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I want to create boxplots with the addition of error bars for data that comes from 6 columns. The seventh column of my dataset contains a binary variable that describes the rest of the data. I do have the code to these plots with seaborn package only including the data of the 6 columns. My problem is how to make the same graphs while taking into account the data from the seventh column.

Here is how my dataframe looks like:

A B C D E F Binary
12 12.5 13 15 15 13 yes
11 11 12 15 12 11 no
10 12 14 15 17 16 yes

For the case without the binary column, here is the code that I have been implementing:

fig, ax = plt.subplots()
sns.boxplot(x="variable", y="value", data=pd.melt(mvel), ax = ax)
sns.set_palette("muted")
sns.pointplot(x="variable", y="value", data=pd.melt(mvel), 
 dodge=True, join=False, ax = ax, color='k', ci=95)
plt.xlabel('Parts', fontsize=14)
plt.ylabel('Speed', fontsize=14)
plt.setp(ax.get_xticklabels(), rotation=60)
plt.savefig('fig', dpi=400, bbox_inches='tight')

So for every column (A-F) I want a boxplot for the "yes" and "no" variables. For comparison purposes, these two plots have to be next to each other.

seaborn pandas-groupby boxplot errorbar

share|improve this question

asked Mar 27 at 22:13

katerinaDkaterinaD

1555 bronze badges

add a comment | 

0

I want to create boxplots with the addition of error bars for data that comes from 6 columns. The seventh column of my dataset contains a binary variable that describes the rest of the data. I do have the code to these plots with seaborn package only including the data of the 6 columns. My problem is how to make the same graphs while taking into account the data from the seventh column.

Here is how my dataframe looks like:

A B C D E F Binary
12 12.5 13 15 15 13 yes
11 11 12 15 12 11 no
10 12 14 15 17 16 yes

For the case without the binary column, here is the code that I have been implementing:

fig, ax = plt.subplots()
sns.boxplot(x="variable", y="value", data=pd.melt(mvel), ax = ax)
sns.set_palette("muted")
sns.pointplot(x="variable", y="value", data=pd.melt(mvel), 
 dodge=True, join=False, ax = ax, color='k', ci=95)
plt.xlabel('Parts', fontsize=14)
plt.ylabel('Speed', fontsize=14)
plt.setp(ax.get_xticklabels(), rotation=60)
plt.savefig('fig', dpi=400, bbox_inches='tight')

So for every column (A-F) I want a boxplot for the "yes" and "no" variables. For comparison purposes, these two plots have to be next to each other.

seaborn pandas-groupby boxplot errorbar

share|improve this question

asked Mar 27 at 22:13

katerinaDkaterinaD

1555 bronze badges

add a comment | 

I want to create boxplots with the addition of error bars for data that comes from 6 columns. The seventh column of my dataset contains a binary variable that describes the rest of the data. I do have the code to these plots with seaborn package only including the data of the 6 columns. My problem is how to make the same graphs while taking into account the data from the seventh column.

Here is how my dataframe looks like:

A B C D E F Binary
12 12.5 13 15 15 13 yes
11 11 12 15 12 11 no
10 12 14 15 17 16 yes

For the case without the binary column, here is the code that I have been implementing:

fig, ax = plt.subplots()
sns.boxplot(x="variable", y="value", data=pd.melt(mvel), ax = ax)
sns.set_palette("muted")
sns.pointplot(x="variable", y="value", data=pd.melt(mvel), 
 dodge=True, join=False, ax = ax, color='k', ci=95)
plt.xlabel('Parts', fontsize=14)
plt.ylabel('Speed', fontsize=14)
plt.setp(ax.get_xticklabels(), rotation=60)
plt.savefig('fig', dpi=400, bbox_inches='tight')

So for every column (A-F) I want a boxplot for the "yes" and "no" variables. For comparison purposes, these two plots have to be next to each other.

seaborn pandas-groupby boxplot errorbar

share|improve this question

asked Mar 27 at 22:13

katerinaDkaterinaD

1555 bronze badges

A B C D E F Binary
12 12.5 13 15 15 13 yes
11 11 12 15 12 11 no
10 12 14 15 17 16 yes

fig, ax = plt.subplots()
sns.boxplot(x="variable", y="value", data=pd.melt(mvel), ax = ax)
sns.set_palette("muted")
sns.pointplot(x="variable", y="value", data=pd.melt(mvel), 
 dodge=True, join=False, ax = ax, color='k', ci=95)
plt.xlabel('Parts', fontsize=14)
plt.ylabel('Speed', fontsize=14)
plt.setp(ax.get_xticklabels(), rotation=60)
plt.savefig('fig', dpi=400, bbox_inches='tight')

share|improve this question

asked Mar 27 at 22:13

katerinaDkaterinaD

1555 bronze badges

share|improve this question

asked Mar 27 at 22:13

katerinaDkaterinaD

1555 bronze badges

asked Mar 27 at 22:13

katerinaDkaterinaD

1555 bronze badges

asked Mar 27 at 22:13

katerinaDkaterinaD

1555 bronze badges