Tight_Layout: Attribute Error . 'AxesSubplot' object has no attribute 'tight

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

asked Nov 20 '18 at 10:34

JazzyJazz

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

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

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.

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

asked Nov 20 '18 at 10:34

JazzyJazz

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

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

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.

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

asked Nov 20 '18 at 10:34

JazzyJazz

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.

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

JazzyJazz

asked Nov 20 '18 at 10:34

JazzyJazz

asked Nov 20 '18 at 10:34

JazzyJazz

asked Nov 20 '18 at 10:34

JazzyJazz

asked Nov 20 '18 at 10:34

JazzyJazz

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

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

add a comment |

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

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

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

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

add a comment |

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

DavidG

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
1

active

oldest

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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

DavidG

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

DavidG

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

DavidG

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

DavidG

10.8k103041

answered Nov 20 '18 at 10:57

DavidG

10.8k103041

answered Nov 20 '18 at 10:57

DavidG

10.8k103041

answered Nov 20 '18 at 10:57

DavidG

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

add a comment |

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