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fix seaborn example
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@bugzpodder
bugzpodder committed Sep 18, 2023
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60 changes: 24 additions & 36 deletions docs/src/content/docs/examples/MainThread/example3_seaborn.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -5,52 +5,40 @@ title: seaborn
import PySandboxExample from "../../../../components/PySandboxExample.astro";

<PySandboxExample code={`import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
sns.set_theme(style="white", context="talk")
rs = np.random.RandomState(8)
# Create the data
# Set up the matplotlib figure
rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)
# Initialize the FacetGrid object
# Generate some sequential data
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=.5, palette=pal)
x = np.array(list("ABCDEFGHIJ"))
y1 = np.arange(1, 11)
sns.barplot(x=x, y=y1, palette="rocket", ax=ax1)
ax1.axhline(0, color="k", clip_on=False)
ax1.set_ylabel("Sequential")
# Draw the densities in a few steps
# Center the data to make it diverging
g.map(sns.kdeplot, "x",
bw_adjust=.5, clip_on=False,
fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=.5)
y2 = y1 - 5.5
sns.barplot(x=x, y=y2, palette="vlag", ax=ax2)
ax2.axhline(0, color="k", clip_on=False)
ax2.set_ylabel("Diverging")
# passing color=None to refline() uses the hue mapping
# Randomly reorder the data to make it qualitative
g.refline(y=0, linewidth=2, linestyle="-", color=None, clip_on=False)
y3 = rs.choice(y1, len(y1), replace=False)
sns.barplot(x=x, y=y3, palette="deep", ax=ax3)
ax3.axhline(0, color="k", clip_on=False)
ax3.set_ylabel("Qualitative")
# Define and use a simple function to label the plot in axes coordinates
# Finalize the plot
def label(x, color, label):
ax = plt.gca()
ax.text(0, .2, label, fontweight="bold", color=color,
ha="left", va="center", transform=ax.transAxes)
g.map(label, "x")
# Set the subplots to overlap
g.figure.subplots_adjust(hspace=-.25)
# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[], ylabel="")
g.despine(bottom=True, left=True)
sns.despine(bottom=True)
plt.setp(f.axes, yticks=[])
plt.tight_layout(h_pad=2)
display(plt, target=current_target())`} target="out" />
60 changes: 24 additions & 36 deletions docs/src/content/docs/examples/MainThreadRestricted/example3_seaborn.mdx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,52 +5,40 @@ title: seaborn
import PySandboxExample from "../../../../components/PySandboxExample.astro";

<PySandboxExample code={`import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
sns.set_theme(style="white", context="talk")
rs = np.random.RandomState(8)
# Create the data
# Set up the matplotlib figure
rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)
# Initialize the FacetGrid object
# Generate some sequential data
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=.5, palette=pal)
x = np.array(list("ABCDEFGHIJ"))
y1 = np.arange(1, 11)
sns.barplot(x=x, y=y1, palette="rocket", ax=ax1)
ax1.axhline(0, color="k", clip_on=False)
ax1.set_ylabel("Sequential")
# Draw the densities in a few steps
# Center the data to make it diverging
g.map(sns.kdeplot, "x",
bw_adjust=.5, clip_on=False,
fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=.5)
y2 = y1 - 5.5
sns.barplot(x=x, y=y2, palette="vlag", ax=ax2)
ax2.axhline(0, color="k", clip_on=False)
ax2.set_ylabel("Diverging")
# passing color=None to refline() uses the hue mapping
# Randomly reorder the data to make it qualitative
g.refline(y=0, linewidth=2, linestyle="-", color=None, clip_on=False)
y3 = rs.choice(y1, len(y1), replace=False)
sns.barplot(x=x, y=y3, palette="deep", ax=ax3)
ax3.axhline(0, color="k", clip_on=False)
ax3.set_ylabel("Qualitative")
# Define and use a simple function to label the plot in axes coordinates
# Finalize the plot
def label(x, color, label):
ax = plt.gca()
ax.text(0, .2, label, fontweight="bold", color=color,
ha="left", va="center", transform=ax.transAxes)
g.map(label, "x")
# Set the subplots to overlap
g.figure.subplots_adjust(hspace=-.25)
# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[], ylabel="")
g.despine(bottom=True, left=True)
sns.despine(bottom=True)
plt.setp(f.axes, yticks=[])
plt.tight_layout(h_pad=2)
display(plt, target=current_target())`} restricted="1" target="out" />
60 changes: 24 additions & 36 deletions docs/src/content/docs/examples/Worker/example3_seaborn.mdx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,52 +5,40 @@ title: seaborn
import PySandboxExample from "../../../../components/PySandboxExample.astro";

<PySandboxExample code={`import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
sns.set_theme(style="white", context="talk")
rs = np.random.RandomState(8)
# Create the data
# Set up the matplotlib figure
rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)
# Initialize the FacetGrid object
# Generate some sequential data
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=.5, palette=pal)
x = np.array(list("ABCDEFGHIJ"))
y1 = np.arange(1, 11)
sns.barplot(x=x, y=y1, palette="rocket", ax=ax1)
ax1.axhline(0, color="k", clip_on=False)
ax1.set_ylabel("Sequential")
# Draw the densities in a few steps
# Center the data to make it diverging
g.map(sns.kdeplot, "x",
bw_adjust=.5, clip_on=False,
fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=.5)
y2 = y1 - 5.5
sns.barplot(x=x, y=y2, palette="vlag", ax=ax2)
ax2.axhline(0, color="k", clip_on=False)
ax2.set_ylabel("Diverging")
# passing color=None to refline() uses the hue mapping
# Randomly reorder the data to make it qualitative
g.refline(y=0, linewidth=2, linestyle="-", color=None, clip_on=False)
y3 = rs.choice(y1, len(y1), replace=False)
sns.barplot(x=x, y=y3, palette="deep", ax=ax3)
ax3.axhline(0, color="k", clip_on=False)
ax3.set_ylabel("Qualitative")
# Define and use a simple function to label the plot in axes coordinates
# Finalize the plot
def label(x, color, label):
ax = plt.gca()
ax.text(0, .2, label, fontweight="bold", color=color,
ha="left", va="center", transform=ax.transAxes)
g.map(label, "x")
# Set the subplots to overlap
g.figure.subplots_adjust(hspace=-.25)
# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[], ylabel="")
g.despine(bottom=True, left=True)
sns.despine(bottom=True)
plt.setp(f.axes, yticks=[])
plt.tight_layout(h_pad=2)
display(plt, target=current_target())`} target="out" worker="1" />
60 changes: 24 additions & 36 deletions docs/src/content/docs/examples/WorkerRestricted/example3_seaborn.mdx
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5,52 +5,40 @@ title: seaborn
import PySandboxExample from "../../../../components/PySandboxExample.astro";

<PySandboxExample code={`import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
sns.set_theme(style="white", context="talk")
rs = np.random.RandomState(8)
# Create the data
# Set up the matplotlib figure
rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)
# Initialize the FacetGrid object
# Generate some sequential data
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=.5, palette=pal)
x = np.array(list("ABCDEFGHIJ"))
y1 = np.arange(1, 11)
sns.barplot(x=x, y=y1, palette="rocket", ax=ax1)
ax1.axhline(0, color="k", clip_on=False)
ax1.set_ylabel("Sequential")
# Draw the densities in a few steps
# Center the data to make it diverging
g.map(sns.kdeplot, "x",
bw_adjust=.5, clip_on=False,
fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=.5)
y2 = y1 - 5.5
sns.barplot(x=x, y=y2, palette="vlag", ax=ax2)
ax2.axhline(0, color="k", clip_on=False)
ax2.set_ylabel("Diverging")
# passing color=None to refline() uses the hue mapping
# Randomly reorder the data to make it qualitative
g.refline(y=0, linewidth=2, linestyle="-", color=None, clip_on=False)
y3 = rs.choice(y1, len(y1), replace=False)
sns.barplot(x=x, y=y3, palette="deep", ax=ax3)
ax3.axhline(0, color="k", clip_on=False)
ax3.set_ylabel("Qualitative")
# Define and use a simple function to label the plot in axes coordinates
# Finalize the plot
def label(x, color, label):
ax = plt.gca()
ax.text(0, .2, label, fontweight="bold", color=color,
ha="left", va="center", transform=ax.transAxes)
g.map(label, "x")
# Set the subplots to overlap
g.figure.subplots_adjust(hspace=-.25)
# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[], ylabel="")
g.despine(bottom=True, left=True)
sns.despine(bottom=True)
plt.setp(f.axes, yticks=[])
plt.tight_layout(h_pad=2)
display(plt, target=current_target())`} target="out" worker="1" restricted="1" />

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