pip install streamlit
streamlit hello
Create a file called first_app.py. To run this file you need to type streamlit run first_app.py.
import streamlit as st
import numpy as np
import pandas as pd
Set Title
st.title('My First Application in Streamlit')
Write in the page
x=4
st.write(x, 'square is', x*x)
Write in page with magic method
x=4
x, 'square is', x*x
Write a dataframe
st.write("Here's our first attempt at using data to create a table:")
st.write(pd.DataFrame({
'first column': [1, 2, 3, 4],
'second column': [10, 20, 30, 40]
}))
"""
# Title
Here's our first attempt at using data to create a table:
"""
df = pd.DataFrame({
'first column': [1, 2, 3, 4],
'second column': [10, 20, 30, 40]
})
df
Create a chart
chart_data = pd.DataFrame(
np.random.randn(20, 3),
columns=['a', 'b', 'c'])
st.line_chart(chart_data)
Create a map data
map_data = pd.DataFrame(
np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
columns=['lat', 'lon'])
st.map(map_data)
Add a checkbox to show or hide the chart
if st.checkbox('Show dataframe'):
Adding a Slider
x = st.slider('x')
st.writer(x, 'square', x*x)
Add an option
option = st.selectbox(
'Which number do you like best?',
df['first column'])
'You selected: ', option
option_side = st.sidebar.selectbox(
'Which number do you like best?',
["hello", "world", "!"])
'You selected:', option_side
Adding a slider
import time
'Starting a long computation...'
# Initializing the variables
latest_iteration = st.empty()
bar = st.progress(0)
for i in range(100):
# Update the progress bar with each iteration.
latest_iteration.text(f'Iteration {i+1}')
bar.progress(i + 1)
time.sleep(0.1)
'...and now we\'re done!'
Selectbox
add_selectbox = st.sidebar.selectbox(
'How would you like to be contacted?',
('Email', 'Home phone', 'Mobile phone')
)
Slider
add_slider = st.sidebar.slider(
'Select a range of values',
0.0, 100.0, (25.0, 75.0)
)
create a document called uber_pickups.py
start
import streamlit as st
import pandas as pd
import numpy as np
Set Title
st.title('Uber Pickups')
Importing data
DATE_COLUMN = 'Date/Time'
DATA_URL = ('https://s3-us-west-2.amazonaws.com/streamlit-demo-data/uber-raw-data-sep14.csv.gz')
Create a Function to load data
def load_data(nrows):
data = pd.read_csv(DATA_URL, parse_dates=[DATE_COLUMN], nrows=nrows)
return data
Now we add a text to notify the data is being loaded
# Create a text element and let the reader know the data is loading.
data_load_state = st.text('Loading data...')
# Load 10,000 rows of data into the dataframe.
data = load_data(10000)
# Notify the reader that the data was successfully loaded.
data_load_state.text("Done!")
Let's displya the data
st.subheader('Raw data')
st.write(data)
Remember if you wish to hide the table you can use a checkbox
if st.checkbox('Show raw data'):
Let's create a histogram
st.subheader('Number of pickups by hour')
hist_values = np.histogram(
data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
st.bar_chart(hist_values)
Let's create a map for the data
st.subheader('Map of all pickups')
st.map(data)
Why not add a filter
hour_to_filter = 17
filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
st.subheader(f'Map of all pickups at {hour_to_filter}:00')
st.map(filtered_data)
That's boring, let's create a dynamic filter
hour_to_filter = st.slider('hour', 0, 23, 17)
filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
st.subheader(f'Map of all pickups at {hour_to_filter}:00')
st.map(filtered_data)