情节:如何从 x 轴删除空日期? [英] Plotly: How to remove empty dates from x axis?
问题描述
我有一个数据框
Date Category Sum
0 2019-06-03 "25M" 34
1 2019-06-03 "25M" 60
2 2019-06-03 "50M" 23
3 2019-06-04 "25M" 67
4 2019-06-05 "50M" -90
5 2019-06-05 "50M" 100
6 2019-06-06 "100M" 6
7 2019-06-07 "25M" -100
8 2019-06-08 "100M" 67
9 2019-06-09 "25M" 450
10 2019-06-10 "50M" 600
11 2019-06-11 "25M" -9
12 2019-07-12 "50M" 45
13 2019-07-13 "50M" 67
14 2019-07-14 "100M" 130
15 2019-07-14 "50M" 45
16 2019-07-15 "100M" 100
17 2019-07-16 "25M" -90
18 2019-07-17 "25M" 700
19 2019-07-18 "25M" -9
我想创建一个情节图，显示在每个描述的日期上为不同的类别"添加总和"，但想要删除日期，如果他们没有任何数据.
I want to create a plotly graph showing the addition of "Sum" for different "Category" on Every described date, but want to remove dates, if they don't have any data.
代码
df["Date"]=pd.to_datetime(df["Date"], format=("%Y%m%d"))
df=df.sort_values(["Date","Category","Sum"],ascending=False)
df=round(df.groupby(["Date","Category"]).agg({"Sum":"sum"}).reset_index(),1)
fig = px.bar(df, x=df["Date"] , y='Sum',barmode="group",color="Category")
fig.update_xaxes(
rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=1, label="day", step="day", stepmode="todate"),
dict(count=24, label="montly", step="month", stepmode="todate"),
dict(count=1, label="year", step="year", stepmode="todate"),
dict(step="all")
])
))
fig.show()
我得到这样的图表，但我想从绘图中删除空日期
推荐答案
这个问题来自于将你的 'Date' 解释为 dates 并创建一个连续的最旧和最新时间戳之间的时间段，有效地将没有关联数据的日期显示为间隔.一种解决方案是取日期列中的第一个和最后一个日期，并在该时间段内制作一个完整日期列表，然后找出哪些日期没有观察，并将其存储在名为 dt_breaks 的变量中.然后，最后，您可以将这些日期包含在:
This problem comes from the fact that plotly interprets your 'Date' as dates and creates a continuous period between the oldest and newest timestamp, effectively showing dates with no associated data as gaps. One solution is to take the first and last date in your date column, and make a complete list of dates in that period, and then sort out which dates do not have any observations, and store that in a variable named dt_breaks. Then, at last, you can include those dates in:
fig.update_xaxes(
rangebreaks=[dict(values=dt_breaks)] # hide dates with no values
)
这将在您的可视化中删除这些日期，并保持 x 值的格式为日期，以便您可以使用按钮对数据进行子集:
This will drop those dates in your visualization, and keep the x-values formatted as dates so that you can subset the data using your buttons:
正如您已经知道的，这里是没有 rangebreaks=[dict(values=dt_breaks)] 的相同可视化:
And here, as you already know, is the same visualization without rangebreaks=[dict(values=dt_breaks)]:
为了使这项工作尽可能简单，我使用 df=df.sort_values(["Date","Category","Sum"],ascending=True) 重新排列日期列 而不是 df=df.sort_values(["Date","Category","Sum"],ascending=False) 就像您的原始代码片段
To make this work as simply as possible, I rearranged the date column using df=df.sort_values(["Date","Category","Sum"],ascending=True) instead of df=df.sort_values(["Date","Category","Sum"],ascending=False) as in your original code snippet
完整代码:
import pandas as pd
import plotly.express as px
df = pd.DataFrame({'Date': {0: '2019-06-03',
1: '2019-06-03',
2: '2019-06-03',
3: '2019-06-04',
4: '2019-06-05',
5: '2019-06-05',
6: '2019-06-06',
7: '2019-06-07',
8: '2019-06-08',
9: '2019-06-09',
10: '2019-06-10',
11: '2019-06-11',
12: '2019-07-12',
13: '2019-07-13',
14: '2019-07-14',
15: '2019-07-14',
16: '2019-07-15',
17: '2019-07-16',
18: '2019-07-17',
19: '2019-07-18'},
'Category': {0: '"25M"',
1: '"25M"',
2: '"50M"',
3: '"25M"',
4: '"50M"',
5: '"50M"',
6: '"100M"',
7: '"25M"',
8: '"100M"',
9: '"25M"',
10: '"50M"',
11: '"25M"',
12: '"50M"',
13: '"50M"',
14: '"100M"',
15: '"50M"',
16: '"100M"',
17: '"25M"',
18: '"25M"',
19: '"25M"'},
'Sum': {0: 34,
1: 60,
2: 23,
3: 67,
4: -90,
5: 100,
6: 6,
7: -100,
8: 67,
9: 450,
10: 600,
11: -9,
12: 45,
13: 67,
14: 130,
15: 45,
16: 100,
17: -90,
18: 700,
19: -9}})
df["Date"]=pd.to_datetime(df["Date"], format=("%Y-%m-%d"))
df=df.sort_values(["Date","Category","Sum"],ascending=True)
df=round(df.groupby(["Date","Category"]).agg({"Sum":"sum"}).reset_index(),1)
dt_all = pd.date_range(start=df['Date'].iloc[0],end=df['Date'].iloc[-1])
dt_obs = [d.strftime("%Y-%m-%d") for d in df['Date']]
dt_breaks = [d for d in dt_all.strftime("%Y-%m-%d").tolist() if not d in dt_obs]
df=df.set_index('Date')
#fig = px.bar(df, x=df.index.strftime("%Y/%m/%d") , y='Sum',barmode="group",color="Category")
fig = px.bar(df, x=df.index , y='Sum',barmode="group",color="Category")
fig.update_xaxes(
rangebreaks=[dict(values=dt_breaks)] # hide dates with no values
)
fig.update_xaxes(
rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=1, label="day", step="day", stepmode="todate"),
dict(count=24, label="montly", step="month", stepmode="todate"),
dict(count=1, label="year", step="year", stepmode="todate"),
dict(step="all")
])
))
fig.show()
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