add salary visualization tool and results

2 files changed, 67 insertions, 2 deletions

diff --git a/content/salary.md b/content/salary.md
index f82a2ee..2656dc1 100644
--- a/content/salary.md
+++ b/content/salary.md
@@ -3,7 +3,9 @@ title = "Salary"
 draft = false
 +++
 
-# Salary Transparency
+<link rel="stylesheet" href="https://unpkg.com/charts.css/dist/charts.min.css">
+
+## Salary Transparency
 
 The data below details the base salary information for each job I've held. This
 information is posted publicly to ensure others in my position have a solid
@@ -24,7 +26,7 @@ paid differently (expertise, years of experience, certifications, education,
 etc.) and that the data in this table should only be used as a single point of
 reference, not the whole story.
 
-# Salary Data
+## Salary Data
 
 Note: When in a role that gives periodic raises, I will create a new record with
 the new base salary in the table below. See the KPMG records for an example of a
@@ -43,6 +45,13 @@ raise while in the same role.
 | Teaching Assistant                             | University of Nebraska | Lincoln, NE    | 2017-08 | 2018-05 | $7/hour  |
 | Community Management Intern                    | Walgreens              | Lincoln, NE    | 2017-06 | 2018-02 | $14/hour |
 
+## Salary Visualization
+
+See the graph below for a visualization of each job's salary over time, as well
+as percentage increases or decreases between jobs.
+
+![Salary Visualization](https://img.cleberg.net/blog/salary/salary.png)
+
 This page was inspired by [Xe](https://xeiaso.net/salary-transparency/), and I'm
 quoting the following wording from them as I want to reiterate this piece:
 
diff --git a/salary_visualization.py b/salary_visualization.py
new file mode 100644
index 0000000..25d2134
--- /dev/null
+++ b/salary_visualization.py
@@ -0,0 +1,56 @@
+# %pip install plotly pandas
+
+import pandas as pd
+import plotly.graph_objs as go
+import locale
+
+locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')
+
+df = pd.read_csv('~/Downloads/salary.csv')
+
+# Reverse the order of the DataFrame
+df = df.iloc[::-1].reset_index(drop=True)
+
+# Calculate the percentage increase
+df['Percentage Increase'] = df['Salary'].pct_change() * 100
+
+# Initialize the plot
+fig = go.Figure()
+
+# Adding each data point as a separate trace to display the text
+for index, row in df.iterrows():
+    title_company = f"{row['Title']} ({row['Company']})"
+    salary_formatted = format_currency(row['Salary'])
+    if pd.notna(row['Percentage Increase']):
+        text = f"{salary_formatted} ({row['Percentage Increase']:.2f}%)"
+    else:
+        text = salary_formatted
+    fig.add_trace(go.Scatter(
+        x=[row['Start'], row['End']],
+        y=[row['Salary'], row['Salary']],
+        text=[text],
+        mode='lines+text',
+        name=title_company,  # Combine title and company for legend
+        textposition='top center'
+    ))
+
+fig.update_layout(
+    title="Salary Data Over Time (annualized)",
+    xaxis_title="Time",
+    yaxis_title="Salary",
+    font=dict(
+        family="monospace",
+        size=16
+    ),
+    margin=dict(l=50, r=50, t=50, b=100),  # Adjust bottom margin for legend space
+    legend=dict(
+        orientation="h",  # Horizontal orientation
+        yanchor="top",    # Anchor to the top of the legend box
+        y=-0.3,           # Position below the graph
+        xanchor="center", # Center the legend horizontally
+        x=0.5             # Center the legend
+    ),
+    height=800  # Increase vertical size
+)
+
+fig.show()