(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)
<小时>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:
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 102.378682fig=px.line(x=df.index, y = df.columns)这会引发错误:
<块引用>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`
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
fig = px.line(df, x='id', y='value', color='variable')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:
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
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
yis 3, whereas the length of previous arguments ['x'] is 100`
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?
np.nango)fid.add_traces()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
px)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
df = pd.melt(df, id_vars='id', value_vars=df.columns[:-1])
fig = px.line(df, x='id', y='value', color='variable')
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()
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