reindex

简明释义

英[riːˈɪndɛks]美[riˈɪndɛks]

vt. 重建索引；重订指标；变换符号

n. 索引命令；变换符号

英英释义

单词用法

同义词

反义词

例句

1.Offers a paid inclusion program called SiteMatch (see Resources) that promises to reindex your pages within 48 hours.

提供一个付费的包含程序SiteMatch(参见参考资料)，它承诺在48小时内对您的页面重新编制索引。

2.Offers a paid inclusion program called SiteMatch (see Resources) that promises to reindex your pages within 48 hours.

提供一个付费的包含程序SiteMatch(参见参考资料)，它承诺在48小时内对您的页面重新编制索引。

3.A reindex is also needed due to internal changes, but overall the upgrade process should be fairly simple.

由于一些内部修改，导致应用需要重新创建索引(reindex)，但总体来说，升级过程相当简单。

4.The database administrator decided to reindex the tables to improve query performance.

数据库管理员决定重新索引表以提高查询性能。

5.After modifying the dataset, we need to reindex the DataFrame to ensure all entries are correctly aligned.

在修改数据集后，我们需要重新索引数据框，以确保所有条目正确对齐。

6.If the data structure changes, it may be necessary to reindex the entire dataset.

如果数据结构发生变化，可能需要重新索引整个数据集。

7.We can use the reindex method to change the index of a pandas Series.

我们可以使用重新索引方法来更改pandas系列的索引。

8.To reflect the changes in the data, you must reindex your time series.

为了反映数据的变化，您必须重新索引您的时间序列。

作文

In the world of data science and programming, managing datasets efficiently is crucial for analysis and decision-making. One common operation that data analysts often perform is to reindex their datasets. The term reindex refers to the process of changing the index of a data structure, such as a DataFrame in Python's pandas library. This can be particularly useful when we want to align data from different sources or when we need to change the order of our data for better readability and organization.For example, imagine you are working with a dataset that contains information about sales figures for different products over several months. Initially, the dataset might be indexed by product names. However, if you want to analyze the data based on time, you may need to reindex the dataset to have dates as the primary index. This allows for easier time-series analysis and visualization.The process of reindexing can also involve filling in missing values or dropping certain indices that are no longer relevant. In many cases, when we reindex, we can specify how we want to handle gaps in the data. For instance, we may choose to fill missing entries with zeros or use forward filling techniques to carry the last known value forward. Such flexibility makes reindexing an essential tool for data cleaning and preparation.Furthermore, reindexing can enhance the performance of data manipulation tasks. For instance, when merging two datasets, ensuring that they are both indexed similarly can significantly speed up the process. If one dataset is indexed by date and the other by product ID, trying to merge them without reindexing will lead to inefficiencies and potentially incorrect results. Therefore, understanding how to reindex properly is vital for anyone working with large datasets.In addition to practical applications, the concept of reindexing can also be metaphorically applied to other areas of life. Just as we might reindex a dataset to improve clarity and organization, we can also reindex our priorities and goals. For instance, during challenging times, individuals may find it beneficial to reassess their objectives and focus on what truly matters to them. By reindexing their life goals, they can create a clearer path towards achieving personal fulfillment.In conclusion, reindexing is not just a technical term used in data analysis; it embodies the idea of re-evaluating and restructuring for better outcomes. Whether in data science or personal development, the ability to reindex effectively can lead to improved clarity, efficiency, and success. As we continue to navigate through complex datasets and life situations, the skill of reindexing will undoubtedly remain a valuable asset.

在数据科学和编程的世界中，有效管理数据集对于分析和决策至关重要。数据分析师经常执行的一项常见操作是对他们的数据集进行reindex。术语reindex指的是更改数据结构的索引的过程，例如Python的pandas库中的DataFrame。当我们想要对齐来自不同来源的数据或需要更改数据的顺序以提高可读性和组织性时，这尤其有用。例如，想象一下您正在处理一个包含不同产品在几个月内销售数字的信息的数据集。最初，数据集可能是按产品名称进行索引的。然而，如果您想根据时间分析数据，则可能需要将数据集reindex为以日期作为主要索引。这使得时间序列分析和可视化变得更加容易。Reindex的过程还可以涉及填补缺失值或删除某些不再相关的索引。在许多情况下，当我们reindex时，可以指定我们希望如何处理数据中的空白。例如，我们可以选择用零填充缺失条目，或者使用前向填充技术将最后一个已知值向前推送。这种灵活性使得reindex成为数据清理和准备的重要工具。此外，reindex还可以提高数据操作任务的性能。例如，在合并两个数据集时，确保它们的索引相似可以显著加快处理速度。如果一个数据集按日期索引，而另一个按产品ID索引，那么在没有reindex的情况下尝试合并它们将导致效率低下，并可能产生不正确的结果。因此，了解如何有效地reindex对于任何处理大型数据集的人来说都是至关重要的。除了实际应用，reindex的概念也可以隐喻性地应用于生活的其他领域。就像我们可能会reindex一个数据集以改善清晰度和组织性一样，我们也可以reindex我们的优先事项和目标。例如，在困难时期，个人可能会发现重新评估他们的目标并专注于真正重要的事情是有益的。通过reindex他们的生活目标，他们可以为实现个人满足感创造更清晰的路径。总之，reindex不仅仅是数据分析中使用的一个技术术语；它体现了重新评估和重组以获得更好结果的理念。无论是在数据科学还是个人发展中，有效的reindex能力都能带来更好的清晰度、效率和成功。随着我们继续在复杂的数据集和生活情境中导航，reindex的技能无疑将继续成为一项宝贵的资产。