diff --git a/draft/API_specification/dataframe_object.html b/draft/API_specification/dataframe_object.html index bcfcdb8a..c146c7cc 100644 --- a/draft/API_specification/dataframe_object.html +++ b/draft/API_specification/dataframe_object.html @@ -505,6 +505,8 @@
  • DataFrame.is_null()
  • +
  • DataFrame.iter_columns() +
  • DataFrame.join()
  • DataFrame.max() @@ -695,6 +697,8 @@
  • DataFrame.is_null()
  • +
  • DataFrame.iter_columns() +
  • DataFrame.join()
  • DataFrame.max() @@ -1331,6 +1335,11 @@ but note that the Standard makes no guarantees about them.

    +
    +iter_columns() Iterator[Column]
    +

    Return iterator over columns.

    +
    +
    join(other: Self, *, how: Literal['left', 'inner', 'outer'], left_on: str | list[str], right_on: str | list[str]) Self

    Join with other dataframe.

    @@ -1393,22 +1402,19 @@ at most once per dataframe, and as late as possible in the pipeline.

    For example, do this

    df: DataFrame
    -features = []
     result = df.std() > 0
     result = result.persist()
    -for column_name in df.column_names:
    -    if result.col(column_name).get_value(0):
    -        features.append(column_name)
    +features = [col.name for col in df.iter_columns() if col.get_value(0)]
     

    instead of this:

    df: DataFrame
    -features = []
    -for column_name in df.column_names:
    -    # Do NOT call `persist` on a `DataFrame` within a for-loop!
    -    # This may re-trigger the same computation multiple times
    -    if df.persist().col(column_name).std() > 0:
    -        features.append(column_name)
    +result = df.std() > 0
    +features = [
    +    # Do NOT do this! This will trigger execution of the entire
    +    # pipeline for element in the for-loop!
    +    col.name for col in df.iter_columns() if col.get_value(0).persist()
    +]
     
    diff --git a/draft/API_specification/index.html b/draft/API_specification/index.html index 43568211..8febd64f 100644 --- a/draft/API_specification/index.html +++ b/draft/API_specification/index.html @@ -597,6 +597,7 @@
  • DataFrame.group_by()
  • DataFrame.is_nan()
  • DataFrame.is_null()
  • +
  • DataFrame.iter_columns()
  • DataFrame.join()
  • DataFrame.max()
  • DataFrame.mean()
  • diff --git a/draft/_sources/design_topics/execution_model.md.txt b/draft/_sources/design_topics/execution_model.md.txt index c81c7767..94f93a14 100644 --- a/draft/_sources/design_topics/execution_model.md.txt +++ b/draft/_sources/design_topics/execution_model.md.txt @@ -11,17 +11,13 @@ not be supported in some cases. For example, let's consider the following: ```python df: DataFrame -features = [] -for column_name in df.column_names: - if df.col(column_name).std() > 0: - features.append(column_name) -return features +features = [col.name for col in df.iter_columns() if col.std() > 0] ``` -If `df` is a lazy dataframe, then the call `df.col(column_name).std() > 0` returns +If `df` is a lazy dataframe, then the call `col.std() > 0` returns a (ducktyped) Python boolean scalar. No issues so far. Problem is, -what happens when `if df.col(column_name).std() > 0` is called? +what happens when `if col.std() > 0` is called? -Under the hood, Python will call `(df.col(column_name).std() > 0).__bool__()` in +Under the hood, Python will call `(col.std() > 0).__bool__()` in order to extract a Python boolean. This is a problem for "lazy" implementations, as the laziness needs breaking in order to evaluate the above. diff --git a/draft/design_topics/execution_model.html b/draft/design_topics/execution_model.html index 7b23b894..41ae3b9d 100644 --- a/draft/design_topics/execution_model.html +++ b/draft/design_topics/execution_model.html @@ -338,17 +338,13 @@

    Scope
    df: DataFrame
    -features = []
    -for column_name in df.column_names:
    -    if df.col(column_name).std() > 0:
    -        features.append(column_name)
    -return features
    +features = [col.name for col in df.iter_columns() if col.std() > 0]
     
    -

    If df is a lazy dataframe, then the call df.col(column_name).std() > 0 returns +

    If df is a lazy dataframe, then the call col.std() > 0 returns a (ducktyped) Python boolean scalar. No issues so far. Problem is, -what happens when if df.col(column_name).std() > 0 is called?

    -

    Under the hood, Python will call (df.col(column_name).std() > 0).__bool__() in +what happens when if col.std() > 0 is called?

    +

    Under the hood, Python will call (col.std() > 0).__bool__() in order to extract a Python boolean. This is a problem for “lazy” implementations, as the laziness needs breaking in order to evaluate the above.

    Dask and Polars both require that .compute (resp. .collect) be called beforehand diff --git a/draft/genindex.html b/draft/genindex.html index 68898d4b..8815617a 100644 --- a/draft/genindex.html +++ b/draft/genindex.html @@ -692,6 +692,8 @@

    I

  • iso_weekday() (Column method) +
  • +
  • iter_columns() (DataFrame method)
  • diff --git a/draft/objects.inv b/draft/objects.inv index d09f4109..de46be50 100644 Binary files a/draft/objects.inv and b/draft/objects.inv differ diff --git a/draft/searchindex.js b/draft/searchindex.js index 5a7f9758..71accabb 100644 --- a/draft/searchindex.js +++ b/draft/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["API_specification/column_object", "API_specification/dataframe_object", "API_specification/generated/dataframe_api.Bool", "API_specification/generated/dataframe_api.Date", "API_specification/generated/dataframe_api.Datetime", "API_specification/generated/dataframe_api.Duration", "API_specification/generated/dataframe_api.Float32", "API_specification/generated/dataframe_api.Float64", "API_specification/generated/dataframe_api.Int16", "API_specification/generated/dataframe_api.Int32", "API_specification/generated/dataframe_api.Int64", "API_specification/generated/dataframe_api.Int8", "API_specification/generated/dataframe_api.String", 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"any() (groupby method)": [[25, "dataframe_api.GroupBy.any"]], "max() (groupby method)": [[25, "dataframe_api.GroupBy.max"]], "mean() (groupby method)": [[25, "dataframe_api.GroupBy.mean"]], "median() (groupby method)": [[25, "dataframe_api.GroupBy.median"]], "min() (groupby method)": [[25, "dataframe_api.GroupBy.min"]], "prod() (groupby method)": [[25, "dataframe_api.GroupBy.prod"]], "size() (groupby method)": [[25, "dataframe_api.GroupBy.size"]], "std() (groupby method)": [[25, "dataframe_api.GroupBy.std"]], "sum() (groupby method)": [[25, "dataframe_api.GroupBy.sum"]], "var() (groupby method)": [[25, "dataframe_api.GroupBy.var"]]}}) \ No newline at end of file