Pandas，Bokeh或使用任何绘图库移动x轴以获取季节性数据(第7个月-> 12-> 6个月或7月1日-6月30日) [英] Pandas, Bokeh, or using any plotting library for shifting the x-axis for seasonal data (months 7 -> 12 -> 6 or July 01 - June 30)

问题描述

我想显示7月1日至6月30日这一季节年度的季节性降雪数据.

I want to display seasonal snow data for the seasonal year from July 01 - June 30.

df = pd.DataFrame({'date1':['1954-03-20','1955-02-23','1956-01-01','1956-11-21','1958-01-07'],
               'date2':['1954-03-25','1955-02-26','1956-02-11','1956-11-30','1958-01-17']},
              index=['1954','1955','1956','1957','1958'])

这是对我之前的问题的扩展

It is an extension to my previous question Pandas: Visualizing Changes in Event Dates for Multiple Years using Bokeh or any other plotting library

斯科特·波士顿(Scott Boston)在回答该问题时建议使用Range1D并在.它适用于连续标量，但我无法使其适用于[182:366]，[1:181]等不连续范围.

Scott Boston, in his answer to my comment in that question, suggested using Range1D and modifyng the answer in How can I accomplish `set_xlim` or `set_ylim` in Bokeh?. It works for continuous scalars, but I couldn't get it to work with a discontinuous ranges like [182:366], [1:181].

添加x_range = Range1d(182，366)可以显示季节性年份的上半年，但我无法获得季节性年份的下半年(1，181).

Adding x_range=Range1d(182, 366) shows me the first half of the seasonal year, but I can't get the second half of the seasonal year (1, 181).

df['date2'] = pd.to_datetime(df['date2'])

df['date1'] = pd.to_datetime(df['date1'])

df=df.assign(date2_DOY=df.date2.dt.dayofyear)
df=df.assign(date1_DOY=df.date1.dt.dayofyear)

from bokeh.plotting import figure, show
from bokeh.io import output_notebook
from bokeh.models import FuncTickFormatter, FixedTicker
p1 = figure(plot_width=1000, plot_height=300,x_range=Range1d(180, 366))

p1.circle(df.date1_DOY,df.index, color='red', legend='Date1')
p1.circle(df.date2_DOY,df.index, color='green', legend='Date2')
p1.xaxis[0].ticker=FixedTicker(ticks=[1,32,60,91,121,152,182,213,244,274,305,335,366])
p1.xaxis.formatter = FuncTickFormatter(code="""
 var labels = {'1':'Jan',32:'Feb',60:'Mar',91:'Apr',121:'May',152:'Jun',182:'Jul',213:'Aug',244:'Sep',274:'Oct',305:'Nov',335:'Dec',366:'Jan'}
 return labels[tick];
""")
show(p1)

#(Code from Scott's answer to my previous question.)

推荐答案

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.DataFrame({'date1':['1954-03-20','1955-02-23','1956-01-01','1956-11-21','1958-01-07'],
               'date2':['1954-03-25','1955-02-26','1956-02-11','1956-11-30','1958-01-17']},
              index=['1954','1955','1956','1957','1958'])
df['date2'] = pd.to_datetime(df['date2'])

df['date1'] = pd.to_datetime(df['date1'])

从6月1日开始调整到轴的一年中的映射天.

Adjusted mapping day of year to axis starting with Jun 1st.

df['date2_DOY_map'] = np.where(df['date2'].dt.dayofyear<151,df['date2'].dt.dayofyear-151+365,df['date2'].dt.dayofyear-151)

df['date1_DOY_map'] = np.where(df['date1'].dt.dayofyear<151,df['date1'].dt.dayofyear-151+365,df['date1'].dt.dayofyear-151)

from bokeh.plotting import figure, show
from bokeh.io import output_notebook

添加Range1d导入表单bokeh.models

Add Range1d import form bokeh.models

from bokeh.models import FuncTickFormatter, FixedTicker,Range1d
p1 = figure(plot_width=1000, plot_height=300,x_range=Range1d(1, 366))

p1.circle(df.date1_DOY_map,df.index, color='red', legend='Date1')
p1.circle(df.date2_DOY_map,df.index, color='green', legend='Date2')

修复了X标记和标签，使其与6月1日开始匹配

Fixed x-ticks and labels to match Jun 1st start

p1.xaxis[0].ticker=FixedTicker(ticks=[1,31,62,93,123,154,184,215,246,274,305,335,366])
p1.xaxis.formatter = FuncTickFormatter(code="""
 var labels = {'1':'Jun',31:'Jul',62:'Aug',93:'Sep',123:'Oct',154:'Nov',184:'Dec',215:'Jan',246:'Feb',274:'Mar',305:'Apr',335:'May',366:'Jun'}
 return labels[tick];
""")
show(p1)

非常容易修复，我们只需要修改日期的x轴映射并重做刻度线和标签.

Pretty easy to fix, we just need to modify the x-axis mapping of dates and redo the ticks and lables.

使用181对151，因为7月1日是第181天，而6月1日是第151天.

Use 181 vs 151 because Jul 1st is the 181st day where June 1st was the 151st day.

df['date2_DOY_map'] = np.where(df['date2'].dt.dayofyear<181,df['date2'].dt.dayofyear-181+365,df['date2'].dt.dayofyear-181)

df['date1_DOY_map'] = np.where(df['date1'].dt.dayofyear<181,df['date1'].dt.dayofyear-181+365,df['date1'].dt.dayofyear-181)

p1.xaxis[0].ticker=FixedTicker(ticks=[1,32,63,93,124,154,185,216,244,275,305,336,366])
p1.xaxis.formatter = FuncTickFormatter(code="""
 var labels = {'1':'Jul',32:'Aug',63:'Sep',93:'Oct',124:'Nov',154:'Dec',185:'Jan',216:'Feb',244:'Mar',275:'Apr',305:'May',336:'Jun',366:'Jul'}
 return labels[tick];
""")

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