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pip install streamlit
streamlit hello

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streamlit run app.py

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import streamlit as st
import numpy as np 
import pandas as pd

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st.title('My first app')
st.write("Here's our first attempt at using data to create a table:")

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data = pd.DataFrame({'first column': [1, 2, 3, 4],
                     'second column': [10, 20, 30, 40]
                    })
# display data table in web app
# st.write(data)
data # magic command to show the dataframe table in web app

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chart_data = pd.DataFrame(
     np.random.randn(20, 3),
     columns=['a', 'b', 'c'])
st.line_chart(chart_data)

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map_data = pd.DataFrame(
    np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
    columns=['lat', 'lon'])
st.map(map_data)

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if st.checkbox('Show dataframe'):
    chart_data = pd.DataFrame(
       np.random.randn(20, 3),
       columns=['a', 'b', 'c'])
    chart_data
    # st.line_chart(chart_data)

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option = st.selectbox(
    'Which number do you like best?',
     data['first column'])
'You selected: ', option

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option = st.sidebar.selectbox(
    'Which number do you like best?',
     chart_data['a'])
'You selected:', option
# most of the elements can be putted into sidebar usinf .sidebar
# st.sidebar.map(map_data)

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left_column, right_column = st.beta_columns(2)

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pressed = left_column.button('Press me?')
if pressed:
    right_column.write("Woohoo!")

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expander = st.beta_expander("FAQ")
expander.write("Here you could put in some really, really long explanations...")

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import time
'Starting a long computation...'
# Add a placeholder
latest_iteration = st.empty()
bar = st.progress(0)
for i in range(40):
  # 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!'

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import streamlit as st  
import pandas as pd
import numpy as np

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from PIL import Image
#img = Image.open("file_name.png")
# pass that variable as page_icon value to use custom icon or logo
# another way
PAGE_CONFIG = {"page_title":"Jesse", "page_icon":":smiley:", "layout":"centered"}
st.set_page_config(**PAGE_CONFIG)
# OR
st.set_page_config(page_title='Sumit', page_icon=':smiley:')

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st.title("Uber Pickups in NYC")

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DATE_COLUMN = 'date/time'
DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
            'streamlit-demo-data/uber-raw-data-sep14.csv.gz')

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# function to download raw data and convert text datatype into datetime
@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

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data_load_state = st.text('Loading data ...')
data = load_data(10000)
data_load_state.text('Dataset loaded using Streamlit caching!')

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if st.checkbox('Show raw data'):
    st.subheader('Raw Data')
    st.write(data)

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st.subheader('Number of picks by hour')
hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0, 24))[0]
st.bar_chart(hist_values)

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st.subheader('Map of all picks')
st.map(data)

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hour_to_filter = 17
filter_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]
st.subheader(f'Map of all the pickups at {hour_to_filter}:00')
st.map(filter_data)

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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)

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st.subheader('Seaborn scatterplot of Median Family Income by Percent Unemployed')
plt.figure()
sns.set_style('darkgrid')
plt.title('Median Family Income versus Unemployment')
sns.scatterplot(x='Median Family Income',y='Percent Unemployed',data=df,hue='Income Quartile',palette="Dark2")
st.pyplot()

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# Histogram for median family income
st.subheader('Seaborn distplot of Median Family Income')
plt.figure()
plt.title('County Level Income Distribution')
sns.set_style('darkgrid')
sns.distplot(df['Median Family Income'])
st.pyplot()