Before /home/pythonscripts/mpxdatacheck/LINELIST_PAHO2024_01_31_04_52_55.csv After /home/pythonscripts/mpxdatacheck/LINELIST_PAHO2024_02_01_04_52_54.csv --------------------------- health_worker --------------------------- Answers changed: -> Before NO 93.882753 YES 5.281045 UNK 0.769128 9 0.058132 0 0.008943 Name: health_worker, dtype: float64 -> After NO 93.945767 YES 5.284589 UNK 0.769644 Name: health_worker, dtype: float64 DataComPy Comparison -------------------- DataFrame Summary ----------------- DataFrame Columns Rows 0 df1 34 60553 1 df2 34 60553 Column Summary -------------- Number of columns in common: 34 Number of columns in df1 but not in df2: 0 Number of columns in df2 but not in df1: 0 Row Summary ----------- Matched on: index Any duplicates on match values: No Absolute Tolerance: 0 Relative Tolerance: 0 Number of rows in common: 60,553 Number of rows in df1 but not in df2: 0 Number of rows in df2 but not in df1: 0 Number of rows with some compared columns unequal: 15 Number of rows with all compared columns equal: 60,538 Column Comparison ----------------- Number of columns compared with some values unequal: 1 Number of columns compared with all values equal: 33 Total number of values which compare unequal: 15 Columns with Unequal Values or Types ------------------------------------ Column df1 dtype df2 dtype # Unequal Max Diff # Null Diff 0 health_worker object object 15 0 15 Sample Rows with Unequal Values ------------------------------- health_worker (df1) health_worker (df2) recordid reporting_country BRA00001019 BRAZIL 9 NaN BRA00009118 BRAZIL 9 NaN BRA00002644 BRAZIL 9 NaN BRA00009517 BRAZIL 9 NaN BRA00007460 BRAZIL 9 NaN BRA00004223 BRAZIL 9 NaN BRA00004954 BRAZIL 9 NaN BRA00010461 BRAZIL 0 NaN BRA00010398 BRAZIL 0 NaN BRA00004622 BRAZIL 9 NaN