What is the translation of " DATAFRAME " in English?

• dataframe

Examples of using Dataframe in Chinese and their translations into English

将数据加载到DataFrame中.
Inserting data into the dataframe.

我们还将把该dataframe转换为一套常规NumPy数组。
We also convert the dataframe into a regular NumPy array.

Pandas库有两种数据结构，Series和DataFrame。
Pandas has two data structures: Series and DataFrame.

我们也将该dataframe转换为了一个常规的NumPy数组。
We also convert the dataframe into a regular NumPy array.

Pandas常用的数据结构有两种：Series和DataFrame。
Pandas has two data structures: Series and DataFrame.

Return:序列的PandasDataFrame转为监督学习。
Return: Pandas DataFrame of series framed for supervised learning.

Pandas有两个主要的数据结构：Series和DataFrame。
Pandas has two data structures: Series and DataFrame.

Pandas-Python数据分析库，包含dataframe等结构.
Pandas- Python data analysis library, including structures such as dataframes.

Ml提供了构建在DataFrame之上的更高级API，用于构建ML管道。
Ml provides higher-level API built on top of DataFrames for constructing ML pipelines.

它同时支持SQL查询和新的DataFrameAPI。
It powers both SQL queries and the new DataFrame API.

此API采用SparkSQL的DataFrame以支持各种数据类型。
This API adopts the DataFrame from Spark SQL in order to support a variety of data types.

DataFrame的每一列都有一个名称（标题），每一行都由一个数字标识。
Each column of the DataFrame has a name(a header), and each row is identified by a number.

首先，我们定义一个函数，它将从DataFrame中获取每一个元素作为自己的参数。
First, we define a Python function that takes an element from the DataFrame as its parameter.

它提供了一个称为DataFrame的编程抽象，并且可以充当分布式SQL查询引擎。
It provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine.

现在，通过另外调用head方法，我们可以确认dataframe不再包含rank列。
Now, with another call to the head function, we can confirm that the dataframe no longer contains a rank column.

相反，DataFrame仍然是它们的主接口，并且类似于这些语言中的单节点数据帧概念。
Instead, DataFrame remains the primary programing abstraction, which is analogous to the single-node data frame notion in these languages.

Pandas为Python带来了两个新的数据结构，即PandasSeries和PandasDataFrame。
Pandas enables you to create two new types of Python objects: the Pandas Series and the Pandas DataFrame.

在完成label列之后，我们将其从dataframe当中移除，这样我们就只剩下20项用于描述输入内容的特征。
Once we're done with the label column we remove it from the dataframe, so that we're left with the 20 features that describe the input.

Str()方法，同时我们也可以使用applymap()将一个pythoncallable映射到DataFrame中的每个元素上。
Str() methods again here, we could also use applymap() to map a Python callable to each element of the DataFrame.

在上面的代码中，knn是指「K-NearestNeighbors」，df指的是「DataFrame」--无处不在的Pandas数据结构。
In the code above, knn refers to“K-Nearest Neighbors”, and df refers to“DataFrame”, the ubiquitous pandas data structure.

SparkSQL专注于结构化数据的处理，使用从R和Python(Pandas)借来的dataframe方法。
Spark SQL is focused on the processing of structured data, using a dataframe approach borrowed from R and Python(in Pandas).

统一Scala和Java中DataFrames和Datasets：从Spark2.0开始，DataFrame只是DatasetofRow的类型别名。
Unifying DataFrames and Datasets in Scala/Java: Starting in Spark 2.0, DataFrame is just a type alias for Dataset of Row.

我们还没有谈到索引，但索引在上面的DataFrame中是左边的东西，在Date下面。
We haven't talked about indexes yet, but the index is what's on the left on the above dataframe, under'Date'.

统一Scala和Java中DataFrames和Datasets的API：从Spark2.0开始，DataFrame仅仅是Dataset的一个别名。
Unifying DataFrames and Datasets in Scala/Java: Starting in Spark 2.0, DataFrame is just a type alias for Dataset of Row.

对于用于统计计算的R语言的用户，DataFrame名称将是熟悉的，因为该对象以类似的Rdata.frame对象命名。
For users of the R statistical computing language, the DataFrame name will be familiar, as it was named after the similar R data. frame object.

尤其是，我喜欢将其用于其数据结构(比如DataFrame)、时间序列操纵和分析以及数字数据表。
In particular, I enjoy using it for its data structures, such as the DataFrame, the time series manipulation and analysis, and the numerical data tables.

SparkML（spark.ml）包提供了构建在DataFrame之上的机器学习API，它已经成了SparkSQL库的核心部分。
Spark ML(spark. ml) package provides machine learning API built on the DataFrames which are becoming the core part of Spark SQL library.

Pandas数据操作库建立在NumPy上，但它不是使用数组，而是使用另外两个基本的数据结构：Series和DataFrame；.
The Pandas data manipulation library builds on NumPy, but instead of the arrays, it makes use of two other fundamental data structures: Series and DataFrames.

看起来将文件作为一个datatableframe读取，然后将其转换为Pandasdataframe比直接读取Pandasdataframe的方式所花费的时间更少。
SIt appears that reading a file as a datatable frame and then converting it to pandas dataframe takes less time than reading through pandas dataframe..