小编use*_*077的帖子

当我尝试使用逻辑表达式对 Pandas 数据框进行切片时，出现异常。

我的数据具有以下形式：

df
    GDP_norm    SP500_Index_deflated_norm
Year        
1980    2.121190    0.769400
1981    2.176224    0.843933
1982    2.134638    0.700833
1983    2.233525    0.829402
1984    2.395658    0.923654
1985    2.497204    0.922986
1986    2.584896    1.09770

df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 38 entries, 1980 to 2017
Data columns (total 2 columns):
GDP_norm                     38 non-null float64
SP500_Index_deflated_norm    38 non-null float64
dtypes: float64(2)
memory usage: 912.0 bytes

命令如下：

df[((df['GDP_norm'] >=3.5 & df['GDP_norm'] <= 4.5) & (df['SP500_Index_deflated_norm'] > 3)) | (

   (df['GDP_norm'] >= 4.0 & df['GDP_norm'] <= 5.0) & (df['SP500_Index_deflated_norm'] < …

我正在尝试使用 to_datetime() 方法将 Pandas 数据帧的索引转换为日期时间，但出现异常。

对于玩具可重现的示例：

我的数据：

json 格式：

'{"0":{"Id":1,"Name":"Lewis Alexander","Organization":"Nomura Securities International","Dec 2018":3.25,"June 2019":3.25,"Dec 2019":3.0,"June 2020":3.0,"Dec 2020":2.88},"1":{"Id":2,"Name":"Scott Anderson","Organization":"Bank of the West","Dec 2018":3.19,"June 2019":3.5,"Dec 2019":3.47,"June 2020":3.1,"Dec 2020":2.6},"2":{"Id":3,"Name":"Paul Ashworth","Organization":"Capital Economics","Dec 2018":3.25,"June 2019":3.0,"Dec 2019":2.5,"June 2020":2.25,"Dec 2020":2.25},"3":{"Id":4,"Name":"Daniel Bachman","Organization":"Deloitte LP","Dec 2018":3.2,"June 2019":3.4,"Dec 2019":3.5,"June 2020":3.6,"Dec 2020":3.7},"4":{"Id":5,"Name":"Bernard Baumohl","Organization":"Economic Outlook Group","Dec 2018":3.1,"June 2019":3.35,"Dec 2019":3.6,"June 2020":3.9,"Dec 2020":4.2}}'

当我尝试时：

df3.index.to_datetime()

我收到以下错误消息：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-53-5ff789e24651> in <module>()
----> 1 df3.index.to_datetime()

AttributeError: 'Index' object has no attribute 'to_datetime'

我在Seaborn遇到了一个奇怪的例外。

对于可重现的示例：

toy_data.to_json()
'{"X":{"0":0.12765045,"1":0.0244816152,"2":0.1263715245,"3":0.0246376768,"4":0.1108581319,"5":0.1406719382,"6":0.1358105564,"7":0.1245863432,"8":0.1175445352,"9":0.1188479018,"10":0.1113148159,"11":0.117455495,"12":0.110555662,"13":0.1328567106,"14":0.103064284,"15":0.1119474442,"16":0.119390455,"17":0.1246727756,"18":0.1117827155,"19":0.1169972547},"Y":{"0":0.1241083714,"1":0.1394242378,"2":0.1225010796,"3":0.0077080173,"4":0.1198371354,"5":0.0029026989,"6":0.1259473297,"7":0.0,"8":0.0,"9":0.1214620231,"10":0.1204110739,"11":0.0,"12":0.1194570059,"13":0.0014971676,"14":0.1184584731,"15":0.1212061305,"16":0.1221438778,"17":0.0,"18":0.1209991075,"19":0.0},"Label":{"0":"17","1":"3","2":"17","3":"0","4":"14","5":"21","6":"16","7":"23","8":"20","9":"15","10":"14","11":"20","12":"14","13":"22","14":"13","15":"14","16":"15","17":"23","18":"14","19":"20"},"Probability":{"0":1.0,"1":1.0,"2":1.0,"3":1.0,"4":1.0,"5":1.0,"6":1.0,"7":1.0,"8":1.0,"9":1.0,"10":1.0,"11":1.0,"12":1.0,"13":1.0,"14":1.0,"15":1.0,"16":1.0,"17":1.0,"18":0.9101796407,"19":1.0}}'

toy_data.head()
       X           Y    Label   Probability
0   0.127650    0.124108    17  1.0
1   0.024482    0.139424    3   1.0
2   0.126372    0.122501    17  1.0
3   0.024638    0.007708    0   1.0
4   0.110858    0.119837    14  1.0

sns.scatterplot(x = toy_data.X, y = toy_data.Y, hue = toy_data.Label.values, alpha = 0.5)

AttributeError: 'str' object has no attribute 'view'

使用此语法的类似异常：

sns.scatterplot(x = 'X', y = 'Y', data = toy_data, hue = 'Label', alpha = 0.5)

我知道有一个方法 .argmax() 返回轴上最大值的索引。

但是，如果我们想要获取某个轴上 10 个最高值的索引该怎么办？

这怎么可能实现呢？

例如：

data = pd.DataFrame(np.random.random_sample((50, 40)))

我有一个从Jupyter Notebook运行的简单Python脚本。但是，我传递给它的参数似乎被忽略了，这导致异常：

two_digits.py

import sys
input = sys.stdin.read()
tokens = input.split()
a = int(tokens[0])
b = int(tokens[1])
print(a + b)

%run two_digits 3 5

ndexError                                Traceback (most recent call last)
D:\Mint_ns\two_digits.py in <module>()
      5 tokens = input.split()
      6 
----> 7 a = int(tokens[0])
      8 
      9 b = int(tokens[1])

IndexError: list index out of range

我编写了以下自定义评估函数与 xgboost 一起使用，以优化 F1。不幸的是，它在使用 xgboost 运行时会返回异常。

评价函数如下：

def F1_eval(preds, labels):

    t = np.arange(0, 1, 0.005)
    f = np.repeat(0, 200)
    Results = np.vstack([t, f]).T

    P = sum(labels == 1)

    for i in range(200):
        m = (preds >= Results[i, 0])
        TP = sum(labels[m] == 1)
        FP = sum(labels[m] == 0)

        if (FP + TP) > 0:
            Precision = TP/(FP + TP)

        Recall = TP/P

        if (Precision + Recall >0) :
            F1 = 2 * Precision * Recall / (Precision + Recall)                
        else: …

我想知道为什么我的代码不起作用。我预计它会返回 11 而是创建一个异常：

def f():

   counter = 1

   def f1():

      global counter

      counter += 1

   while True:

       f1()

       if counter>10:

           return(counter)

f()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-219-0ec059b9bfe1> in <module>()
----> 1 f()

<ipython-input-218-50c23b042204> in f()
      9         counter += 1
     10 
---> 11     f1()
     12 
     13     if counter>10:

<ipython-input-218-50c23b042204> in f1()
      7         global counter
      8 
----> 9         counter += 1
     10 
     11     f1()

NameError: name 'counter' is not defined

由于 counter 被声明为全局变量，并且由于它在 f1() 的周围环境中出现和定义 --inside f()-- 为什么我会收到此错误消息？

我想在 Pandas 中创建一个列联表。我可以用下面的代码来做，但我想知道是否有一个 Pandas 函数可以为我做这件事。

对于可重现的示例：

toy_data #json
'{"Light":{"321":"no_light","476":"night_light","342":"lamp","454":"lamp","25":"night_light","53":"night_light","120":"night_light","346":"night_light","360":"lamp","55":"no_light","391":"night_light","243":"no_light","101":"night_light","377":"night_light","124":"no_light","368":"lamp","400":"no_light","247":"night_light","270":"lamp","208":"night_light"},"Nearsightedness":{"321":"No","476":"Yes","342":"Yes","454":"Yes","25":"No","53":"Yes","120":"Yes","346":"No","360":"No","55":"Yes","391":"Yes","243":"No","101":"No","377":"Yes","124":"No","368":"No","400":"No","247":"No","270":"Yes","208":"No"}}'

toy_data.head()
    Light       Nearsightedness
321 no_light       No
476 night_light    Yes
342 lamp           Yes
454 lamp           Yes
25  night_light    No

df = pd.DataFrame(toy_data.groupby(['Light', 'Nearsightedness']).size())

df = df.unstack('Nearsightedness')

df.columns = df.columns.droplevel()

df
Nearsightedness No  Yes
Light       
lamp             2  3
night_light      5  5
no_light         4  1

以下代码在与 Keras 一起打包的 MNIST 数据上运行一个 Sequential Keras 模型，非常简单。

在运行以下代码时，我得到一个异常。

代码很容易重现。

import tensorflow as tf

class myCallback(tf.keras.callbacks.Callback):
      def on_epoch_end(self, epoch, logs={}):
        if(logs.get('acc')>0.99):
          print("\nReached 99% accuracy so cancelling training!")
          self.model.stop_training = True

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

callbacks = myCallback()

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])

例外是：

Epoch 1/10
59296/60000 [============================>.] - ETA: 0s - loss: 0.2005 - accuracy: …

我正在学习使用 Tensorboard——Tensorflow 2.0。

特别是，我想实时监控学习曲线，并直观地检查和传达模型的架构。

下面我将提供可重现示例的代码。

我有三个问题：

1. 虽然训练结束后我得到了学习曲线，但我不知道应该做什么来实时监控它们

2. 我从Tensorboard得到的学习曲线与history.history的情节不符。事实上，它的逆转是奇怪且难以解释的。

3. 我无法理解该图表。我训练了一个具有 5 个密集层和中间的 dropout 层的顺序模型。Tensorboard 向我展示的是其中包含更多元素的东西。

我的代码如下：

from keras.datasets import boston_housing

(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()

inputs = Input(shape = (train_data.shape[1], ))
x1 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(inputs)
x1a = Dropout(0.5)(x1)
x2 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x1a)
x2a = Dropout(0.5)(x2)
x3 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x2a)
x3a = Dropout(0.5)(x3)
x4 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x3a)
x4a = Dropout(0.5)(x4)
x5 …