assert_frame_equal()是pytest库中的一个函数,可以用来断言两个DataFrame是否相等。该函数会同时比较DataFrame的形状、索引和值。如果所有条件都满足,则测试通过;否则,测试失败并抛出异常。首先,你需要安装pytest库(如果尚未安装): pip install pytest 然后,在Python脚本或交互式环境中导入asser
Pandas的testing模块中有一个assert_frame_equal()方法,可以用来比较两个数据框是否完全相同。assert_frame_equal()方法比较两个数据框的每个元素,并在两个数据框不相同时抛出一个AssertionError异常。示例代码如下: importpandasaspdfrompandas.testingimportassert_frame_equal# 定义数据框1df1=pd.DataFrame({'A':[1...
assert_frame_equal是比较两个df是否完全一模一样。index都要一样! import pandas as pd import numpy as np from pandas.testing import assert_frame_equal df1 = pd.DataFrame(np.arange(12).reshape(3, -1)) df2 = pd.DataFrame(np.arange(12).reshape(3, -1)) df1 == df2 assert_frame_equal(df1...
pandas.testing.assert_frame_equal(left, right, check_dtype=True, check_index_type='equiv', check_column_type='equiv', check_frame_type=True, check_less_precise=NoDefault.no_default, check_names=True, by_blocks=False, check_exact=False, check_datetimelike_compat=False, check_categorical=True,...
Pandas有一个assert_frame_equal方法,也可以判断DataFrame是否相同: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 >>> from pandas.testing import assert_frame_equal >>> assert_frame_equal( ... movie_boolean, movie_mask, check_dtype=False ... ) 更多 比较这两个条件的速度: 代码语言:javascript...
assert_frame_equal()来自pandas.testing包，而不是unittest.TestCase类。替换：如果你导入这个，问题就...
print(pd.testing.assert_series_equal(df.a, df.b, check_names=False, check_dtype=False)) print() # assert_frame_equal函数查看是否相同,异常则输出 df_new = df.copy() pd.testing.assert_frame_equal(df, df_new) # Use NumPy without importing NumPy ...
在pandas.testing子程序包中,在创建单元测试时,存在一个分析人员可以使用的函数。如果两个DataFrame不相等,则assert_frame_equal函数将引发AssertionError;如果两个DataFrame相等,则返回None。 >>>frompandas.testingimportassert_frame_equal>>>assert_frame_equal(college_ugds,college_ugds)isNoneTrue ...
movie_equal.all().all() False movie_equal.size - movie_equal.sum().sum() 2654 movie.isnull().sum().sum() 2654 比较两个DataFrame最直接的方法是使用equals()方法 frompandas.testingimportassert_frame_equal assert_frame_equal(movie, movie) ...
on a conversion viapa.Table.to_pandas()and then leveragingpandas.testing.assert_frame_equals()to garner better output and an idea of where the data issues may lie. The conversion can be costly with larger datasets both in terms of time and accuracy (conversions andassert_frame_equalssettings)...