presentation.qmd

---
title: "From R to Python: A Gentle Introduction"
date: "2025-04-27"
author: Federica Gazzelloni<br><span style="color:#ff66c4;">R-Ladies Rome</span>
footer: "*[From R to Python: A Gentle Introduction](https://github.com/Fgazzelloni/From-R-to-Python-A-Gentle-Introduction)* | [rladiesrome.org](http://www.rladiesrome.org)"
logo: images/logo.png 
logo-alt: "rladiesrome"
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# Welcome Everyone! {background-image="images/logo.png" background-size="500px" background-position="right"}

# Disclaimer {.smaller}

This talk is recorded and will be posted on [Youtube \@rladiesrome](https://www.youtube.com/@rladiesrome)

Please remember that all our attendees are expected to adhere to our `Code of Conduct` 👉 <https://rladies.org/coc/>

<br>

**`We ensure a safe, inclusive, and harassment-free space for learning. We are committed to providing a respectful environment where everyone feels welcome to learn, connect, and grow.`**

# Who we are?

```         
rladies_global |>
  filter(city %in% c('Rome'))
```

**`R-Ladies Rome` is a local chapter of `R-Ladies Global` dedicated to promoting `continous learning` in the Open Source Community. Our monthly meetings provide a platform to discuss current trends and hot topics, such as `Python` 🐍**

# Join us! {background-image="images/background-joinus.png" background-size="800px" background-position="right"}

If you’d like to join our team and contribute to our community, we have a few open positions!

<br>

Fill out the form here 👇 <https://forms.gle/SkBiab8LhLcehpKu6>

## Learning Objectives 🤓 {background-image="images/background-rtopy.png" background-size="800px" background-position="right"}

::: notes
Explore similarities and key differences to build on your existing skills From data wrangling to basic summaries and plots Use pandas, matplotlib, and others to analyze and visualize data.
:::

-   Transition from R to Python
-   Learn how to perform basic tasks in Python
-   Get hands-on with essential Python libraries

## Python Quick Takes

::: notes
Provides the global interpreter lock (GIL), a mechanism that prevents the interpreter from executing more than one Python instruction at a time
:::

. . .

> 📣 Python is a programming language that lets you work quickly and integrate systems more effectively.

-   First appearance in 1991
-   Integrates C, C++, and FORTRAN code (Glue)
-   Defined as an interpreted programming language
-   Provides the global interpreter lock (GIL)

## R [vs]{style="color:#ff66c4;"} Python 🤺 {.smaller}

| Use Case | R | Python |
|------------------|---------------------------|---------------------------|
| **Best for** | Statistical analysis, data visualization | General-purpose programming |
| **Learning curve** | Intuitive for data analysis and stats | Accessible for general programming |
| **Visualization** | `ggplot2` | `matplotlib` |
| **Data handling** | `data.frame`, `tidyverse` | `pandas`, `numpy` |
| **Machine Learning** | `caret`, `tidymodels` | `scikit-learn`, `TensorFlow` |
| **Reproducible reporting** | Quarto, RMarkdown | Jupyter Notebooks, Quarto |
| **Community** | Strong in academia and research | Strong in industry and web development |

## Essential Python Libraries📚 {.smaller}

::: notes
data frames are built into the R programming language and its standard library. As a result, many features found in pandas are typically either part of the R core implementation or provided by add-on packages
:::

-   `NumPy` (Numerical Python)
-   `pandas` (high-level data structures and functions)
-   `matplotlib` (plots and other two-dimensional data visualizations)
-   `SciPy` (collection of scientific packages)
-   `scikit-learn` (machine learning toolkit)
-   `statsmodels` (statistical modeling)

## Install Python

::: notes
integrated development environment (IDE)
:::

-   Download: <https://www.python.org/downloads/>
-   Install the latest version of `Python3`
-   Install an IDE (Integrated Development Environment):
    -   `VSCode` - Visual Studio Code (IDE)

# Hands-on: Python Basics

## Install Python libraries

-   Use `pip` (Python package manager) to install libraries
-   Open a terminal and type:

. . .

``` bash
python3 -m pip install pandas
python3 -m pip install matplotlib
python3 -m pip install numpy
```

## Load libraries

-   Use `import` to load libraries
-   Use `as` to give a library an alias

. . .

``` python
import pandas as pd
```

## Use a function from a library

::: notes
You do need to specify which library the functions you are using come from - such as pd.--- Use the library name followed by a dot and the function name
:::

. . .

::::: columns
::: {.column width="50%"}
```{r}
#| eval: false
#| echo: true
data.frame(a = c(1, 2, 3, 4),
           b = c(5, 6, 7, 8))
```
:::

::: {.column width="50%"}
``` python
pd.DataFrame({'a': [1,2,3,4], 
              'b': [5,6,7,8]})
```
:::
:::::

## Resources

-   [Python for Data Analysis, 3E](https://wesmckinney.com/book) by Wes McKinney

-   [The Carpenteries: Python Novice gapminder](https://swcarpentry.github.io/python-novice-gapminder/)

-   [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) by Jake VanderPlas

-   [Python for Everybody](https://www.py4e.com/) by Charles Severance

-   [Python Crash Course](https://ehmatthes.github.io/pcc_2e/) by Eric Matthes

-   [Automate the Boring Stuff with Python](https://automatetheboringstuff.com/) by Al Sweigart