为了检查值是否为 NaN,可以使用isnull()或notnull()函数。 In [1]: import numpy as np In [2]: import pandas as pd In [3]: ser = pd.Series([1, 2, np.nan, 4]) In [4]: pd.isnull(ser) Out[4]: 0 False 1 False 2 True 3 False dtype: bool ...
pandas.errors.ParserError: Error tokenizing data. C error: Expected 3 fields in line 3, saw 4 Example 3 There are less then 3 fields in the 2nd row. Again here is no warning or error. The missing field is set with NaN. And here it does not matter if you give the number of (expec...
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_...
import pandas as pd Importing the dataset train_dataset = pd.read_csv('train.csv') test_dataset = pd.read_csv('test.csv') X = train_dataset.iloc[:, 1:94].values y = train_dataset.iloc[:, 94].values Encoding categorical data ...
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...