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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/data-analysis-example-a.md
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---
id: 5e9a093a74c4063ca6f7c14d
title: Data Analysis Example A
title: 數據分析 案例 A
challengeType: 11
videoId: nVAaxZ34khk
dashedName: data-analysis-example-a
@ -8,34 +8,34 @@ dashedName: data-analysis-example-a
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資料：
- [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 shape of our dataframe tell us?
數據框架的形狀表示了什麼意義？從中我們又能夠了解到什麼？
## --answers--
The size in gigabytes the dataframe we loaded into memory is.
我們加載到內存中的數據幀大小爲千兆字節。
---
How many rows and columns our dataframe has.
我們的數據框架有多少行和多少列？
---
How many rows the source data had before loading.
在加載數據前，源數據中有多少行？
---
How many columns the source data had before loading.
在加載數據前，源數據中有多少列？
## --video-solution--

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/data-analysis-example-b.md
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# --description--
*您可以使用 Google Colab，而不是像視頻中顯示的那樣使用 notebooks.ai。*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
更多資源：

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-and-visualizations.md
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---
id: 5e9a093a74c4063ca6f7c160
title: Data Cleaning and Visualizations
title: 數據清理和可視化
challengeType: 11
videoId: mHjxzFS5_Z0
dashedName: data-cleaning-and-visualizations
@ -8,18 +8,18 @@ dashedName: data-cleaning-and-visualizations
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [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--
When using Matplotlib's global API, what does the order of numbers mean here?
當使用 Matplotlib 的全局 API 時，數字的順序在這裏意味着什麼？
```py
plt.subplot(1, 2, 1)
@ -27,15 +27,15 @@ plt.subplot(1, 2, 1)
## --answers--
My figure will have one column, two rows, and I am going to start drawing in the first (left) plot.
這裏將創建一個圖像包括一列、兩行，並且我將開始在第一個圖（左）繪圖。
---
I am going to start drawing in the first (left) plot, my figure will have two rows, and my figure will have one column.
我將開始在第一個圖表（左）繪圖，同時我的圖像將有兩行，也將有一列。
---
My figure will have one row, two columns, and I am going to start drawing in the first (left) plot.
這裏將創建一個圖像包括一行、兩列，並且我將開始在第一個圖表（左）繪圖。
## --video-solution--

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-duplicates.md
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---
id: 5e9a093a74c4063ca6f7c15f
title: Data Cleaning Duplicates
title: 數據 清理重複項
challengeType: 11
videoId: kj7QqjXhH6A
dashedName: data-cleaning-duplicates
@ -8,30 +8,30 @@ dashedName: data-cleaning-duplicates
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
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--
The Python method `.duplicated()` returns a boolean Series for your DataFrame. `True` is the return value for rows that:
Python 方法 `.duplicated()` 將針對你的 DataFrame 返回一個儲存着布爾值的 Series。 `True` 是行的返回值：
## --answers--
contain a duplicate, where the value for the row contains the first occurrence of that value.
包含一個重複值，並且它表示了在這一行這一重複值第一次出現。
---
contain a duplicate, where the value for the row is at least the second occurrence of that value.
包含一個重複值，並且它表示了在這一行這一重複值至少第二次出現。
---
contain a duplicate, where the value for the row contains either the first or second occurrence.
包含一個重複值，並且它表示了在這一行這一重複值第一次或第二次出現。
## --video-solution--

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-introduction.md
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@ -8,7 +8,7 @@ dashedName: data-cleaning-introduction
# --description--
*您可以使用 Google Colab，而不是像視頻中顯示的那樣使用 notebooks.ai。*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
以下有更多的資料：

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-with-dataframes.md
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---
id: 5e9a093a74c4063ca6f7c15e
title: Data Cleaning with DataFrames
title: DataFrames 中的數據清理
challengeType: 11
videoId: sTMN_pdI6S0
dashedName: data-cleaning-with-dataframes
@ -8,18 +8,18 @@ dashedName: data-cleaning-with-dataframes
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
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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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/how-to-use-jupyter-notebooks-intro.md
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---
id: 5e9a093a74c4063ca6f7c14f
title: How to use Jupyter Notebooks Intro
title: 如何使用 Jupyter Notebook
challengeType: 11
videoId: h8caJq2Bb9w
dashedName: how-to-use-jupyter-notebooks-intro
@ -8,19 +8,18 @@ dashedName: how-to-use-jupyter-notebooks-intro
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
- [Twitter Cheat Sheet](https://twitter.com/rmotr_com/status/1122176794696847361)
- [在 GitHub 平臺的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [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 is **not** allowed in a Jupyter Notebook's cell?
以下哪個是 Jupyter Notebook 單元格中**不**允許的？
## --answers--
@ -28,11 +27,11 @@ Markdown
---
Python code
Python 代碼
---
An Excel sheet
Excel 工作表
## --video-solution--

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/introduction-to-data-analysis.md
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---
id: 5e9a093a74c4063ca6f7c14c
title: Introduction to Data Analysis
title: 數據分析簡介
challengeType: 11
videoId: VJrP2FUzKP0
dashedName: introduction-to-data-analysis
@ -8,33 +8,32 @@ dashedName: introduction-to-data-analysis
# --description--
More resources:
以下有更多的資料：
\- [Slides](https://docs.google.com/presentation/d/1fDpjlyMiOMJyuc7_jMekcYLPP2XlSl1eWw9F7yE7byk)
\- [幻燈片](https://docs.google.com/presentation/d/1cUIt8b2ySz-85_ykfeuuWsurccwTAuFPn782pZBzFsU/edit?usp=sharing)
# --question--
## --text--
Why should you choose R over Python for data analysis?
以下哪一項 **不是** 數據分析的一部分？
## --answers--
It's simple to learn.
建立統計模型和數據可視化。
---
It's better at dealing with advanced statistical methods.
爲分析選擇所需的結論。
---
There are many powerful libraries that support R.
修復不正確的值並刪除無效數據。
---
It's free and open source.
將數據轉換爲適當的數據結構。
## --video-solution--
2

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---
id: 5e9a093a74c4063ca6f7c150
title: Jupyter Notebooks Cells
title: Jupyter Notebooks 單元格
challengeType: 11
videoId: 5PPegAs9aLA
dashedName: jupyter-notebooks-cells
@ -8,31 +8,30 @@ dashedName: jupyter-notebooks-cells
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
- [Twitter Cheat Sheet](https://twitter.com/rmotr_com/status/1122176794696847361)
- [在 GitHub 平臺的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [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 are the three main types of Jupyter Notebook Cell?
Jupyter Notebook 單元格支持的三種主要類型是什麼？
## --answers--
Code, Markdown, and Python
Code、Markdown 和 Python
---
Code, Markdown, and Raw
Code、Markdown 和 Raw
---
Markdown, Python, and Raw
Markdown、Python 和 Raw
## --video-solution--

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/jupyter-notebooks-importing-and-exporting-data.md
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---
id: 5e9a093a74c4063ca6f7c151
title: Jupyter Notebooks Importing and Exporting Data
title: 在 Jupyter Notebooks 中導入和導出數據
challengeType: 11
videoId: k1msxD3JIxE
dashedName: jupyter-notebooks-importing-and-exporting-data
@ -8,39 +8,38 @@ dashedName: jupyter-notebooks-importing-and-exporting-data
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
- [Twitter Cheat Sheet](https://twitter.com/rmotr_com/status/1122176794696847361)
- [在 GitHub 平臺的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [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 kind of data can you import and work with in a Jupyter Notebook?
你可以在 Jupyter Notebook 中導入和使用什麼樣的數據？
## --answers--
Excel files.
Excel 文件。
---
CSV files.
CSV 文件。
---
XML files.
XML 文件
---
Data from an API.
來自 API 的數據
---
All of the above.
以上全部內容
## --video-solution--

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---
id: 5e9a093a74c4063ca6f7c157
title: Numpy Algebra and Size
title: Numpy 代數和大小
challengeType: 11
videoId: XAT97YLOKD8
dashedName: numpy-algebra-and-size
@ -8,34 +8,34 @@ dashedName: numpy-algebra-and-size
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
- [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-intro-to-numpy)
- [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 is the relationship between size of objects (such as lists and datatypes) in memory in Python's standard library and the NumPy library? Knowing this, what are the implications for performance?
內存中，對象的大小（例如列表和數據類型）在 Python 標準庫和 NumPy 庫之間有什麼關係？ 知道這一點，對性能有何影響？
## --answers--
Standard Python objects take up much more memory to store than NumPy objects; operations on comparable standard Python and NumPy objects complete in roughly the same time.
標準的 Python 對象佔用了比 NumPy 對象更多的內存；標準的 Python 和 NumPy 對象完成的操作時間是大致相同的。
---
NumPy objects take up much more memory than standard Python objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.
Numpy 對象比標準的 Python 對象佔用更多的內存；Numpy 的對象相比較標準的 Python 更快地完成操作。
---
NumPy objects take up much less memory than Standard Python objects; operations on Standard Python objects complete very quickly compared to comparable objects on NumPy Object.
Numpy 對象比標準的 Python 對象佔用更少的內存；標準 Python 的對象相比較 Numpy 的對象更快地完成操作。
---
Standard Python objects take up more memory than NumPy objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.
標準 Python 的對象比 Numpy 的對象佔用更多的內存；Numpy 的對象相比較標準 Python 的對象更快地完成操作。
## --video-solution--

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curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/numpy-arrays.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c154
title: Numpy Arrays
title: Numpy 數組
challengeType: 11
videoId: VDYVFHBL1AM
dashedName: numpy-arrays
@ -8,18 +8,18 @@ dashedName: numpy-arrays
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
- [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-intro-to-numpy)
- [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
A = np.array([

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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c156
title: Numpy Boolean Arrays
title: Numpy 布爾值的數組
challengeType: 11
videoId: N1ttsMmcVMM
dashedName: numpy-boolean-arrays
@ -8,18 +8,18 @@ dashedName: numpy-boolean-arrays
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
- [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-intro-to-numpy)
- [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
a = np.arange(5)

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---
id: 5e9a093a74c4063ca6f7c152
title: Numpy Introduction A
title: Numpy 簡介 A
challengeType: 11
videoId: P-JjV6GBCmk
dashedName: numpy-introduction-a
@ -8,30 +8,30 @@ dashedName: numpy-introduction-a
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
- [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-intro-to-numpy)
- [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--
Why is Numpy an important, but unpopular Python library?
爲什麼 Numpy 是一個重要的卻又不流行的 Python 庫？
## --answers--
Often you won't work directly with Numpy.
你常常不會直接使用 Numpy。
---
It is extremely slow.
它是極其緩慢的。
---
Working with Numpy is difficult.
使用 Numpy 的難度是很大的。
## --video-solution--

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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c153
title: Numpy Introduction B
title: Numpy 簡介 B
challengeType: 11
videoId: YIqgrNLAZkA
dashedName: numpy-introduction-b
@ -8,34 +8,34 @@ dashedName: numpy-introduction-b
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
- [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-intro-to-numpy)
- [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--
About how much memory does the integer `5` consume in plain Python?
整數 `5` 在 Python 中消耗多少內存？
## --answers--
32 bits
32 位
---
20 bytes
20 字節
---
16 bytes
16 字節
---
8 bits
8 位
## --video-solution--

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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c155
title: Numpy Operations
title: Numpy 的運算
challengeType: 11
videoId: eqSVcJbaPdk
dashedName: numpy-operations
@ -8,18 +8,18 @@ dashedName: numpy-operations
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
- [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-intro-to-numpy)
- [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 is the value of `a` after you run the following code?
運行以下代碼後， `a` 的值是多少？
```py
a = np.arange(5)

12
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c15b
title: Pandas Conditional Selection and Modifying DataFrames
title: Pandas 條件選擇和 DataFrames 的修改
challengeType: 11
videoId: BFlH0fN5xRQ
dashedName: pandas-conditional-selection-and-modifying-dataframes
@ -8,18 +8,18 @@ dashedName: pandas-conditional-selection-and-modifying-dataframes
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
- [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-intro-to-pandas)
- [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

12
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c15c
title: Pandas Creating Columns
title: Pandas 創建列
challengeType: 11
videoId: _sSo2XZoB3E
dashedName: pandas-creating-columns
@ -8,18 +8,18 @@ dashedName: pandas-creating-columns
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
- [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-intro-to-pandas)
- [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 code would add a "Certificates per month" column to the `certificates_earned` DataFrame like the one below?
哪段代碼可以向 DataFrame `certificates_earned` 中添加一個 “Certificates per month” 列，就像下面所展示的？
<pre> Certificates Time (in months) Certificates per month
Tom 8 16 0.50

10
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@ -8,18 +8,18 @@ dashedName: pandas-dataframes
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
- [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-intro-to-pandas)
- [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

12
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c159
title: Pandas Indexing and Conditional Selection
title: Pandas 索引和條件選擇
challengeType: 11
videoId: '-ZOrgV_aA9A'
dashedName: pandas-indexing-and-conditional-selection
@ -8,18 +8,18 @@ dashedName: pandas-indexing-and-conditional-selection
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
- [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-intro-to-pandas)
- [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

12
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c158
title: Pandas Introduction
title: Pandas 簡介
challengeType: 11
videoId: 0xACW-8cZU0
dashedName: pandas-introduction
@ -8,18 +8,18 @@ dashedName: pandas-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
- [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-intro-to-pandas)
- [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

12
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c164
title: Parsing HTML and Saving Data
title: 解析 HTML 和保存數據
challengeType: 11
videoId: bJaqnTWQmb0
dashedName: parsing-html-and-saving-data
@ -8,18 +8,18 @@ dashedName: parsing-html-and-saving-data
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas)
- [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/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-17-reading-html-tables/files)
- [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 Python library has the `.read_html()` method we can we use for parsing HTML documents and extracting tables?
爲了解析 HTML 文本和提取表格，`.read_html()` 位於哪個 Python 的庫？
## --answers--

18
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c166
title: Python Functions and Collections
title: Python 函數和集合
challengeType: 11
videoId: NzpU17ZVlUw
dashedName: python-functions-and-collections
@ -8,30 +8,30 @@ dashedName: python-functions-and-collections
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
- [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/ds-content-python-under-10-minutes)
- [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 is the main difference between lists and tuples in Python?
在 Python 中，列表和元組有什麼主要的區別？
## --answers--
Tuples are immutable.
元組是不可改變的。
---
Lists are ordered.
列表是有順序的。
---
Tuples are unordered.
元組是無序的。
## --video-solution--

20
curriculum/challenges/chinese-traditional/08-data-analysis-with-python/data-analysis-with-python-course/python-introduction.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c165
title: Python Introduction
title: Python 簡介
challengeType: 11
videoId: PrQV9JkLhb4
dashedName: python-introduction
@ -8,34 +8,34 @@ dashedName: python-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
- [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/ds-content-python-under-10-minutes)
- [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--
How do we define blocks of code in the body of functions in Python?
我們如何在 Python 函數中定義代碼塊？
## --answers--
We use a set of curly braces, one on either side of each new block of our code.
我們可以在代碼的每個新區塊的兩側使用一組大括號。
---
We use indentation, usually right-aligned 4 spaces.
我們使用縮進，通常是右對齊的 4 個空格。
---
We do not denote blocks of code.
我們不用指示出代碼塊。
---
We could use curly braces or indentation to denote blocks of code.
我們可以使用大括號或縮進來指示出代碼塊。
## --video-solution--

12
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c167
title: Python Iteration and Modules
title: Python 迭代和模塊
challengeType: 11
videoId: XzosGWLafrY
dashedName: python-iteration-and-modules
@ -8,18 +8,18 @@ dashedName: python-iteration-and-modules
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
更多資源：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
- [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/ds-content-python-under-10-minutes)
- [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--
How would you iterate over and print the keys and values of a dictionary named `user`?
您將如何迭代並打印名爲 `user` 的字典的鍵和值？
## --answers--

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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c162
title: Reading Data CSV and TXT
title: 從 CSV 和 TXT 中讀取數據
challengeType: 11
videoId: ViGEv0zOzUk
dashedName: reading-data-csv-and-txt
@ -8,18 +8,18 @@ dashedName: reading-data-csv-and-txt
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas)
- [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/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-1-reading-csv-and-txt-files/files)
- [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--
How would you import the CSV file `data.csv` and store it in a DataFrame using the Pandas module?
你如何使用 Pandas 模塊導入 CSV 文件 `data.csv` 並且存儲到 DataFrame 中？
## --answers--

18
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c163
title: Reading Data from Databases
title: 從數據庫中讀取數據
challengeType: 11
videoId: MtgXS1MofRw
dashedName: reading-data-from-databases
@ -8,30 +8,30 @@ dashedName: reading-data-from-databases
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas)
- [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/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-11-reading-data-from-relational-databases/files)
- [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 method does a `Cursor` instance have and what does it allow?
`Cursor` 實例中都有什麼方法，這些方法能用來做什麼？
## --answers--
The `Cursor` instance has a `.run()` method which allows you to run SQL queries.
`Cursor` 實例中有方法 `.run()` ，它允許你運行 SQL 查詢語句。
---
The `Cursor` instance has a `.select()` method which allows you to select records.
`Cursor` 實例中有方法 `.select()` ，它允許你選擇記錄。
---
The `Cursor` instance has an `.execute()` method which will receive SQL parameters to run against the database.
`Cursor` 實例有方法 `.execute()` ， 它能夠接收在數據庫中運行的 SQL 參數。
## --video-solution--

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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c161
title: Reading Data Introduction
title: 讀取數據簡介
challengeType: 11
videoId: cDnt02BcHng
dashedName: reading-data-introduction
@ -8,18 +8,18 @@ dashedName: reading-data-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*在視頻中我們使用的編輯器工具是在 notebook.ai 這個平臺，你也可以選擇用其他的平臺，比如說 Google Colab 也是一個不錯的選擇。*
More resources:
以下有更多的資料：
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas)
- [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/RDP-Reading-Data-with-Python-and-Pandas/tree/master/unit-1-reading-data-with-python-and-pandas/lesson-1-reading-csv-and-txt-files/files)
- [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--
Given a file named `certificates.csv` with these contents:
文件 `certificates.csv` 有以下內容：
<pre>
Name$Certificates$Time (in months)
@ -29,7 +29,7 @@ Ahmad$5$9
Beau$6$12
</pre>
Fill in the blanks for the missing arguments below:
請填寫以下缺失的參數：
```py
import csv

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---
id: 5e46f7e5ac417301a38fb929
title: Demographic Data Analyzer
title: 人口統計數據分析器
challengeType: 10
forumTopicId: 462367
dashedName: demographic-data-analyzer
---
# --description--
In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database.
在這個挑戰中，你必須使用 Pandas 對人口統計進行分析。 你將獲得從 1994 年人口普查數據庫中提取的人口統計數據數據集。
You can access [the full project description and starter code on Repl.it](https://repl.it/github/freeCodeCamp/boilerplate-demographic-data-analyzer).
你可以[在 Replit 上查看整個項目的具體描述和初始代碼](https://replit.com/github/freeCodeCamp/boilerplate-demographic-data-analyzer)。
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。 一旦你根據 “README.md” 中的說明完成項目，請提交你的項目鏈接到下面。
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，你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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@ -1,23 +1,24 @@
---
id: 5e46f7f8ac417301a38fb92a
title: Medical Data Visualizer
title: 醫療數據可視化工具
challengeType: 10
forumTopicId: 462368
dashedName: medical-data-visualizer
---
# --description--
In this project, you will visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas.
在這個項目中，你將要使用 matplotlib，seaborn 和 pandas 來對健康檢查數據進行數據可視化和計算。
You can access [the full project description and starter code on Repl.it](https://repl.it/github/freeCodeCamp/boilerplate-medical-data-visualizer).
你可以[在 Replit 上查看整個項目的具體描述和初始代碼](https://replit.com/github/freeCodeCamp/boilerplate-medical-data-visualizer)。
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.
打開此鏈接後，fork 這個項目。 一旦你根據 “README.md” 中的說明完成項目，請提交你的項目到下面的鏈接。
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課程部分。 現在，你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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@ -1,23 +1,24 @@
---
id: 5e46f802ac417301a38fb92b
title: Page View Time Series Visualizer
title: 頁面訪問量的時間序列可視化工具
challengeType: 10
forumTopicId: 462369
dashedName: page-view-time-series-visualizer
---
# --description--
For this project you will visualize time series data using a line chart, bar chart, and box plots. You will use Pandas, matplotlib, and seaborn to visualize a dataset containing the number of page views each day on the freeCodeCamp.org forum from 2016-05-09 to 2019-12-03. The data visualizations will help you understand the patterns in visits and identify yearly and monthly growth.
對於這個項目，你將使用線圖、條形圖和箱形圖對時間序列數據進行可視化。 你將要使用 Pandas、matplotlib 和 seaborn 來對數據集進行可視化，這個數據集包含從 2016-05-09 到 2019-12-03 每一天在 freeCodeCamp.org 論壇的頁面訪問量。 這個數據可視化將幫助你瞭解訪問的模式，並且顯示年增長和月增長情況。
You can access [the full project description and starter code on Repl.it](https://repl.it/github/freeCodeCamp/boilerplate-page-view-time-series-visualizer).
你可以 [在 Replit 上查看整個項目的具體描述和初始代碼](https://replit.com/github/freeCodeCamp/boilerplate-page-view-time-series-visualizer)。
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。 一旦你根據 “README.md” 中的說明完成項目，請提交你的項目到下面的鏈接。
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課程部分。 現在，你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

13
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@ -1,23 +1,24 @@
---
id: 5e4f5c4b570f7e3a4949899f
title: Sea Level Predictor
title: 海平面預報器
challengeType: 10
forumTopicId: 462370
dashedName: sea-level-predictor
---
# --description--
In this project, you will analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050.
在本項目中，您將分析自 1880 年以來全球平均海平面變化的數據集。 您將使用這些數據來預測到 2050 年的海平面變化。
You can access [the full project description and starter code on Repl.it](https://repl.it/github/freeCodeCamp/boilerplate-sea-level-predictor).
你可以在 [Replit 上查看整個項目的具體描述和初始代碼](https://replit.com/github/freeCodeCamp/boilerplate-sea-level-predictor)。
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.
打開此鏈接後，fork 這個項目。 一旦你根據 “README.md” 中的說明完成項目，請提交你的項目到下面的鏈接。
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課程部分。 現在，你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

4
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed4
title: 'Accessing and Changing Elements, Rows, Columns'
title: '訪問與更改元素、行和列'
challengeType: 11
videoId: v-7Y7koJ_N0
dashedName: accessing-and-changing-elements-rows-columns
@ -10,7 +10,7 @@ dashedName: accessing-and-changing-elements-rows-columns
## --text--
What code would change the values in the 3rd column of both of the following Numpy arrays to 20?
以下哪行代碼將下面的 Numpy 數組的第三行的數值都更改成 20？
```py
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])

4
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed3
title: Basics of Numpy
title: Numpy 的基礎知識
challengeType: 11
videoId: f9QrZrKQMLI
dashedName: basics-of-numpy
@ -10,7 +10,7 @@ dashedName: basics-of-numpy
## --text--
What will the following code print?
以下代碼將打印出什麼？
```python
b = np.array([[1.0,2.0,3.0],[3.0,4.0,5.0]])

4
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed7
title: Copying Arrays Warning
title: 複製數組警告
challengeType: 11
videoId: iIoQ0_L0GvA
dashedName: copying-arrays-warning
@ -10,7 +10,7 @@ dashedName: copying-arrays-warning
## --text--
What is the value of `a` after running the following code?
運行以下代碼後， `a` 的值是多少？
```py
import numpy as np

4
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed6
title: Initialize Array Problem
title: 初始化數組問題
challengeType: 11
videoId: 0jGfH8BPfOk
dashedName: initialize-array-problem
@ -10,7 +10,7 @@ dashedName: initialize-array-problem
## --text--
What is another way to produce the following array?
產生以下數組的另一種方式是什麼？
```py
[[0. 0. 0. 0. 0. 0. 0.]

4
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed5
title: Initializing Different Arrays
title: 初始化不同的數組
challengeType: 11
videoId: CEykdsKT4U4
dashedName: initializing-different-arrays
@ -10,7 +10,7 @@ dashedName: initializing-different-arrays
## --text--
What will the following code print?
以下代碼將打印出什麼？
```py
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])

4
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed8
title: Mathematics
title: 數學
challengeType: 11
videoId: 7txegvyhtVk
dashedName: mathematics
@ -10,7 +10,7 @@ dashedName: mathematics
## --text--
What is the value of `b` after running the following code?
運行以下代碼後， `b` 的值是多少？
```py
import numpy as np

4
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed9
title: Reorganizing Arrays
title: 重組數組
challengeType: 11
videoId: VNWAQbEM-C8
dashedName: reorganizing-arrays
@ -10,7 +10,7 @@ dashedName: reorganizing-arrays
## --text--
What code would produce the following array?
哪個代碼會生成下面的數組？
```py
[[1. 1.]