You will find pl-compare useful if you find yourself writing various SQL/Dataframe operations to:
- Understand how well two tables Reconcile example
- Find the schema differences between two tables example
- Find counts or examples of rows that exist in one table but not another example
- Find counts or examples of value differences between two tables example
- Assert that two tables are exactly equal (such as for an automated test) example
- Assert that two tables have matching schemas, rows or column values example
Click for a jupyter notebook with example usage
With pl-compare you can:
- Get statistical summaries and/or examples and/or a boolean to indicate:
- Schema differences
- Row differences
- Value differences
- Easily works for Pandas dataframes and other tabular data formats with conversion using Apache arrow
- View differences as a text report
- Get differences as a Polars LazyFrame or DataFrame
- Use LazyFrames for larger than memory comparisons
- Specify the equality calculation that is used to dermine value differences
pip install pl_compare>>> import polars as pl
>>> from pl_compare import compare
>>>
>>> base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": ["1", "2", "3"],
... }
... )
>>> compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1, 2, 3],
... "Example2": [1, 2, 3],
... "Example3": [1, 2, 3],
... },
... )
>>>
>>> compare_result = compare(["ID"], base_df, compare_df)
>>> print("is_schemas_equal:", compare_result.is_schemas_equal())
is_schemas_equal: False
>>> print("is_rows_equal:", compare_result.is_rows_equal())
is_rows_equal: False
>>> print("is_values_equal:", compare_result.is_values_equal())
is_values_equal: False
>>>>>> import polars as pl
>>> from pl_compare import compare
>>>
>>> base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": ["1", "2", "3"],
... }
... )
>>> compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1, 2, 3],
... "Example2": [1, 2, 3],
... "Example3": [1, 2, 3],
... },
... )
>>>
>>> compare_result = compare(["ID"], base_df, compare_df)
>>> print("schemas_summary()")
schemas_summary()
>>> print(compare_result.schemas_summary())
shape: (6, 2)
┌─────────────────────────────────┬───────┐
│ Statistic ┆ Count │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════════════════════════════════╪═══════╡
│ Columns in base ┆ 3 │
│ Columns in compare ┆ 4 │
│ Columns in base and compare ┆ 3 │
│ Columns only in base ┆ 0 │
│ Columns only in compare ┆ 1 │
│ Columns with schema differences ┆ 1 │
└─────────────────────────────────┴───────┘
>>> print("schemas_sample()")
schemas_sample()
>>> print(compare_result.schemas_sample())
shape: (2, 3)
┌──────────┬─────────────┬────────────────┐
│ column ┆ base_format ┆ compare_format │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞══════════╪═════════════╪════════════════╡
│ Example2 ┆ String ┆ Int64 │
│ Example3 ┆ null ┆ Int64 │
└──────────┴─────────────┴────────────────┘
>>>>>> import polars as pl
>>> from pl_compare import compare
>>>
>>> base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": ["1", "2", "3"],
... }
... )
>>> compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1, 2, 3],
... "Example2": [1, 2, 3],
... "Example3": [1, 2, 3],
... },
... )
>>>
>>> compare_result = compare(["ID"], base_df, compare_df)
>>> print("rows_summary()")
rows_summary()
>>> print(compare_result.rows_summary())
shape: (5, 2)
┌──────────────────────────┬───────┐
│ Statistic ┆ Count │
│ --- ┆ --- │
│ str ┆ i64 │
╞══════════════════════════╪═══════╡
│ Rows in base ┆ 3 │
│ Rows in compare ┆ 3 │
│ Rows only in base ┆ 1 │
│ Rows only in compare ┆ 1 │
│ Rows in base and compare ┆ 2 │
└──────────────────────────┴───────┘
>>> print("rows_sample()")
rows_sample()
>>> print(compare_result.rows_sample())
shape: (2, 3)
┌────────────┬──────────┬─────────────────┐
│ ID ┆ variable ┆ value │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞════════════╪══════════╪═════════════════╡
│ 12345678 ┆ status ┆ in base only │
│ 1234567810 ┆ status ┆ in compare only │
└────────────┴──────────┴─────────────────┘
>>>>>> import polars as pl
>>> from pl_compare import compare
>>>
>>> base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": ["1", "2", "3"],
... }
... )
>>> compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1, 2, 3],
... "Example2": [1, 2, 3],
... "Example3": [1, 2, 3],
... },
... )
>>>
>>> compare_result = compare(["ID"], base_df, compare_df)
>>> print("values_summary()")
values_summary()
>>> print(compare_result.values_summary())
shape: (2, 3)
┌─────────────────────────┬───────┬────────────┐
│ Value Differences ┆ Count ┆ Percentage │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════════════════════════╪═══════╪════════════╡
│ Total Value Differences ┆ 1 ┆ 50.0 │
│ Example1 ┆ 1 ┆ 50.0 │
└─────────────────────────┴───────┴────────────┘
>>> print("values_sample()")
values_sample()
>>> print(compare_result.values_sample())
shape: (1, 4)
┌─────────┬──────────┬──────┬─────────┐
│ ID ┆ variable ┆ base ┆ compare │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ i64 ┆ i64 │
╞═════════╪══════════╪══════╪═════════╡
│ 1234567 ┆ Example1 ┆ 6 ┆ 2 │
└─────────┴──────────┴──────┴─────────┘
>>>>>> import polars as pl
>>> from pl_compare import compare
>>>
>>> base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": ["1", "2", "3"],
... }
... )
>>> compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1, 2, 3],
... "Example2": [1, 2, 3],
... "Example3": [1, 2, 3],
... },
... )
>>>
>>> compare_result = compare(["ID"], base_df, compare_df)
>>> compare_result.report()
--------------------------------------------------------------------------------
COMPARISON REPORT
--------------------------------------------------------------------------------
<BLANKLINE>
SCHEMA DIFFERENCES:
shape: (6, 2)
┌─────────────────────────────────┬───────┐
│ Statistic ┆ Count │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════════════════════════════════╪═══════╡
│ Columns in base ┆ 3 │
│ Columns in compare ┆ 4 │
│ Columns in base and compare ┆ 3 │
│ Columns only in base ┆ 0 │
│ Columns only in compare ┆ 1 │
│ Columns with schema differences ┆ 1 │
└─────────────────────────────────┴───────┘
shape: (2, 3)
┌──────────┬─────────────┬────────────────┐
│ column ┆ base_format ┆ compare_format │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞══════════╪═════════════╪════════════════╡
│ Example2 ┆ String ┆ Int64 │
│ Example3 ┆ null ┆ Int64 │
└──────────┴─────────────┴────────────────┘
--------------------------------------------------------------------------------
<BLANKLINE>
ROW DIFFERENCES:
shape: (5, 2)
┌──────────────────────────┬───────┐
│ Statistic ┆ Count │
│ --- ┆ --- │
│ str ┆ i64 │
╞══════════════════════════╪═══════╡
│ Rows in base ┆ 3 │
│ Rows in compare ┆ 3 │
│ Rows only in base ┆ 1 │
│ Rows only in compare ┆ 1 │
│ Rows in base and compare ┆ 2 │
└──────────────────────────┴───────┘
shape: (2, 3)
┌────────────┬──────────┬─────────────────┐
│ ID ┆ variable ┆ value │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞════════════╪══════════╪═════════════════╡
│ 12345678 ┆ status ┆ in base only │
│ 1234567810 ┆ status ┆ in compare only │
└────────────┴──────────┴─────────────────┘
--------------------------------------------------------------------------------
<BLANKLINE>
VALUE DIFFERENCES:
shape: (2, 3)
┌─────────────────────────┬───────┬────────────┐
│ Value Differences ┆ Count ┆ Percentage │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════════════════════════╪═══════╪════════════╡
│ Total Value Differences ┆ 1 ┆ 50.0 │
│ Example1 ┆ 1 ┆ 50.0 │
└─────────────────────────┴───────┴────────────┘
shape: (1, 4)
┌─────────┬──────────┬──────┬─────────┐
│ ID ┆ variable ┆ base ┆ compare │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ i64 ┆ i64 │
╞═════════╪══════════╪══════╪═════════╡
│ 1234567 ┆ Example1 ┆ 6 ┆ 2 │
└─────────┴──────────┴──────┴─────────┘
--------------------------------------------------------------------------------
End of Report
--------------------------------------------------------------------------------
>>>>>> import polars as pl
>>> import pandas as pd # doctest: +SKIP
>>> from pl_compare import compare
>>>
>>> base_df = pd.DataFrame(data=
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": ["1", "2", "3"],
... }
... )# doctest: +SKIP
>>> compare_df = pd.DataFrame(data=
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1, 2, 3],
... "Example2": [1, 2, 3],
... "Example3": [1, 2, 3],
... },
... )# doctest: +SKIP
>>>
>>> compare_result = compare(["ID"], pl.from_pandas(base_df), pl.from_pandas(compare_df))# doctest: +SKIP
>>> compare_result.report()# doctest: +SKIP
--------------------------------------------------------------------------------
COMPARISON REPORT
--------------------------------------------------------------------------------
SCHEMA DIFFERENCES:
shape: (6, 2)
┌─────────────────────────────────┬───────┐
│ Statistic ┆ Count │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════════════════════════════════╪═══════╡
│ Columns in base ┆ 3 │
│ Columns in compare ┆ 4 │
│ Columns in base and compare ┆ 3 │
│ Columns only in base ┆ 0 │
│ Columns only in compare ┆ 1 │
│ Columns with schema differences ┆ 1 │
└─────────────────────────────────┴───────┘
shape: (2, 3)
┌──────────┬─────────────┬────────────────┐
│ column ┆ base_format ┆ compare_format │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞══════════╪═════════════╪════════════════╡
│ Example2 ┆ String ┆ Int64 │
│ Example3 ┆ null ┆ Int64 │
└──────────┴─────────────┴────────────────┘
--------------------------------------------------------------------------------
ROW DIFFERENCES:
shape: (5, 2)
┌──────────────────────────┬───────┐
│ Statistic ┆ Count │
│ --- ┆ --- │
│ str ┆ i64 │
╞══════════════════════════╪═══════╡
│ Rows in base ┆ 3 │
│ Rows in compare ┆ 3 │
│ Rows only in base ┆ 1 │
│ Rows only in compare ┆ 1 │
│ Rows in base and compare ┆ 2 │
└──────────────────────────┴───────┘
shape: (2, 3)
┌────────────┬──────────┬─────────────────┐
│ ID ┆ variable ┆ value │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞════════════╪══════════╪═════════════════╡
│ 12345678 ┆ status ┆ in base only │
│ 1234567810 ┆ status ┆ in compare only │
└────────────┴──────────┴─────────────────┘
--------------------------------------------------------------------------------
VALUE DIFFERENCES:
shape: (2, 3)
┌─────────────────────────┬───────┬────────────┐
│ Value Differences ┆ Count ┆ Percentage │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════════════════════════╪═══════╪════════════╡
│ Total Value Differences ┆ 1 ┆ 50.0 │
│ Example1 ┆ 1 ┆ 50.0 │
└─────────────────────────┴───────┴────────────┘
shape: (1, 4)
┌─────────┬──────────┬──────┬─────────┐
│ ID ┆ variable ┆ base ┆ compare │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ i64 ┆ i64 │
╞═════════╪══════════╪══════╪═════════╡
│ 1234567 ┆ Example1 ┆ 6 ┆ 2 │
└─────────┴──────────┴──────┴─────────┘
--------------------------------------------------------------------------------
End of Report
--------------------------------------------------------------------------------
>>>>>> import polars as pl
>>> from pl_compare import compare
>>>
>>> base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1.111, 6.11, 3.11],
... }
... )
>>>
>>> compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1.114, 6.14, 3.12],
... },
... )
>>>
>>> print("With equality_resolution of 0.01")
With equality_resolution of 0.01
>>> compare_result = compare(["ID"], base_df, compare_df, resolution=0.01)
>>> print(compare_result.values_sample())
shape: (1, 4)
┌─────────┬──────────┬──────┬─────────┐
│ ID ┆ variable ┆ base ┆ compare │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 │
╞═════════╪══════════╪══════╪═════════╡
│ 1234567 ┆ Example1 ┆ 6.11 ┆ 6.14 │
└─────────┴──────────┴──────┴─────────┘
>>> print("With no equality_resolution")
With no equality_resolution
>>> compare_result = compare(["ID"], base_df, compare_df)
>>> print(compare_result.values_sample())
shape: (2, 4)
┌─────────┬──────────┬───────┬─────────┐
│ ID ┆ variable ┆ base ┆ compare │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 │
╞═════════╪══════════╪═══════╪═════════╡
│ 123456 ┆ Example1 ┆ 1.111 ┆ 1.114 │
│ 1234567 ┆ Example1 ┆ 6.11 ┆ 6.14 │
└─────────┴──────────┴───────┴─────────┘
>>>>>> import polars as pl
>>> from pl_compare import compare
>>>
>>> base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": ["1", "2", "3"],
... }
... )
>>> compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "1234567810"],
... "Example1": [1, 2, 3],
... "Example2": [1, 2, 3],
... "Example3": [1, 2, 3],
... },
... )
>>>
>>> compare_result = compare(["ID"],
... base_df,
... compare_df,
... base_alias="before_change",
... compare_alias="after_change")
>>>
>>> print("values_summary()")
values_summary()
>>> print(compare_result.schemas_sample())
shape: (2, 3)
┌──────────┬──────────────────────┬─────────────────────┐
│ column ┆ before_change_format ┆ after_change_format │
│ --- ┆ --- ┆ --- │
│ str ┆ str ┆ str │
╞══════════╪══════════════════════╪═════════════════════╡
│ Example2 ┆ String ┆ Int64 │
│ Example3 ┆ null ┆ Int64 │
└──────────┴──────────────────────┴─────────────────────┘
>>> print("values_sample()")
values_sample()
>>> print(compare_result.values_sample())
shape: (1, 4)
┌─────────┬──────────┬───────────────┬──────────────┐
│ ID ┆ variable ┆ before_change ┆ after_change │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ i64 ┆ i64 │
╞═════════╪══════════╪═══════════════╪══════════════╡
│ 1234567 ┆ Example1 ┆ 6 ┆ 2 │
└─────────┴──────────┴───────────────┴──────────────┘
>>>>>> import polars as pl
>>> import pytest
>>> from pl_compare.compare import compare
>>>
>>> def test_example():
... base_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 3],
... "Example2": [1, 2, 3],
... }
... )
... compare_df = pl.DataFrame(
... {
... "ID": ["123456", "1234567", "12345678"],
... "Example1": [1, 6, 9],
... "Example2": [1, 2, 3],
... }
... )
... comparison = compare(["ID"], base_df, compare_df)
... if not comparison.is_equal():
... raise Exception(comparison.report())
...
>>> test_example() # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 18, in test_example
Exception: --------------------------------------------------------------------------------
COMPARISON REPORT
--------------------------------------------------------------------------------
No Schema differences found.
--------------------------------------------------------------------------------
No Row differences found (when joining by the supplied id_columns).
--------------------------------------------------------------------------------
VALUE DIFFERENCES:
shape: (3, 3)
┌─────────────────────────┬───────┬────────────┐
│ Value Differences ┆ Count ┆ Percentage │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════════════════════════╪═══════╪════════════╡
│ Total Value Differences ┆ 1 ┆ 16.666667 │
│ Example1 ┆ 1 ┆ 33.333333 │
│ Example2 ┆ 0 ┆ 0.0 │
└─────────────────────────┴───────┴────────────┘
shape: (1, 4)
┌──────────┬──────────┬──────┬─────────┐
│ ID ┆ variable ┆ base ┆ compare │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ i64 ┆ i64 │
╞══════════╪══════════╪══════╪═════════╡
│ 12345678 ┆ Example1 ┆ 3 ┆ 9 │
└──────────┴──────────┴──────┴─────────┘
--------------------------------------------------------------------------------
End of Report
--------------------------------------------------------------------------------
>>>- Linting (Ruff)
- Make into python package
- Add makefile for easy linting and tests
- Statistics should indicate which statistics are referencing columns
- Add all statistics frame to tests
- Add schema differences to schema summary
- Make row examples alternate between base only and compare only so that it is more readable.
- Add limit value to the examples.
- Updated value differences summary so that Statistic is something that makes sense.
- Publish package to pypi
- Add difference criterion.
- Add license
- Make package easy to use (i.e. so you only have to import pl_compare and then you can us pl_compare)
- Add table name labels that can replace 'base' and 'compare'.
- Update code to use a config dataclass that can be passed between the class and functions.
- Write up docstrings
- Write up readme (with code examples)
- Add parameter to hide column differences with 0 differences.
- Add flag to indicate if there are differences between the tables.
- Update report so that non differences are not displayed.
- Seperate out dev dependencies from library dependencies?
- Change 'threshold' to be equality resolution.
- strict MyPy type checking
- Raise error and print examples if duplicates are present.
- Add total number of value differences to the value differences summary.
- Add percentage column so the value differences summary.
- Change id_columns to be named 'join_columns'
- Github actions for publishing
- Update the duplication validation.
- Fix report output when tables are exactly equal.
- Github actions for testing
- Github actions for linting
- [] Test for large amounts of data
- [] Benchmark for different sizes of data.
- [] Investigate use for very large datasets 50GB-100GB. Can this be done using LazyFrames only?
- [] Simplify custom equality checks and add example.
- [] Add a count of the number of rows that have any differences to the value differences summary.
- [] add a test that checks that abritrary join conditions work.
