From fae4e2d6094de57a438ee4188ce47fc5b01b96fe Mon Sep 17 00:00:00 2001
From: ksonj <kson@siberie.de>
Date: Thu, 7 May 2015 01:02:00 -0700
Subject: [PATCH] [SPARK-7035] Encourage __getitem__ over __getattr__ on column
access in the Python DataFrame API
Author: ksonj <kson@siberie.de>
Closes #5971 from ksonj/doc and squashes the following commits:
dadfebb [ksonj] __getitem__ is cleaner than __getattr__
---
docs/sql-programming-guide.md | 11 ++++++++---
1 file changed, 8 insertions(+), 3 deletions(-)
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index b8233ae06f..df4c123bdd 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -139,7 +139,6 @@ DataFrames provide a domain-specific language for structured data manipulation i
Here we include some basic examples of structured data processing using DataFrames:
-
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
@@ -242,6 +241,12 @@ df.groupBy("age").count().show();
</div>
<div data-lang="python" markdown="1">
+In Python it's possible to access a DataFrame's columns either by attribute
+(`df.age`) or by indexing (`df['age']`). While the former is convenient for
+interactive data exploration, users are highly encouraged to use the
+latter form, which is future proof and won't break with column names that
+are also attributes on the DataFrame class.
+
{% highlight python %}
from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
@@ -270,14 +275,14 @@ df.select("name").show()
## Justin
# Select everybody, but increment the age by 1
-df.select(df.name, df.age + 1).show()
+df.select(df['name'], df['age'] + 1).show()
## name (age + 1)
## Michael null
## Andy 31
## Justin 20
# Select people older than 21
-df.filter(df.age > 21).show()
+df.filter(df['age'] > 21).show()
## age name
## 30 Andy
--
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