Addition of the python line styling

Desired nonexistent pages of R only code showed styling line plots as needing some Python contributions, so added seaborn.objects styling into this section plus the 2x2 grid of it. Basically a replica of what follows in R (except it had to be aggregated per year because seaborn.object is still... odd... with dates :[  )

Presentation/Figures/styling_line_graphs.md

@@ -18,6 +18,132 @@ There are several ways of styling line graphs. The following examples demonstrat
 
 # Implementation
 
+## Python
+```python
+import pandas as pd
+import seaborn.objects as so
+import numpy as np
+import matplotlib.pyplot as plt
+
+# 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)
+
+# Disclaimer on seaborn.objects.
+"""
+seaborn.objects is still under development.. Which means some things are sub-optimal
+This includes how it handles datetime x-variables, for example.
+It skips far too many years on the xticks, so, a way around that,
+is to manually extract just the year value from the date column
+and plot that. However, that would mean you have to aggregate per year, which could be
+not what you want.
+"""
+df = (
+    economics
+    .assign(
+        date = lambda df: pd.to_datetime(df['date']),
+        year = lambda df: df['date'].dt.year
+        )
+    .groupby(by='year')
+    .aggregate(
+        Mean_Unemploy = ('unemploy', 'mean'),
+        Mean_psavert = ('psavert', 'mean'),
+        Mean_unempmed = ('uempmed', 'mean')
+        )
+    .reset_index()
+)
+
+
+# Basic Plot with the most basic renamings
+p1 = (
+    so.Plot(data=df, x='year', y='Mean_unempmed')
+    .add(so.Line())
+
+    )
+
+p1
+
+
+
+
+## Change line color and chart labels
+## 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.
+# Howeever, in our case, we can pass a color directly.
+p2 = (
+    so.Plot(data=df, x='year', y='Mean_unempmed')
+    .add(so.Line(color='purple'))
+    .label(title='Annual avg of median unemployment duration', x='Year', y='Mean Unemployed')
+
+    )
+
+p2
+
+
+
+
+
+## plotting multiple charts (of different line types and sizes)
+p3 = (
+    so.Plot(data=df)
+    .add(so.Line(color='darkblue', linewidth=5), x='year', y='Mean_unempmed')
+    .add(so.Line(color='red', linewidth=2, linestyle='dotted'), x='year', y='Mean_psavert')
+    .label(title='Economics', 
+           x='Year', 
+           y='')
+
+    )
+
+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['Mean_unempmed'] = df2['Mean_unempmed'] - 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='year', y='Mean_unempmed', color='fac')
+    .add(so.Line())
+    .label(title = "Median Duration of Unemployment",
+           x = "Date",
+           y = "",
+           color='Random Factor')
+)
+
+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):
+
+![Four Styled Line Graphs in Python]({{ "/Presentation/Figures/Images/Styling-Line-Graphs/styling_line_graphs_Python.png" | relative_url }})
+
+
+
 ## R
 ```r
 ## If necessary