8000 chore: fix typos (#844) · PhVHoang/datafusion-python@57eb959 · GitHub
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chore: fix typos (apache#844)
- run [codespell](https://github.com/codespell-project/codespell) on the source code - change name of parameter in db-benchmark.dockerfile based on spelling suggestion and the documentation: https://www.rdocumentation.org/packages/utils/versions/3.6.2/topics/install.packages
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‎benchmarks/db-benchmark/db-benchmark.dockerfile

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@@ -58,7 +58,7 @@ RUN cd pandas && \
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RUN cd modin && \
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virtualenv py-modin --python=/usr/bin/python3.10
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RUN Rscript -e 'install.packages(c("jsonlite","bit64","devtools","rmarkdown"), dependecies=TRUE, repos="https://cloud.r-project.org")'
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RUN Rscript -e 'install.packages(c("jsonlite","bit64","devtools","rmarkdown"), dependencies=TRUE, repos="https://cloud.r-project.org")'
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SHELL ["/bin/bash", "-c"]
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‎docs/mdbook/src/index.md

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DataFusion is a blazing fast query engine that lets you run data analyses quickly and reliably.
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DataFusion is written in Rust, but also exposes Python and SQL bindings, so you can easily query data in your langauge of choice. You don't need to know any Rust to be a happy and productive user of DataFusion.
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DataFusion is written in Rust, but also exposes Python and SQL bindings, so you can easily query data in your language of choice. You don't need to know any Rust to be a happy and productive user of DataFusion.
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DataFusion lets you run queries faster than pandas. Let's compare query runtimes for a 5GB CSV file with 100 million rows of data.
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‎docs/source/_static/theme_overrides.css

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/* This is the bootstrap CSS style for "table-striped". Since the theme does
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not yet provide an easy way to configure this globaly, it easier to simply
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not yet provide an easy way to configure this globally, it easier to simply
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include this snippet here than updating each table in all rst files to
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add ":class: table-striped" */
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‎docs/source/conf.py

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# specific language governing permissions and limitations
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# under the License.
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"""Documenation generation."""
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"""Documentation generation."""
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# Configuration file for the Sphinx documentation builder.
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#

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

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===========
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In DataFusion an expression is an abstraction that represents a computation.
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Expressions are used as the primary inputs and ouputs for most functions within
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Expressions are used as the primary inputs and outputs for most functions within
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DataFusion. As such, expressions can be combined to create expression trees, a
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concept shared across most compilers and databases.
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‎examples/export.py

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pylist = df.to_pylist()
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assert pylist == [{"a": 1, "b": 4}, {"a": 2, "b": 5}, {"a": 3, "b": 6}]
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# export to Pyton dictionary of columns
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# export to Python dictionary of columns
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pydict = df.to_pydict()
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assert pydict == {"a": [1, 2, 3], "b": [4, 5, 6]}

‎examples/python-udf-comparisons.py

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# question "return all of the rows that have a specific combination of these
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# values". We have the combinations we care about provided as a python
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# list of tuples. There is no built in function that supports this operation,
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# but it can be explicilty specified via a single expression or we can
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# but it can be explicitly specified via a single expression or we can
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# use a user defined function.
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ctx = SessionContext()

‎examples/tpch/q02_minimum_cost_supplier.py

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# create a column of that value. We can then filter down any rows for which the cost and
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# minimum do not match.
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# The default window frame as of 5/6/2024 is from unbounded preceeding to the current row.
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# The default window frame as of 5/6/2024 is from unbounded preceding to the current row.
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# We want to evaluate the entire data frame, so we specify this.
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window_frame = datafusion.WindowFrame("rows", None, None)
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df = df.with_column(

‎examples/tpch/q04_order_priority_checking.py

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# Limit results to cases where commitment date before receipt date
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# Aggregate the results so we only get one row to join with the order table.
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# Alterately, and likely more idomatic is instead of `.aggregate` you could
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# Alternately, and likely more idiomatic is instead of `.aggregate` you could
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# do `.select_columns("l_orderkey").distinct()`. The goal here is to show
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# mulitple examples of how to use Data Fusion.
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# multiple examples of how to use Data Fusion.
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df_lineitem = df_lineitem.filter(col("l_commitdate") < col("l_receiptdate")).aggregate(
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[col("l_orderkey")], []
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)

‎examples/tpch/q06_forecasting_revenue_change.py

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revenue = df.collect()[0]["revenue"][0].as_py()
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# Note: the output value from this query may be dependant on the size of the database generated
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# Note: the output value from this query may be dependent on the size of the database generated
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print(f"Potential lost revenue: {revenue:.2f}")

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