.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/annoy/plot_annoy_to_NPY_CSV.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_annoy_plot_annoy_to_NPY_CSV.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_annoy_plot_annoy_to_NPY_CSV.py:


annoy.Index to NPY or CSV with examples
=======================================

An example showing the :py:class:`~scikitplot.annoy.Index` class.

.. seealso::
    * :py:obj:`~scikitplot.annoy.Index.from_low_level`
    * https://docs.python.org/3/library/pickle.html#what-can-be-pickled-and-unpickled

.. GENERATED FROM PYTHON SOURCE LINES 16-23

.. code-block:: Python

    import random; random.seed(0)

    # from annoy import Annoy, AnnoyIndex
    from scikitplot.annoy import Annoy, AnnoyIndex, Index

    print(AnnoyIndex.__doc__)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


        High-level Pythonic Annoy wrapper with picklable (or pickle-able).

        Minimal modify spotify/annoy low-level C-API to extend Python API.

        .. seealso::
            * :py:obj:`~scikitplot.annoy.Index.from_low_level`
            * https://docs.python.org/3/library/pickle.html#what-can-be-pickled-and-unpickled
    




.. GENERATED FROM PYTHON SOURCE LINES 24-44

.. code-block:: Python


    import random
    from pathlib import Path

    random.seed(0)

    HERE = Path.cwd().resolve()
    OUT = HERE / "../../../scikitplot/annoy/tests" / "test_v2.tree"

    f = 10
    n = 1000
    idx = AnnoyIndex(f, "angular")
    for i in range(n):
        idx.add_item(i, [random.gauss(0, 1) for _ in range(f)])

    idx.build(10)
    idx.save(str(OUT))
    print("Wrote", OUT)
    idx





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Wrote /home/circleci/repo/galleries/examples/annoy/../../../scikitplot/annoy/tests/test_v2.tree

    Annoy(f=10, metric='angular', n_items=1000, n_trees=10, on_disk_path=/home/circleci/repo/galleries/examples/annoy/../../../scikitplot/annoy/tests/test_v2.tree)



.. GENERATED FROM PYTHON SOURCE LINES 45-46

Small subset → DataFrame/CSV

.. GENERATED FROM PYTHON SOURCE LINES 46-49

.. code-block:: Python

    df = idx.to_dataframe(start=0, stop=1000)
    df.to_csv("sample.csv", index=False)








.. GENERATED FROM PYTHON SOURCE LINES 50-54

.. code-block:: Python

    import pandas as pd

    pd.read_csv("sample.csv")






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>id</th>
          <th>feature_0</th>
          <th>feature_1</th>
          <th>feature_2</th>
          <th>feature_3</th>
          <th>feature_4</th>
          <th>feature_5</th>
          <th>feature_6</th>
          <th>feature_7</th>
          <th>feature_8</th>
          <th>feature_9</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>0.941715</td>
          <td>-1.396578</td>
          <td>-0.679714</td>
          <td>0.370504</td>
          <td>-1.016349</td>
          <td>-0.072120</td>
          <td>0.179196</td>
          <td>-0.831099</td>
          <td>-1.309037</td>
          <td>0.193888</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>0.993250</td>
          <td>-0.646982</td>
          <td>-0.333668</td>
          <td>1.645672</td>
          <td>-0.558890</td>
          <td>-0.514157</td>
          <td>2.404119</td>
          <td>-1.531083</td>
          <td>0.796466</td>
          <td>-2.003649</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2</td>
          <td>-0.596963</td>
          <td>1.503681</td>
          <td>1.221436</td>
          <td>-0.901120</td>
          <td>-0.453699</td>
          <td>0.080233</td>
          <td>-1.258103</td>
          <td>0.552220</td>
          <td>2.227577</td>
          <td>-1.355241</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3</td>
          <td>-1.981533</td>
          <td>0.288244</td>
          <td>-0.119123</td>
          <td>1.804330</td>
          <td>-0.160362</td>
          <td>-0.050660</td>
          <td>-0.190874</td>
          <td>-0.990606</td>
          <td>0.673030</td>
          <td>-1.324083</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4</td>
          <td>1.166490</td>
          <td>0.008376</td>
          <td>0.503630</td>
          <td>-0.552765</td>
          <td>-0.920194</td>
          <td>1.800263</td>
          <td>0.468550</td>
          <td>1.207003</td>
          <td>0.187123</td>
          <td>2.611608</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>995</th>
          <td>995</td>
          <td>-0.764022</td>
          <td>0.174524</td>
          <td>-0.816212</td>
          <td>0.623093</td>
          <td>-0.395465</td>
          <td>0.193787</td>
          <td>-0.769984</td>
          <td>-0.147106</td>
          <td>0.377592</td>
          <td>-0.230512</td>
        </tr>
        <tr>
          <th>996</th>
          <td>996</td>
          <td>0.812510</td>
          <td>-1.125429</td>
          <td>-0.725055</td>
          <td>1.007468</td>
          <td>-1.236581</td>
          <td>-0.339250</td>
          <td>0.958843</td>
          <td>-0.857818</td>
          <td>1.487129</td>
          <td>0.667199</td>
        </tr>
        <tr>
          <th>997</th>
          <td>997</td>
          <td>1.509753</td>
          <td>0.877829</td>
          <td>-0.604218</td>
          <td>0.013888</td>
          <td>-0.597203</td>
          <td>1.374362</td>
          <td>0.723732</td>
          <td>-1.195797</td>
          <td>0.084885</td>
          <td>-0.644913</td>
        </tr>
        <tr>
          <th>998</th>
          <td>998</td>
          <td>-0.479956</td>
          <td>-0.314434</td>
          <td>2.384329</td>
          <td>-1.387915</td>
          <td>1.522265</td>
          <td>0.047036</td>
          <td>0.547916</td>
          <td>0.307560</td>
          <td>-0.234338</td>
          <td>-0.743033</td>
        </tr>
        <tr>
          <th>999</th>
          <td>999</td>
          <td>1.797861</td>
          <td>0.535607</td>
          <td>0.371127</td>
          <td>0.373999</td>
          <td>1.999118</td>
          <td>-1.771545</td>
          <td>-0.133898</td>
          <td>-0.841187</td>
          <td>-0.977023</td>
          <td>-0.905645</td>
        </tr>
      </tbody>
    </table>
    <p>1000 rows × 11 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 55-56

Streaming CSV (warning: huge)

.. GENERATED FROM PYTHON SOURCE LINES 56-58

.. code-block:: Python

    idx.to_csv("annoy_vectors.csv", start=0, stop=100_000)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'annoy_vectors.csv'



.. GENERATED FROM PYTHON SOURCE LINES 59-63

.. code-block:: Python

    import pandas as pd

    pd.read_csv("annoy_vectors.csv")






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>id</th>
          <th>feature_0</th>
          <th>feature_1</th>
          <th>feature_2</th>
          <th>feature_3</th>
          <th>feature_4</th>
          <th>feature_5</th>
          <th>feature_6</th>
          <th>feature_7</th>
          <th>feature_8</th>
          <th>feature_9</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>0.941715</td>
          <td>-1.396578</td>
          <td>-0.679714</td>
          <td>0.370504</td>
          <td>-1.016349</td>
          <td>-0.072120</td>
          <td>0.179196</td>
          <td>-0.831099</td>
          <td>-1.309037</td>
          <td>0.193888</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>0.993250</td>
          <td>-0.646982</td>
          <td>-0.333668</td>
          <td>1.645672</td>
          <td>-0.558890</td>
          <td>-0.514157</td>
          <td>2.404119</td>
          <td>-1.531083</td>
          <td>0.796466</td>
          <td>-2.003649</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2</td>
          <td>-0.596963</td>
          <td>1.503681</td>
          <td>1.221436</td>
          <td>-0.901120</td>
          <td>-0.453699</td>
          <td>0.080233</td>
          <td>-1.258103</td>
          <td>0.552220</td>
          <td>2.227577</td>
          <td>-1.355242</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3</td>
          <td>-1.981533</td>
          <td>0.288244</td>
          <td>-0.119123</td>
          <td>1.804330</td>
          <td>-0.160362</td>
          <td>-0.050660</td>
          <td>-0.190874</td>
          <td>-0.990606</td>
          <td>0.673030</td>
          <td>-1.324082</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4</td>
          <td>1.166490</td>
          <td>0.008376</td>
          <td>0.503630</td>
          <td>-0.552765</td>
          <td>-0.920194</td>
          <td>1.800263</td>
          <td>0.468550</td>
          <td>1.207003</td>
          <td>0.187123</td>
          <td>2.611608</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>995</th>
          <td>995</td>
          <td>-0.764022</td>
          <td>0.174524</td>
          <td>-0.816212</td>
          <td>0.623093</td>
          <td>-0.395465</td>
          <td>0.193787</td>
          <td>-0.769984</td>
          <td>-0.147106</td>
          <td>0.377592</td>
          <td>-0.230512</td>
        </tr>
        <tr>
          <th>996</th>
          <td>996</td>
          <td>0.812510</td>
          <td>-1.125429</td>
          <td>-0.725055</td>
          <td>1.007468</td>
          <td>-1.236580</td>
          <td>-0.339250</td>
          <td>0.958843</td>
          <td>-0.857817</td>
          <td>1.487129</td>
          <td>0.667198</td>
        </tr>
        <tr>
          <th>997</th>
          <td>997</td>
          <td>1.509753</td>
          <td>0.877829</td>
          <td>-0.604218</td>
          <td>0.013888</td>
          <td>-0.597203</td>
          <td>1.374362</td>
          <td>0.723732</td>
          <td>-1.195797</td>
          <td>0.084885</td>
          <td>-0.644913</td>
        </tr>
        <tr>
          <th>998</th>
          <td>998</td>
          <td>-0.479956</td>
          <td>-0.314434</td>
          <td>2.384329</td>
          <td>-1.387915</td>
          <td>1.522265</td>
          <td>0.047036</td>
          <td>0.547916</td>
          <td>0.307560</td>
          <td>-0.234338</td>
          <td>-0.743033</td>
        </tr>
        <tr>
          <th>999</th>
          <td>999</td>
          <td>1.797861</td>
          <td>0.535607</td>
          <td>0.371127</td>
          <td>0.373999</td>
          <td>1.999118</td>
          <td>-1.771545</td>
          <td>-0.133898</td>
          <td>-0.841187</td>
          <td>-0.977023</td>
          <td>-0.905645</td>
        </tr>
      </tbody>
    </table>
    <p>1000 rows × 11 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 64-66

Large export → memory-safe .npy
Exports items [0, n_items) into a memmapped .npy

.. GENERATED FROM PYTHON SOURCE LINES 66-68

.. code-block:: Python

    idx.save_vectors_npy("annoy_vectors.npy")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'annoy_vectors.npy'



.. GENERATED FROM PYTHON SOURCE LINES 69-73

.. code-block:: Python

    import numpy as np

    np.load("annoy_vectors.npy")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    array([[ 0.9417154 , -1.3965781 , -0.67971444, ..., -0.8310992 ,
            -1.3090373 ,  0.19388774],
           [ 0.9932497 , -0.64698166, -0.333668  , ..., -1.5310826 ,
             0.7964658 , -2.0036485 ],
           [-0.59696275,  1.5036808 ,  1.2214364 , ...,  0.55222   ,
             2.2275772 , -1.3552415 ],
           ...,
           [ 1.5097532 ,  0.8778289 , -0.6042179 , ..., -1.1957974 ,
             0.0848854 , -0.64491284],
           [-0.47995627, -0.31443435,  2.3843286 , ...,  0.30755976,
            -0.23433805, -0.7430332 ],
           [ 1.7978611 ,  0.53560704,  0.37112716, ..., -0.8411868 ,
            -0.9770226 , -0.90564495]], shape=(1000, 10), dtype=float32)



.. GENERATED FROM PYTHON SOURCE LINES 74-75

Range-only export (strict, sized)

.. GENERATED FROM PYTHON SOURCE LINES 75-77

.. code-block:: Python

    idx.save_vectors_npy("chunk_0_1m.npy", start=0, stop=1_000_000)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'chunk_0_1m.npy'



.. GENERATED FROM PYTHON SOURCE LINES 78-83

.. code-block:: Python

    import numpy as np

    np.load("chunk_0_1m.npy")






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    array([[ 0.9417154 , -1.3965781 , -0.67971444, ..., -0.8310992 ,
            -1.3090373 ,  0.19388774],
           [ 0.9932497 , -0.64698166, -0.333668  , ..., -1.5310826 ,
             0.7964658 , -2.0036485 ],
           [-0.59696275,  1.5036808 ,  1.2214364 , ...,  0.55222   ,
             2.2275772 , -1.3552415 ],
           ...,
           [ 1.5097532 ,  0.8778289 , -0.6042179 , ..., -1.1957974 ,
             0.0848854 , -0.64491284],
           [-0.47995627, -0.31443435,  2.3843286 , ...,  0.30755976,
            -0.23433805, -0.7430332 ],
           [ 1.7978611 ,  0.53560704,  0.37112716, ..., -0.8411868 ,
            -0.9770226 , -0.90564495]], shape=(1000, 10), dtype=float32)



.. GENERATED FROM PYTHON SOURCE LINES 84-88

.. tags::

   level: beginner
   purpose: showcase


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.068 seconds)


.. _sphx_glr_download_auto_examples_annoy_plot_annoy_to_NPY_CSV.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-plots/scikit-plots/main?urlpath=lab/tree/notebooks/auto_examples/annoy/plot_annoy_to_NPY_CSV.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/annoy/plot_annoy_to_NPY_CSV.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_annoy_to_NPY_CSV.ipynb <plot_annoy_to_NPY_CSV.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_annoy_to_NPY_CSV.py <plot_annoy_to_NPY_CSV.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_annoy_to_NPY_CSV.zip <plot_annoy_to_NPY_CSV.zip>`


.. include:: plot_annoy_to_NPY_CSV.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_