Pandas Usage#

Overview#

The main goal of datacompy is to provide a human-readable output describing differences between two dataframes. For example, if you have two dataframes containing data like:

df1

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

10000001238

1.05

Lucille Bluth

2017-01-01

10000001238

1.05

Loose Seal Bluth

2017-01-01

df2

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

Set up like:

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
10000001238,1.05,Lucille Bluth,,2017-01-01
10000001238,1.05,Loose Seal 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 Object Setup#

There are currently two supported methods for joining your dataframes - by join column(s) or by index.

compare = 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')

# OR

compare = datacompy.Compare(df1, df2, join_columns=['acct_id', 'name'])

# OR

compare = datacompy.Compare(df1, df2, on_index=True)

Reports#

A report is generated by calling Compare.report(), which returns a string. Here is a sample report generated by datacompy for the two tables above, joined on acct_id (Note: if you don’t specify df1_name and/or df2_name, then any instance of “original” or “new” in the report is replaced with “df1” and/or “df2”.):

DataComPy Comparison
--------------------

DataFrame Summary
-----------------

  DataFrame  Columns  Rows
0  original        5     6
1       new        4     5

Column Summary
--------------

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

Row Summary
-----------

Matched on: acct_id
Any duplicates on match values: Yes
Absolute Tolerance: 0.0001
Relative Tolerance: 0
Number of rows in common: 5
Number of rows in original but not in new: 1
Number of rows in new but not in original: 0

Number of rows with some compared columns unequal: 5
Number of rows with all compared columns equal: 0

Column Comparison
-----------------

Number of columns compared with some values unequal: 3
Number of columns compared with all values equal: 1
Total number of values which compare unequal: 7

Columns with Unequal Values or Types
------------------------------------

       Column original dtype new dtype  # Unequal  Max Diff  # Null Diff
0  dollar_amt        float64   float64          1    0.0500            0
1   float_fld        float64   float64          4    0.0005            3
2        name         object    object          2    0.0000            0

Sample Rows with Unequal Values
-------------------------------

       acct_id  dollar_amt (original)  dollar_amt (new)
0  10000001234                 123.45             123.4

       acct_id  float_fld (original)  float_fld (new)
0  10000001234            14530.1555        14530.155
5  10000001238                   NaN          111.000
2  10000001236                   NaN            1.000
1  10000001235                1.0000              NaN

       acct_id name (original)            name (new)
0  10000001234  George Maharis  George Michael Bluth
3  10000001237      Bob Loblaw         Robert Loblaw

Sample Rows Only in original (First 10 Columns)
-----------------------------------------------

       acct_id  dollar_amt           name  float_fld    date_fld
4  10000001238        1.05  Lucille Bluth        NaN  2017-01-01

Convenience Methods#

There are a few convenience methods available after the comparison has been run:

print(compare.intersect_rows[['name_df1', 'name_df2', 'name_match']])
#            name_df1              name_df2  name_match
# 0    George Maharis  George Michael Bluth       False
# 1     Michael Bluth         Michael Bluth        True
# 2      George Bluth          George Bluth        True
# 3        Bob Loblaw         Robert Loblaw       False
# 5  Loose Seal Bluth      Loose Seal Bluth        True

print(compare.df1_unq_rows)
#        acct_id  dollar_amt           name  float_fld    date_fld
# 4  10000001238        1.05  Lucille Bluth        NaN  2017-01-01

print(compare.df2_unq_rows)
# Empty DataFrame
# Columns: [acct_id, dollar_amt, name, float_fld]
# Index: []

print(compare.intersect_columns())
# {'float_fld', 'acct_id', 'name', 'dollar_amt'}

print(compare.df1_unq_columns())
# {'date_fld'}

print(compare.df2_unq_columns())
# set()

Duplicate rows#

Datacompy will try to handle rows that are duplicate in the join columns. It does this behind the scenes by generating a unique ID within each unique group of the join columns. For example, if you have two dataframes you’re trying to join on acct_id:

acct_id

name

1

George Maharis

1

Michael Bluth

2

George Bluth

acct_id

name

1

George Maharis

1

Michael Bluth

1

Tony Wonder

2

George Bluth

Datacompy will generate a unique temporary ID for joining:

acct_id

name

temp_id

1

George Maharis

0

1

Michael Bluth

1

2

George Bluth

0

acct_id

name

temp_id

1

George Maharis

0

1

Michael Bluth

1

1

Tony Wonder

2

2

George Bluth

0

And then merge the two dataframes on a combination of the join_columns you specified and the temporary ID, before dropping the temp_id again. So the first two rows in the first dataframe will match the first two rows in the second dataframe, and the third row in the second dataframe will be recognized as uniquely in the second.

Caveats#

  • Duplicate matching is resilient to nulls in your join columns - it will convert the join columns to strings and fill null values with 'DATACOMPY_NULL' before generating the temporary ID. If you already have 'DATACOMPY_NULL' as a value in your join columns, the merge step will fail with a ValueError. You can also fill null values with a value of your choice before initializing the Compare class, based on what you know about the data.

  • The duplicate matching is somewhat naïve when it comes to picking which rows to match when there are duplicates. Datacompy sorts by the other fields before generating the temporary ID, then matches directly on that field. If there are a lot of duplicates you may need to join on more columns, or handle them separately.

Limitations#

There’s a number of limitations with datacompy:

  1. The dataframes that you’re comparing have to fit in memory. In comparison with SAS PROC COMPARE which can operate on datasets that are on disk, this could be a constraint if you’re using very large dataframes.

  2. If you only need to check whether or not two dataframes are exactly the same, you should look at the testing capabilities within Pandas and Numpy:

    from pandas.testing import assert_series_equal
from pandas.testing import assert_frame_equal
import numpy.testing as npt

#Compare two series
assert_series_equal(df1['some_field'], df2['some_field'])

#Compare two dataframes
assert_frame_equal(df1, df2)

#Numpy testing
npt.assert_array_equal(arr1, arr2)
npt.assert_almost_equal(obj1, obj2)