Typos: the the

Author: jakevdp

notebooks/00.00-Preface.ipynb

@@ -111,7 +111,7 @@
  "\n",
  "Each chapter of this book focuses on a particular package or tool that contributes a fundamental piece of the Python Data Sciece story.\n",
  "\n",
- "1. IPython and Jupyter: these packages provide the the computational environment in which many Python-using data scientists work.\n",
+ "1. IPython and Jupyter: these packages provide the computational environment in which many Python-using data scientists work.\n",
  "2. NumPy: this library provides the ``ndarray`` for efficient storage and manipulation of dense data arrays in Python.\n",
  "3. Pandas: this library provides the ``DataFrame`` for efficient storage and manipulation of labeled/columnar data in Python.\n",
  "4. Matplotlib: this library provides capabilities for a flexible range of data visualizations in Python.\n",

notebooks/05.11-K-Means.ipynb

@@ -412,7 +412,7 @@
  "We might imagine using the same trick to allow *k*-means to discover non-linear boundaries.\n",
  "\n",
  "One version of this kernelized *k*-means is implemented in Scikit-Learn within the ``SpectralClustering`` estimator.\n",
- "It uses the the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm:"
+ "It uses the graph of nearest neighbors to compute a higher-dimensional representation of the data, and then assigns labels using a *k*-means algorithm:"
  ]
  },
  {