chore(i18n,learn): processed translations (#45432)
This commit is contained in:
camperbot
GitHub
@@ -38,9 +38,13 @@ assert(myVar === 10);
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`myVar = myVar - 1;` debe cambiarse.
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```js
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assert(
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/let\s*myVar\s*=\s*11;\s*\/*.*\s*([-]{2}\s*myVar|myVar\s*[-]{2});/.test(code)
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);
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assert(!code.match(/myVar\s*=\s*myVar\s*[-]\s*1.*?;?/));
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```
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No debes asignar `myVar` con `10`.
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```js
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assert(!code.match(/myVar\s*=\s*10.*?;?/));
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```
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Debes usar el operador `--` en `myVar`.
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@@ -1,37 +1,41 @@
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---
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id: 5e9a093a74c4063ca6f7c15f
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title: Data Cleaning Duplicates
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title: Limpieza de datos duplicados
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challengeType: 11
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videoId: kj7QqjXhH6A
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bilibiliIds:
|
||||
aid: 675611672
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||||
bvid: BV1VU4y1A7tu
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cid: 409019368
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dashedName: data-cleaning-duplicates
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---
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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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*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
|
||||
|
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More resources:
|
||||
Más recursos:
|
||||
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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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- [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
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- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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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El método Python `.duplicated()` devuelve una serie booleana para su DataFrame. `True` es el valor de retorno de las filas que:
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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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contiene un duplicado, donde el valor de la fila contiene la primera coincidencia de ese valor.
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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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contiene un duplicado, donde el valor de la fila es al menos la segunda coincidencia de ese valor.
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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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contiene un duplicado, donde el valor de la fila es ya sea la primera o segunda coincidencia.
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## --video-solution--
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@@ -1,25 +1,29 @@
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---
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id: 5e9a093a74c4063ca6f7c15d
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title: Data Cleaning Introduction
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title: Introducción a la limpieza de datos
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challengeType: 11
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videoId: ovYNhnltVxY
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bilibiliIds:
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||||
aid: 250574398
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||||
bvid: BV1Pv411A7GN
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||||
cid: 409018611
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dashedName: data-cleaning-introduction
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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.*
|
||||
*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
|
||||
|
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More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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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¿Qué imprimirá el siguiente código?
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```py
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import pandas as pd
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@@ -1,25 +1,29 @@
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---
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id: 5e9a093a74c4063ca6f7c15e
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title: Data Cleaning with DataFrames
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title: Limpieza de datos con DataFrames
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challengeType: 11
|
||||
videoId: sTMN_pdI6S0
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bilibiliIds:
|
||||
aid: 505597026
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||||
bvid: BV1Yg411c7bx
|
||||
cid: 409018948
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dashedName: data-cleaning-with-dataframes
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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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¿Qué imprimirá el siguiente código?
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```py
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import pandas as pd
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@@ -1,25 +1,29 @@
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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: Cómo usar la introducción de Notebooks de Jupyter
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challengeType: 11
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||||
videoId: h8caJq2Bb9w
|
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bilibiliIds:
|
||||
aid: 293035919
|
||||
bvid: BV1Hf4y1n7qr
|
||||
cid: 409002965
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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.*
|
||||
*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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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¿Qué **no está** permitido en la celda de un Jupyter Notebook?
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## --answers--
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@@ -27,11 +31,11 @@ Markdown
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---
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Python code
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Código Python
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---
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An Excel sheet
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Una hoja de Excel
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## --video-solution--
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@@ -1,38 +1,42 @@
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---
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id: 5e9a093a74c4063ca6f7c14c
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title: Introduction to Data Analysis
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title: Introducción al Análisis de Datos
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challengeType: 11
|
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videoId: VJrP2FUzKP0
|
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bilibiliIds:
|
||||
aid: 378034466
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||||
bvid: BV19f4y1c7nu
|
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cid: 409001487
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dashedName: introduction-to-data-analysis
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---
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# --description--
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More resources:
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Más recursos:
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\- [Slides](https://docs.google.com/presentation/d/1cUIt8b2ySz-85_ykfeuuWsurccwTAuFPn782pZBzFsU/edit?usp=sharing)
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\- [Diapositivas](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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Which of the following is **not** part of Data Analysis?
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¿Cuál de las siguientes opciones no **es** parte del Análisis de Datos?
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## --answers--
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Building statistical models and data visualizations.
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Construcción de modelos estadísticos y visualización de datos.
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---
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Picking a desired conclusion for the analysis.
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Elegir una conclusión deseada para el análisis.
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---
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Fixing incorrect values and removing invalid data.
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Corregir valores incorrectos y eliminar datos no válidos.
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---
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Transforming data into an appropriate data structure.
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Transformación de datos en una estructura de datos apropiada.
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## --video-solution--
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@@ -1,37 +1,41 @@
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---
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||||
id: 5e9a093a74c4063ca6f7c150
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title: Jupyter Notebooks Cells
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title: Celdas de Notebooks de Jupyter
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challengeType: 11
|
||||
videoId: 5PPegAs9aLA
|
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bilibiliIds:
|
||||
aid: 420510493
|
||||
bvid: BV19341117Hq
|
||||
cid: 409003280
|
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dashedName: jupyter-notebooks-cells
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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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¿Cuáles son los tres tipos principales de celdas de Jupyter Notebook?
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## --answers--
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Code, Markdown, and Python
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Código, Markdown y Python
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---
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Code, Markdown, and Raw
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Código, Markdown y Raw
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|
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---
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Markdown, Python, and Raw
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Markdown, Python y Raw
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## --video-solution--
|
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|
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@@ -1,45 +1,49 @@
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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: Importación y exportación de datos en Jupyter Notebooks
|
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challengeType: 11
|
||||
videoId: k1msxD3JIxE
|
||||
bilibiliIds:
|
||||
aid: 975540688
|
||||
bvid: BV1n44y1b7Gi
|
||||
cid: 409006337
|
||||
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.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notebooks en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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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¿Qué tipo de datos puede importar y trabajar en un Jupyter Notebook?
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|
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## --answers--
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Excel files.
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Archivos de Excel.
|
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|
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---
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CSV files.
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Archivos CSV.
|
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|
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---
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|
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XML files.
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Archivos XML.
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|
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---
|
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|
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Data from an API.
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Datos de una API.
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|
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---
|
||||
|
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All of the above.
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Todo lo anterior.
|
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|
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## --video-solution--
|
||||
|
||||
|
||||
@@ -1,41 +1,45 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c157
|
||||
title: Numpy Algebra and Size
|
||||
title: NumPy Álgebra y tamaño
|
||||
challengeType: 11
|
||||
videoId: XAT97YLOKD8
|
||||
bilibiliIds:
|
||||
aid: 250621433
|
||||
bvid: BV1hv41137uM
|
||||
cid: 409013128
|
||||
dashedName: numpy-algebra-and-size
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Cuál es la relación entre el tamaño de los objetos (como listas y tipos de datos) en la memoria de la biblioteca estándar de Python y la biblioteca NumPy? Sabiendo esto, ¿cuáles son las implicaciones para el rendimiento?
|
||||
|
||||
## --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.
|
||||
Objetos estándar de Python ocupan mucha más memoria que los objetos de NumPy; operaciones comprables en Python estándar y objectos de NumPy se completan en casi el mismo tiempo.
|
||||
|
||||
---
|
||||
|
||||
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.
|
||||
Los objectos de NumPy toman mucha más memoria que los de Python estándar; las operaciones con objectos de NumPy se terminan muy rápido comparadas con las de los objetos de Python estándar.
|
||||
|
||||
---
|
||||
|
||||
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.
|
||||
Los objetos de NumPy tomas mucha menos memoria que los de Python Estándar; las operaciones en Python Estándar se completan muy rápido comparando con objetos similares en NumPy.
|
||||
|
||||
---
|
||||
|
||||
Standard Python objects take up more memory than NumPy objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.
|
||||
Los objetos de Python Estándar toman más memoria que los de NumPy; operaciones con objetos de Numpy se terminan rápidamente comparando con objetos de Python Estándar.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c154
|
||||
title: Numpy Arrays
|
||||
title: Arreglos de NumPy
|
||||
challengeType: 11
|
||||
videoId: VDYVFHBL1AM
|
||||
bilibiliIds:
|
||||
aid: 890607366
|
||||
bvid: BV1zP4y1h7FR
|
||||
cid: 409011400
|
||||
dashedName: numpy-arrays
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
¿Qué imprimirá el siguiente código?
|
||||
|
||||
```py
|
||||
A = np.array([
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c156
|
||||
title: Numpy Boolean Arrays
|
||||
title: Arreglos Booleanos en Numpy
|
||||
challengeType: 11
|
||||
videoId: N1ttsMmcVMM
|
||||
bilibiliIds:
|
||||
aid: 208091324
|
||||
bvid: BV1Qh411p7V8
|
||||
cid: 409012711
|
||||
dashedName: numpy-boolean-arrays
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
¿Qué imprimirá el siguiente código?
|
||||
|
||||
```py
|
||||
a = np.arange(5)
|
||||
|
||||
@@ -1,37 +1,41 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c152
|
||||
title: Numpy Introduction A
|
||||
title: Numpy Introducción A
|
||||
challengeType: 11
|
||||
videoId: P-JjV6GBCmk
|
||||
bilibiliIds:
|
||||
aid: 718079611
|
||||
bvid: BV18Q4y1k7om
|
||||
cid: 409007080
|
||||
dashedName: numpy-introduction-a
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Por qué es Numpy una biblioteca importante, pero poco popular en Python?
|
||||
|
||||
## --answers--
|
||||
|
||||
Often you won't work directly with Numpy.
|
||||
A menudo no trabajarás directamente con Numpy.
|
||||
|
||||
---
|
||||
|
||||
It is extremely slow.
|
||||
Es muy lento.
|
||||
|
||||
---
|
||||
|
||||
Working with Numpy is difficult.
|
||||
Trabajar con Numpy es complicado.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c153
|
||||
title: Numpy Introduction B
|
||||
title: Introducción a Numpy B
|
||||
challengeType: 11
|
||||
videoId: YIqgrNLAZkA
|
||||
bilibiliIds:
|
||||
aid: 250503382
|
||||
bvid: BV1kv411w7vB
|
||||
cid: 409010193
|
||||
dashedName: numpy-introduction-b
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En lugar de usar notebooks.ai como se muestra en el vídeo, puede utilizar Google Colab en su lugar.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿cuanta memoria consume el entero `5` en python plano?
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c155
|
||||
title: Numpy Operations
|
||||
title: Operaciones en NumPy
|
||||
challengeType: 11
|
||||
videoId: eqSVcJbaPdk
|
||||
bilibiliIds:
|
||||
aid: 378057123
|
||||
bvid: BV13f4y1w7od
|
||||
cid: 409012507
|
||||
dashedName: numpy-operations
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Cuál es el valor de `a` después de ejecutar el siguiente código?
|
||||
|
||||
```py
|
||||
a = np.arange(5)
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c15b
|
||||
title: Pandas Conditional Selection and Modifying DataFrames
|
||||
title: Selección condicional de Pandas y modificación de DataFrames
|
||||
challengeType: 11
|
||||
videoId: BFlH0fN5xRQ
|
||||
bilibiliIds:
|
||||
aid: 505598518
|
||||
bvid: BV1vg411c72y
|
||||
cid: 409113534
|
||||
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.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
¿Qué imprimirá el siguiente código?
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
@@ -40,17 +44,17 @@ print(certificates_earned)
|
||||
## --answers--
|
||||
|
||||
<pre>
|
||||
Tom 13
|
||||
Kris 11
|
||||
Ahmad 9
|
||||
Beau 7
|
||||
Name: Longest streak, dtype: int64
|
||||
Tom 13
|
||||
Kris 11
|
||||
Ahmad 9
|
||||
Beau 7
|
||||
Nombre: Racha más larga, tipo: int64
|
||||
</pre>
|
||||
|
||||
---
|
||||
|
||||
<pre>
|
||||
Certificates Time (in months) Longest streak
|
||||
Certificados Tiempo (en meses) Racha más larga
|
||||
Tom 8 16 13
|
||||
Kris 2 5 11
|
||||
Ahmad 5 9 9
|
||||
@@ -60,7 +64,7 @@ Beau 6 12 7
|
||||
---
|
||||
|
||||
<pre>
|
||||
Certificates Longest streak
|
||||
Certificados Racha más larga
|
||||
Tom 8 13
|
||||
Kris 2 11
|
||||
Ahmad 5 9
|
||||
|
||||
@@ -1,27 +1,31 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c15c
|
||||
title: Pandas Creating Columns
|
||||
title: Creando columnas en Pandas
|
||||
challengeType: 11
|
||||
videoId: _sSo2XZoB3E
|
||||
bilibiliIds:
|
||||
aid: 975568901
|
||||
bvid: BV1b44y1b7Cg
|
||||
cid: 409018052
|
||||
dashedName: pandas-creating-columns
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Qué código añadiría una columna "Certificados por mes" a la `certificates_earned` DataFrame como la de abajo?
|
||||
|
||||
<pre> Certificates Time (in months) Certificates per month
|
||||
<pre> Certificados Tiempo (en meses) Certificados por mes
|
||||
Tom 8 16 0.50
|
||||
Kris 2 5 0.40
|
||||
Ahmad 5 9 0.56
|
||||
|
||||
@@ -3,23 +3,27 @@ id: 5e9a093a74c4063ca6f7c15a
|
||||
title: Pandas DataFrames
|
||||
challengeType: 11
|
||||
videoId: 7SgFBYXaiH0
|
||||
bilibiliIds:
|
||||
aid: 890503235
|
||||
bvid: BV1TP4y1h7qq
|
||||
cid: 409014039
|
||||
dashedName: pandas-dataframes
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
¿Qué imprimirá el siguiente código?
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
@@ -41,23 +45,23 @@ Tom 16
|
||||
Kris 5
|
||||
Ahmad 9
|
||||
Beau 12
|
||||
Name: Time (in months), dtype: int64
|
||||
Nombre: Tiempo (en meses), dtype: int64
|
||||
</pre>
|
||||
|
||||
---
|
||||
|
||||
<pre>
|
||||
Certificates 6
|
||||
Time (in months) 12
|
||||
Name: Beau, dtype: int64
|
||||
Certificados 6
|
||||
Tiempo (en meses) 12
|
||||
Nombre: Beau, dtype: int64
|
||||
</pre>
|
||||
|
||||
---
|
||||
|
||||
<pre>
|
||||
Certificates 5
|
||||
Time (in months) 9
|
||||
Name: Ahmad, dtype: int64
|
||||
Certificados 5
|
||||
Tiempo (en meses) 9
|
||||
Nombre: Ahmad, dtype: int64
|
||||
</pre>
|
||||
|
||||
## --video-solution--
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c159
|
||||
title: Pandas Indexing and Conditional Selection
|
||||
title: Indexación y Selección Condicional en Pandas
|
||||
challengeType: 11
|
||||
videoId: '-ZOrgV_aA9A'
|
||||
bilibiliIds:
|
||||
aid: 720604139
|
||||
bvid: BV1FQ4y1k7tC
|
||||
cid: 409013650
|
||||
dashedName: pandas-indexing-and-conditional-selection
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
¿Qué imprimirá el siguiente código?
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
@@ -45,18 +49,18 @@ dtype: int64
|
||||
---
|
||||
|
||||
<pre>
|
||||
Tom 8
|
||||
Ahmad 5
|
||||
Beau 6
|
||||
dtype: int64
|
||||
Tom 8
|
||||
Ahmad 5
|
||||
Beau 6
|
||||
tipo: int64
|
||||
</pre>
|
||||
|
||||
---
|
||||
|
||||
<pre>
|
||||
Tom 8
|
||||
Beau 6
|
||||
dtype: int64
|
||||
Tom 8
|
||||
Beau 6
|
||||
tipo: int64
|
||||
</pre>
|
||||
|
||||
## --video-solution--
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c158
|
||||
title: Pandas Introduction
|
||||
title: Introducción a Pandas
|
||||
challengeType: 11
|
||||
videoId: 0xACW-8cZU0
|
||||
bilibiliIds:
|
||||
aid: 975510116
|
||||
bvid: BV1u44y1b7fD
|
||||
cid: 409013433
|
||||
dashedName: pandas-introduction
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
|
||||
# --question--
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print out?
|
||||
¿Qué imprimirá el siguiente código?
|
||||
|
||||
```py
|
||||
import pandas as pd
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c164
|
||||
title: Parsing HTML and Saving Data
|
||||
title: Analizando HTML y guardando datos
|
||||
challengeType: 11
|
||||
videoId: bJaqnTWQmb0
|
||||
bilibiliIds:
|
||||
aid: 335522976
|
||||
bvid: BV1RA411F7vi
|
||||
cid: 409023170
|
||||
dashedName: parsing-html-and-saving-data
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [Notebooks on GitHub](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)
|
||||
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
- [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Qué biblioteca de Python tiene el método `.read_html()` que podemos usar para analizar documentos HTML y extraer tablas?
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@@ -1,37 +1,41 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c166
|
||||
title: Python Functions and Collections
|
||||
title: Funciones y colecciones de Python
|
||||
challengeType: 11
|
||||
videoId: NzpU17ZVlUw
|
||||
bilibiliIds:
|
||||
aid: 675544435
|
||||
bvid: BV1pU4y1N7JC
|
||||
cid: 409023833
|
||||
dashedName: python-functions-and-collections
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Cuál es la principal diferencia entre las listas y las tuplas en Python?
|
||||
|
||||
## --answers--
|
||||
|
||||
Tuples are immutable.
|
||||
Los tuplas son inmutables.
|
||||
|
||||
---
|
||||
|
||||
Lists are ordered.
|
||||
Las listas están ordenadas.
|
||||
|
||||
---
|
||||
|
||||
Tuples are unordered.
|
||||
Los tuplas no están ordenadas.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,41 +1,45 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c165
|
||||
title: Python Introduction
|
||||
title: Introducción a Python
|
||||
challengeType: 11
|
||||
videoId: PrQV9JkLhb4
|
||||
bilibiliIds:
|
||||
aid: 805597530
|
||||
bvid: BV1634y1S7gD
|
||||
cid: 409023550
|
||||
dashedName: python-introduction
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Cómo definimos bloques de código en el cuerpo de funciones de Python?
|
||||
|
||||
## --answers--
|
||||
|
||||
We use a set of curly braces, one on either side of each new block of our code.
|
||||
Utilizamos un conjunto de llaves, uno a cada lado de cada nuevo bloque de nuestro código.
|
||||
|
||||
---
|
||||
|
||||
We use indentation, usually right-aligned 4 spaces.
|
||||
Utilizamos indentación, habitualmente 4 espacios alineados.
|
||||
|
||||
---
|
||||
|
||||
We do not denote blocks of code.
|
||||
No denotamos bloques de código.
|
||||
|
||||
---
|
||||
|
||||
We could use curly braces or indentation to denote blocks of code.
|
||||
Podríamos usar llaves o indentación para indicar bloques de código.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c167
|
||||
title: Python Iteration and Modules
|
||||
title: Iteración y módulos en Python
|
||||
challengeType: 11
|
||||
videoId: XzosGWLafrY
|
||||
bilibiliIds:
|
||||
aid: 633068913
|
||||
bvid: BV1db4y127M4
|
||||
cid: 409024056
|
||||
dashedName: python-iteration-and-modules
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [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)
|
||||
- [Notas en GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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`?
|
||||
¿Cómo iterarías e imprimirías las claves y valores de un diccionario llamado`user`?
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@@ -1,25 +1,29 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c162
|
||||
title: Reading Data CSV and TXT
|
||||
title: Leyendo datos CSV y TXT
|
||||
challengeType: 11
|
||||
videoId: ViGEv0zOzUk
|
||||
bilibiliIds:
|
||||
aid: 505575354
|
||||
bvid: BV1tg411c7GH
|
||||
cid: 409020451
|
||||
dashedName: reading-data-csv-and-txt
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [Notebooks on GitHub](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)
|
||||
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
- [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Cómo importaría el archivo CSV `data.csv` y lo almacenaría en un DataFrame usando el módulo Pandas?
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@@ -1,37 +1,41 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c163
|
||||
title: Reading Data from Databases
|
||||
title: Leyendo datos de bases de datos
|
||||
challengeType: 11
|
||||
videoId: MtgXS1MofRw
|
||||
bilibiliIds:
|
||||
aid: 890546354
|
||||
bvid: BV1JP4y1h7gk
|
||||
cid: 409020851
|
||||
dashedName: reading-data-from-databases
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [Notebooks on GitHub](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)
|
||||
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
- [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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?
|
||||
¿Qué método tiene una instancia de `Cursor` y qué permite?
|
||||
|
||||
## --answers--
|
||||
|
||||
The `Cursor` instance has a `.run()` method which allows you to run SQL queries.
|
||||
La instancia `Cursor` tiene un método `.run()` que permite ejecutar consultas SQL.
|
||||
|
||||
---
|
||||
|
||||
The `Cursor` instance has a `.select()` method which allows you to select records.
|
||||
La instancia `Cursor` tiene un método `.select()` que permite seleccionar registros.
|
||||
|
||||
---
|
||||
|
||||
The `Cursor` instance has an `.execute()` method which will receive SQL parameters to run against the database.
|
||||
La instancia `Cursor` tiene un método `.execute()` que recibirá parámetros SQL para ejecutarse contra la base de datos.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,35 +1,39 @@
|
||||
---
|
||||
id: 5e9a093a74c4063ca6f7c161
|
||||
title: Reading Data Introduction
|
||||
title: Introducción a Lectura de datos
|
||||
challengeType: 11
|
||||
videoId: cDnt02BcHng
|
||||
bilibiliIds:
|
||||
aid: 548023524
|
||||
bvid: BV1Nq4y1K7iV
|
||||
cid: 409020187
|
||||
dashedName: reading-data-introduction
|
||||
---
|
||||
|
||||
# --description--
|
||||
|
||||
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
|
||||
*En vez de usar notebooks.ai como aparece en el video, puedes usar Google Colab.*
|
||||
|
||||
More resources:
|
||||
Más recursos:
|
||||
|
||||
- [Notebooks on GitHub](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)
|
||||
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
|
||||
- [Notas en GitHub](https://github.com/krishnatray/RDP-Reading-Data-with-Python-and-Pandas)
|
||||
- [Cómo abrir Notebooks desde GitHub utilizando Google Colab.](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:
|
||||
Dado un archivo llamado `certificates.csv` con estos contenidos:
|
||||
|
||||
<pre>
|
||||
Name$Certificates$Time (in months)
|
||||
Nombre$Certificates$Tiempo (en meses)
|
||||
Tom$8$16
|
||||
Kris$2$5
|
||||
Ahmad$5$9
|
||||
Beau$6$12
|
||||
</pre>
|
||||
|
||||
Fill in the blanks for the missing arguments below:
|
||||
Rellena los espacios en blanco de los argumentos que faltan a continuación:
|
||||
|
||||
```py
|
||||
import csv
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed4
|
||||
title: 'Accessing and Changing Elements, Rows, Columns'
|
||||
title: 'Acceder y cambiar elementos, filas, columnas'
|
||||
challengeType: 11
|
||||
videoId: v-7Y7koJ_N0
|
||||
bilibiliIds:
|
||||
aid: 590517748
|
||||
bvid: BV1Eq4y1f7Fa
|
||||
cid: 409025392
|
||||
dashedName: accessing-and-changing-elements-rows-columns
|
||||
---
|
||||
|
||||
@@ -10,7 +14,7 @@ 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?
|
||||
¿Qué código cambiaría los valores en la tercera columna de los siguientes matrices Numpy a 20?
|
||||
|
||||
```py
|
||||
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed3
|
||||
title: Basics of Numpy
|
||||
title: Fundamentos de Numpy
|
||||
challengeType: 11
|
||||
videoId: f9QrZrKQMLI
|
||||
bilibiliIds:
|
||||
aid: 763014202
|
||||
bvid: BV1K64y1a7bu
|
||||
cid: 409025169
|
||||
dashedName: basics-of-numpy
|
||||
---
|
||||
|
||||
@@ -10,7 +14,7 @@ dashedName: basics-of-numpy
|
||||
|
||||
## --text--
|
||||
|
||||
What will the following code print?
|
||||
¿Qué hará el siguiente código?
|
||||
|
||||
```python
|
||||
b = np.array([[1.0,2.0,3.0],[3.0,4.0,5.0]])
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5e9a0a8e09c5df3cc3600ed2
|
||||
title: What is NumPy
|
||||
title: Qué es NumPy
|
||||
challengeType: 11
|
||||
videoId: 5Nwfs5Ej85Q
|
||||
bilibiliIds:
|
||||
aid: 293086867
|
||||
bvid: BV1Tf4y1E7QZ
|
||||
cid: 409024791
|
||||
dashedName: what-is-numpy
|
||||
---
|
||||
|
||||
@@ -10,23 +14,23 @@ dashedName: what-is-numpy
|
||||
|
||||
## --text--
|
||||
|
||||
Why are Numpy arrays faster than regular Python lists?
|
||||
¿Por qué los arreglos Numpy son más rápidos que las listas de Python?
|
||||
|
||||
## --answers--
|
||||
|
||||
Numpy does not perform type checking while iterating through objects.
|
||||
¿Por qué los arreglos Numpy son más rápidas que las listas normales de Python.
|
||||
|
||||
---
|
||||
|
||||
Numpy uses fixed types.
|
||||
Numpy usa tipos fijos.
|
||||
|
||||
---
|
||||
|
||||
Numpy uses contiguous memory.
|
||||
Numpy usa memoria contigua.
|
||||
|
||||
---
|
||||
|
||||
All of the above.
|
||||
Todo lo anterior.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 587d8247367417b2b2512c36
|
||||
title: Install and Require Helmet
|
||||
title: Instalar y Requerir Helmet
|
||||
challengeType: 2
|
||||
forumTopicId: 301581
|
||||
dashedName: install-and-require-helmet
|
||||
@@ -8,25 +8,25 @@ dashedName: install-and-require-helmet
|
||||
|
||||
# --description--
|
||||
|
||||
Working on these challenges will involve you writing your code using one of the following methods:
|
||||
Trabajar en estos desafíos implica escribir tu código usando uno de los siguientes métodos:
|
||||
|
||||
- Clone [this GitHub repo](https://github.com/freeCodeCamp/boilerplate-infosec/) and complete these challenges locally.
|
||||
- Use [our Replit starter project](https://replit.com/github/freeCodeCamp/boilerplate-infosec) to complete these challenges.
|
||||
- Use a site builder of your choice to complete the project. Be sure to incorporate all the files from our GitHub repo.
|
||||
- Clona [este repositorio de Github](https://github.com/freeCodeCamp/boilerplate-infosec/) y completa estos desafíos localmente.
|
||||
- Use [nuestro proyecto inicial de Replit](https://replit.com/github/freeCodeCamp/boilerplate-infosec) para completar estos desafios.
|
||||
- Utilice un constructor de sitios de su elección para completar el proyecto. Asegúrese de incorporar todos los archivos de nuestro repositorio de GitHub.
|
||||
|
||||
When you are done, make sure a working demo of your project is hosted somewhere public. Then submit the URL to it in the `Solution Link` field.
|
||||
Cuando haya terminado, asegúrese de que un demo funcional de su proyecto esté alojado en algún lugar público. A continuación, envíe la URL en el campo `Solution Link`.
|
||||
|
||||
Helmet helps you secure your Express apps by setting various HTTP headers.
|
||||
Helmet te ayuda a proteger tus aplicaciones Express configurando varias cabeceras HTTP.
|
||||
|
||||
# --instructions--
|
||||
|
||||
All your code for these lessons goes in the `myApp.js` file between the lines of code we have started you off with. Do not change or delete the code we have added for you.
|
||||
Todo su código para estas lecciones va en el archivo `myApp.js` entre las líneas de código con las que hemos iniciado. No cambie o elimine el código que hemos añadido para usted.
|
||||
|
||||
Install Helmet version `3.21.3`, then require it. You can install a specific version of a package with `npm install --save-exact package@version`, or by adding it to your `package.json` directly.
|
||||
Instale la versión `3.21.3` de Helmet, luego requiérala. Puede instalar una versión específica de un paquete con `npm install --save-exact package@version`, o agregándolo a su paquete `package.json` directamente.
|
||||
|
||||
# --hints--
|
||||
|
||||
`helmet` version `3.21.3` should be in `package.json`
|
||||
`helmet` version `3.21.3` debería estar en `package.json`
|
||||
|
||||
```js
|
||||
(getUserInput) =>
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5ea9997bbec2e9bc47e94db0
|
||||
title: Creating a TCP Client
|
||||
title: Creando un Cliente TCP
|
||||
challengeType: 11
|
||||
videoId: ugYfJNTawks
|
||||
bilibiliIds:
|
||||
aid: 933058124
|
||||
bvid: BV16M4y1g7zL
|
||||
cid: 409034338
|
||||
dashedName: creating-a-tcp-client
|
||||
---
|
||||
|
||||
@@ -10,7 +14,7 @@ dashedName: creating-a-tcp-client
|
||||
|
||||
## --text--
|
||||
|
||||
Which socket object method lets you set the maximum amount of data your client accepts at once?
|
||||
¿Qué método del objeto Socket le permite definir la cantidad máxima de data que tu cliente acepta por vez?
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5ea9997bbec2e9bc47e94db2
|
||||
title: Developing an Nmap Scanner part 2
|
||||
title: Desarrollando un escáner de Nmap parte 2
|
||||
challengeType: 11
|
||||
videoId: a98PscnUsTg
|
||||
bilibiliIds:
|
||||
aid: 505526943
|
||||
bvid: BV1Hg411c7oE
|
||||
cid: 409034761
|
||||
dashedName: developing-an-nmap-scanner-part-2
|
||||
---
|
||||
|
||||
@@ -10,7 +14,7 @@ dashedName: developing-an-nmap-scanner-part-2
|
||||
|
||||
## --text--
|
||||
|
||||
Which of the following allows you to scan for UDP ports between 21 to 443?
|
||||
¿Cuál de las siguientes opciones le permite buscar puertos UDP entre 21 a 443?
|
||||
|
||||
## --answers--
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: a3f503de51cf954ede28891d
|
||||
title: Find the Symmetric Difference
|
||||
title: Encuentra la diferencia simétrica
|
||||
challengeType: 5
|
||||
forumTopicId: 301611
|
||||
dashedName: find-the-symmetric-difference
|
||||
@@ -8,77 +8,77 @@ dashedName: find-the-symmetric-difference
|
||||
|
||||
# --description--
|
||||
|
||||
The mathematical term <dfn>symmetric difference</dfn> (`△` or `⊕`) of two sets is the set of elements which are in either of the two sets but not in both. For example, for sets `A = {1, 2, 3}` and `B = {2, 3, 4}`, `A △ B = {1, 4}`.
|
||||
El término matemático <dfn>diferencia simétrica</dfn> (`△` or `⊕`) de dos conjuntos es el conjunto de elementos que están en cualquiera de los dos conjuntos, pero no en ambos. Por ejemplo, para los conjuntos `A = {1, 2, 3}` y `B = {2, 3, 4}`, `A △ B = {1, 4}`.
|
||||
|
||||
Symmetric difference is a binary operation, which means it operates on only two elements. So to evaluate an expression involving symmetric differences among *three* elements (`A △ B △ C`), you must complete one operation at a time. Thus, for sets `A` and `B` above, and `C = {2, 3}`, `A △ B △ C = (A △ B) △ C = {1, 4} △ {2, 3} = {1, 2, 3, 4}`.
|
||||
Diferencia simétrica es una operación binaria, significa que opera en solo dos elementos. Entonces, para evaluar una expresión que involucra diferencias simétricas entre * tres * elementos (`A △ B △ C`), tienes que completar una operación a la vez. Asi, para los conjuntos `A` y `B` encima, y `C = {2, 3}`, `A △ B △ C = (A △ B) △ C = {1, 4} △ {2, 3} = {1, 2, 3, 4}`.
|
||||
|
||||
# --instructions--
|
||||
|
||||
Create a function that takes two or more arrays and returns an array of their symmetric difference. The returned array must contain only unique values (*no duplicates*).
|
||||
Cree una función que tome dos o más arrays y devuelva una array de sus diferencias. La array que se devuelve debe contener solo valores únicos (*no duplicados*).
|
||||
|
||||
# --hints--
|
||||
|
||||
`sym([1, 2, 3], [5, 2, 1, 4])` should return `[3, 4, 5]`.
|
||||
`sym([1, 2, 3], [5, 2, 1, 4])` debería retornar `[3, 4, 5]`.
|
||||
|
||||
```js
|
||||
assert.sameMembers(sym([1, 2, 3], [5, 2, 1, 4]), [3, 4, 5]);
|
||||
```
|
||||
|
||||
`sym([1, 2, 3], [5, 2, 1, 4])` should contain only three elements.
|
||||
`sym([1, 2, 3], [5, 2, 1, 4])`debería contener solo tres elementos.
|
||||
|
||||
```js
|
||||
assert.equal(sym([1, 2, 3], [5, 2, 1, 4]).length, 3);
|
||||
```
|
||||
|
||||
`sym([1, 2, 3, 3], [5, 2, 1, 4])` should return `[3, 4, 5]`.
|
||||
`sym([1, 2, 3, 3], [5, 2, 1, 4])` debería retornar `[3, 4, 5]`.
|
||||
|
||||
```js
|
||||
assert.sameMembers(sym([1, 2, 3, 3], [5, 2, 1, 4]), [3, 4, 5]);
|
||||
```
|
||||
|
||||
`sym([1, 2, 3, 3], [5, 2, 1, 4])` should contain only three elements.
|
||||
`sym([1, 2, 3, 3], [5, 2, 1, 4])` debería contener solo tres elementos.
|
||||
|
||||
```js
|
||||
assert.equal(sym([1, 2, 3, 3], [5, 2, 1, 4]).length, 3);
|
||||
```
|
||||
|
||||
`sym([1, 2, 3], [5, 2, 1, 4, 5])` should return `[3, 4, 5]`.
|
||||
`sym([1, 2, 3], [5, 2, 1, 4, 5])` debería retornar `[3, 4, 5]`.
|
||||
|
||||
```js
|
||||
assert.sameMembers(sym([1, 2, 3], [5, 2, 1, 4, 5]), [3, 4, 5]);
|
||||
```
|
||||
|
||||
`sym([1, 2, 3], [5, 2, 1, 4, 5])` should contain only three elements.
|
||||
`sym([1, 2, 3], [5, 2, 1, 4, 5])` debería contener solo tres elementos.
|
||||
|
||||
```js
|
||||
assert.equal(sym([1, 2, 3], [5, 2, 1, 4, 5]).length, 3);
|
||||
```
|
||||
|
||||
`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` should return `[1, 4, 5]`
|
||||
`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` debería retornar `[1, 4, 5]`
|
||||
|
||||
```js
|
||||
assert.sameMembers(sym([1, 2, 5], [2, 3, 5], [3, 4, 5]), [1, 4, 5]);
|
||||
```
|
||||
|
||||
`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` should contain only three elements.
|
||||
`sym([1, 2, 5], [2, 3, 5], [3, 4, 5])` debería contener solo tres elementos.
|
||||
|
||||
```js
|
||||
assert.equal(sym([1, 2, 5], [2, 3, 5], [3, 4, 5]).length, 3);
|
||||
```
|
||||
|
||||
`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` should return `[1, 4, 5]`.
|
||||
`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` debería retornar `[1, 4, 5]`.
|
||||
|
||||
```js
|
||||
assert.sameMembers(sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5]), [1, 4, 5]);
|
||||
```
|
||||
|
||||
`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` should contain only three elements.
|
||||
`sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5])` debería contener solo tres elementos.
|
||||
|
||||
```js
|
||||
assert.equal(sym([1, 1, 2, 5], [2, 2, 3, 5], [3, 4, 5, 5]).length, 3);
|
||||
```
|
||||
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` should return `[2, 3, 4, 6, 7]`.
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` debería retornar `[2, 3, 4, 6, 7]`.
|
||||
|
||||
```js
|
||||
assert.sameMembers(
|
||||
@@ -87,7 +87,7 @@ assert.sameMembers(
|
||||
);
|
||||
```
|
||||
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` should contain only five elements.
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3])` debería contener solo cinco elementos.
|
||||
|
||||
```js
|
||||
assert.equal(
|
||||
@@ -96,7 +96,7 @@ assert.equal(
|
||||
);
|
||||
```
|
||||
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` should return `[1, 2, 4, 5, 6, 7, 8, 9]`.
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` debería retornar `[1, 2, 4, 5, 6, 7, 8, 9]`.
|
||||
|
||||
```js
|
||||
assert.sameMembers(
|
||||
@@ -112,7 +112,7 @@ assert.sameMembers(
|
||||
);
|
||||
```
|
||||
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` should contain only eight elements.
|
||||
`sym([3, 3, 3, 2, 5], [2, 1, 5, 7], [3, 4, 6, 6], [1, 2, 3], [5, 3, 9, 8], [1])` debería contener solo ocho elementos.
|
||||
|
||||
```js
|
||||
assert.equal(
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 8d5123c8c441eddfaeb5bdef
|
||||
title: Implement Bubble Sort
|
||||
title: Implementar ordenación de burbuja
|
||||
challengeType: 1
|
||||
forumTopicId: 301612
|
||||
dashedName: implement-bubble-sort
|
||||
@@ -8,23 +8,23 @@ dashedName: implement-bubble-sort
|
||||
|
||||
# --description--
|
||||
|
||||
This is the first of several challenges on sorting algorithms. Given an array of unsorted items, we want to be able to return a sorted array. We will see several different methods to do this and learn some tradeoffs between these different approaches. While most modern languages have built-in sorting methods for operations like this, it is still important to understand some of the common basic approaches and learn how they can be implemented.
|
||||
Este es el primero de varios retos en algoritmos de ordenación. Dado un arreglo de elementos desordenados, queremos ser capaces de devolver un arreglo ordenado. Veremos diferentes métodos para hacerlo y aprenderemos algunos pros y contras entre estos diferentes enfoques. Mientras que la mayoría de los lenguajes modernos tienen métodos de ordenamiento incorporados para este tipo de operaciones, sigue siendo importante entender algunos de los enfoques básicos y aprender cómo pueden implementarse.
|
||||
|
||||
Here we will see bubble sort. The bubble sort method starts at the beginning of an unsorted array and 'bubbles up' unsorted values towards the end, iterating through the array until it is completely sorted. It does this by comparing adjacent items and swapping them if they are out of order. The method continues looping through the array until no swaps occur at which point the array is sorted.
|
||||
Aquí veremos el algoritmo de burbuja. El método de ordenación de burbuja comienza al inicio de un arreglo desordenado y 'burbujea hacia arriba' los valores no ordenados, iterando a través del arreglo hasta que esté completamente ordenado. Esto se hace comparando elementos adyacentes e intercambiándolos si no están en orden. El método continúa iterando sobre el arreglo hasta que no hay mas intercambios, lo que significa que el arreglo esta ordenado.
|
||||
|
||||
This method requires multiple iterations through the array and for average and worst cases has quadratic time complexity. While simple, it is usually impractical in most situations.
|
||||
Este método requiere múltiples iteraciones a través del arreglo y para el promedio y el peor de los casos tiene complejidad de tiempo cuadrática. Si bien es simple, suele ser poco práctico en la mayoría de las situaciones.
|
||||
|
||||
**Instructions:** Write a function `bubbleSort` which takes an array of integers as input and returns an array of these integers in sorted order from least to greatest.
|
||||
**Instrucciones:** Escribe una función `bubbleSort` que tomará un arreglo de números enteros y retornará un arreglo con estos números, ordenados de menor a mayor.
|
||||
|
||||
# --hints--
|
||||
|
||||
`bubbleSort` should be a function.
|
||||
`bubbleSort` Debería ser una función.
|
||||
|
||||
```js
|
||||
assert(typeof bubbleSort == 'function');
|
||||
```
|
||||
|
||||
`bubbleSort` should return a sorted array (least to greatest).
|
||||
`bubbleSort` Debería retornar un arreglo ordenado (de menor a mayor).
|
||||
|
||||
```js
|
||||
assert(
|
||||
@@ -52,7 +52,7 @@ assert(
|
||||
);
|
||||
```
|
||||
|
||||
`bubbleSort` should return an array that is unchanged except for order.
|
||||
`bubbleSort([1,4,2,8,345,123,43,32,5643,63,123,43,2,55,1,234,92])` Debería retornar el mismo arreglo cambiando solo el orden de los números.
|
||||
|
||||
```js
|
||||
assert.sameMembers(
|
||||
@@ -79,7 +79,7 @@ assert.sameMembers(
|
||||
);
|
||||
```
|
||||
|
||||
`bubbleSort` should not use the built-in `.sort()` method.
|
||||
`bubbleSort` No debería usar el método incorporado `.sort()`.
|
||||
|
||||
```js
|
||||
assert(isBuiltInSortUsed());
|
||||
@@ -113,8 +113,6 @@ function bubbleSort(array) {
|
||||
return array;
|
||||
// Only change code above this line
|
||||
}
|
||||
|
||||
bubbleSort([1, 4, 2, 8, 345, 123, 43, 32, 5643, 63, 123, 43, 2, 55, 1, 234, 92]);
|
||||
```
|
||||
|
||||
# --solutions--
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 587d8259367417b2b2512c86
|
||||
title: Implement Insertion Sort
|
||||
title: Implementar Orden de Inserción
|
||||
challengeType: 1
|
||||
forumTopicId: 301613
|
||||
dashedName: implement-insertion-sort
|
||||
@@ -8,19 +8,19 @@ dashedName: implement-insertion-sort
|
||||
|
||||
# --description--
|
||||
|
||||
The next sorting method we'll look at is insertion sort. This method works by building up a sorted array at the beginning of the list. It begins the sorted array with the first element. Then it inspects the next element and swaps it backwards into the sorted array until it is in sorted position. It continues iterating through the list and swapping new items backwards into the sorted portion until it reaches the end. This algorithm has quadratic time complexity in the average and worst cases.
|
||||
El siguiente método de clasificación que veremos es el orden de las inserciones. Este método funciona construyendo un arreglo ordenado al principio de la lista. Comienza el arreglo ordenado con el primer elemento. Luego inspecciona el siguiente elemento, lo intercambia de atrás hacia adelante dentro de el arreglo clasificado hasta que esté en posición ordenada. Continúa iterando a través de la lista y cambiando nuevos elementos hacia atrás en la porción ordenada hasta llegar al final. Este algoritmo tiene una complejidad temporal cuadrática en el caso medio y en el peor.
|
||||
|
||||
**Instructions:** Write a function `insertionSort` which takes an array of integers as input and returns an array of these integers in sorted order from least to greatest.
|
||||
**Instrucciones:** Escribe una función `insertionSort` que toma un "array" de enteros como entrada y devuelve un array de estos enteros ordenados de menor a mayor.
|
||||
|
||||
# --hints--
|
||||
|
||||
`insertionSort` should be a function.
|
||||
`insertionSort` debería ser una función.
|
||||
|
||||
```js
|
||||
assert(typeof insertionSort == 'function');
|
||||
```
|
||||
|
||||
`insertionSort` should return a sorted array (least to greatest).
|
||||
`insertionSort` debería devolver un arreglo ordenado (de menor al más grande).
|
||||
|
||||
```js
|
||||
assert(
|
||||
@@ -48,7 +48,7 @@ assert(
|
||||
);
|
||||
```
|
||||
|
||||
`insertionSort` should return an array that is unchanged except for order.
|
||||
`insertionSort([1,4,2,8,345,123,43,32,5643,63,123,43,2,55,1,234,92])` debe devolver un arreglo sin cambios excepto por el orden.
|
||||
|
||||
```js
|
||||
assert.sameMembers(
|
||||
@@ -75,7 +75,13 @@ assert.sameMembers(
|
||||
);
|
||||
```
|
||||
|
||||
`insertionSort` should not use the built-in `.sort()` method.
|
||||
`insertionSort([5, 4, 33, 2, 8])` debe devolver `[2, 4, 5, 8, 33]`.
|
||||
|
||||
```js
|
||||
assert.deepEqual(insertionSort([5, 4, 33, 2, 8]), [2, 4, 5, 8, 33])
|
||||
```
|
||||
|
||||
`insertionSort` no debe utilizar el método "buil-in" `.sort()`.
|
||||
|
||||
```js
|
||||
assert(isBuiltInSortUsed());
|
||||
@@ -109,8 +115,6 @@ function insertionSort(array) {
|
||||
return array;
|
||||
// Only change code above this line
|
||||
}
|
||||
|
||||
insertionSort([1, 4, 2, 8, 345, 123, 43, 32, 5643, 63, 123, 43, 2, 55, 1, 234, 92]);
|
||||
```
|
||||
|
||||
# --solutions--
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610cc
|
||||
title: Deep Learning Demystified
|
||||
title: Deep Learning Desmitificado
|
||||
challengeType: 11
|
||||
videoId: bejQ-W9BGJg
|
||||
dashedName: deep-learning-demystified
|
||||
@@ -10,23 +10,23 @@ dashedName: deep-learning-demystified
|
||||
|
||||
## --text--
|
||||
|
||||
How should you assign weights to input neurons before training your network for the first time?
|
||||
¿Cómo deberías asignar pesos a las neuronas de entrada antes de entrenar tu red por primera vez?
|
||||
|
||||
## --answers--
|
||||
|
||||
From smallest to largest.
|
||||
De más pequeño a más grande.
|
||||
|
||||
---
|
||||
|
||||
Completely randomly.
|
||||
Completamente al azar.
|
||||
|
||||
---
|
||||
|
||||
Alphabetically.
|
||||
Alfabéticamente.
|
||||
|
||||
---
|
||||
|
||||
None of the above.
|
||||
Ninguna de las anteriores.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610cd
|
||||
title: How Convolutional Neural Networks work
|
||||
title: Cómo funcionan las Redes Neuronales Convolucionales
|
||||
challengeType: 11
|
||||
videoId: Y5M7KH4A4n4
|
||||
dashedName: how-convolutional-neural-networks-work
|
||||
@@ -10,19 +10,19 @@ dashedName: how-convolutional-neural-networks-work
|
||||
|
||||
## --text--
|
||||
|
||||
When are Convolutional Neural Networks not useful?
|
||||
¿Cuándo las Redes Neurales Convolucionales no son útiles?
|
||||
|
||||
## --answers--
|
||||
|
||||
If your data can't be made to look like an image, or if you can rearrange elements of your data and it's still just as useful.
|
||||
Si tus datos no pueden ser hechos para parecer una imagen, o si puedes reorganizar elementos de tus datos y es igualmente útil.
|
||||
|
||||
---
|
||||
|
||||
If your data is made up of different 2D or 3D images.
|
||||
Si sus datos se componen de diferentes imágenes 2D o 3D.
|
||||
|
||||
---
|
||||
|
||||
If your data is text or sound based.
|
||||
Si sus datos son basados en texto o sonido.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610ca
|
||||
title: How Deep Neural Networks Work
|
||||
title: Cómo Funcionan las Redes Neuronales Profundas
|
||||
challengeType: 11
|
||||
videoId: zvalnHWGtx4
|
||||
dashedName: how-deep-neural-networks-work
|
||||
@@ -10,19 +10,19 @@ dashedName: how-deep-neural-networks-work
|
||||
|
||||
## --text--
|
||||
|
||||
Why is it better to calculate the gradient (slope) directly rather than numerically?
|
||||
¿Por qué es mejor calcular la gradiente (pendiente) directamente que numéricamente?
|
||||
|
||||
## --answers--
|
||||
|
||||
It is computationally expensive to go back through the entire neural network and adjust the weights for each layer of the neural network.
|
||||
Es computacionalmente caro volver a través de toda la red neuronal y ajustar los pesos para cada capa de la red neuronal.
|
||||
|
||||
---
|
||||
|
||||
It is more accurate.
|
||||
Es más preciso.
|
||||
|
||||
---
|
||||
|
||||
There is no difference between the two methods.
|
||||
No hay diferencia entre ambos métodos.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
---
|
||||
id: 5e9a0e9ef99a403d019610cb
|
||||
title: Recurrent Neural Networks RNN and Long Short Term Memory LSTM
|
||||
title: Redes Neurales Recurrentes RNN y Memoria a Largo Plazo LSTM
|
||||
challengeType: 11
|
||||
videoId: UVimlsy9eW0
|
||||
dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
|
||||
@@ -10,19 +10,19 @@ dashedName: recurrent-neural-networks-rnn-and-long-short-term-memory-lstm
|
||||
|
||||
## --text--
|
||||
|
||||
What are the main neural network components that make up a Long Short Term Memory network?
|
||||
¿Cuáles son los principales componentes de la red neuronal que componen una red de memoria a largo plazo?
|
||||
|
||||
## --answers--
|
||||
|
||||
New information and prediction.
|
||||
Nueva información y predicción.
|
||||
|
||||
---
|
||||
|
||||
Prediction, collected possibilities, and selection.
|
||||
Predicción, posibilidades recolectadas y selección.
|
||||
|
||||
---
|
||||
|
||||
Prediction, ignoring, forgetting, and selection.
|
||||
Predicción, ignoración, olvido, y selección.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d99
|
||||
title: 'Convolutional Neural Networks: Evaluating the Model'
|
||||
title: 'Redes Neurales Convolucionales: Evaluando el Modelo'
|
||||
challengeType: 11
|
||||
videoId: eCATNvwraXg
|
||||
bilibiliIds:
|
||||
aid: 933030136
|
||||
bvid: BV1hM4y1g7Bx
|
||||
cid: 409132265
|
||||
dashedName: convolutional-neural-networks-evaluating-the-model
|
||||
---
|
||||
|
||||
@@ -10,19 +14,19 @@ dashedName: convolutional-neural-networks-evaluating-the-model
|
||||
|
||||
## --text--
|
||||
|
||||
What is **not** a good way to increase the accuracy of a convolutional neural network?
|
||||
¿Qué **no** es una buena manera de incrementar la precisión de una red neuronal convolucional?
|
||||
|
||||
## --answers--
|
||||
|
||||
Augmenting the data you already have.
|
||||
Aumentando los datos que ya tiene.
|
||||
|
||||
---
|
||||
|
||||
Using a pre-trained model.
|
||||
Usando un model pre-entrenado.
|
||||
|
||||
---
|
||||
|
||||
Using your test data to retrain the model.
|
||||
Usando tus datos de prueba para re entrenar el modelo.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72d9a
|
||||
title: 'Convolutional Neural Networks: Picking a Pretrained Model'
|
||||
title: 'Redes Neuronales Convolucionales: Eligiendo un Modelo Pre-entrenado'
|
||||
challengeType: 11
|
||||
videoId: h1XUt1AgIOI
|
||||
bilibiliIds:
|
||||
aid: 463063633
|
||||
bvid: BV1qL411x73q
|
||||
cid: 409132626
|
||||
dashedName: convolutional-neural-networks-picking-a-pretrained-model
|
||||
---
|
||||
|
||||
@@ -10,7 +14,7 @@ dashedName: convolutional-neural-networks-picking-a-pretrained-model
|
||||
|
||||
## --text--
|
||||
|
||||
Fill in the blanks below to use Google's pre-trained MobileNet V2 model as a base for a convolutional neural network:
|
||||
Completa los siguientes espacios en blanco para utilizar el modelo pre-entrenado MobileNet V2 de Google como base para una red neuronal convolucional:
|
||||
|
||||
```py
|
||||
base_model = tf.__A__.applications.__B__(input_shape=(160, 160, 3),
|
||||
|
||||
@@ -1,8 +1,12 @@
|
||||
---
|
||||
id: 5e8f2f13c4cdbe86b5c72da3
|
||||
title: Reinforcement Learning With Q-Learning
|
||||
title: Aprendizaje Reforzado con Q-Learning
|
||||
challengeType: 11
|
||||
videoId: Cf7DSU0gVb4
|
||||
bilibiliIds:
|
||||
aid: 463025802
|
||||
bvid: BV1iL411x7L6
|
||||
cid: 409138811
|
||||
dashedName: reinforcement-learning-with-q-learning
|
||||
---
|
||||
|
||||
@@ -10,19 +14,19 @@ dashedName: reinforcement-learning-with-q-learning
|
||||
|
||||
## --text--
|
||||
|
||||
The key components of reinforcement learning are...
|
||||
Los componente clave del Aprendizaje Reforzado son...
|
||||
|
||||
## --answers--
|
||||
|
||||
environment, representative, state, reaction, and reward.
|
||||
entorno, representatividad, estado, reacción, y recompensa.
|
||||
|
||||
---
|
||||
|
||||
environment, agent, state, action, and reward.
|
||||
entorno, agente, estado, acción, y recompensa.
|
||||
|
||||
---
|
||||
|
||||
habitat, agent, state, action, and punishment.
|
||||
habitat, agente, estado, acción, y castigo.
|
||||
|
||||
## --video-solution--
|
||||
|
||||
|
||||
Reference in New Issue
Block a user