Number of columns in common: 4 Number of columns in original but not in new: 1 Number of columns in new but not in original: 0 Import Datacompy & Compare two dataframesĬompare = datacompy.Compare( df1, df2, join_columns=’acct_id’, #You can also specify a list of columns abs_tol=0.0001, rel_tol=0, df1_name=’original’, df2_name=’new’) Generate the output (in the form of report )ĭataFrame Columns Rows 0 original 5 7 1 new 4 6
So if, for example, you have a column with decimal.Decimal values in one dataframe and an identically-named column with float64 data type in another, it will tell you that the dtypes are different but will still try to compare the values. It will try to join two dataframes either on a list of join columns, or on indexes.Ĭolumn-wise comparisons attempt to match values even when dtypes doesn't match. Installing datacompy pip install datacompy Details :ĭatacompy takes two dataframes as input and gives us a human-readable report containing statistics that lets us know the similarities and dissimilarities between the two dataframes. Let’s see how can we make use of this library. Originally started as a replacement for SAS’s PROC COMPARE for Pandas DataFrames with some more functionality than just (Pandas.DataFrame)
Beyond compare script examples how to#
In this article ,we will be exploring how to compare two large files/datasets efficiently while creating meaningful summery using Python Library “datacompy”ĭatacompy : is a package to compare two DataFrames. There are lot of file comparison tools available in the market like beyond compare.
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