nan

简明释义

英[næn]美[næn;nɑːn]

n. 奶奶（小孩儿语）；印度，巴基斯坦式的微微发酵的面包

n. （Nan）人名；（越）难；（日）难（姓）；（东南亚国家华语）南；（柬、印尼）南

英英释义

单词用法

同义词

反义词

例句

1.Nan and Bet are like to her in this.

南恩和白特在这一点上都和她一样。

2."If government troops suffer a lot of casualties, government troops turn on the villagers in the area, " said La Nan.

“如果政府军遭受了重大伤亡，那么政府军就会把报复目标转向该地区村民。”La Nan说。

3.This trip is not only about pushing our limits, but also about looking for adventure," said Nan.

“这次旅行不只是为了提升我们的极限，也出于想寻找冒险，”南至超说。

4.One penny every week the good priest that teacheth me shall have; mother, Nan, and Bet the other four.

我每个礼拜要给一个便士给教我的那位好心的神甫；剩下的四个就给妈妈、南恩和白特。

5.Who should I be, but thy sister Nan?

除了你的姐姐南恩，我还能是谁？

6.Nan Shan would have marked the first deal for the former Citigroup Inc.

南山应该会把前花旗集团，亚洲投资银行首席当作交易头选。

7.One thing to remember when using NaN is that it is incomparable with any other value including itself.

在使用NaN时需要记住一点，这个值无法与任何值进行比较，包括它本身。

8.I love eating nan with my curry.

我喜欢和我的咖喱一起吃南。

9.The restaurant serves freshly baked nan every day.

这家餐厅每天提供新鲜出炉的南。

10.Can you pass me the nan from the table?

你能把桌子上的南递给我吗？

11.I prefer nan over rice for Indian dishes.

对于印度菜，我更喜欢南而不是米饭。

12.We ordered butter nan and garlic nan for dinner.

我们点了黄油南和大蒜南作为晚餐。

作文

In the world of computing, particularly in programming and data analysis, the term nan stands for 'Not a Number.' This designation is used to represent a value that does not have a defined or valid numerical representation. For example, if you perform a mathematical operation that does not yield a valid number, such as dividing zero by zero, the result is nan. Understanding nan is crucial for developers and data scientists alike because it helps them identify and handle errors in their calculations and datasets. When working with large datasets, encountering nan values can be common. These values may arise from various sources, such as missing data, invalid entries, or errors during data collection. For instance, if a survey respondent skips a question, the corresponding entry in the dataset may be recorded as nan. In this case, recognizing nan allows analysts to decide how to handle these gaps—whether to exclude them from calculations, replace them with estimates, or use statistical methods to infer their values. The presence of nan can significantly affect the results of statistical analyses. Many algorithms assume that all input data is valid and will produce misleading results if nan values are not accounted for. Therefore, it is essential to preprocess data by identifying and managing nan values before conducting any analyses. This preprocessing step might include filtering out rows with nan entries, filling in missing values using techniques like mean substitution, or employing more sophisticated imputation methods. Moreover, programming languages like Python and R provide built-in functions to detect and handle nan values easily. For example, in Python's NumPy library, one can use functions like `numpy.isnan()` to check for nan values in an array. Similarly, R has functions like `is.na()` for identifying nan values in data frames. By utilizing these tools, programmers can write cleaner and more efficient code that gracefully handles nan values. In conclusion, the concept of nan—'Not a Number'—is an integral part of data analysis and programming. It serves as a critical indicator of invalid or undefined numerical values within datasets. By understanding how to identify and manage nan, developers and analysts can ensure the integrity of their analyses and improve the accuracy of their results. As data continues to grow in complexity and volume, mastering the handling of nan values will remain a vital skill in the toolkit of anyone working with data.

在计算机世界中，尤其是在编程和数据分析中，术语nan代表“不是一个数字”。这个术语用于表示没有定义或有效数值表示的值。例如，如果你进行一个数学运算而没有产生有效数字，比如将零除以零，结果就是nan。理解nan对开发人员和数据科学家来说至关重要，因为它帮助他们识别和处理计算和数据集中的错误。 在处理大型数据集时，遇到nan值是很常见的。这些值可能来自多种来源，例如缺失数据、无效条目或数据收集过程中的错误。例如，如果一名调查受访者跳过一个问题，则数据集中相应的条目可能被记录为nan。在这种情况下，识别nan使分析师能够决定如何处理这些空缺——是排除在计算之外，还是用估计值替换，或者使用统计方法推断其值。 nan的存在可能会显著影响统计分析的结果。许多算法假设所有输入数据都是有效的，如果不考虑nan值，可能会产生误导性结果。因此，在进行任何分析之前，必须通过识别和管理nan值来预处理数据。这一步骤可能包括过滤掉包含nan条目的行，使用均值替代等技术填补缺失值，或采用更复杂的插补方法。 此外，像Python和R这样的编程语言提供了内置函数，以便轻松检测和处理nan值。例如，在Python的NumPy库中，可以使用`numpy.isnan()`函数检查数组中的nan值。同样，R有`is.na()`函数用于识别数据框中的nan值。通过利用这些工具，程序员可以编写更简洁和高效的代码，优雅地处理nan值。 总之，概念nan——“不是一个数字”——是数据分析和编程的重要组成部分。它作为数据集中无效或未定义数值的关键指示符。通过理解如何识别和管理nan，开发人员和分析师可以确保分析的完整性，提高结果的准确性。随着数据继续增长的复杂性和量，掌握处理nan值的能力将始终是任何从事数据工作的人必备的技能。