==================== Options and settings ==================== .. currentmodule:: pyspark.pandas Koalas has an options system that lets you customize some aspects of its behaviour, display-related options being those the user is most likely to adjust. Options have a full "dotted-style", case-insensitive name (e.g. ``display.max_rows``). You can get/set options directly as attributes of the top-level ``options`` attribute: .. code-block:: python >>> import pyspark.pandas as ks >>> ks.options.display.max_rows 1000 >>> ks.options.display.max_rows = 10 >>> ks.options.display.max_rows 10 The API is composed of 3 relevant functions, available directly from the ``koalas`` namespace: * :func:`get_option` / :func:`set_option` - get/set the value of a single option. * :func:`reset_option` - reset one or more options to their default value. **Note:** Developers can check out `pyspark.pandas/config.py `_ for more information. .. code-block:: python >>> import pyspark.pandas as ks >>> ks.get_option("display.max_rows") 1000 >>> ks.set_option("display.max_rows", 101) >>> ks.get_option("display.max_rows") 101 Getting and setting options --------------------------- As described above, :func:`get_option` and :func:`set_option` are available from the koalas namespace. To change an option, call ``set_option('option name', new_value)``. .. code-block:: python >>> import pyspark.pandas as ks >>> ks.get_option('compute.max_rows') 1000 >>> ks.set_option('compute.max_rows', 2000) >>> ks.get_option('compute.max_rows') 2000 All options also have a default value, and you can use ``reset_option`` to do just that: .. code-block:: python >>> import pyspark.pandas as ks >>> ks.reset_option("display.max_rows") .. code-block:: python >>> import pyspark.pandas as ks >>> ks.get_option("display.max_rows") 1000 >>> ks.set_option("display.max_rows", 999) >>> ks.get_option("display.max_rows") 999 >>> ks.reset_option("display.max_rows") >>> ks.get_option("display.max_rows") 1000 ``option_context`` context manager has been exposed through the top-level API, allowing you to execute code with given option values. Option values are restored automatically when you exit the `with` block: .. code-block:: python >>> with ks.option_context("display.max_rows", 10, "compute.max_rows", 5): ... print(ks.get_option("display.max_rows")) ... print(ks.get_option("compute.max_rows")) 10 5 >>> print(ks.get_option("display.max_rows")) >>> print(ks.get_option("compute.max_rows")) 1000 1000 Operations on different DataFrames ---------------------------------- Koalas disallows the operations on different DataFrames (or Series) by default to prevent expensive operations. It internally performs a join operation which can be expensive in general. This can be enabled by setting `compute.ops_on_diff_frames` to `True` to allow such cases. See the examples below. .. code-block:: python >>> import pyspark.pandas as ks >>> ks.set_option('compute.ops_on_diff_frames', True) >>> kdf1 = ks.range(5) >>> kdf2 = ks.DataFrame({'id': [5, 4, 3]}) >>> (kdf1 - kdf2).sort_index() id 0 -5.0 1 -3.0 2 -1.0 3 NaN 4 NaN >>> ks.reset_option('compute.ops_on_diff_frames') .. code-block:: python >>> import pyspark.pandas as ks >>> ks.set_option('compute.ops_on_diff_frames', True) >>> kdf = ks.range(5) >>> kser_a = ks.Series([1, 2, 3, 4]) >>> # 'kser_a' is not from 'kdf' DataFrame. So it is considered as a Series not from 'kdf'. >>> kdf['new_col'] = kser_a >>> kdf id new_col 0 0 1.0 1 1 2.0 3 3 4.0 2 2 3.0 4 4 NaN >>> ks.reset_option('compute.ops_on_diff_frames') Default Index type ------------------ In Koalas, the default index is used in several cases, for instance, when Spark DataFrame is converted into Koalas DataFrame. In this case, internally Koalas attaches a default index into Koalas DataFrame. There are several types of the default index that can be configured by `compute.default_index_type` as below: **sequence**: It implements a sequence that increases one by one, by PySpark's Window function without specifying partition. Therefore, it can end up with whole partition in single node. This index type should be avoided when the data is large. This is default. See the example below: .. code-block:: python >>> import pyspark.pandas as ks >>> ks.set_option('compute.default_index_type', 'sequence') >>> kdf = ks.range(3) >>> ks.reset_option('compute.default_index_type') >>> kdf.index Int64Index([0, 1, 2], dtype='int64') This is conceptually equivalent to the PySpark example as below: .. code-block:: python >>> from pyspark.sql import functions as F, Window >>> import pyspark.pandas as ks >>> spark_df = ks.range(3).to_spark() >>> sequential_index = F.row_number().over( ... Window.orderBy(F.monotonically_increasing_id().asc())) - 1 >>> spark_df.select(sequential_index).rdd.map(lambda r: r[0]).collect() [0, 1, 2] **distributed-sequence**: It implements a sequence that increases one by one, by group-by and group-map approach in a distributed manner. It still generates the sequential index globally. If the default index must be the sequence in a large dataset, this index has to be used. Note that if more data are added to the data source after creating this index, then it does not guarantee the sequential index. See the example below: .. code-block:: python >>> import pyspark.pandas as ks >>> ks.set_option('compute.default_index_type', 'distributed-sequence') >>> kdf = ks.range(3) >>> ks.reset_option('compute.default_index_type') >>> kdf.index Int64Index([0, 1, 2], dtype='int64') This is conceptually equivalent to the PySpark example as below: .. code-block:: python >>> import pyspark.pandas as ks >>> spark_df = ks.range(3).to_spark() >>> spark_df.rdd.zipWithIndex().map(lambda p: p[1]).collect() [0, 1, 2] **distributed**: It implements a monotonically increasing sequence simply by using PySpark's `monotonically_increasing_id` function in a fully distributed manner. The values are indeterministic. If the index does not have to be a sequence that increases one by one, this index should be used. Performance-wise, this index almost does not have any penalty comparing to other index types. See the example below: .. code-block:: python >>> import pyspark.pandas as ks >>> ks.set_option('compute.default_index_type', 'distributed') >>> kdf = ks.range(3) >>> ks.reset_option('compute.default_index_type') >>> kdf.index Int64Index([25769803776, 60129542144, 94489280512], dtype='int64') This is conceptually equivalent to the PySpark example as below: .. code-block:: python >>> from pyspark.sql import functions as F >>> import pyspark.pandas as ks >>> spark_df = ks.range(3).to_spark() >>> spark_df.select(F.monotonically_increasing_id()) \ ... .rdd.map(lambda r: r[0]).collect() [25769803776, 60129542144, 94489280512] .. warning:: It is very unlikely for this type of index to be used for computing two different dataframes because it is not guaranteed to have the same indexes in two dataframes. If you use this default index and turn on `compute.ops_on_diff_frames`, the result from the operations between two different DataFrames will likely be an unexpected output due to the indeterministic index values. Available options ----------------- =============================== ============== ===================================================== Option Default Description =============================== ============== ===================================================== display.max_rows 1000 This sets the maximum number of rows Koalas should output when printing out various output. For example, this value determines the number of rows to be shown at the repr() in a dataframe. Set `None` to unlimit the input length. Default is 1000. compute.max_rows 1000 'compute.max_rows' sets the limit of the current Koalas DataFrame. Set `None` to unlimit the input length. When the limit is set, it is executed by the shortcut by collecting the data into the driver, and then using the pandas API. If the limit is unset, the operation is executed by PySpark. Default is 1000. compute.shortcut_limit 1000 'compute.shortcut_limit' sets the limit for a shortcut. It computes specified number of rows and use its schema. When the dataframe length is larger than this limit, Koalas uses PySpark to compute. compute.ops_on_diff_frames False This determines whether or not to operate between two different dataframes. For example, 'combine_frames' function internally performs a join operation which can be expensive in general. So, if `compute.ops_on_diff_frames` variable is not True, that method throws an exception. compute.default_index_type 'sequence' This sets the default index type: sequence, distributed and distributed-sequence. compute.ordered_head False 'compute.ordered_head' sets whether or not to operate head with natural ordering. Koalas does not guarantee the row ordering so `head` could return some rows from distributed partitions. If 'compute.ordered_head' is set to True, Koalas performs natural ordering beforehand, but it will cause a performance overhead. plotting.max_rows 1000 'plotting.max_rows' sets the visual limit on top-n- based plots such as `plot.bar` and `plot.pie`. If it is set to 1000, the first 1000 data points will be used for plotting. Default is 1000. plotting.sample_ratio None 'plotting.sample_ratio' sets the proportion of data that will be plotted for sample-based plots such as `plot.line` and `plot.area`. This option defaults to 'plotting.max_rows' option. plotting.backend 'plotly' Backend to use for plotting. Default is plotly. Supports any package that has a top-level `.plot` method. Known options are: [matplotlib, plotly]. =============================== ============== =====================================================