python - plotly python中的多个绘图错误

Question

我有这个数据框

    Id  Timestamp               Data    Group
0   1   2013-08-12 10:29:19.673 40.0    1
1   2   2013-08-13 10:29:20.687 50.0    2
2   3   2013-09-14 10:29:20.687 40.0    3
3   4   2013-10-14 10:29:20.687 30.0    4
4   5   2013-11-15 10:29:20.687 50.0    5
                    ...

我可以使用

import plotly.express as px
df1 = df[df['Group'] ==1]
fig = px.line(df1, 'Timestamp', 'Data',width=1000, height=500)
fig.show()

然后我想按 Group 对数据进行分组列并为每个唯一的 Group 绘制图表.我用了

import plotly.express as px
df1 = df.groupby(df['Group'])
fig = px.line(df1, 'Timestamp', 'Data',width=1000, height=500)
fig.show()

并产生错误

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-10-f8ccd9a83ce9> in <module>()
      2 df1 = df.groupby(df['Group'])
      3 
----> 4 fig = px.line(df1, 'Timestamp', 'Data',width=1000, height=500)
      5 fig.show()

4 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py in _make_wrapper(self, name)
    602                 "using the 'apply' method".format(kind, name, type(self).__name__)
    603             )
--> 604             raise AttributeError(msg)
    605 
    606         self._set_group_selection()

AttributeError: Cannot access attribute 'columns' of 'DataFrameGroupBy' objects, try using the 'apply' method

我引用了几个类似的 posts并尝试了一些东西，但没有奏效。我该怎么做？谢谢

Answer 1

你已经用 plotly 标记了这个问题，到目前为止只得到了一个 matplotlib 答案，所以这里有一个 plotly 方法:

在您提供的数据样本中，有 no duplicate values for 'Group' ，但 timestamp似乎是连续的。虽然您的问题很好，但是您的数据集并没有为您尝试做的事情奠定合理的基础，特别是如果您不想以任何方式聚合分组值。因此，我假设您真正在这里工作的实际上更像是:

    Id  Timestamp               Data    Group
0   1   2013-08-12 10:29:19.673 40.0    1
1   2   2013-08-13 10:29:20.687 50.0    1
2   3   2013-09-14 10:29:20.687 40.0    1
3   4   2013-10-14 10:29:20.687 30.0    1
4   5   2013-11-15 10:29:20.687 50.0    1
5   6   2013-08-12 10:29:19.673 60.0    2
6   7   2013-08-13 10:29:20.687 70.0    2
7   8   2013-09-14 10:29:20.687 60.0    2
8   9   2013-10-14 10:29:20.687 40.0    2
9   10   2013-11-15 10:29:20.687 60.0    2
10   11   2013-08-12 10:29:19.673 80.0    3
11   12   2013-08-13 10:29:20.687 100.0    3
12   13   2013-09-14 10:29:20.687 80.0    3
13   14   2013-10-14 10:29:20.687 60.0    3
14   15   2013-11-15 10:29:20.687 100.0    3

如果是这种情况，您可以选择在相同的时间戳上绘制每个组以获得:

使用枢轴值绘制每组一条轨迹:



或者您可以选择为每个组制作一个单独的图，如下所示:

使用分组数据绘图以获得每组一个数字:



完整的代码片段:

带有枢轴值以获得每组一条跟踪的代码:

import pandas as pd
import plotly.graph_objects as go

df= pd.DataFrame({'Id': {(0, 1): '2013-08-12',
          (1, 2): '2013-08-13',
          (2, 3): '2013-09-14',
          (3, 4): '2013-10-14',
          (4, 5): '2013-11-15',
          (5, 6): '2013-08-12',
          (6, 7): '2013-08-13',
          (7, 8): '2013-09-14',
          (8, 9): '2013-10-14',
          (9, 10): '2013-11-15',
          (10, 11): '2013-08-12',
          (11, 12): '2013-08-13',
          (12, 13): '2013-09-14',
          (13, 14): '2013-10-14',
          (14, 15): '2013-11-15'},
         'Timestamp': {(0, 1): '10:29:19.673',
          (1, 2): '10:29:20.687',
          (2, 3): '10:29:20.687',
          (3, 4): '10:29:20.687',
          (4, 5): '10:29:20.687',
          (5, 6): '10:29:19.673',
          (6, 7): '10:29:20.687',
          (7, 8): '10:29:20.687',
          (8, 9): '10:29:20.687',
          (9, 10): '10:29:20.687',
          (10, 11): '10:29:19.673',
          (11, 12): '10:29:20.687',
          (12, 13): '10:29:20.687',
          (13, 14): '10:29:20.687',
          (14, 15): '10:29:20.687'},
         'Data': {(0, 1): 40.0,
          (1, 2): 50.0,
          (2, 3): 40.0,
          (3, 4): 30.0,
          (4, 5): 50.0,
          (5, 6): 60.0,
          (6, 7): 70.0,
          (7, 8): 60.0,
          (8, 9): 40.0,
          (9, 10): 60.0,
          (10, 11): 80.0,
          (11, 12): 100.0,
          (12, 13): 80.0,
          (13, 14): 60.0,
          (14, 15): 100.0},
         'Group': {(0, 1): 1,
          (1, 2): 1,
          (2, 3): 1,
          (3, 4): 1,
          (4, 5): 1,
          (5, 6): 2,
          (6, 7): 2,
          (7, 8): 2,
          (8, 9): 2,
          (9, 10): 2,
          (10, 11): 3,
          (11, 12): 3,
          (12, 13): 3,
          (13, 14): 3,
          (14, 15): 3}})

# pivot values to get one trace per group
dfp = pd.pivot_table(df,
                     values='Data',
                     index=['Timestamp'],
                     columns=['Group'],
               )
dfp.tail()

# plotly
fig = go.Figure()
for col in dfp.columns:
    fig.add_trace(go.Scatter(x=dfp.index, y=dfp[col], name='Group_'+str(col)))

fig.show()

带有分组数据的代码以获得每组一个数字:

# imports
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# data
df= pd.DataFrame({'Id': {(0, 1): '2013-08-12',
          (1, 2): '2013-08-13',
          (2, 3): '2013-09-14',
          (3, 4): '2013-10-14',
          (4, 5): '2013-11-15',
          (5, 6): '2013-08-12',
          (6, 7): '2013-08-13',
          (7, 8): '2013-09-14',
          (8, 9): '2013-10-14',
          (9, 10): '2013-11-15',
          (10, 11): '2013-08-12',
          (11, 12): '2013-08-13',
          (12, 13): '2013-09-14',
          (13, 14): '2013-10-14',
          (14, 15): '2013-11-15'},
         'Timestamp': {(0, 1): '10:29:19.673',
          (1, 2): '10:29:20.687',
          (2, 3): '10:29:20.687',
          (3, 4): '10:29:20.687',
          (4, 5): '10:29:20.687',
          (5, 6): '10:29:19.673',
          (6, 7): '10:29:20.687',
          (7, 8): '10:29:20.687',
          (8, 9): '10:29:20.687',
          (9, 10): '10:29:20.687',
          (10, 11): '10:29:19.673',
          (11, 12): '10:29:20.687',
          (12, 13): '10:29:20.687',
          (13, 14): '10:29:20.687',
          (14, 15): '10:29:20.687'},
         'Data': {(0, 1): 40.0,
          (1, 2): 50.0,
          (2, 3): 40.0,
          (3, 4): 30.0,
          (4, 5): 50.0,
          (5, 6): 60.0,
          (6, 7): 70.0,
          (7, 8): 60.0,
          (8, 9): 40.0,
          (9, 10): 60.0,
          (10, 11): 80.0,
          (11, 12): 100.0,
          (12, 13): 80.0,
          (13, 14): 60.0,
          (14, 15): 100.0},
         'Group': {(0, 1): 1,
          (1, 2): 1,
          (2, 3): 1,
          (3, 4): 1,
          (4, 5): 1,
          (5, 6): 2,
          (6, 7): 2,
          (7, 8): 2,
          (8, 9): 2,
          (9, 10): 2,
          (10, 11): 3,
          (11, 12): 3,
          (12, 13): 3,
          (13, 14): 3,
          (14, 15): 3}})

dfp = pd.pivot_table(df,
                     values='Data',
                     index=['Timestamp'],
                     columns=['Group'],
               )

# data dimensions
nrows = len(dfp.columns)

fig = make_subplots(rows=nrows,
                    cols=1,
                    subplot_titles=['Group'+str(c) for c in dfp.columns])

# add traces
x = 1
for i, col in enumerate(dfp.columns):
    fig.add_trace(go.Scatter(x=dfp.index, y=dfp[col].values,
                             name = 'Group_'+str(col),
                             mode = 'lines',
                             ),
                  row=i+1,
                  col=1)

fig.update_layout(height=nrows*200)

fig.show()