Anaconda Enterprise: Documentation

# Basic Queries

Here we give a quick overview of some of the more common query functionality.

We use the well known iris dataset

    >>> from blaze import data
    >>> from blaze.utils import example
    >>> iris = data(example('iris.csv'))
    >>> iris.peek()
        sepal_length  sepal_width  petal_length  petal_width      species
    0            5.1          3.5           1.4          0.2  Iris-setosa
    1            4.9          3.0           1.4          0.2  Iris-setosa
    2            4.7          3.2           1.3          0.2  Iris-setosa
    3            4.6          3.1           1.5          0.2  Iris-setosa
    ...

## Column Access

Select individual columns using attributes

    >>> iris.species
            species
    0   Iris-setosa
    1   Iris-setosa
    2   Iris-setosa
    3   Iris-setosa
    ...

Or item access

    >>> iris['species']
            species
    0   Iris-setosa
    1   Iris-setosa
    2   Iris-setosa
    3   Iris-setosa
    ...

Select many columns using a list of names

    >>> iris[['sepal_length', 'species']]
        sepal_length      species
    0            5.1  Iris-setosa
    1            4.9  Iris-setosa
    2            4.7  Iris-setosa
    3            4.6  Iris-setosa
    ...

## Mathematical operations

Use mathematical operators and functions as normal

    >>> from blaze import log
    >>> log(iris.sepal_length * 10)
        sepal_length
    0       3.931826
    1       3.891820
    2       3.850148
    3       3.828641
    ...

Note that mathematical functions like `log` should be imported from `blaze`. These will translate to `np.log`, `math.log`, `sqlalchemy.sql.func.log`, etc. based on the backend.

## Reductions

As with many Blaze operations reductions like `sum` and `mean` may be used either as methods or as base functions.

    >>> iris.sepal_length.mean()
    5.84333333333333...

    >>> from blaze import mean
    >>> mean(iris.sepal_length)
    5.84333333333333...

## Split-Apply-Combine

The `by` operation expresses split-apply-combine computations. It has the general format

    >>> by(table.grouping_columns, name_1=table.column.reduction(),
    ...                            name_2=table.column.reduction(),
    ...                            ...)

Here is a concrete example. Find the shortest, longest, and average petal length by species.

    >>> from blaze import by
    >>> by(iris.species, shortest=iris.petal_length.min(),
    ...                   longest=iris.petal_length.max(),
    ...                   average=iris.petal_length.mean())
               species  average  longest  shortest
    0      Iris-setosa    1.462      1.9       1.0
    1  Iris-versicolor    4.260      5.1       3.0
    2   Iris-virginica    5.552      6.9       4.5

This simple model can be extended to include more complex groupers and more complex reduction expressions.

## Add Computed Columns

Add new columns using the `transform` function

    >>> transform(iris, sepal_ratio = iris.sepal_length / iris.sepal_width,
    ...                 petal_ratio = iris.petal_length / iris.petal_width)
        sepal_length  sepal_width  petal_length  petal_width      species  \
    0            5.1          3.5           1.4          0.2  Iris-setosa
    1            4.9          3.0           1.4          0.2  Iris-setosa
    2            4.7          3.2           1.3          0.2  Iris-setosa
    3            4.6          3.1           1.5          0.2  Iris-setosa

        sepal_ratio  petal_ratio
    0      1.457143     7.000000
    1      1.633333     7.000000
    2      1.468750     6.500000
    3      1.483871     7.500000
    ...

## Text Matching

Match text with glob strings, specifying columns with keyword arguments.

    >>> iris[iris.species.like('*versicolor')]
        sepal_length  sepal_width  petal_length  petal_width          species
    50           7.0          3.2           4.7          1.4  Iris-versicolor
    51           6.4          3.2           4.5          1.5  Iris-versicolor
    52           6.9          3.1           4.9          1.5  Iris-versicolor

## Relabel Column names

    >>> iris.relabel(petal_length='PETAL-LENGTH', petal_width='PETAL-WIDTH')
        sepal_length  sepal_width  PETAL-LENGTH  PETAL-WIDTH      species
    0            5.1          3.5           1.4          0.2  Iris-setosa
    1            4.9          3.0           1.4          0.2  Iris-setosa
    2            4.7          3.2           1.3          0.2  Iris-setosa

# Examples

Blaze can help solve many common problems that data analysts and scientists encounter. Here are a few examples of common issues that can be solved using blaze.

## Combining separate, gzipped csv files.

    >>> from blaze import odo
    >>> from pandas import DataFrame
    >>> odo(example('accounts_*.csv.gz'), DataFrame)
       id      name  amount
    0   1     Alice     100
    1   2       Bob     200
    2   3   Charlie     300
    3   4       Dan     400
    4   5     Edith     500

## Split-Apply-Combine

    >>> from blaze import data, by
    >>> t = data('sqlite:///%s::iris' % example('iris.db'))
    >>> t.peek()
        sepal_length  sepal_width  petal_length  petal_width      species
    0            5.1          3.5           1.4          0.2  Iris-setosa
    1            4.9          3.0           1.4          0.2  Iris-setosa
    2            4.7          3.2           1.3          0.2  Iris-setosa
    3            4.6          3.1           1.5          0.2  Iris-setosa
    4            5.0          3.6           1.4          0.2  Iris-setosa
    5            5.4          3.9           1.7          0.4  Iris-setosa
    6            4.6          3.4           1.4          0.3  Iris-setosa
    7            5.0          3.4           1.5          0.2  Iris-setosa
    8            4.4          2.9           1.4          0.2  Iris-setosa
    9            4.9          3.1           1.5          0.1  Iris-setosa
    ...
    >>> by(t.species, max=t.petal_length.max(), min=t.petal_length.min())
               species  max  min
    0      Iris-setosa  1.9  1.0
    1  Iris-versicolor  5.1  3.0
    2   Iris-virginica  6.9  4.5