Plotly:如何为 x 轴上的时间序列设置主要刻度线/网格线的值?
Plotly: How to set values for major ticks / gridlines for timeseries on x-axis?
背景:
此问题与 . A similar question has also been asked but not answered for matplotlib here: How do I show major ticks as the first day of each months and minor ticks as each day?
相关,但不完全相同
Plotly 很棒,也许唯一困扰我的是刻度线/网格线的自动选择以及为 x 轴选择的标签,如下图所示:
地块 1:
我认为这里显示的自然是每个月的第一天(当然取决于时间段)。或者甚至可能只是一个缩写的月份名称,例如每个刻度上的 'Jan'
。由于所有月份的长度都不相等,我意识到技术上什至视觉上的挑战。但是有人知道怎么做吗?
可重现的片段:
import plotly
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import pandas as pd
import numpy as np
from IPython.display import HTML
from IPython.core.display import display, HTML
import copy
# setup
init_notebook_mode(connected=True)
np.random.seed(123)
cf.set_config_file(theme='pearl')
# Random data using cufflinks
df = cf.datagen.lines()
#df = df['UUN.XY']
fig = df.iplot(asFigure=True, kind='scatter',
xTitle='Dates',yTitle='Returns',title='Returns')
iplot(fig)
(plotly 新版本的更新答案)
使用较新版本的 plotly,您可以指定 dtick = 'M1'
在每个月初设置网格线。也可以通过tickformat
:
格式化月份的显示
片段 1
fig.update_xaxes(dtick="M2",
tickformat="%b\n%Y"
)
地块 1
如果您想每两个月设置一次网格线,只需将 "M1"
更改为 "M2"
地块 2
完整代码:
# imports
import pandas as pd
import plotly.express as px
# data
df = px.data.stocks()
df = df.tail(40)
colors = px.colors.qualitative.T10
# plotly
fig = px.line(df,x = 'date',
y = [c for c in df.columns if c != 'date'],
template = 'plotly_dark',
color_discrete_sequence = colors,
title = 'Stocks',
)
fig.update_xaxes(dtick="M2",
tickformat="%b\n%Y"
)
fig.show()
旧解:
如何设置网格线将完全取决于您要显示的内容以及图形的构建方式在您尝试编辑设置之前。但是要得到题中指定的结果,可以这样做。
第一步:
为 fig['data']
中的每个系列编辑 fig['data'][series]['x']
。
第二步:
在以下位置设置刻度模式和刻度文本:
go.Layout(xaxis = go.layout.XAxis(tickvals = [some_values]
ticktext = [other_values])
)
结果:
Jupyter Notebook 的完整代码:
# imports
import plotly
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import pandas as pd
import numpy as np
from IPython.display import HTML
from IPython.core.display import display, HTML
import copy
import plotly.graph_objs as go
# setup
init_notebook_mode(connected=True)
np.random.seed(123)
cf.set_config_file(theme='pearl')
#%qtconsole --style vim
# Random data using cufflinks
df = cf.datagen.lines()
# create figure setup
fig = df.iplot(asFigure=True, kind='scatter',
xTitle='Dates',yTitle='Returns',title='Returns')
# create df1 to mess around with while
# keeping the source intact in df
df1 = df.copy(deep = True)
df1['idx'] = range(0, len(df))
# time variable operations and formatting
df1['yr'] = df1.index.year
df1['mth'] = df1.index.month_name()
# function to replace month name with
# abbreviated month name AND year
# if the month is january
def mthFormat(month):
dDict = {'January':'jan','February':'feb', 'March':'mar',
'April':'apr', 'May':'may','June':'jun', 'July':'jul',
'August':'aug','September':'sep', 'October':'oct',
'November':'nov', 'December':'dec'}
mth = dDict[month]
return(mth)
# replace month name with abbreviated month name
df1['mth'] = [mthFormat(m) for m in df1['mth']]
# remove adjacent duplicates for year and month
df1['yr'][df1['yr'].shift() == df1['yr']] = ''
df1['mth'][df1['mth'].shift() == df1['mth']] = ''
# select and format values to be displayed
df1['idx'][df1['mth']!='']
df1['display'] = df1['idx'][df1['mth']!='']
display = df1['display'].dropna()
displayVal = display.values.astype('int')
df_display = df1.iloc[displayVal]
df_display['display'] = df_display['display'].astype('int')
df_display['yrmth'] = df_display['mth'] + '<br>' + df_display['yr'].astype(str)
# set properties for each trace
for ser in range(0,len(fig['data'])):
fig['data'][ser]['x'] = df1['idx'].values.tolist()
fig['data'][ser]['text'] = df1['mth'].values.tolist()
fig['data'][ser]['hoverinfo']='all'
# layout for entire figure
f2Data = fig['data']
f2Layout = go.Layout(
xaxis = go.layout.XAxis(
tickmode = 'array',
tickvals = df_display['display'].values.tolist(),
ticktext = df_display['yrmth'].values.tolist(),
zeroline = False)#,
)
# plot figure with specified major ticks and gridlines
fig2 = go.Figure(data=f2Data, layout=f2Layout)
iplot(fig2)
一些重要细节:
1。 iplot()
的灵活性和限制:
这种使用 iplot()
并编辑所有这些设置的方法有点笨拙,但它在数据集中的列/变量数量方面非常灵活,并且可以说比手动构建每个跟踪更可取,例如 trace1 = go.Scatter()
对于 df 中的每一列。
2。为什么你必须编辑每个系列/轨迹?
如果你尝试用
跳过中间部分
for ser in range(0,len(fig['data'])):
fig['data'][ser]['x'] = df1['idx'].values.tolist()
fig['data'][ser]['text'] = df1['mth'].values.tolist()
fig['data'][ser]['hoverinfo']='all'
并尝试直接在整个plot上设置tickvals
和ticktext
,不会有任何效果:
我觉得这有点奇怪,但我认为这是由 iplot()
.
发起的一些底层设置引起的
3。还缺一件事:
为了使设置生效,ticvals
和 ticktext
的结构分别为 [0, 31, 59, 90]
和 ['jan<br>2015', 'feb<br>', 'mar<br>', 'apr<br>']
。这导致 xaxis 行 hovertext 显示数据的位置,其中 ticvals
和 ticktext
为空:
非常感谢任何关于如何改进整个事情的建议。比我自己更好的解决方案将立即获得 Accepted Answer 状态!
背景:
此问题与
Plotly 很棒,也许唯一困扰我的是刻度线/网格线的自动选择以及为 x 轴选择的标签,如下图所示:
地块 1:
我认为这里显示的自然是每个月的第一天(当然取决于时间段)。或者甚至可能只是一个缩写的月份名称,例如每个刻度上的 'Jan'
。由于所有月份的长度都不相等,我意识到技术上什至视觉上的挑战。但是有人知道怎么做吗?
可重现的片段:
import plotly
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import pandas as pd
import numpy as np
from IPython.display import HTML
from IPython.core.display import display, HTML
import copy
# setup
init_notebook_mode(connected=True)
np.random.seed(123)
cf.set_config_file(theme='pearl')
# Random data using cufflinks
df = cf.datagen.lines()
#df = df['UUN.XY']
fig = df.iplot(asFigure=True, kind='scatter',
xTitle='Dates',yTitle='Returns',title='Returns')
iplot(fig)
(plotly 新版本的更新答案)
使用较新版本的 plotly,您可以指定 dtick = 'M1'
在每个月初设置网格线。也可以通过tickformat
:
片段 1
fig.update_xaxes(dtick="M2",
tickformat="%b\n%Y"
)
地块 1
如果您想每两个月设置一次网格线,只需将 "M1"
更改为 "M2"
地块 2
完整代码:
# imports
import pandas as pd
import plotly.express as px
# data
df = px.data.stocks()
df = df.tail(40)
colors = px.colors.qualitative.T10
# plotly
fig = px.line(df,x = 'date',
y = [c for c in df.columns if c != 'date'],
template = 'plotly_dark',
color_discrete_sequence = colors,
title = 'Stocks',
)
fig.update_xaxes(dtick="M2",
tickformat="%b\n%Y"
)
fig.show()
旧解:
如何设置网格线将完全取决于您要显示的内容以及图形的构建方式在您尝试编辑设置之前。但是要得到题中指定的结果,可以这样做。
第一步:
为 fig['data']
中的每个系列编辑 fig['data'][series]['x']
。
第二步:
在以下位置设置刻度模式和刻度文本:
go.Layout(xaxis = go.layout.XAxis(tickvals = [some_values]
ticktext = [other_values])
)
结果:
Jupyter Notebook 的完整代码:
# imports
import plotly
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import pandas as pd
import numpy as np
from IPython.display import HTML
from IPython.core.display import display, HTML
import copy
import plotly.graph_objs as go
# setup
init_notebook_mode(connected=True)
np.random.seed(123)
cf.set_config_file(theme='pearl')
#%qtconsole --style vim
# Random data using cufflinks
df = cf.datagen.lines()
# create figure setup
fig = df.iplot(asFigure=True, kind='scatter',
xTitle='Dates',yTitle='Returns',title='Returns')
# create df1 to mess around with while
# keeping the source intact in df
df1 = df.copy(deep = True)
df1['idx'] = range(0, len(df))
# time variable operations and formatting
df1['yr'] = df1.index.year
df1['mth'] = df1.index.month_name()
# function to replace month name with
# abbreviated month name AND year
# if the month is january
def mthFormat(month):
dDict = {'January':'jan','February':'feb', 'March':'mar',
'April':'apr', 'May':'may','June':'jun', 'July':'jul',
'August':'aug','September':'sep', 'October':'oct',
'November':'nov', 'December':'dec'}
mth = dDict[month]
return(mth)
# replace month name with abbreviated month name
df1['mth'] = [mthFormat(m) for m in df1['mth']]
# remove adjacent duplicates for year and month
df1['yr'][df1['yr'].shift() == df1['yr']] = ''
df1['mth'][df1['mth'].shift() == df1['mth']] = ''
# select and format values to be displayed
df1['idx'][df1['mth']!='']
df1['display'] = df1['idx'][df1['mth']!='']
display = df1['display'].dropna()
displayVal = display.values.astype('int')
df_display = df1.iloc[displayVal]
df_display['display'] = df_display['display'].astype('int')
df_display['yrmth'] = df_display['mth'] + '<br>' + df_display['yr'].astype(str)
# set properties for each trace
for ser in range(0,len(fig['data'])):
fig['data'][ser]['x'] = df1['idx'].values.tolist()
fig['data'][ser]['text'] = df1['mth'].values.tolist()
fig['data'][ser]['hoverinfo']='all'
# layout for entire figure
f2Data = fig['data']
f2Layout = go.Layout(
xaxis = go.layout.XAxis(
tickmode = 'array',
tickvals = df_display['display'].values.tolist(),
ticktext = df_display['yrmth'].values.tolist(),
zeroline = False)#,
)
# plot figure with specified major ticks and gridlines
fig2 = go.Figure(data=f2Data, layout=f2Layout)
iplot(fig2)
一些重要细节:
1。 iplot()
的灵活性和限制:
这种使用 iplot()
并编辑所有这些设置的方法有点笨拙,但它在数据集中的列/变量数量方面非常灵活,并且可以说比手动构建每个跟踪更可取,例如 trace1 = go.Scatter()
对于 df 中的每一列。
2。为什么你必须编辑每个系列/轨迹?
如果你尝试用
跳过中间部分for ser in range(0,len(fig['data'])):
fig['data'][ser]['x'] = df1['idx'].values.tolist()
fig['data'][ser]['text'] = df1['mth'].values.tolist()
fig['data'][ser]['hoverinfo']='all'
并尝试直接在整个plot上设置tickvals
和ticktext
,不会有任何效果:
我觉得这有点奇怪,但我认为这是由 iplot()
.
3。还缺一件事:
为了使设置生效,ticvals
和 ticktext
的结构分别为 [0, 31, 59, 90]
和 ['jan<br>2015', 'feb<br>', 'mar<br>', 'apr<br>']
。这导致 xaxis 行 hovertext 显示数据的位置,其中 ticvals
和 ticktext
为空:
非常感谢任何关于如何改进整个事情的建议。比我自己更好的解决方案将立即获得 Accepted Answer 状态!