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        content="Chapter 15 of Python Zero to Hero: delve into data analysis with pandas and NumPy—arrays, Series, DataFrame operations, and basic plotting."/>
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        content="Python, pandas, NumPy, DataFrame, Series, array, data analysis, aggregation, plotting"/>
  <meta name="author" content="Luca Bocaletto"/>
  <title>Chapter 15 – Data Analysis with pandas & NumPy | Python Zero to Hero</title>

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      <h1 class="display-6">Chapter 15: Data Analysis with pandas &amp; NumPy</h1>
      <p class="text-muted">
        Learn how to manipulate numerical arrays with NumPy and tabular data with pandas, including aggregation and basic plotting.
      </p>
      <a href="src/chapter15.py" download class="btn btn-outline-primary btn-sm btn-py">
        Download <code>chapter15.py</code>
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    <!-- Objectives -->
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      <h2>Objectives</h2>
      <ul>
        <li>Create and manipulate <code>numpy.ndarray</code> objects.</li>
        <li>Load and inspect data using <code>pandas.Series</code> and <code>DataFrame</code>.</li>
        <li>Perform indexing, slicing, and boolean selection in pandas.</li>
        <li>Compute summary statistics and group-by aggregations.</li>
        <li>Handle missing data and apply functions with <code>apply</code>.</li>
        <li>Visualize data with built-in pandas plotting (matplotlib).</li>
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    <!-- 1. NumPy Arrays -->
    <section id="numpy" class="mb-5">
      <h2>1. NumPy Arrays</h2>
      <pre><code class="python">import numpy as np

# 1D array
arr = np.array([1, 2, 3, 4])
# 2D array
mat = np.arange(9).reshape(3, 3)
print(arr)
print(mat)</code></pre>
      <pre><code class="python"># elementwise operations
print(arr + 10)
# statistics
print("mean:", arr.mean(), "sum:", arr.sum())</code></pre>
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    <!-- 2. pandas Series & DataFrame -->
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      <h2>2. pandas Series &amp; DataFrame</h2>
      <pre><code class="python">import pandas as pd

# create Series
s = pd.Series([10, 20, 30], index=['a','b','c'])
# create DataFrame
df = pd.DataFrame({
    'col1': [1,2,3],
    'col2': ['x','y','z']
})
print(s)
print(df.head())</code></pre>
      <pre><code class="python"># read CSV
df = pd.read_csv('data.csv')
print(df.info())
print(df.describe())</code></pre>
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    <!-- 3. Indexing & Selection -->
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      <h2>3. Indexing &amp; Selection</h2>
      <pre><code class="python"># label-based
print(df.loc[0, 'col1'])
# position-based
print(df.iloc[0:2, 0:2])
# boolean mask
print(df[df['col1'] > 1])</code></pre>
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    <!-- 4. Aggregation & GroupBy -->
    <section id="groupby" class="mb-5">
      <h2>4. Aggregation &amp; GroupBy</h2>
      <pre><code class="python"># summary statistics
print(df['col1'].mean(), df['col1'].sum())

# group by
grouped = df.groupby('col2')['col1'].agg(['mean','count'])
print(grouped)</code></pre>
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    <!-- 5. Missing Data & Apply -->
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      <h2>5. Missing Data &amp; apply()</h2>
      <pre><code class="python"># fill missing values
df['col1'] = df['col1'].fillna(0)
# apply function
df['col3'] = df['col1'].apply(lambda x: x**2)</code></pre>
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    <!-- 6. Plotting -->
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      <h2>6. Basic Plotting</h2>
      <pre><code class="python">import matplotlib.pyplot as plt

# line plot
df['col1'].plot(title='Col1 over index')
plt.show()

# bar plot
df.groupby('col2')['col1'].sum().plot(kind='bar')
plt.show()</code></pre>
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      <h2>Exercises</h2>
      <ol>
        <li>Load the Iris dataset into a DataFrame and compute the average petal length by species.</li>
        <li>Identify and drop rows with missing values in a sample CSV.</li>
        <li>Plot a histogram of a numeric column and save it to a file.</li>
        <li>Use <code>apply</code> to normalize a column (min–max scaling).</li>
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