python - 数据集不可调用问题

Question

我试图估算 NaN 值，但首先我想检查计算该值的最佳方法。我是使用这种方法的新手，所以我想使用我发现的代码来处理不同的回归量并选择最好的。原代码是这样的：

from sklearn.experimental import enable_iterative_imputer  # noqa
from sklearn.datasets import fetch_california_housing
from sklearn.impute import SimpleImputer
from sklearn.impute import IterativeImputer
from sklearn.linear_model import BayesianRidge
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_val_score

N_SPLITS = 5

rng = np.random.RandomState(0)

X_full, y_full = fetch_california_housing(return_X_y=True)
# ~2k samples is enough for the purpose of the example.
Remove the following two lines for a slower run with different error bars.
X_full = X_full[::10]
y_full = y_full[::10]
n_samples, n_features = X_full.shape

fetch_california_housing 是他的数据集。

因此，当我尝试将此代码改编为我的案例时，我编写了以下代码：

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


from numpy import genfromtxt
data = genfromtxt('documents/datasets/df.csv', delimiter=',')
features = data[:, :2]
targets = data[:, 2]

N_SPLITS = 5
rng = np.random.RandomState(0)
X_full, y_full = data(return_X_y= True)
# ~2k samples is enough for the purpose of the example.
# Remove the following two lines for a slower run with different error bars.
X_full = X_full[::10]
y_full = y_full[::10]
n_samples, n_features = X_full.shape

我总是遇到同样的错误：

AttributeError: 'numpy.ndarray' object is not callable

在我使用我的 DF 作为 csv (df.csv) 之前，错误是一样的

AttributeError: 'Dataset' object is not callable

完整的错误是这样的：

ypeError Traceback (most recent call last) <ipython-input-8-3b63ca34361e>    in <module> 
3 rng = np.random.RandomState(0) 4 
----> 5 X_full, y_full = df(return_X_y=True) 
6 # ~2k samples is enough for the purpose of the example. 
7 # Remove the following two lines for a slower run with different error     bars. 
TypeError: 'DataFrame' object is not callable 

而且我不知道如何解决两个错误中的一个以消失

我希望能很好地解释我的问题，因为我的英语不是很好

标签： pythonpandasregressionmissing-dataimputation