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import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
#######
# Data loading
#######
df = pd.read_csv("gapminder.csv")
year_values = (int(df["year"].min()), int(df["year"].max()))
metrics = ["lifeExp", "pop", "gdpPercap"]
dimension = ["country", "continent", "year"]
#######
# Helper functions
#######
def get_filtered_data(
continents="All",
countries="All",
min_year=year_values[0],
max_year=year_values[1],
):
if isinstance(continents, str) and continents != "All":
mask_continent = df["continent"] == continents
else:
mask_continent = df["continent"].isin(continents)
if isinstance(countries, str) and countries != "All":
mask_country = df["country"] == countries
else:
mask_country = df["country"].isin(countries)
mask_year = ((df["year"] >= min_year) & (df["year"] <= max_year))
return df[mask_continent & mask_country & mask_year]
def box_plot(df, x, y):
fig = px.box(
df, x=x, y=y, hover_data=df[dimension + [x]],
points="all", color=x)
return fig
def scatter_plot(df, x, y, hue):
fig = px.scatter(
df, x=x, y=y,
color=hue, symbol=hue)
return fig
def line_plot(df, y_axis, label, highlighted):
fig = go.Figure()
if label=="continent":
df = df.groupby(["continent", "year"]).agg({
"lifeExp": "mean",
"pop": "sum",
"gdpPercap": "mean",
}).reset_index()
for i in df[label].unique():
if i == highlighted:
continue
data = df[df[label]==i]
x = data["year"]
y = data[y_axis]
fig.add_trace(go.Scatter(x=x, y=y,
hovertext=[
f"{label}: {i}<br>year: {year}<br>{y_axis}: {value}"
for year, value in zip(x,y)
],
hoverinfo="text",
mode='lines',
line = dict(color='gray', width=1),
# name=i
))
data = df[df[label]==highlighted]
x = data["year"]
y = data[y_axis]
fig.add_trace(go.Scatter(x=x, y=y,
hovertext=[
f"{label}: {highlighted}<br>year: {year}<br>{y_axis}: {value}"
for year, value in zip(x,y)
],
hoverinfo="text",
mode='lines',
line = dict(color='orange', width=10),
# name=highlighted
))
fig.update_layout(showlegend=False)
return fig
#######
# Streamlit app code
#######
st.title('[Gapminder] Exploratory Data Analysis')
st.markdown("## Gapminder Table")
selected_continents = st.multiselect("Select Continents:", df["continent"].unique(), key="table_continent")
selected_countries = st.multiselect("Select Countries:", df.loc[df["continent"].isin(selected_continents), "country"].unique(), key="table_country")
min_year, max_year = st.slider("Select Year:", year_values[0], year_values[1], year_values, key="table_year")
st.dataframe(get_filtered_data(selected_continents, selected_countries, min_year, max_year))
st.markdown("## Gapminder Boxplot")
col1, col2 = st.columns(2)
with col1:
x = st.selectbox("Select x Axis", dimension, 1, key="boxplot_x")
with col2:
y = st.selectbox("Select y Axis", metrics, key="boxplot_y")
st.plotly_chart(box_plot(df, x, y))
st.markdown('## Gapminder Lineplot')
col1, col2, col3 = st.columns(3)
with col3:
label = st.radio("Select label", ["country", "continent"], key="lineplot_label")
with col1:
highlighted = st.selectbox("Select value to hightlight", df[label].unique(), key="lineplot_highlighting")
with col2:
y = st.selectbox("Select hue", metrics, key="lineplot_y")
st.plotly_chart(line_plot(df, y, label, highlighted))
st.markdown('## Gapminder Scatterplot')
col1, col2, col3 = st.columns(3)
with col1:
x = st.selectbox("Select x Axis", metrics, key="scatterplot_x")
with col2:
y = st.selectbox("Select y Axis", metrics, key="scatterplot_y")
with col3:
hue = st.radio("Select hue", ["country", "continent"], key="scatterplot_hue")
st.plotly_chart(scatter_plot(df, x, y, hue))
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