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I have this data frame
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
...
I could plot a single graph using
import plotly.express as px
df1 = df[df['Group'] ==1]
fig = px.line(df1, 'Timestamp', 'Data',width=1000, height=500)
fig.show()
Then I want to group the data by Group column and plot a graph for each unique Group. I used
import plotly.express as px
df1 = df.groupby(df['Group'])
fig = px.line(df1, 'Timestamp', 'Data',width=1000, height=500)
fig.show()
and incurred error
---------------------------------------------------------------------------
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
I referred to a few similar posts and tried a few things but didn't work. How should I do this? Thanks
779
You've tagged the question with plotly and only gotten a matplotlib answer so far, so here's a plotly approach:
In your provided data sample, there are no duplicate values for 'Group', but the timestamp seems to be continous. Your question is good though, but your dataset does not make a reasonable foundation for what you're trying to do, particularly not if you don't want to aggregate grouped values in any way. Therefore I'm assuming that what you're really working on here is actually more like:
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
If this is the case, you could chose to plot each group over the same timestamps to get a:
Plot wiht pivoted values to get one trace per group:
Or you could chose to make an individual plot for each group like this:
Plot with grouped data to get one figure per group:
Complete code snippets:
Code wiht pivoted values to get one trace per group:
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()
Code with grouped data to get one figure per group:
# 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()
The problem you are having is because of the object you are generating from from your groupby(). Which is a DataFrameGroupBy. This happens you are not passing any column to perform the aggregation on, neither are you passing the function to us as agg.
Depending on what you want to specifically do, you should resolve the issue with your groupby() first. An example that should work is:
import plotly.express as px
df1 = df.groupby(['Group'])['Data'].sum()
fig = px.line(df1, 'Timestamp', 'Data',width=1000, height=500)
fig.show()
Of course then adapt this code to fit your needs, or specify them in the question/comment and I'll edit this post accordingly.
Based on the discussion in the comments I offer 2 different solutions:
1) This is a simpler, yet harder to individualize the parameters:
import pandas as pd
import matplotlib.pyplot as plt
data = {'group':[1,2,3,4,5,1,2,3,4,5],'Timestamp':['2013-08-12','2013-08-13','2013-08-14','2013-08-15','2013-08-16','2013-08-17','2013-08-18','2013-08-18','2013-08-19','2013-08-19'],'Data':[40,50,40,30,50,20,20,10,40,30]}
df = pd.DataFrame(data)
for i in df['group'].value_counts().reset_index()['index'].tolist():
plt.plot('Timestamp','Data',data=df[df['group'] == i],marker='',color='red')
plt.show()
2) This is the longer option, but very customizable:
import pandas as pd
import matplotlib.pyplot as plt
data = {'group':[1,2,3,4,5,1,2,3,4,5],'Timestamp':['2013-08-12','2013-08-13','2013-08-14','2013-08-15','2013-08-16','2013-08-17','2013-08-18','2013-08-18','2013-08-19','2013-08-19'],'Data':[40,50,40,30,50,20,20,10,40,30]}
df = pd.DataFrame(data)
plt.plot('Timestamp','Data',data=df[df['group'] ==1],marker='',color='blue')
plt.plot('Timestamp','Data',data=df[df['group'] ==2],marker='',color='red')
plt.plot('Timestamp','Data',data=df[df['group'] ==3],marker='',color='green')
plt.plot('Timestamp','Data',data=df[df['group'] ==4],marker='',color='yellow')
plt.plot('Timestamp','Data',data=df[df['group'] ==5],marker='',color='olive')
plt.show()
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