PANDAS in English translation

• pandas

Examples of using Pandas in Chinese and their translations into English

我们还看到了pandas软件包，它支持灵活的数据结构，旨在简化数据集的处理。
We also saw the pandas package, which supports flexible data structures and is designed to make working with datasets easy.

除了pandas、scikit-learn和matplotlib，还要学习一些用Python进行数据科学的新技巧。
Go beyond pandas, scikit-learn, and matplotlib and learn some new tricks for doing data science in Python.

Pandas可用于数据分析，Matplotlib库可用于绘制图形。
The pandas library can be used for the data analysis, and the matplotlib library can be used for plotting graphs.

Pandas项目包括基于Python编程语言的数据结构和数据分析工具。
The Pandas project includes data structures and data analysis tools based on the Python programming language.

作为数据科学家，我们对pandas、SQL或任何其他关系数据库非常熟悉。
We, as data scientists have gotten quite comfortable with Pandas or SQL or any other relational database.

SQL，一种可能会一直存在下去的语言，还可以转移到其他技术，如Pandas。
SQL as a language exists practically forever and is the skill that can be transferred to other technologies, like Pandas.

截至目前，径向坐标可视化在Seaborn中并未实现，所以我们结合Pandas的radviz函数和Seaborn的sns.
As of this writing, radial visualizations are not yet available in Seaborn, so we can use the Pandas radviz function together with the Seaborn sns.

对于许多数据科学家来说，数据操作起始于Pandas或Tidyverse。
For many data scientists, data manipulation begins and ends with Pandas or the Tidyverse.

使用这个方法所能导入完整的文件格式清单是在Pandas文档中。
A full list of the file formats you can import using this function is in the Pandas documentation.

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.

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).

对NumPy数组的深入理解可帮助数据科学家有效地利用Pandas。
A deep understanding of the NumPy arrays helps in the effective use of Pandas for data scientists.

一些自定义（如添加标题和标签）可以使用pandasplot函数轻松搞定。
Some of the customizations(like adding titles and labels) are very simple to use with the pandas plot function.

但是无论如何，如果要学Python，你就要学习使用一些开发库，例如学习Pandas来操作数据框架以及学习matplotlib来制作图表。
But anyway, to learn Python you need to learn a few libraries, such as Pandas for manipulating data frames and matplotlib for making charts.

对于数据科学和机器学习，开发人员通常使用NumPy、Pandas、Matplotlib，机器学习专用的库(如scikit-learn、TensorFlow和Keras)也很流行。
For data science and machine learning, developers typically use NumPy, Pandas, Matplotlib, with machine learning-specific libraries such as scikit-learn, TensorFlow and Keras also being popular.

Pandas是一个提供快速，灵活和表达性数据结构的Python包，旨在使“关系”或“标记”数据变得简单直观。
Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with"relational" or"labeled" data both easy and intuitive.

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.

Pandas这个名字源于paneldata（面板数据，这是多维结构化数据集在计量经济学中的术语）以及Pythondataanalysis（Python数据分析）。
The pandas name itself is derived from panel data, an econometrics term for multidimensional structured data sets, and Python data analysis itself.

如果你正考虑学习其中一种框架且具备Python、numpy、pandas、sklearn和matplotlib的技能，我建议你从Keras入手。
If you are considering learning one of these frameworks and have Python, numpy, pandas, sklearn, and matplotlib skills, I suggest you start with Keras.

如果你才刚刚开始，那么就专注于一些能让你建立Python技巧的课，例如介绍Jupyternotebooks,scikit-learn和pandas.
If you're just getting started, focus on something that lets you build your Python skills, and that introduces you to Jupyter notebooks, scikit-learn, and pandas.