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Photo by me! + I pretty recently switched jobs (from one IT consultancy to another). This also meant switching client project, and subsequently I would be working with another tech stack than what I had been using the last few years. I was going from microservices in csharp/.NET to "data apps" with Python, Pandas and Streamlit. @@ -71,6 +74,70 @@ Now we're ready to explore some more features 😄. ### Dataframes +Dataframes are the primary data structure in the pandas library. +It is used to store tabular data, i.e. data which consists of rows and columns, like a spreadsheet. + +Streamlit makes it very simple visualize dataframes. + +After some googling for a sort-of interesting dataset to play with, I found out Stavanger kommune publishes +[data from bike counter sensors publically](https://opencom.no/dataset/sykkeldata), +and the most recent dataset (as of April 2024) is available as [a CSV file here]( +https://opencom.no/dataset/90cef5d5-601e-4412-87e4-3e9e8dc59245/resource/4c71d19a-adc4-42e0-9bed-c990316479be/download/sykkeldata.csv). + +Let's update application.py to demonstrate parsing this CSV to a dataframe and visualizing the results as a table: + +```python +import pandas as pd +import streamlit as st + +BIKE_COUNT_DATA_URL: str = ( + "https://opencom.no/dataset/90cef5d5-601e-4412-87e4-3e9e8dc59245/resource/4c71d19a-adc4-42e0-9bed-c990316479be/download/sykkeldata.csv" +) + + +@st.cache_data(ttl=3600) +def load_count_data() -> pd.DataFrame: + df = pd.read_csv(BIKE_COUNT_DATA_URL) + return df + + +st.title("Streamlit demo") + +df = load_count_data() +st.dataframe(df) +``` + +And that's it! +Go back to your web browser and hit the "Rerun" button in the top right and notice we now have a fully interactive table of the dataset to play with! + +![streamlit app with dataframe](dataframe_demo.png) + ### Plots +For a quick plotting demo, let's visualize bike counts (across all counter locations) by date. +We'll add a line to application.py: + +```patch +--- a/application.py ++++ b/application.py +@@ -16,3 +16,4 @@ st.title("Streamlit demo") + + df = load_count_data() + st.dataframe(df) ++st.bar_chart(df, x="Date", y="Count") +``` + +And here we go: + +![streamlit app with bar chart](bar_chart_demo.png) + +Streamlit supports several different native chart types, as well as rendering charts from popular libraries from the ecosystem, such as matplotlib and plotly, +so there are lots of options and possibilities! + +We could also use pandas to transform our dataframe in different ways prior to plotting the data as needed. + ### More + +This was just the tip of the iceberg with regards to data apps on Streamlit. +Hopefully it was useful and/or inspires you to try it out and learn more 😄. +