn(100,3)df=pd.DataFrame(X, columns=['a','b','c'])df=df.cumsum()df['id']=df.index# 长格式数据帧df = pd.melt(df, id_vars='id', value_vars=df.columns[:-1])# 情节表达fig = px.line(df, x='id', y='值', color='变量')图.show()

(This is a self-answered post to help others shorten their answers to plotly questions by not having to explain how plotly best handles data of long and wide format)

I'd like to build a plotly figure based on a pandas dataframe in as few lines as possible. I know you can do that using plotly.express, but this fails for what I would call a standard pandas dataframe; an index describing row order, and column names describing the names of a value in a dataframe:

Sample dataframe:

    a           b           c
0   100.000000  100.000000  100.000000
1   98.493705   99.421400   101.651437
2   96.067026   98.992487   102.917373
3   95.200286   98.313601   102.822664
4   96.691675   97.674699   102.378682

An attempt:

fig=px.line(x=df.index, y = df.columns)

This raises an error:

ValueError: All arguments should have the same length. The length of argument y is 3, whereas the length of previous arguments ['x'] is 100`

解决方案

Here you've tried to use a pandas dataframe of a wide format as a source for px.line. And plotly.express is designed to be used with dataframes of a long format, often referred to as tidy data (and please take a look at that. No one explains it better that Wickham). Many, particularly those injured by years of battling with Excel, often find it easier to organize data in a wide format. So what's the difference?

Wide format:

data is presented with each different data variable in a separate column each column has only one data type missing values are often represented by np.nan works best with plotly.graphobjects (go) lines are often added to a figure using fid.add_traces() colors are normally assigned to each trace

Example:

            a          b           c
0   -1.085631    0.997345   0.282978
1   -2.591925    0.418745   1.934415
2   -5.018605   -0.010167   3.200351
3   -5.885345   -0.689054   3.105642
4   -4.393955   -1.327956   2.661660
5   -4.828307    0.877975   4.848446
6   -3.824253    1.264161   5.585815
7   -2.333521    0.328327   6.761644
8   -3.587401   -0.309424   7.668749
9   -5.016082   -0.449493   6.806994

Long format:

data is presented with one column containing all the values and another column listing the context of the value missing values are simply not included in the dataset. works best with plotly.express (px) colors are set by a default color cycle and are assigned to each unique variable

Example:

    id  variable    value
0   0   a        -1.085631
1   1   a        -2.591925
2   2   a        -5.018605
3   3   a        -5.885345
4   4   a        -4.393955
... ... ... ...
295 95  c        -4.259035
296 96  c        -5.333802
297 97  c        -6.211415
298 98  c        -4.335615
299 99  c        -3.515854

How to go from wide to long?

df = pd.melt(df, id_vars='id', value_vars=df.columns[:-1])

The two snippets below will produce the very same plot:

How to use px to plot long data?

fig = px.line(df, x='id', y='value', color='variable')

How to use go to plot wide data?

colors = px.colors.qualitative.Plotly
fig = go.Figure()
fig.add_traces(go.Scatter(x=df['id'], y = df['a'], mode = 'lines', line=dict(color=colors[0])))
fig.add_traces(go.Scatter(x=df['id'], y = df['b'], mode = 'lines', line=dict(color=colors[1])))
fig.add_traces(go.Scatter(x=df['id'], y = df['c'], mode = 'lines', line=dict(color=colors[2])))
fig.show()

By the looks of it, go is more complicated and offers perhaps more flexibility? Well, yes. And no. You can easily build a figure using px and add any go object you'd like!

Complete go snippet:

import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

# dataframe of a wide format
np.random.seed(123)
X = np.random.randn(100,3)  
df=pd.DataFrame(X, columns=['a','b','c'])
df=df.cumsum()
df['id']=df.index

# plotly.graph_objects
colors = px.colors.qualitative.Plotly
fig = go.Figure()
fig.add_traces(go.Scatter(x=df['id'], y = df['a'], mode = 'lines', line=dict(color=colors[0])))
fig.add_traces(go.Scatter(x=df['id'], y = df['b'], mode = 'lines', line=dict(color=colors[1])))
fig.add_traces(go.Scatter(x=df['id'], y = df['c'], mode = 'lines', line=dict(color=colors[2])))
fig.show()

Complete px snippet:

import numpy as np
import pandas as pd
import plotly.express as px
from plotly.offline import iplot

# dataframe of a wide format
np.random.seed(123)
X = np.random.randn(100,3)  
df=pd.DataFrame(X, columns=['a','b','c'])
df=df.cumsum()
df['id']=df.index

# dataframe of a long format
df = pd.melt(df, id_vars='id', value_vars=df.columns[:-1])

# plotly express
fig = px.line(df, x='id', y='value', color='variable')
fig.show()