chore(i18n,curriculum): processed translations (#42734)

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---
id: 5e9a093a74c4063ca6f7c14e
title: Data Analysis Example B
title: 數據分析 案例 B
challengeType: 11
videoId: 0kJz0q0pvgQ
dashedName: data-analysis-example-b
@ -8,30 +8,30 @@ dashedName: data-analysis-example-b
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*您可以使用 Google Colab而不是像視頻中顯示的那樣使用 notebooks.ai。*
More resources:
更多資源:
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
- [在 GitHub 平臺的 Notebooks](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
- [如何使用 Google Colab 來打開 GitHub 上的 Notebooks](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
# --question--
## --text--
What does the `loc` method allow you to do?
`loc` 方法允許您做什麼?
## --answers--
Retrieve a subset of rows and columns by supplying integer-location arguments.
通過提供整數位置參數來獲取一個行和列的子集。
---
Access a group of rows and columns by supplying label(s) arguments.
通過提供標籤參數來訪問一組行和列。
---
Returns the first `n` rows based on the integer argument supplied.
根據提供的整數參數返回前 `n` 行。
## --video-solution--

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---
id: 5e9a093a74c4063ca6f7c15d
title: Data Cleaning Introduction
title: 數據清理簡介
challengeType: 11
videoId: ovYNhnltVxY
dashedName: data-cleaning-introduction
@ -8,18 +8,18 @@ dashedName: data-cleaning-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*您可以使用 Google Colab而不是像視頻中顯示的那樣使用 notebooks.ai。*
More resources:
以下有更多的資料:
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
- [在 GitHub 平臺的 Notebooks](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
- [如何使用 Google Colab 來打開 GitHub 上的 Notebooks](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
# --question--
## --text--
What will the following code print out?
以下代碼會打印出什麼?
```py
import pandas as pd

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---
id: 5e46f7e5ac417301a38fb928
title: Mean-Variance-Standard Deviation Calculator
title: 均值-方差-標準差 計算器
challengeType: 10
forumTopicId: 462366
dashedName: mean-variance-standard-deviation-calculator
---
# --description--
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.
創建一個函數,這個函數可以使用 Numpy 輸出 3 x 3 矩陣的每一行、每一列和所有元素的均值,方差和標準差。
You can access [the full project description and starter code on Repl.it](https://repl.it/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator).
你可以在 [Replit](https://replit.com/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator) 上查看整個項目的具體描述和初始代碼。
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.
點擊此鏈接fork 這個項目。 當你根據 “README.md” 中的說明完成了項目,請在下方提交你的項目鏈接。
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.
我們仍在開發 Python 數據分析課程的交互式教學。 現在,您將需要使用其他資源來學習如何通過這一挑戰。
# --hints--
It should pass all Python tests.
它應該通過所有的 Python 測試。
```js

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---
id: 5e9a0a8e09c5df3cc3600eda
title: Loading Data and Advanced Indexing
title: 加載數據和高級索引
challengeType: 11
videoId: tUdBZ7pF8Jg
dashedName: loading-data-and-advanced-indexing
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## --text--
Given a file named `data.txt` with these contents:
給定一個名爲 `data.txt` 的文件,其中包含以下內容:
<pre>
29,97,32,100,45
15,88,5,75,22
</pre>
What code would produce the following array?
哪段代碼會生成下面的數組?
```py
[29. 32. 45. 15. 5. 22.]

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---
id: 5e9a0a8e09c5df3cc3600ed2
title: What is NumPy
title: Numpy 是什麼?
challengeType: 11
videoId: 5Nwfs5Ej85Q
dashedName: what-is-numpy
@ -10,23 +10,23 @@ dashedName: what-is-numpy
## --text--
Why are Numpy arrays faster than regular Python lists?
爲什麼 Numpy 數組要比常規的 Python 列表更快?
## --answers--
Numpy does not perform type checking while iterating through objects.
Numpy 在遍歷對象時不執行類型檢查。
---
Numpy uses fixed types.
Numpy 使用固定類型。
---
Numpy uses contiguous memory.
Numpy 使用連續內存。
---
All of the above.
上述所有的。
## --video-solution--