diff --git a/Presentation/Figures/Images/Styling-Line-Graphs/styling_line_graphs_Python.png b/Presentation/Figures/Images/Styling-Line-Graphs/styling_line_graphs_Python.png
index 422e2b28..7eb0e741 100644
Binary files a/Presentation/Figures/Images/Styling-Line-Graphs/styling_line_graphs_Python.png and b/Presentation/Figures/Images/Styling-Line-Graphs/styling_line_graphs_Python.png differ
diff --git a/Presentation/Figures/styling_line_graphs.md b/Presentation/Figures/styling_line_graphs.md
index 7a593c19..3f38c0b7 100644
--- a/Presentation/Figures/styling_line_graphs.md
+++ b/Presentation/Figures/styling_line_graphs.md
@@ -24,75 +24,63 @@ 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)
 
-# 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.
-"""
+
+# Quick manipulation of dataframe to convert column to datetime
 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')
+        date = lambda df: pd.to_datetime(df['date'])
         )
-    .reset_index()
 )
 
 
-# Basic Plot with the most basic renamings
+
+# Default plots (Notice the xaxis only has 2 years! We'll fix this in p2)
 p1 = (
-    so.Plot(data=df, x='year', y='Mean_unempmed')
+    so.Plot(data=df, x='date', y='uempmed')
     .add(so.Line())
-
     )
-
 p1
 
 
 
 
-## Change line color and chart labels
+## 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.
-# Howeever, in our case, we can pass a color directly.
+## 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='year', y='Mean_unempmed')
+    so.Plot(data=df, x='date', y='uempmed')
     .add(so.Line(color='purple'))
-    .label(title='Annual avg of median unemployment duration', x='Year', y='Mean Unemployed')
-
+    .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='year', y='Mean_unempmed')
-    .add(so.Line(color='red', linewidth=2, linestyle='dotted'), x='year', y='Mean_psavert')
-    .label(title='Economics', 
+    .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='Year', 
            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
@@ -101,23 +89,21 @@ 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))
-
+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='year', y='Mean_unempmed', color='fac')
+    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
@@ -144,6 +130,7 @@ The four plots generated by the code are (in order p1, p2, then p3 and p4):
 
 
 
+
 ## R
 ```r
 ## If necessary