Streamlit

Now lets create our first app

Create a python file with the following script (Here we create a slider and out put of the slider. We develop an app get result square of a number)

import streamlit as st

x = st.slider('x')

st.write(x, 'squared is', x * x)

Some of important Streamlit Widget sare

1. Slider

streamlit.slider(label, min_value=None, max_value=None, value=None, step=None, format=None)

2. Text Input

import streamlit as st

url = st.text_input('Enter URL')

st.write('The Entered URL is', url)

This is how the app looks:

3. Checkbox

import streamlit as st

import pandas as pd

import numpy as np

df = pd.read_csv("football_data.csv")

if st.checkbox('Show dataframe'):

st.write(df)

4. Select box

import streamlit as st

import pandas as pd

import numpy as np

df = pd.read_csv("football_data.csv")

option = st.selectbox(

'Which Club do you like best?',

df['Club'].unique())

'You selected: ', option

5. Multi Select

import streamlit as st

import pandas as pd

import numpy as np

df = pd.read_csv("football_data.csv")

options = st.multiselect(

'What are your favorite clubs?', df['Club'].unique())

st.write('You selected:', options)

To see more styles of API see: https://streamlit.io/docs/api.html

More practical example

import streamlit as st

x = st.slider('x')

st.write(x, 'squared is', x * x)

import streamlit as st

import pandas as pd

import numpy as np

st.title('Uber pickups in NYC')

DATE_COLUMN = 'date/time'

DATA_URL = ('https://s3-us-west-2.amazonaws.com/'

'streamlit-demo-data/uber-raw-data-sep14.csv.gz')

@st.cache

def load_data(nrows):

data = pd.read_csv(DATA_URL, nrows=nrows)

lowercase = lambda x: str(x).lower()

data.rename(lowercase, axis='columns', inplace=True)

data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])

return data

data_load_state = st.text('Loading data...')

data = load_data(10000)

data_load_state.text('Loading data... done!')

if st.checkbox('Show raw data'):

st.subheader('Raw data')

st.write(data)

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)

# Some number in the range 0-23

hour_to_filter = st.slider('hour', 0, 23, 17)

filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]

st.subheader('Map of all pickups at %s:00' % hour_to_filter)

st.map(filtered_data)