Add document about basics of working with expressions (apache#668)

docs/source/user-guide/common-operations/expressions.rst

@@ -0,0 +1,94 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements.  See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership.  The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License.  You may obtain a copy of the License at
+
+..   http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied.  See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+Expressions
+===========
+
+In DataFusion an expression is an abstraction that represents a computation.
+Expressions are used as the primary inputs and ouputs for most functions within
+DataFusion. As such, expressions can be combined to create expression trees, a
+concept shared across most compilers and databases.
+
+Column
+------
+
+The first expression most new users will interact with is the Column, which is created by calling :func:`col`.
+This expression represents a column within a DataFrame. The function :func:`col` takes as in input a string
+and returns an expression as it's output.
+
+Literal
+-------
+
+Literal expressions represent a single value. These are helpful in a wide range of operations where
+a specific, known value is of interest. You can create a literal expression using the function :func:`lit`.
+The type of the object passed to the :func:`lit` function will be used to convert it to a known data type.
+
+In the following example we create expressions for the column named `color` and the literal scalar string `red`.
+The resultant variable `red_units` is itself also an expression.
+
+.. ipython:: python
+
+    red_units = col("color") == lit("red")
+
+Boolean
+-------
+
+When combining expressions that evaluate to a boolean value, you can combine these expressions using boolean operators.
+It is important to note that in order to combine these expressions, you *must* use bitwise operators. See the following
+examples for the and, or, and not operations.
+
+
+.. ipython:: python
+
+    red_or_green_units = (col("color") == lit("red")) | (col("color") == lit("green"))
+    heavy_red_units = (col("color") == lit("red")) & (col("weight") > lit(42))
+    not_red_units = ~(col("color") == lit("red"))
+
+Functions
+---------
+
+As mentioned before, most functions in DataFusion return an expression at their output. This allows us to create
+a wide variety of expressions built up from other expressions. For example, :func:`.alias` is a function that takes
+as it input a single expression and returns an expression in which the name of the expression has changed.
+
+The following example shows a series of expressions that are built up from functions operating on expressions.
+
+.. ipython:: python
+
+    from datafusion import SessionContext
+    from datafusion import column, lit
+    from datafusion import functions as f
+    import random
+
+    ctx = SessionContext()
+    df = ctx.from_pydict(
+        {
+            "name": ["Albert", "Becca", "Carlos", "Dante"],
+            "age": [42, 67, 27, 71],
+            "years_in_position": [13, 21, 10, 54],
+        },
+        name="employees"
+    )
+
+    age_col = col("age")
+    renamed_age = age_col.alias("age_in_years")
+    start_age = age_col - col("years_in_position")
+    started_young = start_age < lit(18)
+    can_retire = age_col > lit(65)
+    long_timer = started_young & can_retire
+
+    df.filter(long_timer).select(col("name"), renamed_age, col("years_in_position"))

docs/source/user-guide/common-operations/index.rst

@@ -23,6 +23,7 @@ Common Operations
 
    basic-info
    select-and-filter
+   expressions
    joins
    functions
    aggregations