sanzoghenzo/compare.py

## compare.py
"""
Compare two Excel sheets
Inspired by https://pbpython.com/excel-diff-pandas-update.html
For the documentation, download this file and type:
python compare.py --help
"""

import argparse

import pandas as pd
import numpy as np


def report_diff(x):
    """Function to use with groupby.apply to highlight value changes."""
    return x[0] if x[0] == x[1] or pd.isna(x).all() else f'{x[0]} ---> {x[1]}'


def strip(x):
    """Function to use with applymap to strip whitespaces from a dataframe."""
    return x.strip() if isinstance(x, str) else x


def diff_pd(old_df, new_df, idx_col):
    """
    Identify differences between two pandas DataFrames using a key column.

    Key column is assumed to have a unique row identifier, i.e. no duplicates.

    Args:
        old_df (pd.DataFrame): first dataframe
        new_df (pd.DataFrame): second dataframe
        idx_col (str|list(str)): column name(s) of the index,
          needs to be present in both DataFrames
    """
    # setting the column name as index for fast operations
    old_df = old_df.set_index(idx_col)
    new_df = new_df.set_index(idx_col)
    # get the added and removed rows
    old_keys = old_df.index
    new_keys = new_df.index
    if isinstance(old_keys, pd.MultiIndex):
        removed_keys = old_keys.difference(new_keys)
        added_keys = new_keys.difference(old_keys)
    else:
        removed_keys = np.setdiff1d(old_keys, new_keys)
        added_keys = np.setdiff1d(new_keys, old_keys)
    # populate the output data with non empty dataframes
    out_data = {}
    removed = old_df.loc[removed_keys]
    if not removed.empty:
        out_data["removed"] = removed
    added = new_df.loc[added_keys]
    if not added.empty:
        out_data["added"] = added
    # focusing on common data of both dataframes
    common_keys = np.intersect1d(old_keys, new_keys, assume_unique=True)
    common_columns = np.intersect1d(
        old_df.columns, new_df.columns, assume_unique=True
    )
    new_common = new_df.loc[common_keys, common_columns].applymap(strip)
    old_common = old_df.loc[common_keys, common_columns].applymap(strip)
    # get the changed rows keys by dropping identical rows
    # (indexes are ignored, so we'll reset them)
    common_data = pd.concat(
        [old_common.reset_index(), new_common.reset_index()], sort=True
    )
    changed_keys = common_data.drop_duplicates(keep=False)[idx_col]
    if isinstance(changed_keys, pd.Series):
        changed_keys = changed_keys.unique()
    else:
        changed_keys = changed_keys.drop_duplicates().set_index(idx_col).index
    # combining the changed rows via multi level columns
    df_all_changes = pd.concat(
        [old_common.loc[changed_keys], new_common.loc[changed_keys]],
        axis='columns',
        keys=['old', 'new']
    ).swaplevel(axis='columns')
    # using report_diff to merge the changes in a single cell with "-->"
    df_changed = df_all_changes.groupby(level=0, axis=1).apply(
        lambda frame: frame.apply(report_diff, axis=1))
    # add changed dataframe to output data only if non empty
    if not df_changed.empty:
        out_data['changed'] = df_changed

    return out_data


def compare_excel(
        path1, path2, out_path, sheet_name, index_col_name, **kwargs
):
    old_df = pd.read_excel(path1, sheet_name=sheet_name, **kwargs)
    new_df = pd.read_excel(path2, sheet_name=sheet_name, **kwargs)
    diff = diff_pd(old_df, new_df, index_col_name)
    if diff:
        with pd.ExcelWriter(out_path) as writer:
            for sname, data in diff.items():
                data.to_excel(writer, sheet_name=sname)
        print(f"Differences saved in {out_path}")
    else:
        print("No differences spotted")


def build_parser():
    cfg = argparse.ArgumentParser(
        description="Compares two Excel sheets and outputs the differences "
                    "to a separate Excel file."
    )
    cfg.add_argument("path1", help="Fist Excel file")
    cfg.add_argument("path2", help="Second Excel file")
    cfg.add_argument("sheetname", help="Name of the sheet to compare.")
    cfg.add_argument(
        "key_column",
        help="Name of the column(s) with unique row identifier. It has to be "
             "the actual text of the first row, not the excel notation."
             "Use multiple times to create a composite index.",
        nargs="+",
    )
    cfg.add_argument("-o", "--output-path", default="compared.xlsx",
                     help="Path of the comparison results")
    cfg.add_argument("--skiprows", help='Excel row containing the table headers',
                     type=int, action='append', default=None)
    return cfg


def main():
    cfg = build_parser()
    opt = cfg.parse_args()
    compare_excel(opt.path1, opt.path2, opt.output_path, opt.sheetname,
                  opt.key_column, skiprows=opt.skiprows)


if __name__ == '__main__':
    main()

## test_compare.py
"""Excel compare test suite."""
import io

import pandas as pd

import compare

def test_parser():
    cfg = compare.build_parser()
    opt = cfg.parse_args(["test1.xlsx", "test2.xlsx", "Sheet 1", "Col1", "Col2", "-o", "output.xlsx"])
    assert opt.path1 == "test1.xlsx"
    assert opt.path2 == "test2.xlsx"
    assert opt.output_path ==  "output.xlsx"
    assert opt.sheetname == "Sheet 1"
    assert opt.key_column == ["Col1", "Col2"]
    assert opt.skiprows is None


def build_excel_stream(df, sheetname):
    """Create an excel workbook as a file-like object."""
    output = io.BytesIO()
    with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
        df.to_excel(writer, sheet_name=sheetname, index=False)
    return output


def sample_xlsx(df_1, df_2):
    xlsx_1 = build_excel_stream(df_1, "Sheet1")
    xlsx_2 = build_excel_stream(df_2, "Sheet1")
    return xlsx_1, xlsx_2


def sample_dfs():
    df_1 = pd.DataFrame({
        "ID": [123456, 654321, 543219, 432198, 765432],
        "Name": ["Lemonade", "Cola", "Orange", "Fruit Punch", "Tobacco"],
        "Flavour Description": ["Fuzzy", "Fuzzy", "Fuzzy", "Fuzzy", "Smoky"],
    })
    df_2 = pd.DataFrame({
        "ID": [123456, 654321, 543219, 432198, 876543],
        "Name": ["Lemonade", "Cola", "Orange", "Fruit Punch", "Soda"],
        "Flavour Description": ["Fuzzy", "Bubbly", "Fuzzy", "Fuzzy", "Sugary"],
    })
    return df_1, df_2


def run_assertion(diff):
    changed = diff["changed"]
    assert len(changed) == 1
    assert changed.iloc[0]["Flavour Description"] == "Fuzzy ---> Bubbly"
    added = diff["added"]
    assert len(added) == 1
    assert added.iloc[0]["Flavour Description"] == "Sugary"
    removed = diff["removed"]
    assert len(removed) == 1
    assert removed.iloc[0]["Flavour Description"] == "Smoky"
    print("OK.")


def test_single_index():
    df_1, df_2 = sample_dfs()
    diff = compare.diff_pd(df_1, df_2, ["ID"])
    run_assertion(diff)


def test_single_index_excel():
    xlsx_1, xlsx_2 = sample_xlsx(*sample_dfs())
    diff_io = io.BytesIO()
    compare.compare_excel(xlsx_1, xlsx_2, diff_io, "Sheet1", "ID")
    diff = pd.read_excel(diff_io, sheet_name=None)
    run_assertion(diff)


def sample_multiindex_dfs():
    df_1 = pd.DataFrame({
        "ID": [123456, 123456, 654321, 543219, 432198, 765432],
        "Name": ["Lemonade", "Lemonade", "Cola", "Orange", "Fruit Punch", "Tobacco"],
        "Flavour ID": [1, 2, None, None, None, None],
        "Flavour Description": ["Fuzzy", "Fuzzy", "Fuzzy", "Fuzzy", "Fuzzy", "Smoky"],
    })
    df_2 = pd.DataFrame({
        "ID": [123456, 123456, 654321, 543219, 432198, 876543],
        "Name": ["Lemonade", "Lemonade", "Cola", "Orange", "Fruit Punch", "Soda"],
        "Flavour ID": [1, 2, None, None, None, None],
        "Flavour Description": ["Fuzzy", "Bubbly", "Fuzzy", "Fuzzy", "Fuzzy", "Sugary"],
    })
    return df_1, df_2


def test_multiindex():
    df_1, df_2 = sample_multiindex_dfs()
    diff = compare.diff_pd(df_1, df_2, ["ID", "Flavour ID"])
    run_assertion(diff)


def test_multiindex_excel():
    xlsx_1, xlsx_2 = sample_xlsx(*sample_multiindex_dfs())
    diff_io = io.BytesIO()
    compare.compare_excel(xlsx_1, xlsx_2, diff_io, "Sheet1", ["ID", "Flavour ID"])
    diff = pd.read_excel(diff_io, sheet_name=None)
    run_assertion(diff)


def test_no_diffs():
    df_1, _ = sample_multiindex_dfs()
    diff = compare.diff_pd(df_1, df_1, ["ID", "Flavour ID"])
    assert not diff
    print("OK.")


if __name__ == '__main__':
    test_multiindex()
    test_multiindex_excel()
    test_single_index()
    test_single_index_excel()
    test_parser()
    test_no_diff()
	"""
	Compare two Excel sheets
	Inspired by https://pbpython.com/excel-diff-pandas-update.html
	For the documentation, download this file and type:
	python compare.py --help
	"""

	import argparse

	import pandas as pd
	import numpy as np


	def report_diff(x):
	"""Function to use with groupby.apply to highlight value changes."""
	return x[0] if x[0] == x[1] or pd.isna(x).all() else f'{x[0]} ---> {x[1]}'


	def strip(x):
	"""Function to use with applymap to strip whitespaces from a dataframe."""
	return x.strip() if isinstance(x, str) else x


	def diff_pd(old_df, new_df, idx_col):
	"""
	Identify differences between two pandas DataFrames using a key column.

	Key column is assumed to have a unique row identifier, i.e. no duplicates.

	Args:
	old_df (pd.DataFrame): first dataframe
	new_df (pd.DataFrame): second dataframe
	idx_col (str\|list(str)): column name(s) of the index,
	needs to be present in both DataFrames
	"""
	# setting the column name as index for fast operations
	old_df = old_df.set_index(idx_col)
	new_df = new_df.set_index(idx_col)
	# get the added and removed rows
	old_keys = old_df.index
	new_keys = new_df.index
	if isinstance(old_keys, pd.MultiIndex):
	removed_keys = old_keys.difference(new_keys)
	added_keys = new_keys.difference(old_keys)
	else:
	removed_keys = np.setdiff1d(old_keys, new_keys)
	added_keys = np.setdiff1d(new_keys, old_keys)
	# populate the output data with non empty dataframes
	out_data = {}
	removed = old_df.loc[removed_keys]
	if not removed.empty:
	out_data["removed"] = removed
	added = new_df.loc[added_keys]
	if not added.empty:
	out_data["added"] = added
	# focusing on common data of both dataframes
	common_keys = np.intersect1d(old_keys, new_keys, assume_unique=True)
	common_columns = np.intersect1d(
	old_df.columns, new_df.columns, assume_unique=True
	)
	new_common = new_df.loc[common_keys, common_columns].applymap(strip)
	old_common = old_df.loc[common_keys, common_columns].applymap(strip)
	# get the changed rows keys by dropping identical rows
	# (indexes are ignored, so we'll reset them)
	common_data = pd.concat(
	[old_common.reset_index(), new_common.reset_index()], sort=True
	)
	changed_keys = common_data.drop_duplicates(keep=False)[idx_col]
	if isinstance(changed_keys, pd.Series):
	changed_keys = changed_keys.unique()
	else:
	changed_keys = changed_keys.drop_duplicates().set_index(idx_col).index
	# combining the changed rows via multi level columns
	df_all_changes = pd.concat(
	[old_common.loc[changed_keys], new_common.loc[changed_keys]],
	axis='columns',
	keys=['old', 'new']
	).swaplevel(axis='columns')
	# using report_diff to merge the changes in a single cell with "-->"
	df_changed = df_all_changes.groupby(level=0, axis=1).apply(
	lambda frame: frame.apply(report_diff, axis=1))
	# add changed dataframe to output data only if non empty
	if not df_changed.empty:
	out_data['changed'] = df_changed

	return out_data


	def compare_excel(
	path1, path2, out_path, sheet_name, index_col_name, **kwargs
	):
	old_df = pd.read_excel(path1, sheet_name=sheet_name, **kwargs)
	new_df = pd.read_excel(path2, sheet_name=sheet_name, **kwargs)
	diff = diff_pd(old_df, new_df, index_col_name)
	if diff:
	with pd.ExcelWriter(out_path) as writer:
	for sname, data in diff.items():
	data.to_excel(writer, sheet_name=sname)
	print(f"Differences saved in {out_path}")
	else:
	print("No differences spotted")


	def build_parser():
	cfg = argparse.ArgumentParser(
	description="Compares two Excel sheets and outputs the differences "
	"to a separate Excel file."
	)
	cfg.add_argument("path1", help="Fist Excel file")
	cfg.add_argument("path2", help="Second Excel file")
	cfg.add_argument("sheetname", help="Name of the sheet to compare.")
	cfg.add_argument(
	"key_column",
	help="Name of the column(s) with unique row identifier. It has to be "
	"the actual text of the first row, not the excel notation."
	"Use multiple times to create a composite index.",
	nargs="+",
	)
	cfg.add_argument("-o", "--output-path", default="compared.xlsx",
	help="Path of the comparison results")
	cfg.add_argument("--skiprows", help='Excel row containing the table headers',
	type=int, action='append', default=None)
	return cfg


	def main():
	cfg = build_parser()
	opt = cfg.parse_args()
	compare_excel(opt.path1, opt.path2, opt.output_path, opt.sheetname,
	opt.key_column, skiprows=opt.skiprows)


	if __name__ == '__main__':
	main()
	"""Excel compare test suite."""
	import io

	import pandas as pd

	import compare

	def test_parser():
	cfg = compare.build_parser()
	opt = cfg.parse_args(["test1.xlsx", "test2.xlsx", "Sheet 1", "Col1", "Col2", "-o", "output.xlsx"])
	assert opt.path1 == "test1.xlsx"
	assert opt.path2 == "test2.xlsx"
	assert opt.output_path == "output.xlsx"
	assert opt.sheetname == "Sheet 1"
	assert opt.key_column == ["Col1", "Col2"]
	assert opt.skiprows is None


	def build_excel_stream(df, sheetname):
	"""Create an excel workbook as a file-like object."""
	output = io.BytesIO()
	with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
	df.to_excel(writer, sheet_name=sheetname, index=False)
	return output


	def sample_xlsx(df_1, df_2):
	xlsx_1 = build_excel_stream(df_1, "Sheet1")
	xlsx_2 = build_excel_stream(df_2, "Sheet1")
	return xlsx_1, xlsx_2


	def sample_dfs():
	df_1 = pd.DataFrame({
	"ID": [123456, 654321, 543219, 432198, 765432],
	"Name": ["Lemonade", "Cola", "Orange", "Fruit Punch", "Tobacco"],
	"Flavour Description": ["Fuzzy", "Fuzzy", "Fuzzy", "Fuzzy", "Smoky"],
	})
	df_2 = pd.DataFrame({
	"ID": [123456, 654321, 543219, 432198, 876543],
	"Name": ["Lemonade", "Cola", "Orange", "Fruit Punch", "Soda"],
	"Flavour Description": ["Fuzzy", "Bubbly", "Fuzzy", "Fuzzy", "Sugary"],
	})
	return df_1, df_2


	def run_assertion(diff):
	changed = diff["changed"]
	assert len(changed) == 1
	assert changed.iloc[0]["Flavour Description"] == "Fuzzy ---> Bubbly"
	added = diff["added"]
	assert len(added) == 1
	assert added.iloc[0]["Flavour Description"] == "Sugary"
	removed = diff["removed"]
	assert len(removed) == 1
	assert removed.iloc[0]["Flavour Description"] == "Smoky"
	print("OK.")


	def test_single_index():
	df_1, df_2 = sample_dfs()
	diff = compare.diff_pd(df_1, df_2, ["ID"])
	run_assertion(diff)


	def test_single_index_excel():
	xlsx_1, xlsx_2 = sample_xlsx(*sample_dfs())
	diff_io = io.BytesIO()
	compare.compare_excel(xlsx_1, xlsx_2, diff_io, "Sheet1", "ID")
	diff = pd.read_excel(diff_io, sheet_name=None)
	run_assertion(diff)


	def sample_multiindex_dfs():
	df_1 = pd.DataFrame({
	"ID": [123456, 123456, 654321, 543219, 432198, 765432],
	"Name": ["Lemonade", "Lemonade", "Cola", "Orange", "Fruit Punch", "Tobacco"],
	"Flavour ID": [1, 2, None, None, None, None],
	"Flavour Description": ["Fuzzy", "Fuzzy", "Fuzzy", "Fuzzy", "Fuzzy", "Smoky"],
	})
	df_2 = pd.DataFrame({
	"ID": [123456, 123456, 654321, 543219, 432198, 876543],
	"Name": ["Lemonade", "Lemonade", "Cola", "Orange", "Fruit Punch", "Soda"],
	"Flavour ID": [1, 2, None, None, None, None],
	"Flavour Description": ["Fuzzy", "Bubbly", "Fuzzy", "Fuzzy", "Fuzzy", "Sugary"],
	})
	return df_1, df_2


	def test_multiindex():
	df_1, df_2 = sample_multiindex_dfs()
	diff = compare.diff_pd(df_1, df_2, ["ID", "Flavour ID"])
	run_assertion(diff)


	def test_multiindex_excel():
	xlsx_1, xlsx_2 = sample_xlsx(*sample_multiindex_dfs())
	diff_io = io.BytesIO()
	compare.compare_excel(xlsx_1, xlsx_2, diff_io, "Sheet1", ["ID", "Flavour ID"])
	diff = pd.read_excel(diff_io, sheet_name=None)
	run_assertion(diff)


	def test_no_diffs():
	df_1, _ = sample_multiindex_dfs()
	diff = compare.diff_pd(df_1, df_1, ["ID", "Flavour ID"])
	assert not diff
	print("OK.")


	if __name__ == '__main__':
	test_multiindex()
	test_multiindex_excel()
	test_single_index()
	test_single_index_excel()
	test_parser()
	test_no_diff()
ID	Name	Flavour ID	Flavour Description
123456	Lemonade	1	Fuzzy
123456	Lemonade	2	Fuzzy
654321	Cola		Fuzzy
543219	Orange		Fuzzy
432198	Fruit Punch		Fuzzy
Numero	Indice	Nombre	Apellido	Edad
1500001	a	Name1	Surname1	39
1500002	b	Name2	Surname2	52
1500003	c	Name3	Surname3	18
1500004	d	Name4	Surname4	76
1500005	e	Name5	Surname5	23
1500006	f	Name6	Surname6	99