srowen commented on a change in pull request #24234:
[WIP][SPARK_26022][PYTHON][DOCS] PySpark Comparison with Pandas
URL: https://github.com/apache/spark/pull/24234#discussion_r270018899
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File path: docs/sql-pyspark-comparison-with-pandas.md
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+---
+layout: global
+title: PySpark Comparison with Pandas
+displayTitle: PySpark Comparison with Pandas
+---
+
+Both PySpark and Pandas cover important use cases and provide a rich set of
features to interact
+with various structural and semistructral data in Python world. Often, PySpark
users are used to
+Pandas. Therefore, this document targets to document the comparison.
+
+* Overview
+* DataFrame APIs
+ * Quick References
+ * Create DataFrame
+ * Load DataFrame
+ * Save DataFrame
+ * Inspect DataFrame
+ * Interaction between PySpark and Pandas
+* Notable Differences
+ * Lazy and Eager Evaluation
+ * Direct assignment
+ * NULL, None, NaN and NaT
+ * Type inference, coercion and cast
+
+
+## Overview
+
+PySpark and Pandas support common functionality to load, save, create,
transform and describe
+DataFrame. PySpark provides conversion from/to Pandas DataFrame, and PySpark
introduced Pandas
+UDFs which allow to use Pandas APIs as are for interoperability between them.
+
+Nevertheless, there are fundamental differences between them to note in
general.
+
+1. PySpark DataFrame is a distributed dataset across multiple nodes whereas
Pandas DataFrame is a
+ local dataset within single node.
+
+ It brings a practical point. If you handle larget dataset, arguably
PySpark brings arguably a
+ better performance in general. If the dataset to process does not fix into
the memory in a
+ single node, using PySpark is probably the way. In case of small dataset,
Pandas might be
+ faster in general since there would not be overhead, for instance, network.
+
+2. PySpark DataFrame is lazy evaluation whereas Pandas DataFrame is eager
evaluation.
+
+ PySpark DataFrame executes lazily whereas Pandas DataFrame executes each
operation
+ immediately against the data set.
+
+3. PySpark DataFrame is immutable in nature whereas Pandas DataFrame is
mutable.
+
+ In PySpark, it creates DataFrame once which cannot be changed. Instead, it
should transform
+ it to another DataFrame whereas Pandas DataFrame is mutable which directly
updates the state
+ of it. Typical example is `String` vs `StringBuilder` in Java.
+
+4. PySpark operations on DataFrame tend to comply SQL.
+
+ It causes some subtleties comparing to Pandas, for instance, about `NaN`,
`None` and `NULL`.
+
+There are similarities and differences between them which might bring
confusion. In this document
+these are described and illuminated by several examples.
+
+
+
+## DataFrame APIs
+
+This chapter describes DataFrame APIs in both PySpark and Pandas.
+
+
+### Quick References
+
+| PySpark | Pandas
|
+| ------------------------------------------------------------------ |
---------------------------------------- |
+| `df.limit(3)` |
`df.head(3)` |
+| `df.filter("a == 1 AND b == 2")` |
`df.filter("(df.a == 1) & (df.b == 2)")` |
+| `df.filter((df.a == 1) & (df.b == 2))` |
`df[(df.a == 1) & (df.b == 2)]` |
+| `df.select("a", "b")` |
`df[["a", "b"]]` |
+| `df.drop_duplicates()` |
`df.drop_duplicates()` |
+| `df.sample(fraction=0.01)` |
`df.sample(frac=0.01)` |
+| `df.groupby("a").count()` |
`df.groupby("a").size()` |
+| `df.groupby("a").agg({"b": "sum"})` |
`df.groupby("a").agg({"b": np.sum})` |
+| `df1.join(df2, on="a")` |
`pandas.merge(df1, df2, on="a")` |
+| `df1.union(df2)` |
`pandas.concat(df1, df2)` |
+| `df = df.select(when(df["a"] < 5, df["a"] * 2).otherwise(df["a"]))`|
`df.loc[pdf['a'] < 5, 'a'] *= 2` |
+
+
+### Create DataFrame
+
+In order to create DataFrame in PySpark and Pandas, you can run the codes
below:
+
+```python
+# PySpark
+data = zip(['Chicago', 'San Francisco', 'New York City'], range(1, 4))
+spark.createDataFrame(list(data), ["city", "rank"])
+```
+
+```python
+# Pandas
+data = {'city': ['Chicago', 'San Francisco', 'New York City'], 'rank':
range(1, 4)}
+pandas.DataFrame(data)
+```
+
+One notable difference when creating DataFrame is that Pandas accepts the data
as below:
+
+```
+data = {
+ 'city': ['Chicago', 'San Francisco', 'New York City'],
+ 'rank': range(1, 4)
+}
+```
+
+and it interprets as:
+
+```
+ city rank
+0 Chicago 1
+1 San Francisco 2
+2 New York City 3
+```
+
+So, a dictionary that contains key and multiple values becomes DataFrame but
PySpark does
+support this. Instead, PySpark can make DataFrame from Pandas DataFrame as
below:
+
+```
+createDataFrame(pandas.DataFrame(data))
+```
+
+For more information see "Interaction between PySpark and Pandas" below.
+
+
+### Load DataFrame
+
+To load DataFrame, there are APIs, usually, `spark.read.xxx` (or
`spark.read.format("xxx")`)
+for PySpark and `pandas.read_xxx` for Pandas.
+
+```python
+# PySpark
+df = spark.read.csv("data.csv")
+```
+
+```python
+# Pandas
+df = pandas.read_csv("data.csv")
+```
+
+There are many sources available in both PySpark and Pandas. For example, CSV,
JSON and
+Parquet are commonly supported but each supports different set of sources.
There are pretty
+different set of options availabe as well, please see the API documentation
for both sides.
+Note that, to match behaviours for PySpark to Pandas, `header=True` and
`inferSchema=True`
+options are required.
+
+
+### Save DataFrame
+
+To load DataFrame, likewise, the APIs are `spark.read.xxx` (or
`spark.read.format("xxx")`) for
+PySpark and `pandas_to_xxx` for Pandas.
+
+```
+df.to_csv("data.csv")
+```
+
+```
+df.write.csv("data.set")
+```
+
+Likewise, different set of sources are supported. Note that, to match
behaviours for PySpark
+to Pandas, `header=True` option is required.
+
+
+### Inspect DataFrame
+
+DataFrame in both PySpark and Pandas can be expected in many ways. One of the
way is to
Review comment:
expected -> inspected
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