Mixing
Tight_Layout: Attribute Error . 'AxesSubplot' object has no attribute 'tight_layout'
I want to plot a stacked-bar graph with data coming from two separate dataframes in pandas (My_means and My_stds). I am using Python 3.7.
The plotting call works fine, however, the labels are cut-off. Setting tight_layout does not help me and generates an "Attribute Error: 'AxesSubplot' object has no attribute 'tight_layout'". What does this mean and how do I overcome this?
I haven´t found any problems related to this error, so I assume I am doing something rather stupid...
Here is my code:
My_means= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.mean)
My_stds= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.std)
plot_it = My_means.plot(kind='bar', color=stack_cols, stacked=True, yerr=My_stds, capsize=3, title='Just some graph')
plot_it.tight_layout()
Thanks for your help!
Jazz
python matplotlib layout label attributeerror
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
add a comment |
I want to plot a stacked-bar graph with data coming from two separate dataframes in pandas (My_means and My_stds). I am using Python 3.7.
The plotting call works fine, however, the labels are cut-off. Setting tight_layout does not help me and generates an "Attribute Error: 'AxesSubplot' object has no attribute 'tight_layout'". What does this mean and how do I overcome this?
I haven´t found any problems related to this error, so I assume I am doing something rather stupid...
Here is my code:
My_means= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.mean)
My_stds= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.std)
plot_it = My_means.plot(kind='bar', color=stack_cols, stacked=True, yerr=My_stds, capsize=3, title='Just some graph')
plot_it.tight_layout()
Thanks for your help!
Jazz
python matplotlib layout label attributeerror
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
You can't applytight_layouton an axes, you ned to do it on a figure. Most often done usingplt.tight_layout()
– DavidG
Nov 20 '18 at 10:37
Hi David, thanks for the quick reply. I thought the way I do it, plot_it would be my figure. However, it seems that it isnt... (new to coding, sorry). How can I get it as my figure object so that tight_layout would work for me?
– JazzyJazz
Nov 20 '18 at 10:48
plot_itis the axes, not the figure. If you have doneimport matplotlib.pyplot as pltthen you can simply doplt.tight_layout()
– DavidG
Nov 20 '18 at 10:52
It works fine! Thanks for you help. Appreciate it!
– JazzyJazz
Nov 20 '18 at 10:57
add a comment |
I want to plot a stacked-bar graph with data coming from two separate dataframes in pandas (My_means and My_stds). I am using Python 3.7.
The plotting call works fine, however, the labels are cut-off. Setting tight_layout does not help me and generates an "Attribute Error: 'AxesSubplot' object has no attribute 'tight_layout'". What does this mean and how do I overcome this?
I haven´t found any problems related to this error, so I assume I am doing something rather stupid...
Here is my code:
My_means= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.mean)
My_stds= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.std)
plot_it = My_means.plot(kind='bar', color=stack_cols, stacked=True, yerr=My_stds, capsize=3, title='Just some graph')
plot_it.tight_layout()
Thanks for your help!
Jazz
python matplotlib layout label attributeerror
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
I want to plot a stacked-bar graph with data coming from two separate dataframes in pandas (My_means and My_stds). I am using Python 3.7.
The plotting call works fine, however, the labels are cut-off. Setting tight_layout does not help me and generates an "Attribute Error: 'AxesSubplot' object has no attribute 'tight_layout'". What does this mean and how do I overcome this?
I haven´t found any problems related to this error, so I assume I am doing something rather stupid...
Here is my code:
My_means= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.mean)
My_stds= ratios.pivot_table('Data', 'Sample ID', 'User ID', aggfunc= np.std)
plot_it = My_means.plot(kind='bar', color=stack_cols, stacked=True, yerr=My_stds, capsize=3, title='Just some graph')
plot_it.tight_layout()
Thanks for your help!
Jazz
python matplotlib layout label attributeerror
python matplotlib layout label attributeerror
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
asked Nov 20 '18 at 10:34
JazzyJazzJazzyJazz
84
84
You can't applytight_layouton an axes, you ned to do it on a figure. Most often done usingplt.tight_layout()
– DavidG
Nov 20 '18 at 10:37
Hi David, thanks for the quick reply. I thought the way I do it, plot_it would be my figure. However, it seems that it isnt... (new to coding, sorry). How can I get it as my figure object so that tight_layout would work for me?
– JazzyJazz
Nov 20 '18 at 10:48
plot_itis the axes, not the figure. If you have doneimport matplotlib.pyplot as pltthen you can simply doplt.tight_layout()
– DavidG
Nov 20 '18 at 10:52
It works fine! Thanks for you help. Appreciate it!
– JazzyJazz
Nov 20 '18 at 10:57
add a comment |
You can't applytight_layouton an axes, you ned to do it on a figure. Most often done usingplt.tight_layout()
– DavidG
Nov 20 '18 at 10:37
Hi David, thanks for the quick reply. I thought the way I do it, plot_it would be my figure. However, it seems that it isnt... (new to coding, sorry). How can I get it as my figure object so that tight_layout would work for me?
– JazzyJazz
Nov 20 '18 at 10:48
plot_itis the axes, not the figure. If you have doneimport matplotlib.pyplot as pltthen you can simply doplt.tight_layout()
– DavidG
Nov 20 '18 at 10:52
It works fine! Thanks for you help. Appreciate it!
– JazzyJazz
Nov 20 '18 at 10:57
You can't apply
tight_layout on an axes, you ned to do it on a figure. Most often done using plt.tight_layout()– DavidG
Nov 20 '18 at 10:37
You can't apply
tight_layout on an axes, you ned to do it on a figure. Most often done using plt.tight_layout()– DavidG
Nov 20 '18 at 10:37
Hi David, thanks for the quick reply. I thought the way I do it, plot_it would be my figure. However, it seems that it isnt... (new to coding, sorry). How can I get it as my figure object so that tight_layout would work for me?
– JazzyJazz
Nov 20 '18 at 10:48
Hi David, thanks for the quick reply. I thought the way I do it, plot_it would be my figure. However, it seems that it isnt... (new to coding, sorry). How can I get it as my figure object so that tight_layout would work for me?
– JazzyJazz
Nov 20 '18 at 10:48
plot_it is the axes, not the figure. If you have done import matplotlib.pyplot as plt then you can simply do plt.tight_layout()– DavidG
Nov 20 '18 at 10:52
plot_it is the axes, not the figure. If you have done import matplotlib.pyplot as plt then you can simply do plt.tight_layout()– DavidG
Nov 20 '18 at 10:52
It works fine! Thanks for you help. Appreciate it!
– JazzyJazz
Nov 20 '18 at 10:57
It works fine! Thanks for you help. Appreciate it!
– JazzyJazz
Nov 20 '18 at 10:57
add a comment |
1 Answer
1
active
oldest
votes
My_means.plot(…) returns an axes object. Whereas tight_layout requires a figure object. There are a number of different approaches you can use:
Perhaps the simplest one would be to use plt.tight_layout() which works on the current figure:
import matplotlib.pyplot as plt
# Your plotting code here
plt.tight_layout()
Alternatively, you can create the figure and axes beforehand, pass the axes as an argument to plot and then use tight_layout on the figure object:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_it = My_means.plot(kind='bar', ax=ax, …)
fig.tight_layout()
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
Thanks again. It works (and looks) fine now!
– JazzyJazz
Nov 20 '18 at 11:09
add a comment |
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1 Answer
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1 Answer
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My_means.plot(…) returns an axes object. Whereas tight_layout requires a figure object. There are a number of different approaches you can use:
Perhaps the simplest one would be to use plt.tight_layout() which works on the current figure:
import matplotlib.pyplot as plt
# Your plotting code here
plt.tight_layout()
Alternatively, you can create the figure and axes beforehand, pass the axes as an argument to plot and then use tight_layout on the figure object:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_it = My_means.plot(kind='bar', ax=ax, …)
fig.tight_layout()
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
Thanks again. It works (and looks) fine now!
– JazzyJazz
Nov 20 '18 at 11:09
add a comment |
My_means.plot(…) returns an axes object. Whereas tight_layout requires a figure object. There are a number of different approaches you can use:
Perhaps the simplest one would be to use plt.tight_layout() which works on the current figure:
import matplotlib.pyplot as plt
# Your plotting code here
plt.tight_layout()
Alternatively, you can create the figure and axes beforehand, pass the axes as an argument to plot and then use tight_layout on the figure object:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_it = My_means.plot(kind='bar', ax=ax, …)
fig.tight_layout()
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
Thanks again. It works (and looks) fine now!
– JazzyJazz
Nov 20 '18 at 11:09
add a comment |
My_means.plot(…) returns an axes object. Whereas tight_layout requires a figure object. There are a number of different approaches you can use:
Perhaps the simplest one would be to use plt.tight_layout() which works on the current figure:
import matplotlib.pyplot as plt
# Your plotting code here
plt.tight_layout()
Alternatively, you can create the figure and axes beforehand, pass the axes as an argument to plot and then use tight_layout on the figure object:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_it = My_means.plot(kind='bar', ax=ax, …)
fig.tight_layout()
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
My_means.plot(…) returns an axes object. Whereas tight_layout requires a figure object. There are a number of different approaches you can use:
Perhaps the simplest one would be to use plt.tight_layout() which works on the current figure:
import matplotlib.pyplot as plt
# Your plotting code here
plt.tight_layout()
Alternatively, you can create the figure and axes beforehand, pass the axes as an argument to plot and then use tight_layout on the figure object:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_it = My_means.plot(kind='bar', ax=ax, …)
fig.tight_layout()
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
answered Nov 20 '18 at 10:57
DavidGDavidG
10.8k103041
10.8k103041
Thanks again. It works (and looks) fine now!
– JazzyJazz
Nov 20 '18 at 11:09
add a comment |
Thanks again. It works (and looks) fine now!
– JazzyJazz
Nov 20 '18 at 11:09
Thanks again. It works (and looks) fine now!
– JazzyJazz
Nov 20 '18 at 11:09
Thanks again. It works (and looks) fine now!
– JazzyJazz
Nov 20 '18 at 11:09
add a comment |
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You can't apply
tight_layouton an axes, you ned to do it on a figure. Most often done usingplt.tight_layout()– DavidG
Nov 20 '18 at 10:37
Hi David, thanks for the quick reply. I thought the way I do it, plot_it would be my figure. However, it seems that it isnt... (new to coding, sorry). How can I get it as my figure object so that tight_layout would work for me?
– JazzyJazz
Nov 20 '18 at 10:48
plot_itis the axes, not the figure. If you have doneimport matplotlib.pyplot as pltthen you can simply doplt.tight_layout()– DavidG
Nov 20 '18 at 10:52
It works fine! Thanks for you help. Appreciate it!
– JazzyJazz
Nov 20 '18 at 10:57