Arranging barplots from seaborn in array
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I am trying to make a plot in seaborn for every category which occurs in column 'Category' in my df. There are 7 unique categories. I managed to do this in one row but the plots are too small. I would like to fit them into two rows (4 in first and 3 in seven). How shall I change the code apart from the fact that I am supposed to change the arguments of subplots to (2,4)?
fig, ax = plt.subplots(1, 7)
for i,g in enumerate(df.Category.unique()):
dfx = df[df['Category'] == g]
sns.set(style="whitegrid", rc={'figure.figsize':(28,6)})
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = g, ax=ax[i])
ax[i].legend(loc = 'lower center')
plt.tight_layout()
plt.show()
python-3.x for-loop seaborn
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
add a comment |
I am trying to make a plot in seaborn for every category which occurs in column 'Category' in my df. There are 7 unique categories. I managed to do this in one row but the plots are too small. I would like to fit them into two rows (4 in first and 3 in seven). How shall I change the code apart from the fact that I am supposed to change the arguments of subplots to (2,4)?
fig, ax = plt.subplots(1, 7)
for i,g in enumerate(df.Category.unique()):
dfx = df[df['Category'] == g]
sns.set(style="whitegrid", rc={'figure.figsize':(28,6)})
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = g, ax=ax[i])
ax[i].legend(loc = 'lower center')
plt.tight_layout()
plt.show()
python-3.x for-loop seaborn
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
add a comment |
I am trying to make a plot in seaborn for every category which occurs in column 'Category' in my df. There are 7 unique categories. I managed to do this in one row but the plots are too small. I would like to fit them into two rows (4 in first and 3 in seven). How shall I change the code apart from the fact that I am supposed to change the arguments of subplots to (2,4)?
fig, ax = plt.subplots(1, 7)
for i,g in enumerate(df.Category.unique()):
dfx = df[df['Category'] == g]
sns.set(style="whitegrid", rc={'figure.figsize':(28,6)})
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = g, ax=ax[i])
ax[i].legend(loc = 'lower center')
plt.tight_layout()
plt.show()
python-3.x for-loop seaborn
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
I am trying to make a plot in seaborn for every category which occurs in column 'Category' in my df. There are 7 unique categories. I managed to do this in one row but the plots are too small. I would like to fit them into two rows (4 in first and 3 in seven). How shall I change the code apart from the fact that I am supposed to change the arguments of subplots to (2,4)?
fig, ax = plt.subplots(1, 7)
for i,g in enumerate(df.Category.unique()):
dfx = df[df['Category'] == g]
sns.set(style="whitegrid", rc={'figure.figsize':(28,6)})
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = g, ax=ax[i])
ax[i].legend(loc = 'lower center')
plt.tight_layout()
plt.show()
python-3.x for-loop seaborn
python-3.x for-loop seaborn
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
asked Nov 24 '18 at 23:22
Blazej KowalskiBlazej Kowalski
98112
98112
add a comment |
add a comment |
1 Answer
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You can loop over the flattened axes array and also use groupby to simplyfy things. So I'd say the code can look like this (untested because no data provided in the question):
sns.set(style="whitegrid")
fig, axes = plt.subplots(2, 4)
for (n, dfx), ax in zip(df.groupby("Category"), axes.flat):
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = n, ax=ax)
ax.legend(loc = 'lower center')
axes[1,3].axis("off")
plt.tight_layout()
plt.show()
Also, as you seem to be using seaborn, you may consider a seaborn.FacetGrid.
This could look like
sns.set(style="whitegrid")
g = sns.FacetGrid(data=df, col = "Category", col_wrap=4)
g.map(sns.barplot, "Month", "measure")
plt.tight_layout()
plt.show()
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can loop over the flattened axes array and also use groupby to simplyfy things. So I'd say the code can look like this (untested because no data provided in the question):
sns.set(style="whitegrid")
fig, axes = plt.subplots(2, 4)
for (n, dfx), ax in zip(df.groupby("Category"), axes.flat):
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = n, ax=ax)
ax.legend(loc = 'lower center')
axes[1,3].axis("off")
plt.tight_layout()
plt.show()
Also, as you seem to be using seaborn, you may consider a seaborn.FacetGrid.
This could look like
sns.set(style="whitegrid")
g = sns.FacetGrid(data=df, col = "Category", col_wrap=4)
g.map(sns.barplot, "Month", "measure")
plt.tight_layout()
plt.show()
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
add a comment |
You can loop over the flattened axes array and also use groupby to simplyfy things. So I'd say the code can look like this (untested because no data provided in the question):
sns.set(style="whitegrid")
fig, axes = plt.subplots(2, 4)
for (n, dfx), ax in zip(df.groupby("Category"), axes.flat):
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = n, ax=ax)
ax.legend(loc = 'lower center')
axes[1,3].axis("off")
plt.tight_layout()
plt.show()
Also, as you seem to be using seaborn, you may consider a seaborn.FacetGrid.
This could look like
sns.set(style="whitegrid")
g = sns.FacetGrid(data=df, col = "Category", col_wrap=4)
g.map(sns.barplot, "Month", "measure")
plt.tight_layout()
plt.show()
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
add a comment |
You can loop over the flattened axes array and also use groupby to simplyfy things. So I'd say the code can look like this (untested because no data provided in the question):
sns.set(style="whitegrid")
fig, axes = plt.subplots(2, 4)
for (n, dfx), ax in zip(df.groupby("Category"), axes.flat):
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = n, ax=ax)
ax.legend(loc = 'lower center')
axes[1,3].axis("off")
plt.tight_layout()
plt.show()
Also, as you seem to be using seaborn, you may consider a seaborn.FacetGrid.
This could look like
sns.set(style="whitegrid")
g = sns.FacetGrid(data=df, col = "Category", col_wrap=4)
g.map(sns.barplot, "Month", "measure")
plt.tight_layout()
plt.show()
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
You can loop over the flattened axes array and also use groupby to simplyfy things. So I'd say the code can look like this (untested because no data provided in the question):
sns.set(style="whitegrid")
fig, axes = plt.subplots(2, 4)
for (n, dfx), ax in zip(df.groupby("Category"), axes.flat):
sns.barplot(x = dfx['Month'], y = dfx['measure'], ci = None, label = n, ax=ax)
ax.legend(loc = 'lower center')
axes[1,3].axis("off")
plt.tight_layout()
plt.show()
Also, as you seem to be using seaborn, you may consider a seaborn.FacetGrid.
This could look like
sns.set(style="whitegrid")
g = sns.FacetGrid(data=df, col = "Category", col_wrap=4)
g.map(sns.barplot, "Month", "measure")
plt.tight_layout()
plt.show()
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
answered Nov 24 '18 at 23:56
ImportanceOfBeingErnestImportanceOfBeingErnest
143k13173252
143k13173252
add a comment |
add a comment |
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