From 32174c6b6305ff87f266fe6dcc9e2a020513add0 Mon Sep 17 00:00:00 2001 From: dataframe-api-bot Date: Thu, 7 Dec 2023 21:59:32 +0000 Subject: [PATCH] Deploy: 8ecbea3728480a69c50249b14f63631586e4aae9 --- draft/API_specification/dataframe_object.html | 26 +++++++++++------- draft/API_specification/index.html | 1 + .../design_topics/execution_model.md.txt | 12 +++----- draft/design_topics/execution_model.html | 12 +++----- draft/genindex.html | 2 ++ draft/objects.inv | Bin 2114 -> 2119 bytes draft/searchindex.js | 2 +- 7 files changed, 28 insertions(+), 27 deletions(-) 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)
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