pandas :使用Bokeh或任何其他绘图库可视化多年事件日期中的更改 [英] Pandas: Visualizing Changes in Event Dates for Multiple Years using Bokeh or any other plotting library

问题描述

我想创建一个图，其中y轴是我拥有数据的季节性年数，x轴以月和日为单位.每个季节年度都有两个日期.

I want to create a plot where the y-axis is the number of seasonal years I have data for and the x-axis is in months and days. Each seasonal year will have two dates.

|1957|...
|1956|             d1--------d2
|1955|                                d1---------d2 
|1954|                                                    d1---------d2
     |June01|...|Jan01...|Feb11|...|Feb23|...|Feb26|...|Mar20|...|Mar25|..

除了x轴涵盖了整个时间范围，而不仅仅是12个月之外，我几乎有了所需的图表.

I almost have the graph I want, except the x-axis covers the entire time span rather than just 12-months.

from bokeh.plotting import figure
p1 = figure(plot_width=1000, plot_height=300, x_axis_type="datetime")
p1.circle(merged.date1, merged.index, color = 'red', legend = 'Date1')
p1.circle(merged.date2, merged.index, color = 'green', legend = 'Date2')
show(p1)

我一直在尝试从日期中剥离年份，但仍将其绘制为日期.下面的第一行有效，但是由于the年，第二行返回实际数据错误(日期超出月份范围). df_snw ['Date1'] = df_snw ['Date1'].map(lambda x:x.strftime('％m-％d')) df_snw = pd.to_datetime(df_snw ['Date1']，format ='％m-％d')

I have been trying to strip the year from the date and still plot it as a date. The first line below works, but because of the leap years the second line returns an error in the real data (day is out of range for month). df_snw['Date1'] = df_snw['Date1'].map(lambda x: x.strftime('%m-%d')) df_snw = pd.to_datetime(df_snw['Date1'], format='%m-%d')

推荐答案

我会将x轴上的date1和date2转换为day of the year，然后将x刻度重新标记为月份.这样，所有数据都将覆盖在1到365个x轴刻度上.

I would convert your date1 and date2 to day of the year for the xaxis and re-label the x ticks as the months. This way all the data is overlayed on a 1 to 365 xaxis scale.

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'])

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)

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)

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