Data Visualization with Streamlit

Transcript

1. Data Visualization With Streamlit Luca Corbucci PyCon Italia - Firenze 04/06/2022
2. • 👨💻 Ph.D. Student @ University of Pisa • 🎤 Podcaster @ PointerPodcast • 🦸 Community Manager @ SuperHeroesValley • 🌏 lucacorbucci.me 👋 Hi, I’m Luca @lucacorbucci
3. Once upon a time, at the end of a university project…
6. How could we report our results without developing a dashboard?
8. What is Streamlit? • It is an open source framework for data scientist and ML engineer • It allows you to develop interfaces for ML projects • It allows you to write your interface using Python • It is easy to use: you don’t need any previous frontend experience • It o ff ers an easy way to deploy your projects
9. Installation
10. Let’s create our first app
11. import streamlit as st import pandas as pd
12. import streamlit as st import pandas as pd st.title("Hello Pycon IT")
13. import streamlit as st import pandas as pd st.title("Hello Pycon IT") df = pd.read_csv("./race-winners.csv") st.dataframe(df)
14. import streamlit as st import pandas as pd st.title("Hello Pycon IT") df = pd.read_csv("./race-winners.csv") st.dataframe(df) df1 = df[df['Class'] == 'MotoGP™'] count = df1['Rider'].value_counts().head(20) st.bar_chart(count)
15. import streamlit as st import pandas as pd st.title("Hello Pycon IT") df = pd.read_csv("./race-winners.csv") st.dataframe(df) class_type = st.radio( "Choose the class you want to display", ('Moto3™', 'Moto2™', 'MotoGP™')) df1 = df[df['Class'] == class_type] count = df1['Rider'].value_counts().head(20) st.bar_chart(count)
16. import streamlit as st import pandas as pd st.title("Hello Pycon IT") df = pd.read_csv("./race-winners.csv") st.dataframe(df) class_type = st.radio( "Choose the class you want to display", ('Moto3™', 'Moto2™', 'MotoGP™')) df1 = df[df['Class'] == class_type] count = df1['Rider'].value_counts().head(20) st.bar_chart(count) option = st.selectbox( 'Select the year you want to display:', (i for i in range(1949, 2023))) df1 = df1[df1['Season'] == option] count = df1['Rider'].value_counts().head(20) st.bar_chart(count)
17. @st.cache is our friend • We don’t want to execute multiple times a slow code • We can mark functions with the decorator @st.cache • It allows us to reuse data avoiding executing slow code multiple times def load_data(path: str) -> pd.DataFrame: data = pd.read_csv(path) return data df = load_data("./race-winners.csv") st.dataframe(df)
18. @st.cache is our friend • We don’t want to execute multiple times a slow code • We can mark functions with the decorator @st.cache • It allows us to reuse data avoiding executing slow code multiple times @st.cache def load_data(path: str) -> pd.DataFrame: data = pd.read_csv(path) return data df = load_data("./race-winners.csv") st.dataframe(df)
19. @st.cache in details • Once we add the decorator, Streamlit will check: • If the input parameters changed • If the body of the function changed • If the body of the functions called inside the function changed • If the variables used inside the function changed • Streamlit stores the data in a local cache, if any of these components changed then it will execute again the function otherwise it will use the cached data
21. Deploy your first data app • Streamlit allows the deployment of the data apps on Streamlit Cloud • Streamlit Cloud is connected with a GitHub repo and it is able to deploy your app • It is a free service (with some limitations)
22. Deploy your first data app
23. Demo!
24. Thank you @lucacorbucci 🌏 lucacorbucci.me 👨💻 github.com/lucacorbucci/ 📥 corbuccilu@gmail.com