how to plot error bar only positive side in seaborn? - seaborn

Question

I´m trying to plot a chart and I have some problems to solve, sorry but I´m new in program language.
First one:
How to plot only one chart? I got that example from the internet and when a plot there is two figure for each code and two of them is blank.
The second one:
Is it possible to plot only a positive error bar?
Third one:
Is it possible to plot these two charts side by side in one figure?
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
Treat1 =pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=1, high=100, size=40)})
Treat2 =pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=1, high=100, size=40)})
df = pd.concat([Treat1, Treat2])
Treat3 =pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=100, high=300, size=40)})
Treat4 =pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=100, high=300, size=40)})
df2 = pd.concat([Treat3, Treat4])
sns.set(style="ticks")
fig, ax = plt.subplots()
color_map = dict(pos="indianred", neg="steelblue")
g = sns.catplot(x= "Treatment", y="weight", hue="Treatment", capsize=.07, ci ="sd",
data=df, kind="bar", palette = 'coolwarm', edgecolor="white")
plt.text(-0.22,99, "B")
plt.text(1.18,99, "A")
plt.ylabel('weight, kg')
plt.xticks([-0.2, 1.2], ['Group 1', 'Group 2'])
plt.ylim(0, 100)
color_map = dict(pos="indianred", neg="steelblue")
g = sns.catplot(x= "Treatment", y="weight", hue="Treatment", capsize=.07, ci ="sd",
data=df2, kind="bar", palette = 'coolwarm', edgecolor="white")
plt.text(-0.22,300, "B")
plt.text(1.18,300, "A")
plt.ylabel('weight, kg')
plt.xticks([-0.2, 1.2], ['Group 1', 'Group 2'])
plt.ylim(0, 300)
Thank you so much!

Answer 1

A seaborn catplot is a figure level plot, which creates and occupies a new figure. To have such a plot as a subplot, sns.barplot can be called directly. Supplying an ax tells into which subplot the barplot should go.
The barplot gets a legend, which in this case is superfluous, but it can be removed.
To only have the upper error bar visible, the rectangles of the bars can be plot on top of them. A zorder larger than the zorder of the lines of the errorbar (2) takes care of this.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
Treat1 = pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=1, high=100, size=40)})
Treat2 = pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=1, high=100, size=40)})
df1 = pd.concat([Treat1, Treat2])
Treat3 = pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=100, high=300, size=40)})
Treat4 = pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=100, high=300, size=40)})
df2 = pd.concat([Treat3, Treat4])
sns.set(style="ticks")
fig, axs = plt.subplots(ncols=2, figsize=(10, 4))
for ax, df, height in zip(axs, [df1, df2], [100, 300]):
color_map = {1: "indianred", 2: "steelblue"}
g = sns.barplot(x="Treatment", y="weight", hue="Treatment", capsize=.07, ci="sd",
data=df, palette=color_map, edgecolor="white", ax=ax)
g.legend_.remove()
for bar in g.patches:
bar.set_zorder(3)
ax.text(-0.2, height * 0.95, "B", ha='center')
ax.text(1.2, height * 0.95, "A", ha='center')
ax.set_ylabel('weight, kg')
ax.set_xticks([-0.2, 1.2])
ax.set_xticklabels(['Group 1', 'Group 2'])
ax.set_ylim(0, height)
plt.tight_layout()
plt.show()
PS: Note that the code can be simplified somewhat if you don't use hue=. This also puts the bars in a more logical position.
fig, axs = plt.subplots(ncols=2, figsize=(10, 4))
for ax, df, height in zip(axs, [df1, df2], [100, 300]):
color_map = {1: "indianred", 2: "steelblue"}
g = sns.barplot(x="Treatment", y="weight", capsize=.07, ci="sd",
data=df, palette=color_map, edgecolor="white", ax=ax)
for bar in g.patches:
bar.set_zorder(3)
ax.text(0, height * 0.97, "B", ha='center', va='top')
ax.text(1, height * 0.97, "A", ha='center', va='top')
ax.set_ylabel('weight, kg')
ax.set_ylim(0, height)
ax.set_xticklabels(['Group 1', 'Group 2'])
plt.tight_layout()
plt.show()