print(f"Differences saved to {output_file_path}") 使用df.compare()的备用解决方案 # find differences result = df1.compare(df2, align_axis=1, keep_equal=True, result_names=('DF1', 'DF2')) # flatten multi-column index (optional) result.columns = [f'{col[1]}-{col[0]}' for col i...
✅ 最佳回答: Use DataFrame.compare: out = (df1.set_index(['pet_name','exam_day']) .compare(df2.set_index(['pet_name','exam_day'])) .stack() .droplevel(-1) .reset_index()) print (out) #40 in df2 is changed to 100 pet_name exam_day result_1 result_2 0 Patrick 2023-...
For a more in-depth explanation check:Report and logic explanation for pd_compare.compare.md. Docstring """Compares two DataFrames, creates a report and returns useful information (see the "Returns" section).**When is this function useful**: This function should be run when `df1.equals(df2...
Awesome. Another aspect of Polars that I understand, so you've mostly so far been talking about Polars being a great choice for people who want to be manipulating dataframes and have more fun, have an easier time with the syntax relative to what they might impede on. But you've previousl...
Versatile Data Structures: Pandas introduce two fundamental data structures: Series: A labeled, one-dimensional array-like structure capable of holding diverse data types. DataFrame: A two-dimensional, table-like structure representing data in rows and columns. It comprises a collection of a Series...
Equality semanticsTwo instances of CategoricalDtype compare equal whenever they have the same categories and order. When comparing two unordered categoricals, the order of the categories is not considered.In [49]: c1 = CategoricalDtype(['a', 'b', 'c'], ordered=False) # Equal, since order ...
In the future pandas will not coerce, and the values not compare equal to the datetime.date . To retain the current behavior, convert the datetime.date to a datetime with pd.Timestamp . #!/bin/python3 0 True 1 False dtype: bool In addition, ordering comparisons will raise a TypeError ...
For example, >>> s = pd.Series([True, False, True]) >>> s.replace({'a string': 'new value', True: False}) # raises TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str' will raise a TypeError because one of the dict keys is not of the correct type for ...
In this case, using the&operator allows the code to compare each element in the two Series objects and return the filtered data without ambiguity. Applying the All and Any Functions Another approach to handle ambiguous truth values is to use theall()andany()functions provided by pandas. These...
An analgous fix for pyogrio is included in pyogrio 0.8.1. Fix to_parquet to write correct metadata in case of 3D geometries (#2824). Fixes for compatibility with psycopg (#3167). Fix to allow appending dataframes with no CRS to PostGIS tables with no CRS (#3328) Fix plotting of ...