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chore(i18n,curriculum): update translations (#42930)

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/data-analysis-example-a.md
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---
id: 5e9a093a74c4063ca6f7c14d
title: Data Analysis Example A
title: Esempio A di analisi dei dati
challengeType: 11
videoId: nVAaxZ34khk
dashedName: data-analysis-example-a
@ -8,34 +8,34 @@ dashedName: data-analysis-example-a
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
- [Come aprire Notebooks da GitHub usando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
# --question--
## --text--
What does the shape of our dataframe tell us?
Cosa ci dice il profilo (shape) del nostro dataframe?
## --answers--
The size in gigabytes the dataframe we loaded into memory is.
La dimensione in gigabyte del dataframe che abbiamo caricato in memoria.
---
How many rows and columns our dataframe has.
Quante righe e colonne ha il nostro database.
---
How many rows the source data had before loading.
Quante righe aveva la sorgente dei dati prima del caricamento.
---
How many columns the source data had before loading.
Quante colonne aveva la sorgente dei dati prima del caricamento.
## --video-solution--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/data-analysis-example-b.md
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---
id: 5e9a093a74c4063ca6f7c14e
title: Data Analysis Example B
title: Esempio B di analisi dei dati
challengeType: 11
videoId: 0kJz0q0pvgQ
dashedName: data-analysis-example-b
@ -8,30 +8,30 @@ dashedName: data-analysis-example-b
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [Notebooks on GitHub](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
- [How to open Notebooks from GitHub using Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example)
- [Come aprire Notebooks da GitHub usando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
# --question--
## --text--
What does the `loc` method allow you to do?
Cosa ti permette di fare il metodo `loc`?
## --answers--
Retrieve a subset of rows and columns by supplying integer-location arguments.
Recuperare un sottoinsieme di righe e colonne fornendo argomenti interi di localizzazione.
---
Access a group of rows and columns by supplying label(s) arguments.
Accedere a un gruppo di righe e colonne fornendo argomenti di etichetta.
---
Returns the first `n` rows based on the integer argument supplied.
Restituisce le prime `n` righe basate sull'argomento intero fornito.
## --video-solution--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-and-visualizations.md
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---
id: 5e9a093a74c4063ca6f7c160
title: Data Cleaning and Visualizations
title: Pulizia e visualizzazione dei dati
challengeType: 11
videoId: mHjxzFS5_Z0
dashedName: data-cleaning-and-visualizations
@ -8,18 +8,18 @@ dashedName: data-cleaning-and-visualizations
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
- [Come aprire Notebooks da GitHub usando Google Colab.](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)
# --question--
## --text--
When using Matplotlib's global API, what does the order of numbers mean here?
Quando si utilizza l'API globale di Matplotlib, cosa significa l'ordine dei numeri?
```py
plt.subplot(1, 2, 1)
@ -27,15 +27,15 @@ plt.subplot(1, 2, 1)
## --answers--
My figure will have one column, two rows, and I am going to start drawing in the first (left) plot.
La mia figura avrà una colonna, due righe, e sto per iniziare a disegnare nel primo grafico (a sinistra).
---
I am going to start drawing in the first (left) plot, my figure will have two rows, and my figure will have one column.
Inizierò a disegnare nel primo grafico (a sinistra), la mia figura avrà due righe, e la mia figura avrà una colonna.
---
My figure will have one row, two columns, and I am going to start drawing in the first (left) plot.
La mia figura avrà una riga, due colonne, e sto per iniziare a disegnare nel primo grafico (a sinistra).
## --video-solution--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-duplicates.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c15f
title: Data Cleaning Duplicates
title: Pulizia di dati duplicati
challengeType: 11
videoId: kj7QqjXhH6A
dashedName: data-cleaning-duplicates
@ -8,30 +8,30 @@ dashedName: data-cleaning-duplicates
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
- [Come aprire Notebooks da GitHub usando 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:
Il metodo di Python `.duplicated()` restituisce una serie booleana per il tuo DataFrame. `True` è restuito per righe che:
## --answers--
contain a duplicate, where the value for the row contains the first occurrence of that value.
contengono un duplicato, dove il valore della riga contiene la prima occorrenza del valore.
---
contain a duplicate, where the value for the row is at least the second occurrence of that value.
contengono un duplicato, dove il valore per quella riga è almeno la seconda occorrenza di quel valore.
---
contain a duplicate, where the value for the row contains either the first or second occurrence.
contengono un duplicato, dove il valore per quella riga è la prima oppure la seconda occorrenza.
## --video-solution--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/data-cleaning-introduction.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c15d
title: Data Cleaning Introduction
title: Introduzione alla pulizia dei dati
challengeType: 11
videoId: ovYNhnltVxY
dashedName: data-cleaning-introduction
@ -8,18 +8,18 @@ dashedName: data-cleaning-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
- [Come aprire Notebooks da GitHub usando 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?
Cosa verrà visualizzato nella console con il seguente codice?
```py
import pandas as pd

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curriculum/challenges/italian/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: Pulizia dati con DataFrames
challengeType: 11
videoId: sTMN_pdI6S0
dashedName: data-cleaning-with-dataframes
@ -8,18 +8,18 @@ dashedName: data-cleaning-with-dataframes
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/data-cleaning-rmotr-freecodecamp)
- [Come aprire Notebooks da GitHub usando 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?
Cosa verrà visualizzato nella console con il seguente codice?
```py
import pandas as pd

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curriculum/challenges/italian/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: Introduzione all'uso di Jupyter Notebooks
challengeType: 11
videoId: h8caJq2Bb9w
dashedName: how-to-use-jupyter-notebooks-intro
@ -8,18 +8,18 @@ dashedName: how-to-use-jupyter-notebooks-intro
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [Come aprire Notebooks da GitHub usando 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?
Cosa **non** è permesso in una cella di un Jupyter Notebook?
## --answers--
@ -27,11 +27,11 @@ Markdown
---
Python code
Codice Python
---
An Excel sheet
Un foglio Excel
## --video-solution--

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curriculum/challenges/italian/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: Celle di Jupyter Notebook
challengeType: 11
videoId: 5PPegAs9aLA
dashedName: jupyter-notebooks-cells
@ -8,30 +8,30 @@ dashedName: jupyter-notebooks-cells
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [Come aprire Notebooks da GitHub usando 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?
Quali sono i tre tipi principali di celle dei Jupyter Notebook?
## --answers--
Code, Markdown, and Python
Codice, Markdown e Python
---
Code, Markdown, and Raw
Codice, Markdown e output diretto (raw)
---
Markdown, Python, and Raw
Markdown, Python, e output diretto (raw)
## --video-solution--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/jupyter-notebooks-importing-and-exporting-data.md
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---
id: 5e9a093a74c4063ca6f7c151
title: Jupyter Notebooks Importing and Exporting Data
title: Importare ed esportare dati usando Jupyter Notebook
challengeType: 11
videoId: k1msxD3JIxE
dashedName: jupyter-notebooks-importing-and-exporting-data
@ -8,38 +8,38 @@ dashedName: jupyter-notebooks-importing-and-exporting-data
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial)
- [Come aprire Notebooks da GitHub usando 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?
Che tipo di dati puoi importare e elaborare in un notebook Jupyter?
## --answers--
Excel files.
File Excel.
---
CSV files.
File CSV.
---
XML files.
File XML.
---
Data from an API.
Dati da un'API.
---
All of the above.
Tutti i precedenti.
## --video-solution--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/numpy-operations.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c155
title: Numpy Operations
title: Operazioni con Numpy
challengeType: 11
videoId: eqSVcJbaPdk
dashedName: numpy-operations
@ -8,18 +8,18 @@ dashedName: numpy-operations
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy)
- [Come aprire Notebooks da GitHub usando 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?
Qual è il valore di `a` dopo aver eseguito il seguente codice?
```py
a = np.arange(5)

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/pandas-dataframes.md
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---
id: 5e9a093a74c4063ca6f7c15a
title: Pandas DataFrames
title: I DataFrame di Panda
challengeType: 11
videoId: 7SgFBYXaiH0
dashedName: pandas-dataframes
@ -8,18 +8,18 @@ dashedName: pandas-dataframes
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
- [Come aprire Notebooks da GitHub usando 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?
Cosa verrà visualizzato nella console con il seguente codice?
```py
import pandas as pd

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/pandas-introduction.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c158
title: Pandas Introduction
title: Introduzione a Pandas
challengeType: 11
videoId: 0xACW-8cZU0
dashedName: pandas-introduction
@ -8,18 +8,18 @@ dashedName: pandas-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas)
- [Come aprire Notebooks da GitHub usando 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?
Cosa verrà visualizzato nella console con il seguente codice?
```py
import pandas as pd

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/parsing-html-and-saving-data.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c164
title: Parsing HTML and Saving Data
title: Analisi HTML e salvataggio dei dati
challengeType: 11
videoId: bJaqnTWQmb0
dashedName: parsing-html-and-saving-data
@ -8,18 +8,18 @@ dashedName: parsing-html-and-saving-data
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su 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)
- [Come aprire Notebooks da GitHub usando 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?
Quale libreria di Python ha un metodo `.read_html()` che possiamo usare per analizzare documenti HTML ed estrarre tabelle?
## --answers--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/python-functions-and-collections.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c166
title: Python Functions and Collections
title: Funzioni e collezioni di Python
challengeType: 11
videoId: NzpU17ZVlUw
dashedName: python-functions-and-collections
@ -8,30 +8,30 @@ dashedName: python-functions-and-collections
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
- [Come aprire Notebooks da GitHub usando 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?
Quale è la principale differenza tra liste e tuple in Python?
## --answers--
Tuples are immutable.
Le tuple sono immutabili.
---
Lists are ordered.
Le liste sono ordinate.
---
Tuples are unordered.
Le tuple non sono ordinate.
## --video-solution--

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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c165
title: Python Introduction
title: Introduzione a Python
challengeType: 11
videoId: PrQV9JkLhb4
dashedName: python-introduction
@ -8,34 +8,34 @@ dashedName: python-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
- [Come aprire Notebooks da GitHub usando 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?
Come definiamo blocchi di codice nel corpo di una funzione in Python?
## --answers--
We use a set of curly braces, one on either side of each new block of our code.
Usiamo una coppia di parentesi graffe, una da ogni lato di un nuovo blocco di codice.
---
We use indentation, usually right-aligned 4 spaces.
Usiamo l'indentazione, in genere quattro spazi a sinistra della riga.
---
We do not denote blocks of code.
Non si possono definire blocchi di codice.
---
We could use curly braces or indentation to denote blocks of code.
Possiamo usare o le parentesi graffe o l'indentazione.
## --video-solution--

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/python-iteration-and-modules.md
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@ -1,6 +1,6 @@
---
id: 5e9a093a74c4063ca6f7c167
title: Python Iteration and Modules
title: Iterazione e moduli in Python
challengeType: 11
videoId: XzosGWLafrY
dashedName: python-iteration-and-modules
@ -8,18 +8,18 @@ dashedName: python-iteration-and-modules
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su GitHub](https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes)
- [Come aprire Notebooks da GitHub usando 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`?
Come faresti per iterare su un dizionario chiamato `user` e scrivere nella console le sue chiavi e valori?
## --answers--

14
curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-course/reading-data-introduction.md
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---
id: 5e9a093a74c4063ca6f7c161
title: Reading Data Introduction
title: Introduzione alla lettura dei dati
challengeType: 11
videoId: cDnt02BcHng
dashedName: reading-data-introduction
@ -8,18 +8,18 @@ dashedName: reading-data-introduction
# --description--
*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*
*Invece di usare notebooks.ai come mostrato nel video, puoi usare Google Colab.*
More resources:
Altre risorse:
- [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)
- [Notebook su 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)
- [Come aprire Notebooks da GitHub usando 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:
Dato un file chiamato `certificates.csv` con questi contenuti:
<pre>
Name$Certificates$Time (in months)
@ -29,7 +29,7 @@ Ahmad$5$9
Beau$6$12
</pre>
Fill in the blanks for the missing arguments below:
Riempi gli spazi vuoti per gli argomenti mancanti qui sotto:
```py
import csv

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer.md
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@ -1,6 +1,6 @@
---
id: 5e46f7e5ac417301a38fb929
title: Demographic Data Analyzer
title: Analizzatore di dati demografici
challengeType: 10
forumTopicId: 462367
dashedName: demographic-data-analyzer
@ -8,17 +8,17 @@ dashedName: demographic-data-analyzer
# --description--
In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database.
In questa sfida è necessario analizzare i dati demografici utilizzando Pandas. Ti viene fornito un insieme di dati demografici estratti dalla banca dati del Census del 1994.
You can access [the full project description and starter code on Replit](https://replit.com/github/freeCodeCamp/boilerplate-demographic-data-analyzer).
Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Replit](https://replit.com/github/freeCodeCamp/boilerplate-demographic-data-analyzer).
After going to that link, fork the project. Once you complete the project based on the instructions in 'README.md', submit your project link below.
Dopo essere andato a quel collegamento, fai un fork del progetto. Una volta completato il progetto in base alle istruzioni riportate in 'README.md', invia il link del progetto qui sotto.
We are still developing the interactive instructional part of the data analysis with Python curriculum. For now, you will have to use other resources to learn how to pass this challenge.
Stiamo ancora sviluppando la parte didattica interattiva del curriculum di analisi dei dati con Python. Per ora, dovrai utilizzare altre risorse per imparare a superare questa sfida.
# --hints--
It should pass all Python tests.
Dovrebbe superare tutti i test Python.
```js

12
curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator.md
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@ -1,6 +1,6 @@
---
id: 5e46f7e5ac417301a38fb928
title: Mean-Variance-Standard Deviation Calculator
title: Calcolatore della Varianza, Media e Deviazione Standard
challengeType: 10
forumTopicId: 462366
dashedName: mean-variance-standard-deviation-calculator
@ -8,17 +8,17 @@ dashedName: mean-variance-standard-deviation-calculator
# --description--
Create a function that uses Numpy to output the mean, variance, and standard deviation of the rows, columns, and elements in a 3 x 3 matrix.
Crea una funzione che usa Numpy per calcolare la media, varianza e deviazione standard delle righe, colonne ed elementi in una matrice 3 x 3.
You can access [the full project description and starter code on Replit](https://replit.com/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator).
Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Replit](https://replit.com/github/freeCodeCamp/boilerplate-mean-variance-standard-deviation-calculator).
After going to that link, fork the project. Once you complete the project based on the instructions in 'README.md', submit your project link below.
Dopo essere andato a quel collegamento, fai un fork del progetto. Una volta completato il progetto in base alle istruzioni riportate in 'README.md', invia il link del progetto qui sotto.
We are still developing the interactive instructional part of the data analysis with Python curriculum. For now, you will have to use other resources to learn how to pass this challenge.
Stiamo ancora sviluppando la parte didattica interattiva del curriculum di analisi dei dati con Python. Per ora, dovrai utilizzare altre risorse per imparare a superare questa sfida.
# --hints--
It should pass all Python tests.
Dovrebbe superare tutti i test Python.
```js

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md
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@ -1,6 +1,6 @@
---
id: 5e46f7f8ac417301a38fb92a
title: Medical Data Visualizer
title: Visualizzatore di Dati Medici
challengeType: 10
forumTopicId: 462368
dashedName: medical-data-visualizer
@ -8,17 +8,17 @@ dashedName: medical-data-visualizer
# --description--
In this project, you will visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas.
In questo progetto visualizzerai e farai calcoli relativi ai dati di esami medici usando matplotlib, seaborn, e pandas.
You can access [the full project description and starter code on Replit](https://replit.com/github/freeCodeCamp/boilerplate-medical-data-visualizer).
Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Replit](https://replit.com/github/freeCodeCamp/boilerplate-medical-data-visualizer).
After going to that link, fork the project. Once you complete the project based on the instructions in 'README.md', submit your project link below.
Dopo essere andato a quel collegamento, fai un fork del progetto. Una volta completato il progetto in base alle istruzioni riportate in 'README.md', invia il link del progetto qui sotto.
We are still developing the interactive instructional part of the data analysis with Python curriculum. For now, you will have to use other resources to learn how to pass this challenge.
Stiamo ancora sviluppando la parte didattica interattiva del curriculum di analisi dei dati con Python. Per ora, dovrai utilizzare altre risorse per imparare a superare questa sfida.
# --hints--
It should pass all Python tests.
Dovrebbe superare tutti i test Python.
```js

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curriculum/challenges/italian/08-data-analysis-with-python/data-analysis-with-python-projects/sea-level-predictor.md
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@ -1,6 +1,6 @@
---
id: 5e4f5c4b570f7e3a4949899f
title: Sea Level Predictor
title: Predittore del livello del mare
challengeType: 10
forumTopicId: 462370
dashedName: sea-level-predictor
@ -8,17 +8,17 @@ dashedName: sea-level-predictor
# --description--
In this project, you will analyze a dataset of the global average sea level change since 1880. You will use the data to predict the sea level change through year 2050.
In questo progetto, analizzerai un set di dati del cambiamento del livello medio globale del mare dal 1880. Userai i dati per predire il cambiamento del livello del mare fino all'anno 2050 incluso.
You can access [the full project description and starter code on Replit](https://replit.com/github/freeCodeCamp/boilerplate-sea-level-predictor).
Puoi accedere [alla descrizione completa del progetto e al codice iniziale su Replit](https://replit.com/github/freeCodeCamp/boilerplate-sea-level-predictor).
After going to that link, fork the project. Once you complete the project based on the instructions in 'README.md', submit your project link below.
Dopo essere andato a quel collegamento, fai un fork del progetto. Una volta completato il progetto in base alle istruzioni riportate in 'README.md', invia il link del progetto qui sotto.
We are still developing the interactive instructional part of the data analysis with Python curriculum. For now, you will have to use other resources to learn how to pass this challenge.
Stiamo ancora sviluppando la parte didattica interattiva del curriculum di analisi dei dati con Python. Per ora, dovrai utilizzare altre risorse per imparare a superare questa sfida.
# --hints--
It should pass all Python tests.
Dovrebbe superare tutti i test Python.
```js

4
curriculum/challenges/italian/08-data-analysis-with-python/numpy/accessing-and-changing-elements,-rows,-columns.md
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed4
title: 'Accessing and Changing Elements, Rows, Columns'
title: 'Accedere e cambiare elementi, righe, colonne'
challengeType: 11
videoId: v-7Y7koJ_N0
dashedName: accessing-and-changing-elements-rows-columns
@ -10,7 +10,7 @@ dashedName: accessing-and-changing-elements-rows-columns
## --text--
What code would change the values in the 3rd column of both of the following Numpy arrays to 20?
Quale codice imposterebbe a 20 il valore della terza colonna per entrambi i seguenti array Numpy?
```py
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])

4
curriculum/challenges/italian/08-data-analysis-with-python/numpy/copying-arrays-warning.md
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed7
title: Copying Arrays Warning
title: Avvertenze sulla copia di array
challengeType: 11
videoId: iIoQ0_L0GvA
dashedName: copying-arrays-warning
@ -10,7 +10,7 @@ dashedName: copying-arrays-warning
## --text--
What is the value of `a` after running the following code?
Qual è il valore di `a` dopo aver eseguito il seguente codice?
```py
import numpy as np

4
curriculum/challenges/italian/08-data-analysis-with-python/numpy/initialize-array-problem.md
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed6
title: Initialize Array Problem
title: Problema di inizializzazione degli array
challengeType: 11
videoId: 0jGfH8BPfOk
dashedName: initialize-array-problem
@ -10,7 +10,7 @@ dashedName: initialize-array-problem
## --text--
What is another way to produce the following array?
Qual è un altro modo di produrre il seguente array?
```py
[[0. 0. 0. 0. 0. 0. 0.]

4
curriculum/challenges/italian/08-data-analysis-with-python/numpy/initializing-different-arrays.md
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed5
title: Initializing Different Arrays
title: Inizializzazione di diversi array
challengeType: 11
videoId: CEykdsKT4U4
dashedName: initializing-different-arrays
@ -10,7 +10,7 @@ dashedName: initializing-different-arrays
## --text--
What will the following code print?
Cosa scriverà il seguente codice?
```py
a = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])

6
curriculum/challenges/italian/08-data-analysis-with-python/numpy/loading-data-and-advanced-indexing.md
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600eda
title: Loading Data and Advanced Indexing
title: Caricamento dei dati e indicizzazione avanzata
challengeType: 11
videoId: tUdBZ7pF8Jg
dashedName: loading-data-and-advanced-indexing
@ -10,14 +10,14 @@ dashedName: loading-data-and-advanced-indexing
## --text--
Given a file named `data.txt` with these contents:
Dato un file chiamato `data.txt` con questi contenuti:
<pre>
29,97,32,100,45
15,88,5,75,22
</pre>
What code would produce the following array?
Quale codice produrrebbe il seguente array?
```py
[29. 32. 45. 15. 5. 22.]

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curriculum/challenges/italian/08-data-analysis-with-python/numpy/what-is-numpy.md
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@ -1,6 +1,6 @@
---
id: 5e9a0a8e09c5df3cc3600ed2
title: What is NumPy
title: Che cos’è NumPy
challengeType: 11
videoId: 5Nwfs5Ej85Q
dashedName: what-is-numpy
@ -10,23 +10,23 @@ dashedName: what-is-numpy
## --text--
Why are Numpy arrays faster than regular Python lists?
Perché gli array Numpy sono più veloci delle normali liste Python?
## --answers--
Numpy does not perform type checking while iterating through objects.
Numpy non esegue il controllo del tipo durante l'iterazione attraverso gli oggetti.
---
Numpy uses fixed types.
Numpy utilizza tipi fissi.
---
Numpy uses contiguous memory.
Numpy utilizza memoria contigua.
---
All of the above.
Tutti i precedenti.
## --video-solution--