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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-duplicates.md
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---
id: 5e9a093a74c4063ca6f7c15f
title: Data Cleaning Duplicates
title: Limpieza de datos duplicados
challengeType: 11
videoId: kj7QqjXhH6A
bilibiliIds:
aid: 675611672
bvid: BV1VU4y1A7tu
cid: 409019368
dashedName: data-cleaning-duplicates
---
# --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)
# --question--
## --text--
The Python method `.duplicated()` returns a boolean Series for your DataFrame. `True` is the return value for rows that:
El método Python `.duplicated()` devuelve una serie booleana para su DataFrame. `True` es el valor de retorno de las filas que:
## --answers--
contain a duplicate, where the value for the row contains the first occurrence of that value.
contiene un duplicado, donde el valor de la fila contiene la primera coincidencia de ese valor.
---
contain a duplicate, where the value for the row is at least the second occurrence of that value.
contiene un duplicado, donde el valor de la fila es al menos la segunda coincidencia de ese valor.
---
contain a duplicate, where the value for the row contains either the first or second occurrence.
contiene un duplicado, donde el valor de la fila es ya sea la primera o segunda coincidencia.
## --video-solution--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-introduction.md
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---
id: 5e9a093a74c4063ca6f7c15d
title: Data Cleaning Introduction
title: Introducción a la limpieza de datos
challengeType: 11
videoId: ovYNhnltVxY
bilibiliIds:
aid: 250574398
bvid: BV1Pv411A7GN
cid: 409018611
dashedName: data-cleaning-introduction
---
# --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)
# --question--
## --text--
What will the following code print out?
¿Qué imprimirá el siguiente código?
```py
import pandas as pd

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-with-dataframes.md
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---
id: 5e9a093a74c4063ca6f7c15e
title: Data Cleaning with DataFrames
title: Limpieza de datos con DataFrames
challengeType: 11
videoId: sTMN_pdI6S0
bilibiliIds:
aid: 505597026
bvid: BV1Yg411c7bx
cid: 409018948
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)
# --question--
## --text--
What will the following code print out?
¿Qué imprimirá el siguiente código?
```py
import pandas as pd

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/how-to-use-jupyter-notebooks-intro.md
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---
id: 5e9a093a74c4063ca6f7c14f
title: How to use Jupyter Notebooks Intro
title: Cómo usar la introducción de Notebooks de Jupyter
challengeType: 11
videoId: h8caJq2Bb9w
bilibiliIds:
aid: 293035919
bvid: BV1Hf4y1n7qr
cid: 409002965
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)
# --question--
## --text--
What is **not** allowed in a Jupyter Notebook's cell?
¿Qué **no está** permitido en la celda de un Jupyter Notebook?
## --answers--
@ -27,11 +31,11 @@ Markdown
---
Python code
Código Python
---
An Excel sheet
Una hoja de Excel
## --video-solution--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/introduction-to-data-analysis.md
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---
id: 5e9a093a74c4063ca6f7c14c
title: Introduction to Data Analysis
title: Introducción al Análisis de Datos
challengeType: 11
videoId: VJrP2FUzKP0
bilibiliIds:
aid: 378034466
bvid: BV19f4y1c7nu
cid: 409001487
dashedName: introduction-to-data-analysis
---
# --description--
More resources:
Más recursos:
\- [Slides](https://docs.google.com/presentation/d/1cUIt8b2ySz-85_ykfeuuWsurccwTAuFPn782pZBzFsU/edit?usp=sharing)
\- [Diapositivas](https://docs.google.com/presentation/d/1cUIt8b2ySz-85_ykfeuuWsurccwTAuFPn782pZBzFsU/edit?usp=sharing)
# --question--
## --text--
Which of the following is **not** part of Data Analysis?
¿Cuál de las siguientes opciones no **es** parte del Análisis de Datos?
## --answers--
Building statistical models and data visualizations.
Construcción de modelos estadísticos y visualización de datos.
---
Picking a desired conclusion for the analysis.
Elegir una conclusión deseada para el análisis.
---
Fixing incorrect values and removing invalid data.
Corregir valores incorrectos y eliminar datos no válidos.
---
Transforming data into an appropriate data structure.
Transformación de datos en una estructura de datos apropiada.
## --video-solution--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/jupyter-notebooks-cells.md
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---
id: 5e9a093a74c4063ca6f7c150
title: Jupyter Notebooks Cells
title: Celdas de Notebooks de Jupyter
challengeType: 11
videoId: 5PPegAs9aLA
bilibiliIds:
aid: 420510493
bvid: BV19341117Hq
cid: 409003280
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)
# --question--
## --text--
What are the three main types of Jupyter Notebook Cell?
¿Cuáles son los tres tipos principales de celdas de Jupyter Notebook?
## --answers--
Code, Markdown, and Python
Código, Markdown y Python
---
Code, Markdown, and Raw
Código, Markdown y Raw
---
Markdown, Python, and Raw
Markdown, Python y Raw
## --video-solution--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/jupyter-notebooks-importing-and-exporting-data.md
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@ -1,45 +1,49 @@
---
id: 5e9a093a74c4063ca6f7c151
title: Jupyter Notebooks Importing and Exporting Data
title: Importación y exportación de datos en Jupyter Notebooks
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)
# --question--
## --text--
What kind of data can you import and work with in a Jupyter Notebook?
¿Qué tipo de datos puede importar y trabajar en un Jupyter Notebook?
## --answers--
Excel files.
Archivos de Excel.
---
CSV files.
Archivos CSV.
---
XML files.
Archivos XML.
---
Data from an API.
Datos de una API.
---
All of the above.
Todo lo anterior.
## --video-solution--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-algebra-and-size.md
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@ -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--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-arrays.md
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@ -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([

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@ -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)

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-introduction-a.md
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@ -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--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-introduction-b.md
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@ -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--

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/numpy-operations.md
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@ -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)

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curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-conditional-selection-and-modifying-dataframes.md
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@ -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

18
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-creating-columns.md
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@ -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

28
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-dataframes.md
View File

@ -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--

30
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-indexing-and-conditional-selection.md
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@ -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--

16
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/pandas-introduction.md
View File

@ -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

16
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/parsing-html-and-saving-data.md
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@ -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--

22
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/python-functions-and-collections.md
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@ -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--

24
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/python-introduction.md
View File

@ -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--

16
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/python-iteration-and-modules.md
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@ -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--

16
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/reading-data-csv-and-txt.md
View File

@ -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--

22
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/reading-data-from-databases.md
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@ -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--

20
curriculum/challenges/espanol/08-data-analysis-with-python/data-analysis-with-python-course/reading-data-introduction.md
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@ -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

8
curriculum/challenges/espanol/08-data-analysis-with-python/numpy/accessing-and-changing-elements,-rows,-columns.md
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@ -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]])

8
curriculum/challenges/espanol/08-data-analysis-with-python/numpy/basics-of-numpy.md
View File

@ -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]])

16
curriculum/challenges/espanol/08-data-analysis-with-python/numpy/what-is-numpy.md
View File

@ -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--