熊猫双括号索引[][重复]

我一直在学习谷歌的机器学习速成课程,他们有一个练习区,教你如何使用熊猫和TensorFlow。在开始时,他们抓取数据帧,然后直接抓取“总房间”和“中值房间”的序列。他们用双括号抓取“总房间”系列,用一组括号抓取“中值房间”系列。我阅读了熊猫的文档,似乎您需要使用双括号来索引一个系列的唯一原因是一次索引2个列,即数据加州住房数据框架[“中位数住房价值”,“总房间”]。在以后使用单括号对数据帧中的一列进行索引时,是否有理由使用双括号对其进行索引,以实现相同的操作?

这是我说的代码。

california_housing_dataframe = pd.read_csv("https://dl.google.com/mlcc/mledu-datasets/california_housing_train.csv", sep=",")
# Define the input feature: total_rooms.
my_feature = california_housing_dataframe[["total_rooms"]]
# Configure a numeric feature column for total_rooms.
feature_columns = [tf.feature_column.numeric_column("total_rooms")]

targets = california_housing_dataframe["median_house_value"]

如果您需要更多的上下文,这里有更多的代码:

california_housing_dataframe = pd.read_csv("https://dl.google.com/mlcc/mledu-datasets/california_housing_train.csv", sep=",")

# Define the input feature: total_rooms.
my_feature = california_housing_dataframe[["total_rooms"]]
# Configure a numeric feature column for total_rooms.
feature_columns = [tf.feature_column.numeric_column("total_rooms")]

targets = california_housing_dataframe["median_house_value"]

def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
    """Trains a linear regression model of one feature.

    Args:
      features: pandas DataFrame of features
      targets: pandas DataFrame of targets
      batch_size: Size of batches to be passed to the model
      shuffle: True or False. Whether to shuffle the data.
      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely
    Returns:
      Tuple of (features, labels) for next data batch
    """

    # Convert pandas data into a dict of np arrays.
    features = {key:np.array(value) for key,value in dict(features).items()}                                           

    # Construct a dataset, and configure batching/repeating.
    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit
    ds = ds.batch(batch_size).repeat(num_epochs)

    # Shuffle the data, if specified.
    if shuffle:
      ds = ds.shuffle(buffer_size=10000)

    # Return the next batch of data.
    features, labels = ds.make_one_shot_iterator().get_next()
    return features, labels

prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)

# Call predict() on the linear_regressor to make predictions.
predictions = linear_regressor.predict(input_fn=prediction_input_fn)

如果您需要更多的上下文,这里是指向包含所有代码的练习的链接: https://colab.research.google.com/notebooks/mlcc/first_steps_with_tensor_flow.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=firststeps-colab&hl=en