TypeError:float（）参数必须是字符串或数字，而不是“function”–Python/Sklearn

Question

下面是一个名为Flights.py的程序的代码片段

...
#Load the dataset
df = dataset
df.isnull().any()
df = df.fillna(lambda x: x.median())

# Define X and Y
X = df.iloc[:, 2:124].values
y = df.iloc[:, 136].values
X_tolist = X.tolist()

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

倒数第二行引发以下错误：

Traceback (most recent call last):

  File "<ipython-input-14-d4add2ccf5ab>", line 3, in <module>
    X_train = sc.fit_transform(X_train)

  File "/Users/<username>/anaconda/lib/python3.6/site-packages/sklearn/base.py", line 494, in fit_transform
    return self.fit(X, **fit_params).transform(X)

  File "/Users/<username>/anaconda/lib/python3.6/site-packages/sklearn/preprocessing/data.py", line 560, in fit
    return self.partial_fit(X, y)

  File "/Users/<username>/anaconda/lib/python3.6/site-packages/sklearn/preprocessing/data.py", line 583, in partial_fit
    estimator=self, dtype=FLOAT_DTYPES)

  File "/Users/<username>/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py", line 382, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)

TypeError: float() argument must be a string or a number, not 'function'

我的数据帧df大小（22587138）

我在看下面的问题以获得灵感：

TypeError: float() argument must be a string or a number, not 'method' in Geocoder

我尝试了以下调整：

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train.as_matrix)
X_test = sc.transform(X_test.as_matrix)

导致以下错误：

AttributeError: 'numpy.ndarray' object has no attribute 'as_matrix'

对于如何扫描数据帧并查找/转换有问题的条目，我目前感到不知所措。

Answer 1

我在使用df = df.fillna(lambda x: x.median())时遇到了同样的问题 我的解决方案是将真实值而不是“函数”放入数据帧：

# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np

我用nan创建数据帧10行，3列

df = pd.DataFrame(np.random.randint(100,size=(10,3)))
df.iloc[3:5,0] = np.nan
df.iloc[4:6,1] = np.nan
df.iloc[5:8,2] = np.nan

属性愚蠢的列标签以便以后使用

df.columns=['Number_of_Holy_Hand_Grenades_of_Antioch', 'Number_of_knight_fleeings', 'Number_of_rabbits_of_Caerbannog']

print df.isnull().any()  # tell if nan per column

对于通过标签的每一列，我们用列本身计算出的中值填充所有nan值。可与mean（）等一起使用

for i in df.columns:     #df.columns[w:] if you have w column of line description 
    df[i] = df[i].fillna(df[i].median() )
print df.isnull().any()

现在df包含用中值替换的nan

print df

例如，你可以

X = df.ix[:,:].values
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

不适用于df = df.fillna(lambda x: x.median()) 我们现在可以使用df into forward方法，因为所有值都是真值，而不是函数；与使用lambda into dataframe.fillna（）的方法相反，例如all proposals using fillna combined to lambda