chore(i18n,curriculum): processed translations (#42734)
This commit is contained in:
camperbot
GitHub
@ -1,6 +1,6 @@
|
||||
---
|
||||
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--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
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
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
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
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
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
|
||||
@ -10,14 +10,14 @@ dashedName: loading-data-and-advanced-indexing
|
||||
|
||||
## --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.]
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
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--
|
||||
|
||||
|
||||
Reference in New Issue
Block a user