Should I add Tableau to my Python learning?

☐ Job posts I target mention Tableau, Power BI or dashboards
☐ My projects produce numbers a manager would read
☐ I already know pandas and SQL basics
☐ I have at least one finished Python project

3 or 4 ticks  → add Tableau now
1 or 2 ticks  → finish core Python first
0 ticks       → skip Tableau for now

# quick scan of job descriptions you saved as text files
from pathlib import Path

skills = ["tableau", "power bi", "django", "fastapi", "pandas", "sql"]
counts = {s: 0 for s in skills}
for f in Path("jds").glob("*.txt"):
    text = f.read_text(encoding="utf-8").lower()
    for s in skills:
        if s in text:
            counts[s] += 1
print(sorted(counts.items(), key=lambda x: -x[1]))

import pandas as pd

df = pd.read_csv("sales_raw.csv")
df["region"] = df["region"].str.strip().str.title()
df["order_date"] = pd.to_datetime(df["order_date"], errors="coerce")
df = df.dropna(subset=["order_date", "amount"])

df.to_csv("sales_clean.csv", index=False)
print(df.shape, "rows ready for Tableau")

import pandas as pd

wide = pd.DataFrame({
    "month": ["Jan", "Feb"],
    "north": [120, 135],
    "south": [90, 110],
})
long = wide.melt(id_vars="month", var_name="region", value_name="sales")
print(long)
# Tableau can now put region on Color and sales on Rows

# in a terminal
pip install tabpy
tabpy
# TabPy starts on port 9004 by default

# In Tableau Desktop:
# Help > Settings and Performance > Manage Analytics Extension Connection
# Choose TabPy, server: localhost, port: 9004, then Test Connection

// Tableau calculated field: score each region's sales
SCRIPT_REAL("
import numpy as np
vals = np.array(_arg1)
return ((vals - vals.mean()) / vals.std()).tolist()
", SUM([Sales]))

Python + Tableau Project Plan
Question to answer: _______________________
Data source: public dataset / API / scraped (allowed) data
Python steps:
  1. Collect
  2. Clean (list each rule)
  3. Reshape to long format
  4. Export CSV
Tableau steps:
  1. Three charts that answer the question
  2. One filter users can change
  3. A title that states the finding
Publish: GitHub (code) + Tableau Public (viz)

# Project title

## Problem
One sentence about the question.

## Data
Source, size, date range.

## Python pipeline
- collect.py: what it does
- clean.py: rules applied
- Tests: what they check

## Dashboard
Link + one screenshot description.

## Findings
Three bullet points.

## What I would do next

60-second project pitch
1. The question (10 s)
2. Where the data came from (10 s)
3. The hardest cleaning problem and how Python solved it (20 s)
4. What the dashboard shows (10 s)
5. What you would improve (10 s)

Practise it aloud 3 times before every interview.