xorbits.pandas.DataFrame.compare#
- DataFrame.compare(other: DataFrame, align_axis: Axis = 1, keep_shape: bool = False, keep_equal: bool = False, result_names: Suffixes = ('self', 'other')) DataFrame [源代码]#
Compare to another DataFrame and show the differences.
- 参数
other (DataFrame) – Object to compare with.
align_axis ({0 or 'index', 1 or 'columns'}, default 1) –
Determine which axis to align the comparison on.
- 0, or ‘index’Resulting differences are stacked vertically
with rows drawn alternately from self and other.
- 1, or ‘columns’Resulting differences are aligned horizontally
with columns drawn alternately from self and other.
keep_shape (bool, default False) – If true, all rows and columns are kept. Otherwise, only the ones with different values are kept.
keep_equal (bool, default False) – If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.
result_names (tuple, default ('self', 'other')) –
Set the dataframes names in the comparison.
1.5.0(pandas) 新版功能.
- 返回
DataFrame that shows the differences stacked side by side.
The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level.
- 返回类型
- 引发
ValueError – When the two DataFrames don’t have identical labels or shape.
参见
Series.compare
Compare with another Series and show differences.
DataFrame.equals
Test whether two objects contain the same elements.
提示
Matching NaNs will not appear as a difference.
Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames
实际案例
>>> df = pd.DataFrame( ... { ... "col1": ["a", "a", "b", "b", "a"], ... "col2": [1.0, 2.0, 3.0, np.nan, 5.0], ... "col3": [1.0, 2.0, 3.0, 4.0, 5.0] ... }, ... columns=["col1", "col2", "col3"], ... ) >>> df col1 col2 col3 0 a 1.0 1.0 1 a 2.0 2.0 2 b 3.0 3.0 3 b NaN 4.0 4 a 5.0 5.0
>>> df2 = df.copy() >>> df2.loc[0, 'col1'] = 'c' >>> df2.loc[2, 'col3'] = 4.0 >>> df2 col1 col2 col3 0 c 1.0 1.0 1 a 2.0 2.0 2 b 3.0 4.0 3 b NaN 4.0 4 a 5.0 5.0
Align the differences on columns
>>> df.compare(df2) col1 col3 self other self other 0 a c NaN NaN 2 NaN NaN 3.0 4.0
Assign result_names
>>> df.compare(df2, result_names=("left", "right")) col1 col3 left right left right 0 a c NaN NaN 2 NaN NaN 3.0 4.0
Stack the differences on rows
>>> df.compare(df2, align_axis=0) col1 col3 0 self a NaN other c NaN 2 self NaN 3.0 other NaN 4.0
Keep the equal values
>>> df.compare(df2, keep_equal=True) col1 col3 self other self other 0 a c 1.0 1.0 2 b b 3.0 4.0
Keep all original rows and columns
>>> df.compare(df2, keep_shape=True) col1 col2 col3 self other self other self other 0 a c NaN NaN NaN NaN 1 NaN NaN NaN NaN NaN NaN 2 NaN NaN NaN NaN 3.0 4.0 3 NaN NaN NaN NaN NaN NaN 4 NaN NaN NaN NaN NaN NaN
Keep all original rows and columns and also all original values
>>> df.compare(df2, keep_shape=True, keep_equal=True) col1 col2 col3 self other self other self other 0 a c 1.0 1.0 1.0 1.0 1 a a 2.0 2.0 2.0 2.0 2 b b 3.0 3.0 3.0 4.0 3 b b NaN NaN 4.0 4.0 4 a a 5.0 5.0 5.0 5.0
警告
This method has not been implemented yet. Xorbits will try to execute it with pandas.
This docstring was copied from pandas.core.frame.DataFrame.