Question

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

我想创建一个绘图图，显示在每个描述的日期上为不同“类别”添加的“总和”，但是要删除日期（如果它们没有任何数据）。

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()

我正在获得这样的图形，但我想从绘图中删除空的日期

Answer 1

此问题来自以下事实：将您的'Date'解释为日期，并在最旧和最新的时间戳之间创建了一个连续的时间段，从而有效地将没有关联数据的日期显示为空白。一种解决方案是在日期栏中获取第一个和最后一个日期，并在该期间内完成日期的完整列表，然后找出没有的日期观测值，并将其存储在名为dt_breaks的变量中。然后，最后，您可以将这些日期添加到：

fig.update_xaxes(
    rangebreaks=[dict(values=dt_breaks)] # hide dates with no values
)

这将在可视化中删除这些日期，和将x值设置为日期格式，以便您可以使用按钮来对数据进行子集化：

正如您已经知道的，这里是没有rangebreaks=[dict(values=dt_breaks)]的相同可视化效果：

为了使这项工作尽可能简单，我使用df=df.sort_values(["Date","Category","Sum"],ascending=True)而不是原始代码段中的df=df.sort_values(["Date","Category","Sum"],ascending=False)重新排列了日期列

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()

Answer 2

我的图表也有同样的问题。只需在布局代码中添加以下内容：

xaxis=dict(type = "category")

注意：我用过import plotly.graph_objs as go 和否 import plotly.express as px

这对我有用。希望它对您也有帮助。

Answer 3

万一有人在这里玩股票数据，下面是隐藏交易时间和周末的代码。

    fig = go.Figure(data=[go.Candlestick(x=df['date'], open=df['Open'], high=df['High'], low=df['Low'], close=df['Close'])])
    fig.update_xaxes(
        rangeslider_visible=True,
        rangebreaks=[
            # NOTE: Below values are bound (not single values), ie. hide x to y
            dict(bounds=["sat", "mon"]),  # hide weekends, eg. hide sat to before mon
            dict(bounds=[16, 9.5], pattern="hour"),  # hide hours outside of 9.30am-4pm
            # dict(values=["2020-12-25", "2021-01-01"])  # hide holidays (Christmas and New Year's, etc)
        ]
    )
    fig.update_layout(
        title='Stock Analysis',
        yaxis_title=f'{symbol} Stock'
    )

    fig.show()

这里是Plotly's doc。

Answer 4

对于跳过空日期、小时数，您应该使用：

import plotly.graph_objects as go

fig.add_trace(go.Candlestick(x=df['begin'], ...)

fig.layout = dict(title=ticker, xaxis = dict(type="category", categoryorder='category ascending'))
fig.show()

这个例子效果很好。祝你好运

密谋：如何从x轴上删除空日期？

4 个答案: