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import streamlit as st
import pandas as pd
import numpy as np
import yfinance as yf
import altair as alt
import plotly.figure_factory as ff
import pydeck as pdk
from vega_datasets import data as vds
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from streamlit_image_comparison import image_comparison
def on_input_change():
user_input = st.session_state.user_input
st.session_state.past.append(user_input)
st.session_state.generated.append(
{"data": "The messages from Bot\nWith new line", "type": "normal"}
)
def on_btn_click():
del st.session_state.past[:]
del st.session_state.generated[:]
def main():
st.title(" All Graphs")
(
col1,
col2,
) = st.columns(2)
with col1:
st.line_chart(
pd.DataFrame(
{
"Apple": yf.download("AAPL", start="2023-01-01", end="2023-07-31")[
"Adj Close"
],
"Google": yf.download(
"GOOGL", start="2023-01-01", end="2023-07-31"
)["Adj Close"],
"Microsoft": yf.download(
"MSFT", start="2023-01-01", end="2023-07-31"
)["Adj Close"],
}
)
)
with col2:
data = pd.DataFrame(
{"X": [1, 2, 3, 4, 5], "Y1": [10, 16, 8, 14, 12], "Y2": [5, 8, 3, 6, 7]}
)
st.area_chart(data)
st.plotly_chart(
ff.create_distplot(
[np.random.randn(200) - 2, np.random.randn(200), np.random.randn(200) + 2],
["Negative Shift", "Normal", "Positive Shift"],
bin_size=[0.1, 0.25, 0.5],
),
use_container_width=True,
)
source = vds.cars()
chart = {
"mark": "point",
"encoding": {
"x": {"field": "Horsepower", "type": "quantitative"},
"y": {"field": "Miles_per_Gallon", "type": "quantitative"},
"color": {"field": "Origin", "type": "nominal"},
"shape": {"field": "Origin", "type": "nominal"},
},
}
tab1, tab2 = st.tabs(["Streamlit theme (default)", "Vega-Lite native theme"])
with tab1:
st.vega_lite_chart(source, chart, theme="streamlit", use_container_width=True)
with tab2:
st.vega_lite_chart(source, chart, theme=None, use_container_width=True)
st.altair_chart(
alt.Chart(
pd.DataFrame(
{
"x": np.random.rand(50),
"y": np.random.rand(50),
"size": np.random.randint(10, 100, 50),
"color": np.random.rand(50),
}
)
)
.mark_circle()
.encode(
x="x",
y="y",
size="size",
color="color",
tooltip=["x", "y", "size", "color"],
)
.properties(width=600, height=400),
use_container_width=True,
)
st.bar_chart(
pd.DataFrame(np.random.randn(20, 3), columns=["Apple", "Banana", "Cherry"])
)
st.pydeck_chart(
pdk.Deck(
map_style=None,
initial_view_state=pdk.ViewState(
latitude=37.76, longitude=-122.4, zoom=11, pitch=50
),
layers=[
pdk.Layer(
"HexagonLayer",
data=pd.DataFrame(
np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
columns=["lat", "lon"],
),
get_position="[lon, lat]",
radius=200,
elevation_scale=4,
elevation_range=[0, 1000],
pickable=True,
extruded=True,
),
pdk.Layer(
"ScatterplotLayer",
data=pd.DataFrame(
np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
columns=["lat", "lon"],
),
get_position="[lon, lat]",
get_color="[200, 30, 0, 160]",
get_radius=200,
),
],
)
)
import datetime
np.random.seed(1)
programmers = ["Alex", "Nicole", "Sara", "Etienne", "Chelsea", "Jody", "Marianne"]
base = datetime.datetime.today()
dates = base - np.arange(180) * datetime.timedelta(days=1)
z = np.random.poisson(size=(len(programmers), len(dates)))
fig = go.Figure(data=go.Heatmap(z=z, x=dates, y=programmers, colorscale="Viridis"))
fig.update_layout(title="GitHub commits per day", xaxis_nticks=36)
st.plotly_chart(fig)
(
col1,
col2,
) = st.columns(2)
with col1:
df = px.data.gapminder().query("year == 2007").query("continent == 'Americas'")
fig = px.pie(
df,
values="pop",
names="country",
title="Population of American continent",
hover_data=["lifeExp"],
labels={"lifeExp": "life expectancy"},
)
fig.update_traces(textposition="inside", textinfo="percent+label")
st.plotly_chart(fig)
with col2:
fig = go.Figure(
go.Sunburst(
labels=[
"Eve",
"Cain",
"Seth",
"Enos",
"Noam",
"Abel",
"Awan",
"Enoch",
"Azura",
],
parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"],
values=[10, 14, 12, 10, 2, 6, 6, 4, 4],
)
)
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
st.plotly_chart(fig)
if __name__ == "__main__":
main()
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