chore(i18n,curriculum): processed translations (#42734)
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---
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id: 5e9a093a74c4063ca6f7c14e
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title: Data Analysis Example B
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title: 数据分析 案例 B
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challengeType: 11
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videoId: 0kJz0q0pvgQ
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dashedName: data-analysis-example-b
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# --description--
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*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
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*您可以使用 Google Colab，而不是像视频中显示的那样使用 notebooks.ai。*
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More resources:
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更多资源：
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- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
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- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
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- [在 GitHub 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
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- [如何使用 Google Colab 来打开 GitHub 上的 Notebooks](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
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# --question--
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## --text--
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What does the `loc` method allow you to do?
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`loc` 方法允许您做什么？
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## --answers--
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Retrieve a subset of rows and columns by supplying integer-location arguments.
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通过提供整数位置参数来获取一个行和列的子集。
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---
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Access a group of rows and columns by supplying label(s) arguments.
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通过提供标签参数来访问一组行和列。
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---
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Returns the first `n` rows based on the integer argument supplied.
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根据提供的整数参数返回前 `n` 行。
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## --video-solution--
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---
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id: 5e9a093a74c4063ca6f7c15d
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title: Data Cleaning Introduction
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title: 数据清理简介
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challengeType: 11
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videoId: ovYNhnltVxY
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dashedName: data-cleaning-introduction
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@ -8,18 +8,18 @@ dashedName: data-cleaning-introduction
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# --description--
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*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
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*您可以使用 Google Colab，而不是像视频中显示的那样使用 notebooks.ai。*
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More resources:
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以下有更多的资料：
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- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
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- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
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- [在 GitHub 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
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- [如何使用 Google Colab 来打开 GitHub 上的 Notebooks](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
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# --question--
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## --text--
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What will the following code print out?
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以下代码会打印出什么？
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```py
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import pandas as pd
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---
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id: 5e46f7e5ac417301a38fb928
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title: Mean-Variance-Standard Deviation Calculator
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title: 均值-方差-标准差 计算器
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challengeType: 10
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forumTopicId: 462366
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dashedName: mean-variance-standard-deviation-calculator
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---
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# --description--
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Create a function that uses Numpy to output the mean, variance, and standard deviation of the rows, columns, and elements in a 3 x 3 matrix.
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创建一个函数，这个函数可以使用 Numpy 输出 3 x 3 矩阵的每一行、每一列和所有元素的均值，方差和标准差。
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You can access [the full project description and starter code on Repl.it](https://repl.it/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator).
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你可以在 [Replit](https://replit.com/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator) 上查看整个项目的具体描述和初始代码。
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After going to that link, fork the project. Once you complete the project based on the instructions in 'README.md', submit your project link below.
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点击此链接，fork 这个项目。 当你根据 “README.md” 中的说明完成了项目，请在下方提交你的项目链接。
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We are still developing the interactive instructional part of the data analysis with Python curriculum. For now, you will have to use other resources to learn how to pass this challenge.
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我们仍在开发 Python 数据分析课程的交互式教学。 现在，您将需要使用其他资源来学习如何通过这一挑战。
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# --hints--
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It should pass all Python tests.
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它应该通过所有的 Python 测试。
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```js
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---
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id: 5e9a0a8e09c5df3cc3600eda
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title: Loading Data and Advanced Indexing
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title: 加载数据和高级索引
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challengeType: 11
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videoId: tUdBZ7pF8Jg
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dashedName: loading-data-and-advanced-indexing
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## --text--
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Given a file named `data.txt` with these contents:
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给定一个名为 `data.txt` 的文件，其中包含以下内容：
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<pre>
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29,97,32,100,45
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15,88,5,75,22
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</pre>
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What code would produce the following array?
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哪段代码会生成下面的数组？
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```py
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[29. 32. 45. 15. 5. 22.]
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---
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id: 5e9a0a8e09c5df3cc3600ed2
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title: What is NumPy
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title: Numpy 是什么？
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challengeType: 11
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videoId: 5Nwfs5Ej85Q
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dashedName: what-is-numpy
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## --text--
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Why are Numpy arrays faster than regular Python lists?
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为什么 Numpy 数组要比常规的 Python 列表更快？
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## --answers--
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Numpy does not perform type checking while iterating through objects.
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Numpy 在遍历对象时不执行类型检查。
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---
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Numpy uses fixed types.
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Numpy 使用固定类型。
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---
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Numpy uses contiguous memory.
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Numpy 使用连续内存。
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---
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All of the above.
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上述所有的。
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## --video-solution--
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