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ass
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...
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 单元测试是软件开发中非常重要的部分,它将确保代码正确运行。Pandas包含成千上万的单元测试,有助...
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) ...
dataframe 新增单列 assign方法 dataframe assign方法,返回一个新对象(副本),不影响旧dataframe对象 import pandas as pd df...= pd.DataFrame({ 'col_1': [0, 1, 2, 3], ...
assert_frame_equal(df1,df2) print("两个df一致") exceptExceptionase: print("出现如下异常:\n%s"%e) 1. 2. 3. 4. 5. 6. is_df_equal(df1=x1,df2=x2) 1. 两个df一致 1. is_df_equal(df1=x1,df2=x3) 1. 出现如下异常: DataFrame.iloc[:, 1] (column name="x2") are different ...
20. 索引值不连续或比较时发现数据一致但索引不一致时,可以重设索引值 df.reset_index(drop=True)#这里drop=True是删除旧的index值,如果不写则会保存旧的索引列为一个新的列 21. 比较dataframe importpandas as pdfrompandas._testingimportassert_frame_equal assert_frame_equal(df1, df2)...
DataFrame({'a': empty_list_array2}) import warnings warnings.simplefilter("always") gives: >>> pd.testing.assert_frame_equal(df1, df2) /pandas/core/dtypes/missing.py:503: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result ...
DataFrame( data=e_data, index=pd.Index(data=e_index, name="row"), columns=pd.Index(data=e_columns, name="col") ) tm.assert_frame_equal(left=actual, right=expected) # fails Issue Description Rows and columns are unexpectedly dropped while creating a pivot table with a single NaN index...