Inferring the type of a column in pandas in general seems complex to impossible. E.g. unstack-ing an int64 dtype that introduces NaNs (missing data) will convert the int64 to float64. We can't really prove anything other than the return value being DataFrame. Even with mypy plugins this...
To utilize df as a name in your if statement, it is necessary to assign it a value. Suitable empty values includeNoneand a dataframe without any rows. Let's assume the latter option. import pandas df = None for i in range(100): if df is None: # Your default dataframe here. # You...
def test_feature_name_validation_missing_columns_drop_passthough(): """Test the interaction between {'drop', 'passthrough'} and missing column names.""" pd = pytest.importorskip("pandas") X = np.ones(shape=(3, 4)) df = pd.DataFrame(X, columns=['a', 'b', 'c', 'd']) df_...
In [5]: ser == np.nan Out[5]: 0 False 1 False 2 False 3 False dtype: bool 这两个函数也被定义为 Series 和 DataFrames 上的方法。 In [6]: ser.isnull() Out[6]: 0 False 1 False 2 True 3 False dtype: bool 在DataFrames 上测试: In [7]: df = pd.DataFrame({'A': [1, ...
def test_feature_name_validation_missing_columns_drop_passthough(): """Test the interaction between {'drop', 'passthrough'} and missing column names.""" pd = pytest.importorskip("pandas") X = np.ones(shape=(3, 4)) df = pd.DataFrame(X, columns=['a', 'b', 'c', 'd']) df_...