类型的参数不可迭代

类型的参数不可迭代

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TypeError:"float"类型的参数不可迭代

本文介绍了TypeError:"float"类型的参数不可迭代的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我是python和TensorFlow的新手.我最近开始理解和执行TensorFlow示例，并遇到了以下示例: https://www.tensorflow.org/versions/r0.10/tutorials/wide_and_deep/index.html

I am new to python and TensorFlow. I recently started understanding and executing TensorFlow examples, and came across this one: https://www.tensorflow.org/versions/r0.10/tutorials/wide_and_deep/index.html

我收到错误消息， TypeError:类型为'float'的参数是不可迭代的，并且我认为问题出在以下代码行:

I got the error, TypeError: argument of type 'float' is not iterable, and I believe that the problem is with the following line of code:

df_train [LABEL_COLUMN] =(df_train ['income_bracket'].apply(lambda x:'> 50K'in x)).astype(int)

df_train[LABEL_COLUMN] = (df_train['income_bracket'].apply(lambda x: '>50K' in x)).astype(int)

(income_bracket是普查数据集的标签列，其中> 50K"是可能的标签值之一，另一个标签是"=< 50K".该数据集被读入df_train.出于上述原因的文档是:由于该任务是二进制分类问题，因此，我们将构建一个名为"label"的标签列，如果收入超过5万，则该列的值为1，否则为0.")

(income_bracket is the label column of the census dataset, with '>50K' being one of the possible label values, and the other label is '=<50K'. The dataset is read into df_train. The explanation provided in the documentation for the reason to do the above is, "Since the task is a binary classification problem, we'll construct a label column named "label" whose value is 1 if the income is over 50K, and 0 otherwise.")

如果任何人都可以解释我到底发生了什么以及我应该如何解决，那将是很棒的.我尝试使用Python2.7和Python3.4，但我不认为问题出在语言的版本上.另外，如果任何人都知道TensorFlow和pandas的新手入门教程，请共享链接.

If anyone could explain me what is exactly happening and how should I fix it, that'll be great. I tried using Python2.7 and Python3.4, and I don't think that the problem is with the version of the language. Also, if anyone is aware of great tutorials for someone who is new to TensorFlow and pandas, please share the links.

完整程序:

import pandas as pd
import urllib
import tempfile
import tensorflow as tf

gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender", keys=["female", "male"])
race = tf.contrib.layers.sparse_column_with_keys(column_name="race", keys=["Amer-Indian-Eskimo", "Asian-Pac-Islander", "Black", "Other", "White"])
education = tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size=1000)
marital_status = tf.contrib.layers.sparse_column_with_hash_bucket("marital_status", hash_bucket_size=100)
relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size=100)
workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100)
occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000)
native_country = tf.contrib.layers.sparse_column_with_hash_bucket("native_country", hash_bucket_size=1000)


age = tf.contrib.layers.real_valued_column("age")
age_buckets = tf.contrib.layers.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
education_num = tf.contrib.layers.real_valued_column("education_num")
capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")

wide_columns = [gender, native_country, education, occupation, workclass, marital_status, relationship, age_buckets, tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4)), tf.contrib.layers.crossed_column([native_country, occupation], hash_bucket_size=int(1e4)), tf.contrib.layers.crossed_column([age_buckets, race, occupation], hash_bucket_size=int(1e6))]

deep_columns = [
  tf.contrib.layers.embedding_column(workclass, dimension=8),
  tf.contrib.layers.embedding_column(education, dimension=8),
  tf.contrib.layers.embedding_column(marital_status, dimension=8),
  tf.contrib.layers.embedding_column(gender, dimension=8),
  tf.contrib.layers.embedding_column(relationship, dimension=8),
  tf.contrib.layers.embedding_column(race, dimension=8),
  tf.contrib.layers.embedding_column(native_country, dimension=8),
  tf.contrib.layers.embedding_column(occupation, dimension=8),
  age, education_num, capital_gain, capital_loss, hours_per_week]

model_dir = tempfile.mkdtemp()
m = tf.contrib.learn.DNNLinearCombinedClassifier(
    model_dir=model_dir,
    linear_feature_columns=wide_columns,
    dnn_feature_columns=deep_columns,
    dnn_hidden_units=[100, 50])


COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
  "marital_status", "occupation", "relationship", "race", "gender",
  "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket"]
LABEL_COLUMN = 'label'
CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation", "relationship", "race", "gender", "native_country"]
CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss", "hours_per_week"]


train_file = tempfile.NamedTemporaryFile()
test_file = tempfile.NamedTemporaryFile()
urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name)
urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name)


df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1)
df_train[LABEL_COLUMN] = (df_train['income_bracket'].apply(lambda x: '>50K' in x)).astype(int)
df_test[LABEL_COLUMN] = (df_test['income_bracket'].apply(lambda x: '>50K' in x)).astype(int)


def input_fn(df):

  continuous_cols = {k: tf.constant(df[k].values)
                     for k in CONTINUOUS_COLUMNS}

  categorical_cols = {k: tf.SparseTensor(
      indices=[[i, 0] for i in range(df[k].size)],
      values=df[k].values,
      shape=[df[k].size, 1])
                      for k in CATEGORICAL_COLUMNS}

  feature_cols = dict(continuous_cols.items() + categorical_cols.items())
  label = tf.constant(df[LABEL_COLUMN].values)
  return feature_cols, label


def train_input_fn():
    return input_fn(df_train)


def eval_input_fn():
    return input_fn(df_test)

m.fit(input_fn=train_input_fn, steps=200)
results = m.evaluate(input_fn=eval_input_fn, steps=1)
for key in sorted(results):
    print("%s: %s" % (key, results[key]))

谢谢

PS:错误的完整堆栈跟踪

PS: Full stack trace for the error

Traceback (most recent call last):

File "/home/jaspreet/PycharmProjects/TicTacTensorFlow/census.py", line 73, in <module>
    df_train[LABEL_COLUMN] = (df_train['income_bracket'].apply(lambda x: '>50K' in x)).astype(int)

File "/usr/lib/python2.7/dist-packages/pandas/core/series.py", line 2023, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)

File "inference.pyx", line 920, in pandas.lib.map_infer (pandas/lib.c:44780)

File "/home/jaspreet/PycharmProjects/TicTacTensorFlow/census.py", line 73, in <lambda>
    df_train[LABEL_COLUMN] = (df_train['income_bracket'].apply(lambda x: '>50K' in x)).astype(int)

TypeError: argument of type 'float' is not iterable

推荐答案

该程序可逐字处理最新版本的熊猫，即0.18.1

The program works verbatim with the latest version of pandas, i.e., 0.18.1

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08-13 19:34
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