英文:
Side by side plot display for different traces in plotly
问题
这是我的数据框的一部分：
这是我的数据框的一部分：
我正在尝试绘制这个图：
我正在尝试绘制这个图：
将 commit_date 转换为日期格式：
将 'commit_date' 转换为日期格式：
将数据按 api_group、commit_date 年份和 type_of_change 分组，计算数量并重置索引：
将数据按 'api_group'、'commit_date' 年份和 'type_of_change' 分组，计算数量并重置索引：
创建图形列表：
创建图形列表：
对于每个 api_group，创建一个分组的数据透视表：
对于每个 'api_group'，创建一个分组的数据透视表：
创建图形对象：
创建图形对象：
添加 Breaking 和 Non-Breaking 的散点图：
添加 Breaking 和 Non-Breaking 的散点图：
更新图形布局：
更新图形布局：
将图形添加到图形列表：
将图形添加到图形列表：
对于每个图形，显示图形：
对于每个图形，显示图形：
英文:
This is a snippet of my dataframe:
api_group commit_date type_of_change count
Other 2015 Non-Breaking 23
Mature 2016 Non-Breaking 96
Developing 2022 Breaking 2
Legacy 2022 Non-Breaking 3
Other 2019 Non-Breaking 148
Legacy 2018 Non-Breaking 5
Early 2019 Breaking 1793
Legacy 2021 Non-Breaking 12
Early 2016 Breaking 711
Mature 2022 Non-Breaking 30
I am trying to plot this graph:
import pandas as pd
import plotly.graph_objs as go
new['commit_date'] = pd.to_datetime(new['commit_date'])
df_grouped = new.groupby([new['api_group'], new['commit_date'].dt.year, 'type_of_change'])['type_of_change'].count().reset_index(name='count')
figs = []
for api_group in df_grouped['api_group'].unique():
breaking_pivot = df_grouped[df_grouped['api_group'] == api_group].pivot_table(index='commit_date', columns='type_of_change', values='count', fill_value=0)
fig = go.Figure()
fig.add_trace(go.Scatter(x=breaking_pivot.index, y=breaking_pivot['Breaking'], name='Breaking', line=dict(dash='solid'), line_width=2.5))
fig.add_trace(go.Scatter(x=breaking_pivot.index, y=breaking_pivot['Non-Breaking'], name='Non-Breaking', line=dict(dash='dot'), line_width=2.5))
fig.update_layout(title=f'Evolution of Breaking Changes Over Time for {api_group}', width=600, height=500, template='ggplot2', xaxis_title='Year', yaxis_title='Number of Releases')
figs.append(fig)
for fig in figs:
fig.show()
For every api_group, it displays the breaking and non breaking changes in it, but the issue is it plots all the graphs for all the 5 groups in a line, but I want them displayed side by side. The graph is this: 
Is there a way I could do this, because I am not sure how to plot multiple traces using subplot function in plotly.
答案1
得分: 1
我略微修改了您的数据以得到更好的示例
数据
import pandas as pd
import plotly.express as px
data = [['开发中', 2022, '破坏', 2],
['早期', 2016, '破坏', 711],
['早期', 2019, '破坏', 1793],
['传统', 2018, '不破坏', 5],
['传统', 2021, '不破坏', 12],
['传统', 2022, '不破坏', 3],
['成熟', 2016, '不破坏', 96],
['成熟', 2022, '不破坏', 30],
['其他', 2015, '不破坏', 23],
['其他', 2019, '不破坏', 148],
['其他', 2017, '破坏', 15],
['其他', 2019, '破坏', 5]]
df = pd.DataFrame(
data,
columns=[
'api_group',
'commit_date',
'type_of_change',
'count'])
现在你想确保数据被正确排序
df = df.sort_values(
["api_group",
"type_of_change",
"commit_date"])\
.reset_index(drop=True)
绘图
# 在这里我们利用plotly express制作子图
fig = px.line(
df,
x="commit_date",
y="count",
facet_col="api_group",
color="type_of_change")
# 在这里我更改为'点'，以防我们正在绘制'不破坏'
for d in fig.data:
if d["legendgroup"] == '不破坏':
d["line"]["dash"] = "dot"
fig.show()
英文:
I slightly changed your data to have a better example
Data
import pandas as pd
import plotly.express as px
data = [['Developing', 2022, 'Breaking', 2],
['Early', 2016, 'Breaking', 711],
['Early', 2019, 'Breaking', 1793],
['Legacy', 2018, 'Non-Breaking', 5],
['Legacy', 2021, 'Non-Breaking', 12],
['Legacy', 2022, 'Non-Breaking', 3],
['Mature', 2016, 'Non-Breaking', 96],
['Mature', 2022, 'Non-Breaking', 30],
['Other', 2015, 'Non-Breaking', 23],
['Other', 2019, 'Non-Breaking', 148],
['Other', 2017, 'Breaking', 15],
['Other', 2019, 'Breaking', 5]]
df = pd.DataFrame(
data,
columns=[
'api_group',
'commit_date',
'type_of_change',
'count'])
Now ou want to be sure data is correclty ordered
df = df.sort_values(
["api_group",
"type_of_change",
"commit_date"])\
.reset_index(drop=True)
Plot
# here we leverage plotly express for
# subplots
fig = px.line(
df,
x="commit_date",
y="count",
facet_col="api_group",
color="type_of_change")
# here I change to `dot` in case we are plotting
# 'Non-Breaking'
for d in fig.data:
if d["legendgroup"] == 'Non-Breaking':
d["line"]["dash"] = "dot"
fig.show()
答案2
得分: 0
当添加的样本数据是数据处理后的数据时，每个api_group的子图将绘制一个使用api_group中提取的数据的折线图。事先准备了一种线型的字典，从列值中搜索线型名称。将子图规格添加到图形规格中，假设每行有5列；对于5行和1列，设置是反向的。
英文:
When the added sample data is the data after data processing, the subplot for each api_group will draw a line graph with the data extracted in the api_group. A dictionary of line types is prepared beforehand, and line type names are searched in the dictionary from the column values. The subplot specification is added to the graph specification, assuming 5 columns per row; for 5 rows and 1 column, the setting is reversed.
import pandas as pd
import plotly.graph_objs as go
from plotly.subplots import make_subplots
line_dashs = {'Breaking': 'solid', 'Non-Breaking': 'dot'}
api_groups = new['api_group'].unique()
#fig = go.Figure()
fig = make_subplots(rows=1, cols=len(api_groups), subplot_titles=api_groups)
for i,api_group in enumerate(new['api_group'].unique()):
breaking_pivot = new.query('api_group == @api_group')
#print(breaking_pivot)
fig.add_trace(go.Scatter(mode='markers+lines',
x=breaking_pivot['commit_date'],
y=breaking_pivot['count'],
name=api_group,
line=dict(dash=line_dashs[breaking_pivot.loc[:,'type_of_change'].unique()[0]]),
line_width=2.5,
), row=1, col=i+1)
fig.update_layout(width=1250,
height=500,
template='ggplot2',
xaxis_title='Year',
yaxis_title='Number of Releases')
fig.update_xaxes(type='date', tickformat='%Y')
fig.show()
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