chore(i18n,chn): manually downloaded curriculum (#42858)
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Mrugesh Mohapatra
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c14d
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title: Data Analysis Example A
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title: 数据分析 案例 A
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challengeType: 11
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videoId: nVAaxZ34khk
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dashedName: data-analysis-example-a
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@ -8,34 +8,34 @@ dashedName: data-analysis-example-a
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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More resources:
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更多资料：
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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 shape of our dataframe tell us?
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数据框架的形状表示了什么意义？从中我们又能够了解到什么？
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## --answers--
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The size in gigabytes the dataframe we loaded into memory is.
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我们加载到内存中的数据帧大小为千兆字节。
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---
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How many rows and columns our dataframe has.
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我们的数据框架有多少行和多少列？
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---
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How many rows the source data had before loading.
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在加载数据前，源数据中有多少行？
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---
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How many columns the source data had before loading.
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在加载数据前，源数据中有多少列？
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## --video-solution--
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@ -8,7 +8,7 @@ dashedName: data-analysis-example-b
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# --description--
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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更多资源：
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c160
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title: Data Cleaning and Visualizations
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title: 数据清理和可视化
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challengeType: 11
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videoId: mHjxzFS5_Z0
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dashedName: data-cleaning-and-visualizations
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@ -8,18 +8,18 @@ dashedName: data-cleaning-and-visualizations
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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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/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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When using Matplotlib's global API, what does the order of numbers mean here?
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当使用 Matplotlib 的全局 API 时，数字的顺序在这里意味着什么？
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```py
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plt.subplot(1, 2, 1)
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@ -27,15 +27,15 @@ plt.subplot(1, 2, 1)
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## --answers--
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My figure will have one column, two rows, and I am going to start drawing in the first (left) plot.
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这里将创建一个图像包括一列、两行，并且我将开始在第一个图（左）绘图。
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---
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I am going to start drawing in the first (left) plot, my figure will have two rows, and my figure will have one column.
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我将开始在第一个图表（左）绘图，同时我的图像将有两行，也将有一列。
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---
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My figure will have one row, two columns, and I am going to start drawing in the first (left) plot.
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这里将创建一个图像包括一行、两列，并且我将开始在第一个图表（左）绘图。
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## --video-solution--
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c15f
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title: Data Cleaning Duplicates
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title: 数据 清理重复项
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challengeType: 11
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videoId: kj7QqjXhH6A
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dashedName: data-cleaning-duplicates
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@ -8,30 +8,30 @@ dashedName: data-cleaning-duplicates
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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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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The Python method `.duplicated()` returns a boolean Series for your DataFrame. `True` is the return value for rows that:
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Python 方法 `.duplicated()` 将针对你的 DataFrame 返回一个储存着布尔值的 Series。 `True` 是行的返回值：
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## --answers--
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contain a duplicate, where the value for the row contains the first occurrence of that value.
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包含一个重复值，并且它表示了在这一行这一重复值第一次出现。
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---
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contain a duplicate, where the value for the row is at least the second occurrence of that value.
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包含一个重复值，并且它表示了在这一行这一重复值至少第二次出现。
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---
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contain a duplicate, where the value for the row contains either the first or second occurrence.
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包含一个重复值，并且它表示了在这一行这一重复值第一次或第二次出现。
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## --video-solution--
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@ -8,7 +8,7 @@ dashedName: data-cleaning-introduction
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# --description--
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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以下有更多的资料：
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c15e
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title: Data Cleaning with DataFrames
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title: DataFrames 中的数据清理
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challengeType: 11
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videoId: sTMN_pdI6S0
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dashedName: data-cleaning-with-dataframes
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@ -8,18 +8,18 @@ dashedName: data-cleaning-with-dataframes
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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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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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c14f
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title: How to use Jupyter Notebooks Intro
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title: 如何使用 Jupyter Notebook
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challengeType: 11
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videoId: h8caJq2Bb9w
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dashedName: how-to-use-jupyter-notebooks-intro
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@ -8,19 +8,18 @@ dashedName: how-to-use-jupyter-notebooks-intro
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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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/ds-content-interactive-jupyterlab-tutorial)
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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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- [Twitter Cheat Sheet](https://twitter.com/rmotr_com/status/1122176794696847361)
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- [在 GitHub 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
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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 is **not** allowed in a Jupyter Notebook's cell?
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以下哪个是 Jupyter Notebook 单元格中**不**允许的？
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## --answers--
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@ -28,11 +27,11 @@ Markdown
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---
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Python code
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Python 代码
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---
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An Excel sheet
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Excel 工作表
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## --video-solution--
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c14c
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title: Introduction to Data Analysis
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title: 数据分析简介
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challengeType: 11
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videoId: VJrP2FUzKP0
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dashedName: introduction-to-data-analysis
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@ -8,33 +8,32 @@ dashedName: introduction-to-data-analysis
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# --description--
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More resources:
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以下有更多的资料：
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\- [Slides](https://docs.google.com/presentation/d/1fDpjlyMiOMJyuc7_jMekcYLPP2XlSl1eWw9F7yE7byk)
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\- [幻灯片](https://docs.google.com/presentation/d/1cUIt8b2ySz-85_ykfeuuWsurccwTAuFPn782pZBzFsU/edit?usp=sharing)
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# --question--
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## --text--
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Why should you choose R over Python for data analysis?
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以下哪一项 **不是** 数据分析的一部分？
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## --answers--
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It's simple to learn.
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建立统计模型和数据可视化。
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---
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It's better at dealing with advanced statistical methods.
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为分析选择所需的结论。
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---
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There are many powerful libraries that support R.
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修复不正确的值并删除无效数据。
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---
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It's free and open source.
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将数据转换为适当的数据结构。
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## --video-solution--
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2
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c150
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title: Jupyter Notebooks Cells
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title: Jupyter Notebooks 单元格
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challengeType: 11
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videoId: 5PPegAs9aLA
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dashedName: jupyter-notebooks-cells
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@ -8,31 +8,30 @@ dashedName: jupyter-notebooks-cells
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
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|
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More resources:
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更多资源：
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|
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- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
|
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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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- [Twitter Cheat Sheet](https://twitter.com/rmotr_com/status/1122176794696847361)
|
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- [在 GitHub 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
|
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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 are the three main types of Jupyter Notebook Cell?
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Jupyter Notebook 单元格支持的三种主要类型是什么？
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## --answers--
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Code, Markdown, and Python
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Code、Markdown 和 Python
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---
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Code, Markdown, and Raw
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Code、Markdown 和 Raw
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---
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Markdown, Python, and Raw
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Markdown、Python 和 Raw
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## --video-solution--
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c151
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title: Jupyter Notebooks Importing and Exporting Data
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title: 在 Jupyter Notebooks 中导入和导出数据
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challengeType: 11
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videoId: k1msxD3JIxE
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dashedName: jupyter-notebooks-importing-and-exporting-data
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@ -8,39 +8,38 @@ dashedName: jupyter-notebooks-importing-and-exporting-data
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|
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# --description--
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|
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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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*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
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|
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More resources:
|
||||
更多资源：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
|
||||
- [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 kind of data can you import and work with in a Jupyter Notebook?
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你可以在 Jupyter Notebook 中导入和使用什么样的数据？
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## --answers--
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Excel files.
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Excel 文件。
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---
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CSV files.
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CSV 文件。
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---
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XML files.
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XML 文件
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---
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Data from an API.
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来自 API 的数据
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---
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All of the above.
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以上全部内容
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||||
## --video-solution--
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||||
|
||||
|
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@ -1,6 +1,6 @@
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---
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id: 5e9a093a74c4063ca6f7c157
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title: Numpy Algebra and Size
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title: Numpy 代数和大小
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [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)
|
||||
|
||||
# --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?
|
||||
内存中，对象的大小（例如列表和数据类型）在 Python 标准库和 NumPy 库之间有什么关系？ 知道这一点，对性能有何影响？
|
||||
|
||||
## --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.
|
||||
标准的 Python 对象占用了比 NumPy 对象更多的内存；标准的 Python 和 NumPy 对象完成的操作时间是大致相同的。
|
||||
|
||||
---
|
||||
|
||||
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 对象比标准的 Python 对象占用更多的内存；Numpy 的对象相比较标准的 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 对象比标准的 Python 对象占用更少的内存；标准 Python 的对象相比较 Numpy 的对象更快地完成操作。
|
||||
|
||||
---
|
||||
|
||||
Standard Python objects take up more memory than NumPy objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.
|
||||
标准 Python 的对象比 Numpy 的对象占用更多的内存；Numpy 的对象相比较标准 Python 的对象更快地完成操作。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c154
|
||||
title: Numpy Arrays
|
||||
title: Numpy 数组
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
以下代码会打印出什么？
|
||||
|
||||
```py
|
||||
A = np.array([
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c156
|
||||
title: Numpy Boolean Arrays
|
||||
title: Numpy 布尔值的数组
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
以下代码会打印出什么？
|
||||
|
||||
```py
|
||||
a = np.arange(5)
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c152
|
||||
title: Numpy Introduction A
|
||||
title: Numpy 简介 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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
Why is Numpy an important, but unpopular Python library?
|
||||
为什么 Numpy 是一个重要的却又不流行的 Python 库？
|
||||
|
||||
## --answers--
|
||||
|
||||
Often you won't work directly with Numpy.
|
||||
你常常不会直接使用 Numpy。
|
||||
|
||||
---
|
||||
|
||||
It is extremely slow.
|
||||
它是极其缓慢的。
|
||||
|
||||
---
|
||||
|
||||
Working with Numpy is difficult.
|
||||
使用 Numpy 的难度是很大的。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c153
|
||||
title: Numpy Introduction B
|
||||
title: Numpy 简介 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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
About how much memory does the integer `5` consume in plain Python?
|
||||
整数 `5` 在 Python 中消耗多少内存？
|
||||
|
||||
## --answers--
|
||||
|
||||
32 bits
|
||||
32 位
|
||||
|
||||
---
|
||||
|
||||
20 bytes
|
||||
20 字节
|
||||
|
||||
---
|
||||
|
||||
16 bytes
|
||||
16 字节
|
||||
|
||||
---
|
||||
|
||||
8 bits
|
||||
8 位
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c155
|
||||
title: Numpy Operations
|
||||
title: Numpy 的运算
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
更多资源：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What is the value of `a` after you run the following code?
|
||||
运行以下代码后， `a` 的值是多少？
|
||||
|
||||
```py
|
||||
a = np.arange(5)
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c15b
|
||||
title: Pandas Conditional Selection and Modifying DataFrames
|
||||
title: Pandas 条件选择和 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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
以下代码会打印出什么？
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c15c
|
||||
title: Pandas Creating Columns
|
||||
title: Pandas 创建列
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What code would add a "Certificates per month" column to the `certificates_earned` DataFrame like the one below?
|
||||
哪段代码可以向 DataFrame `certificates_earned` 中添加一个 “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
|
||||
|
||||
@ -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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
以下代码会打印出什么？
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c159
|
||||
title: Pandas Indexing and Conditional Selection
|
||||
title: Pandas 索引和条件选择
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
以下代码会打印出什么？
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c158
|
||||
title: Pandas Introduction
|
||||
title: Pandas 简介
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
以下代码会打印出什么？
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c164
|
||||
title: Parsing HTML and Saving Data
|
||||
title: 解析 HTML 和保存数据
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
更多资源：
|
||||
|
||||
- [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 平台的 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 来打开 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?
|
||||
为了解析 HTML 文本和提取表格，`.read_html()` 位于哪个 Python 的库？
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c166
|
||||
title: Python Functions and Collections
|
||||
title: Python 函数和集合
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
更多资源：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What is the main difference between lists and tuples in Python?
|
||||
在 Python 中，列表和元组有什么主要的区别？
|
||||
|
||||
## --answers--
|
||||
|
||||
Tuples are immutable.
|
||||
元组是不可改变的。
|
||||
|
||||
---
|
||||
|
||||
Lists are ordered.
|
||||
列表是有顺序的。
|
||||
|
||||
---
|
||||
|
||||
Tuples are unordered.
|
||||
元组是无序的。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c165
|
||||
title: Python Introduction
|
||||
title: Python 简介
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
更多资源：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
How do we define blocks of code in the body of functions in Python?
|
||||
我们如何在 Python 函数中定义代码块？
|
||||
|
||||
## --answers--
|
||||
|
||||
We use a set of curly braces, one on either side of each new block of our code.
|
||||
我们可以在代码的每个新区块的两侧使用一组大括号。
|
||||
|
||||
---
|
||||
|
||||
We use indentation, usually right-aligned 4 spaces.
|
||||
我们使用缩进，通常是右对齐的 4 个空格。
|
||||
|
||||
---
|
||||
|
||||
We do not denote blocks of code.
|
||||
我们不用指示出代码块。
|
||||
|
||||
---
|
||||
|
||||
We could use curly braces or indentation to denote blocks of code.
|
||||
我们可以使用大括号或缩进来指示出代码块。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c167
|
||||
title: Python Iteration and Modules
|
||||
title: Python 迭代和模块
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
更多资源：
|
||||
|
||||
- [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 平台的 Notebooks](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
|
||||
- [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)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
How would you iterate over and print the keys and values of a dictionary named `user`?
|
||||
您将如何迭代并打印名为 `user` 的字典的键和值？
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c162
|
||||
title: Reading Data CSV and TXT
|
||||
title: 从 CSV 和 TXT 中读取数据
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 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 来打开 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 模块导入 CSV 文件 `data.csv` 并且存储到 DataFrame 中？
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c163
|
||||
title: Reading Data from Databases
|
||||
title: 从数据库中读取数据
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料
|
||||
|
||||
- [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 平台的 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 来打开 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` 实例中都有什么方法，这些方法能用来做什么？
|
||||
|
||||
## --answers--
|
||||
|
||||
The `Cursor` instance has a `.run()` method which allows you to run SQL queries.
|
||||
`Cursor` 实例中有方法 `.run()` ，它允许你运行 SQL 查询语句。
|
||||
|
||||
---
|
||||
|
||||
The `Cursor` instance has a `.select()` method which allows you to select records.
|
||||
`Cursor` 实例中有方法 `.select()` ，它允许你选择记录。
|
||||
|
||||
---
|
||||
|
||||
The `Cursor` instance has an `.execute()` method which will receive SQL parameters to run against the database.
|
||||
`Cursor` 实例有方法 `.execute()` ， 它能够接收在数据库中运行的 SQL 参数。
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c161
|
||||
title: Reading Data Introduction
|
||||
title: 读取数据简介
|
||||
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.*
|
||||
*在视频中我们使用的编辑器工具是在 notebook.ai 这个平台，你也可以选择用其他的平台，比如说 Google Colab 也是一个不错的选择。*
|
||||
|
||||
More resources:
|
||||
以下有更多的资料：
|
||||
|
||||
- [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 平台的 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 来打开 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` 有以下内容：
|
||||
|
||||
<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:
|
||||
请填写以下缺失的参数：
|
||||
|
||||
```py
|
||||
import csv
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
id: 5e46f7e5ac417301a38fb929
|
||||
title: Demographic Data Analyzer
|
||||
title: 人口统计数据分析器
|
||||
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.
|
||||
在这个挑战中，你必须使用 Pandas 对人口统计进行分析。 你将获得从 1994 年人口普查数据库中提取的人口统计数据数据集。
|
||||
|
||||
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 上查看整个项目的具体描述和初始代码](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.
|
||||
点击此链接，fork 这个项目。 一旦你根据 “README.md” 中的说明完成项目，请提交你的项目链接到下面。
|
||||
|
||||
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.
|
||||
我们仍在开发 Python 数据分析课程的交互式教学。 现在，你需要使用其他资源来学习如何通过这一挑战。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它应该通过所有的 Python 测试。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
id: 5e46f7f8ac417301a38fb92a
|
||||
title: Medical Data Visualizer
|
||||
title: 医疗数据可视化工具
|
||||
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.
|
||||
在这个项目中，你将要使用 matplotlib，seaborn 和 pandas 来对健康检查数据进行数据可视化和计算。
|
||||
|
||||
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 上查看整个项目的具体描述和初始代码](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.
|
||||
打开此链接后，fork 这个项目。 一旦你根据 “README.md” 中的说明完成项目，请提交你的项目到下面的链接。
|
||||
|
||||
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.
|
||||
我们仍在开发 Python 数据分析课程的交互式课程部分。 现在，你需要使用其他资源来学习如何通过这一挑战。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它应该通过所有的 Python 测试。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
id: 5e46f802ac417301a38fb92b
|
||||
title: Page View Time Series Visualizer
|
||||
title: 页面访问量的时间序列可视化工具
|
||||
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.
|
||||
对于这个项目，你将使用线图、条形图和箱形图对时间序列数据进行可视化。 你将要使用 Pandas、matplotlib 和 seaborn 来对数据集进行可视化，这个数据集包含从 2016-05-09 到 2019-12-03 每一天在 freeCodeCamp.org 论坛的页面访问量。 这个数据可视化将帮助你了解访问的模式，并且显示年增长和月增长情况。
|
||||
|
||||
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 上查看整个项目的具体描述和初始代码](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.
|
||||
点击此链接，fork 这个项目。 一旦你根据 “README.md” 中的说明完成项目，请提交你的项目到下面的链接。
|
||||
|
||||
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.
|
||||
我们仍在开发 Python 数据分析课程的交互式课程部分。 现在，你将需要使用其他资源来学习如何通过这一挑战。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它应该通过所有的 Python 测试。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,23 +1,24 @@
|
||||
---
|
||||
id: 5e4f5c4b570f7e3a4949899f
|
||||
title: Sea Level Predictor
|
||||
title: 海平面预报器
|
||||
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.
|
||||
在本项目中，您将分析自 1880 年以来全球平均海平面变化的数据集。 您将使用这些数据来预测到 2050 年的海平面变化。
|
||||
|
||||
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 上查看整个项目的具体描述和初始代码](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.
|
||||
打开此链接后，fork 这个项目。 一旦你根据 “README.md” 中的说明完成项目，请提交你的项目到下面的链接。
|
||||
|
||||
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.
|
||||
我们仍在开发 Python 数据分析课程的交互式课程部分。 现在，你需要使用其他资源来学习如何通过这一挑战。
|
||||
|
||||
# --hints--
|
||||
|
||||
It should pass all Python tests.
|
||||
它应该通过所有的 Python 测试。
|
||||
|
||||
```js
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed4
|
||||
title: 'Accessing and Changing Elements, Rows, Columns'
|
||||
title: '访问与更改元素、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?
|
||||
以下哪行代码将下面的 Numpy 数组的第三行的数值都更改成 20？
|
||||
|
||||
```py
|
||||
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed3
|
||||
title: Basics of Numpy
|
||||
title: Numpy 的基础知识
|
||||
challengeType: 11
|
||||
videoId: f9QrZrKQMLI
|
||||
dashedName: basics-of-numpy
|
||||
@ -10,7 +10,7 @@ dashedName: basics-of-numpy
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print?
|
||||
以下代码将打印出什么？
|
||||
|
||||
```python
|
||||
b = np.array([[1.0,2.0,3.0],[3.0,4.0,5.0]])
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed7
|
||||
title: Copying Arrays Warning
|
||||
title: 复制数组警告
|
||||
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?
|
||||
运行以下代码后， `a` 的值是多少？
|
||||
|
||||
```py
|
||||
import numpy as np
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed6
|
||||
title: Initialize Array Problem
|
||||
title: 初始化数组问题
|
||||
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?
|
||||
产生以下数组的另一种方式是什么？
|
||||
|
||||
```py
|
||||
[[0. 0. 0. 0. 0. 0. 0.]
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed5
|
||||
title: Initializing Different Arrays
|
||||
title: 初始化不同的数组
|
||||
challengeType: 11
|
||||
videoId: CEykdsKT4U4
|
||||
dashedName: initializing-different-arrays
|
||||
@ -10,7 +10,7 @@ dashedName: initializing-different-arrays
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print?
|
||||
以下代码将打印出什么？
|
||||
|
||||
```py
|
||||
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed8
|
||||
title: Mathematics
|
||||
title: 数学
|
||||
challengeType: 11
|
||||
videoId: 7txegvyhtVk
|
||||
dashedName: mathematics
|
||||
@ -10,7 +10,7 @@ dashedName: mathematics
|
||||
|
||||
## --text--
|
||||
|
||||
What is the value of `b` after running the following code?
|
||||
运行以下代码后， `b` 的值是多少？
|
||||
|
||||
```py
|
||||
import numpy as np
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed9
|
||||
title: Reorganizing Arrays
|
||||
title: 重组数组
|
||||
challengeType: 11
|
||||
videoId: VNWAQbEM-C8
|
||||
dashedName: reorganizing-arrays
|
||||
@ -10,7 +10,7 @@ dashedName: reorganizing-arrays
|
||||
|
||||
## --text--
|
||||
|
||||
What code would produce the following array?
|
||||
哪个代码会生成下面的数组？
|
||||
|
||||
```py
|
||||
[[1. 1.]
|
||||
|
||||
Reference in New Issue
Block a user