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: 數據分析 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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@ -8,30 +8,30 @@ 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,而不是像視頻中顯示的那樣使用 notebooks.ai。*
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More resources:
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更多資源:
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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 平臺的 Notebooks](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
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- [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)
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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` 方法允許您做什麼?
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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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通過提供整數位置參數來獲取一個行和列的子集。
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---
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Access a group of rows and columns by supplying label(s) arguments.
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通過提供標籤參數來訪問一組行和列。
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---
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Returns the first `n` rows based on the integer argument supplied.
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根據提供的整數參數返回前 `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: 數據清理簡介
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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,而不是像視頻中顯示的那樣使用 notebooks.ai。*
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More resources:
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以下有更多的資料:
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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 平臺的 Notebooks](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
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- [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)
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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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以下代碼會打印出什麼?
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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-標準差 計算器
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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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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。
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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) 上查看整個項目的具體描述和初始代碼。
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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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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。
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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。
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# --hints--
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It should pass all Python tests.
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它應該通過所有的 Python 測試。
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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: 加載數據和高級索引
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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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給定一個名爲 `data.txt` 的文件,其中包含以下內容:
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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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哪段代碼會生成下面的數組?
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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 是什麼?
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challengeType: 11
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videoId: 5Nwfs5Ej85Q
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dashedName: what-is-numpy
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@ -10,23 +10,23 @@ 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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爲什麼 Numpy 數組要比常規的 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 在遍歷對象時不執行類型檢查。
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---
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Numpy uses fixed types.
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Numpy 使用固定類型。
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---
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Numpy uses contiguous memory.
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Numpy 使用連續內存。
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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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