Dataframe comparison in Python
DataComPy is a package to compare two Pandas DataFrames. Originally started to
be something of a replacement for SAS's PROC COMPARE
for Pandas DataFrames
with some more functionality than just Pandas.DataFrame.equals(Pandas.DataFrame)
(in that it prints out some stats, and lets you tweak how accurate matches have to be).
Then extended to carry that functionality over to Spark Dataframes.
pip install datacompy
or
conda install datacompy
If you would like to use Spark or any other backends please make sure you install via extras:
pip install datacompy[spark]
pip install datacompy[dask]
pip install datacompy[duckdb]
pip install datacompy[polars]
pip install datacompy[ray]
DataComPy will try to join two dataframes either on a list of join columns, or on indexes. If the two dataframes have duplicates based on join values, the match process sorts by the remaining fields and joins based on that row number.
Column-wise comparisons attempt to match values even when dtypes don't match.
So if, for example, you have a column with decimal.Decimal
values in one
dataframe and an identically-named column with float64
dtype in another,
it will tell you that the dtypes are different but will still try to compare the
values.
from io import StringIO
import pandas as pd
import datacompy
data1 = """acct_id,dollar_amt,name,float_fld,date_fld
10000001234,123.45,George Maharis,14530.1555,2017-01-01
10000001235,0.45,Michael Bluth,1,2017-01-01
10000001236,1345,George Bluth,,2017-01-01
10000001237,123456,Bob Loblaw,345.12,2017-01-01
10000001239,1.05,Lucille Bluth,,2017-01-01
"""
data2 = """acct_id,dollar_amt,name,float_fld
10000001234,123.4,George Michael Bluth,14530.155
10000001235,0.45,Michael Bluth,
10000001236,1345,George Bluth,1
10000001237,123456,Robert Loblaw,345.12
10000001238,1.05,Loose Seal Bluth,111
"""
df1 = pd.read_csv(StringIO(data1))
df2 = pd.read_csv(StringIO(data2))
compare = datacompy.Compare(
df1,
df2,
join_columns='acct_id', #You can also specify a list of columns
abs_tol=0, #Optional, defaults to 0
rel_tol=0, #Optional, defaults to 0
df1_name='Original', #Optional, defaults to 'df1'
df2_name='New' #Optional, defaults to 'df2'
)
compare.matches(ignore_extra_columns=False)
# False
# This method prints out a human-readable report summarizing and sampling differences
print(compare.report())
See docs for more detailed usage instructions and an example of the report output.
You pass in two dataframes (df1
, df2
) to datacompy.Compare
and a
column to join on (or list of columns) to join_columns
. By default the
comparison needs to match values exactly, but you can pass in abs_tol
and/or rel_tol
to apply absolute and/or relative tolerances for numeric columns.
on_index=True
instead of join_columns
to join on
the index instead.The class validates that you passed dataframes, that they contain all of the
columns in join_columns
and have unique column names other than that. The
class also lowercases all column names to disambiguate.
On initialization the class validates inputs, and runs the comparison.
Compare.matches()
will return True
if the dataframes match, False
otherwise.
ignore_extra_columns=True
to not return False
just
because there are non-overlapping column names (will still check on
overlapping columns)pandas.testing.assert_frame_equal
. The main
use case for datacompy
is when you need to interpret the difference
between two dataframes.Compare also has some shortcuts like
intersect_rows
, df1_unq_rows
, df2_unq_rows
for getting
intersection, just df1 and just df2 records (DataFrames)intersect_columns()
, df1_unq_columns()
, df2_unq_columns()
for
getting intersection, just df1 and just df2 columns (Sets)You can turn on logging to see more detailed logs.
Fugue is a Python library that provides a unified interface for data processing on Pandas, DuckDB, Polars, Arrow, Spark, Dask, Ray, and many other backends. DataComPy integrates with Fugue to provide a simple way to compare data across these backends.
The following usage example compares two Pandas dataframes, it is equivalent to the Pandas example above.
from io import StringIO
import pandas as pd
import datacompy
data1 = """acct_id,dollar_amt,name,float_fld,date_fld
10000001234,123.45,George Maharis,14530.1555,2017-01-01
10000001235,0.45,Michael Bluth,1,2017-01-01
10000001236,1345,George Bluth,,2017-01-01
10000001237,123456,Bob Loblaw,345.12,2017-01-01
10000001239,1.05,Lucille Bluth,,2017-01-01
"""
data2 = """acct_id,dollar_amt,name,float_fld
10000001234,123.4,George Michael Bluth,14530.155
10000001235,0.45,Michael Bluth,
10000001236,1345,George Bluth,1
10000001237,123456,Robert Loblaw,345.12
10000001238,1.05,Loose Seal Bluth,111
"""
df1 = pd.read_csv(StringIO(data1))
df2 = pd.read_csv(StringIO(data2))
datacompy.is_match(
df1,
df2,
join_columns='acct_id', #You can also specify a list of columns
abs_tol=0, #Optional, defaults to 0
rel_tol=0, #Optional, defaults to 0
df1_name='Original', #Optional, defaults to 'df1'
df2_name='New' #Optional, defaults to 'df2'
)
# False
# This method prints out a human-readable report summarizing and sampling differences
print(datacompy.report(
df1,
df2,
join_columns='acct_id', #You can also specify a list of columns
abs_tol=0, #Optional, defaults to 0
rel_tol=0, #Optional, defaults to 0
df1_name='Original', #Optional, defaults to 'df1'
df2_name='New' #Optional, defaults to 'df2'
))
In order to compare dataframes of different backends, you just need to replace df1
and df2
with
dataframes of different backends. Just pass in Dataframes such as Pandas dataframes, DuckDB relations,
Polars dataframes, Arrow tables, Spark dataframes, Dask dataframes or Ray datasets. For example,
to compare a Pandas dataframe with a Spark dataframe:
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark_df2 = spark.createDataFrame(df2)
datacompy.is_match(
df1,
spark_df2,
join_columns='acct_id',
)
Notice that in order to use a specific backend, you need to have the corresponding library installed. For example, if you want compare Ray datasets, you must do
pip install datacompy[ray]
DataComPy uses Fugue to partition the two dataframes into chunks, and then compare each chunk in parallel
using the Pandas-based Compare
. The comparison results are then aggregated to produce the final result.
Different from the join operation used in SparkCompare
, the Fugue version uses the cogroup -> map
like semantic (not exactly the same, Fugue adopts a coarse version to achieve great performance), which
guarantees full data comparison with consistent result compared to Pandas-based Compare
.
:::{important}
With version v0.9.0
SparkCompare now uses Null Safe (<=>
) comparisons
:::
DataComPy's SparkCompare
class will join two dataframes either on a list of join
columns. It has the capability to map column names that may be different in each
dataframe, including in the join columns. You are responsible for creating the
dataframes from any source which Spark can handle and specifying a unique join
key. If there are duplicates in either dataframe by join key, the match process
will remove the duplicates before joining (and tell you how many duplicates were
found).
As with the Pandas-based Compare
class, comparisons will be attempted even
if dtypes don't match. Any schema differences will be reported in the output
as well as in any mismatch reports, so that you can assess whether or not a
type mismatch is a problem or not.
The main reasons why you would choose to use SparkCompare
over Compare
are that your data is too large to fit into memory, or you're comparing data
that works well in a Spark environment, like partitioned Parquet, CSV, or JSON
files, or Cerebro tables.
Spark scales incredibly well, so you can use SparkCompare
to compare
billions of rows of data, provided you spin up a big enough cluster. Still,
joining billions of rows of data is an inherently large task, so there are a
couple of things you may want to take into consideration when getting into the
cliched realm of "big data":
SparkCompare
will compare all columns in common in the dataframes and
report on the rest. If there are columns in the data that you don't care to
compare, use a select
statement/method on the dataframe(s) to filter
those out. Particularly when reading from wide Parquet files, this can make
a huge difference when the columns you don't care about don't have to be
read into memory and included in the joined dataframe.cache_intermediates=True
to the SparkCompare
call can help optimize performance by caching certain intermediate dataframes
in memory, like the de-duped version of each input dataset, or the joined
dataframe. Otherwise, Spark's lazy evaluation will recompute those each time
it needs the data in a report or as you access instance attributes. This may
be fine for smaller dataframes, but will be costly for larger ones. You do
need to ensure that you have enough free cache memory before you do this, so
this parameter is set to False by default.
import datetime
import datacompy
from pyspark.sql import Row
# This example assumes you have a SparkSession named "spark" in your environment, as you
# do when running `pyspark` from the terminal or in a Databricks notebook (Spark v2.0 and higher)
data1 = [
Row(acct_id=10000001234, dollar_amt=123.45, name='George Maharis', float_fld=14530.1555,
date_fld=datetime.date(2017, 1, 1)),
Row(acct_id=10000001235, dollar_amt=0.45, name='Michael Bluth', float_fld=1.0,
date_fld=datetime.date(2017, 1, 1)),
Row(acct_id=10000001236, dollar_amt=1345.0, name='George Bluth', float_fld=None,
date_fld=datetime.date(2017, 1, 1)),
Row(acct_id=10000001237, dollar_amt=123456.0, name='Bob Loblaw', float_fld=345.12,
date_fld=datetime.date(2017, 1, 1)),
Row(acct_id=10000001239, dollar_amt=1.05, name='Lucille Bluth', float_fld=None,
date_fld=datetime.date(2017, 1, 1))
]
data2 = [
Row(acct_id=10000001234, dollar_amt=123.4, name='George Michael Bluth', float_fld=14530.155),
Row(acct_id=10000001235, dollar_amt=0.45, name='Michael Bluth', float_fld=None),
Row(acct_id=10000001236, dollar_amt=1345.0, name='George Bluth', float_fld=1.0),
Row(acct_id=10000001237, dollar_amt=123456.0, name='Robert Loblaw', float_fld=345.12),
Row(acct_id=10000001238, dollar_amt=1.05, name='Loose Seal Bluth', float_fld=111.0)
]
base_df = spark.createDataFrame(data1)
compare_df = spark.createDataFrame(data2)
comparison = datacompy.SparkCompare(spark, base_df, compare_df, join_columns=['acct_id'])
# This prints out a human-readable report summarizing differences
comparison.report()
virtualenv venv; source venv/bin/activate
)/usr/lib/spark
but may
differ based on your installation)export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.10.4-src.zip:$SPARK_HOME/python:$PYTHONPATH
(note that your version of py4j may differ depending on the version of Spark you're using)python setup.py bdist_egg
from the repo root directory.import datacompy
in a notebook attached to the cluster that the library is attached to and enjoy!We welcome and appreciate your contributions! Before we can accept any contributions, we ask that you please be sure to sign the Contributor License Agreement (CLA).
This project adheres to the Open Source Code of Conduct. By participating, you are expected to honor this code.
Roadmap details can be found here