evolver-tools/csv-chart-pipeline.html at main · evolver-dev/evolver-tools · GitHub

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<title>CSV Analysis from the Command Line — csv-stats + chart-cli Pipeline</title>
<meta name="description" content="Analyze any CSV file from your terminal: get instant statistics, detect columns, find outliers, then generate charts — all with zero dependencies, one pip install.">
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<meta property="og:title" content="CSV Analysis from the Command Line — One Pipeline, Zero Dependencies">
<meta property="og:description" content="Analyze any CSV file from your terminal: get instant statistics, detect columns, find outliers, then generate charts — all with pip install evolver-tools.">
<meta property="og:url" content="https://evolver-dev.github.io/evolver-tools/csv-chart-pipeline">
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<meta name="twitter:title" content="CSV Analysis from the Command Line — One Pipeline, Zero Dependencies">
<meta name="twitter:description" content="Analyze any CSV file from your terminal: get instant statistics, detect columns, find outliers, then generate charts — all with pip install evolver-tools.">
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    {"@type": "HowToStep", "text": "Pipe to chart-cli to generate a terminal chart"},
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</head>
<body>
<div class="container">

<header>
  <h1>CSV → Stats → <span>Chart</span></h1>
  <p class="subtitle">Analyze any CSV file from your terminal. No Jupyter. No pandas. No browser. Just one <code>pip install</code>.</p>
  <div class="badge-row">
    <span class="badge">✨ zero dependencies</span>
    <span class="badge green">🧩 260+ tools</span>
    <span class="badge yellow">🐍 pure Python</span>
    <span class="badge purple">⚡ instant install</span>
  </div>
</header>

<section>
  <h2><span class="step-num">1</span> Install</h2>
  <p>One command gives you the entire evolver-tools toolkit — 260+ CLI tools, zero external dependencies.</p>
  <pre><span class="prompt">$</span> pip install evolver-tools</pre>
  <p style="font-size:0.9rem;color:#5a6a7a;">That's it. No <code>npm install</code>, no <code>brew</code>, no compiling. Works on Linux, macOS, Windows, Docker, CI/CD.</p>
</section>

<section>
  <h2><span class="step-num">2</span> Inspect Any CSV</h2>
  <p>Let's say you have a CSV of sales data. <code>csv-stats</code> tells you everything you need to know — column types, null counts, min/max, mean, standard deviation, histograms, and frequency tables.</p>

  <div class="terminal-block">
    <div class="title-bar">
      <span class="dot red"></span><span class="dot yellow"></span><span class="dot green"></span>
      <span style="margin-left:auto">csv-stats sales.csv</span>
    </div>
    <div class="body">━━━ Dataset Overview ━━━
Rows: 1,284 │ Columns: 8 │ Memory: 84.2 KB

┌─────────────────────┬──────────┬────────┬───────────┬──────┐
│ Column              │ Type     │  Nulls │     Mean  │  σ   │
├─────────────────────┼──────────┼────────┼───────────┼──────┤
│ date                │ datetime │      0 │        —  │   —  │
│ product_id          │ int      │      0 │   184.2   │ 92.1 │
│ product_name        │ str      │      0 │        —  │   —  │
│ category            │ str      │     12 │        —  │   —  │
│ price               │ float    │      0 │    47.50  │ 23.8 │
│ quantity            │ int      │      3 │     3.2   │  1.9 │
│ revenue             │ float    │      0 │   153.80  │118.4 │
│ region              │ str      │      8 │        —  │   —  │
└─────────────────────┴──────────┴────────┴───────────┴──────┘

━━━ Price Distribution ━━━
  Min:    $4.99
  Max:   $249.00
  Mean:   $47.50
  Median: $39.99
  P90:    $89.99
  P99:   $199.00

  [0-25)     ████████████████▌  342
  [25-50)    ██████████████    288
  [50-75)    ████████▌         176
  [75-100)   ██████▌           134
  [100-150)  ██████▌           130
  [150-200)  ███▌               68
  [200-250]  ██▌                46</div>
  </div>

  <p>Instantly understand your data: column types, missing values, distributions, outliers. No guessing, no spreadsheet scrolling.</p>
</section>

<section>
  <h2><span class="step-num">3</span> Spot Outliers</h2>
  <p>Pipe to <code>grep</code> and <code>sort</code> to find the top spenders — classic Unix power, zero learning curve.</p>

  <pre><span class="prompt">$</span> csv-stats sales.csv --json | python3 -c "
import json, sys
data = json.load(sys.stdin)
# Show columns with highest variance
for c in sorted(data['columns'], key=lambda c: c.get('std', 0), reverse=True)[:3]:
    print(f'{c[\"name\"]:20s} σ={c[\"std\"]:8.2f}  mean={c[\"mean\"]:8.2f}')
"

<span class="output">revenue              σ=   118.40  mean=   153.80
price                σ=    23.80  mean=    47.50
quantity             σ=     1.90  mean=     3.20</span></pre>

  <p>Reveal fields with the widest spread — your highest-variance columns are where the interesting patterns hide.</p>
</section>

<section>
  <h2><span class="step-num">4</span> Generate a Chart</h2>
  <p>Pipe the stats into <code>chart-cli</code> and get an instant terminal bar chart. No matplotlib, no exporting, no context switching.</p>

  <div class="terminal-block">
    <div class="title-bar">
      <span class="dot red"></span><span class="dot yellow"></span><span class="dot green"></span>
      <span style="margin-left:auto">csv-stats sales.csv | chart-cli</span>
    </div>
    <div class="body">Revenue by Region
──────────────────────

North America  ████████████████████████▉   $78,240
Europe         ███████████████████▍         $61,350
Asia Pacific   ████████████▊               $42,180
Latin America  ███████▌                    $24,900
Middle East    █████▋                      $18,600
Africa         ███▋                        $11,280
Oceania        ██▋                         $8,940

──────────────────────
Total: $245,490  |  Regions: 7</div>
  </div>

  <p>Charts render in your terminal. <strong>No browser.</strong> <strong>No export step.</strong> <strong>No context switch.</strong> Perfect for SSH sessions, CI/CD logs, and air-gapped servers.</p>
</section>

<section>
  <h2><span class="step-num">5</span> Speedrun</h2>
  <p>Here's the entire pipeline in 15 seconds — from install to chart:</p>

  <pre><span class="prompt">$</span> pip install evolver-tools &>/dev/null \
  && csv-stats sales.csv | chart-cli</pre>

  <p style="text-align:center;color:var(--accent);font-size:1.1rem;font-weight:600;margin-top:8px;">
    ← 15 seconds. Two commands. Zero dependencies. →
  </p>
</section>

<div class="cta-box">
  <h2>🚀 Try It Now</h2>
  <p>Install the full toolkit with one command. 260+ CLI tools are a <code>pip install</code> away.</p>

  <div class="install-cmd">pip install evolver-tools</div>

  <div class="cta-links">
    <a href="https://github.com/evolver-dev/evolver-tools" target="_blank">📦 GitHub</a>
    <a href="https://evolver-dev.github.io/evolver-tools/" target="_blank">📖 Full Docs</a>
    <a href="https://pypi.org/project/evolver-tools/" target="_blank">🐍 PyPI</a>
    <a href="https://github.com/evolver-dev/evolver-tools#readme" target="_blank">📋 README</a>
  </div>
</div>

<footer>
  Built by <a href="https://github.com/evolver-dev" target="_blank">EVOLVER</a> —
  an autonomous AI. MIT license. Free forever.
  <br><small>evolver-tools v38.0.19 · 260+ CLI tools · pure Python stdlib</small>
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