import pandas as pd
+import seaborn.objects as so
+import numpy as np
+import matplotlib.pyplot as plt
+from seaborn import axes_style
+
+
+
+
+# Download the economics dataset (from ggplot2 so comparison is apples-to-apples)
+url = "https://raw.githubusercontent.com/tidyverse/ggplot2/main/data-raw/economics.csv"
+economics = pd.read_csv(url)
+
+
+# Quick manipulation of dataframe to convert column to datetime
+df = (
+ economics
+ .assign(
+ date = lambda df: pd.to_datetime(df['date'])
+ )
+)
+
+
+
+# Default plots (Notice the xaxis only has 2 years! We'll fix this in p2)
+p1 = (
+ so.Plot(data=df, x='date', y='uempmed')
+ .add(so.Line())
+ )
+p1
+
+
+
+
+## Change line color and chart labels, and fix xaxis
+## Note here that color is inside of the Line call, so this would color the line.
+## If color were instead *inside* the so.Plot() object, SO would assign it
+## a different line for each value of the factor variable (column), colored differently. (Commonly referred to as hue in seaborn)
+# However, in our case, we can pass a color directly.
+p2 = (
+ so.Plot(data=df, x='date', y='uempmed')
+ .add(so.Line(color='purple'))
+ .label(title='Median Duration of Unemploymeny', x='Date', y='')
+ .scale(x=so.Temporal().tick(upto=10)) #Needed for current configuration of seaborn.objects so xaxis prints more than 2 ticks
+ .theme(axes_style("whitegrid")) #use a function from parent seaborn library, that will pass a prebuilt selection based on what you pass
+ )
+
+p2
+
+
+
+## plotting multiple charts (of different line types and sizes)
+p3 = (
+ so.Plot(data=df)
+ .add(so.Line(color='darkblue', linewidth=5), x='date', y='uempmed')
+ .add(so.Line(color='red', linewidth=2, linestyle='dotted'), x='date', y='psavert')
+ .label(title='Unemployment Duration (Blue)\n & Savings Rate (Red)',
+ x='Date',
+ y='')
+ .scale(x=so.Temporal().tick(upto=10)) #Needed for current configuration of seaborn.objects so xaxis prints more than 2 ticks
+ .theme(axes_style("whitegrid")) #use a function from parent seaborn library, that will pass a prebuilt selection based on what you pass
+ )
+
+p3
+
+
+## Plotting a different line type for each group
+## There isn't a natural factor in this data so let's just duplicate the data and make one up
+df['fac'] = 1
+df2 = df.copy()
+df2['fac'] = 2
+df2['uempmed'] = df2['uempmed'] - 2 + np.random.normal(size=len(df2))
+df_final = pd.concat([df, df2], ignore_index=True).astype({'fac':'category'})
+
+
+p4 = (
+ so.Plot(data=df_final, x='date', y='uempmed', color='fac')
+ .add(so.Line())
+ .label(title = "Median Duration of Unemployment",
+ x = "Date",
+ y = "",
+ color='Random Factor')
+ .scale(x=so.Temporal().tick(upto=10)) #Needed for current configuration of seaborn.objects so xaxis prints more than 2 ticks
+ .theme(axes_style("whitegrid")) #use a function from parent seaborn library, that will pass a prebuilt selection based on what you pass
+)
+
+p4
+
+
+
+# Plot all 4 plots
+fig, axs = plt.subplots(2, 2, figsize=(10, 8))
+# Draw each plot in the corresponding subplot
+p1.on(axs[0, 0]).plot()
+p2.on(axs[0, 1]).plot()
+p3.on(axs[1, 0]).plot()
+p4.on(axs[1, 1]).plot()
+
+# Adjust layout to avoid overlap
+plt.tight_layout()
+
+# Show the combined plot
+plt.show()
+The four plots generated by the code are (in order p1, p2, then p3 and p4):
+