def main():
# Loading the data
df = get_data_ex4()
page = st.sidebar.selectbox("Choose a page", ["Homepage", "Exploration"])
if page == "Homepage":
st.header('Ъда Covid-19 Vaccines Doses Applied Dashboard Ъда ')
st.write("Please select a page on the left.")
st.sidebar.markdown('Ъда **Covid-19 Dashborad** Ъда ')
st.sidebar.markdown('''
This app is to give insights about Covid-19 Vaccines in PERU.
The data considered for this analysis was obtained from MINSA Open data website
https://www.datosabiertos.gob.pe/dataset/vacunaci%C3%B3n-contra-covid-19-ministerio-de-salud-minsa
Select the different options to play with the Visualization
All the Charts are interactive.
Scroll the mouse over the Charts to feel the interactive features like Tool tip, Zoom, Pan
Designed by:
**Javier Caparo** ''')
#st.write(df.dtypes)
today = datetime.date.today()
st.write('Today is:', today)
st.write('# of doses already applied', len(df.index))
st.dataframe(df.head(5))
# Load a lottie animation
lottie_url = "https://assets3.lottiefiles.com/packages/lf20_1pf6yomw.json"
lottie_json = load_lottieurl(lottie_url)
st_lottie(lottie_json)
elif page == "Exploration":
st.title("Data Exploration")
st.header('Grouped by SEX & RISK GROUP')
visualize_data(df)
from matplotlib.figure import Figure
from PIL import Image
import gender_guesser.detector as gender
from streamlit_lottie import st_lottie
import requests
st.set_page_config(layout="wide")
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_book = load_lottieurl('https://assets4.lottiefiles.com/temp/lf20_aKAfIn.json')
st_lottie(lottie_book, speed=1, height=200, key="initial")
matplotlib.use("agg")
_lock = RendererAgg.lock
sns.set_style('darkgrid')
row0_spacer1, row0_1, row0_spacer2, row0_2, row0_spacer3 = st.columns(
(.1, 2, .2, 1, .1))
row0_1.title('Analyzing Your Goodreads Reading Habits')
with row0_2:
import pandas as pd
import requests
password_attempt = st.text_input('Please Enter The Password')
if password_attempt != 'example_password':
st.write('Incorrect Password!')
st.stop()
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_airplane = load_lottieurl('https://assets4.lottiefiles.com/packages/lf20_jhu1lqdz.json')
st_lottie(lottie_airplane, speed=1, height=200, key="initial")
st.title('Major US Airline Job Application')
st.write('by Tyler Richards')
st.subheader('Question 1: Airport Distance')
'''
The first exercise asks us 'Given the table of airports and
locations (in latitude and longitude) below,
write a function that takes an airport code as input and
returns the airports listed from nearest to furthest from
the input airport.' There are three steps here:
1. Load Data
2. Implement Distance Algorithm
3. Apply distance formula across all airports other than the input
4. Return sorted list of airports Distance
import datetime as dt
import matplotlib.pyplot as plt
from streamlit_lottie import st_lottie
import requests
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_tree = load_lottieurl(
'https://assets7.lottiefiles.com/temp/lf20_yww8EW.json')
st_lottie(lottie_tree, speed=1, height=100, key="initial")
st.title('SF Trees')
st.write('This app analyses trees in San Francisco using'
' a dataset kindly provided by SF DPW. The '
'histogram below is filtered by tree owner.')
#load trees dataset, add age column in days
trees_df = pd.read_csv('trees.csv')
trees_df['age'] = (pd.to_datetime('today') -
pd.to_datetime(trees_df['date'])).dt.days
#add tree owner filter to sidebar, then filter, get color
owners = st.sidebar.multiselect('Tree Owner Filter',
trees_df['caretaker'].unique())
graph_color = st.sidebar.color_picker('Graph Colors')
if owners:
#st.image(image, use_column_width = False)
from streamlit_lottie import st_lottie
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_url = "https://assets6.lottiefiles.com/packages/lf20_arq6m8vt.json"
lottie_json = load_lottieurl(lottie_url)
st_lottie(lottie_json)
#st_lottie('https://assets6.lottiefiles.com/packages/lf20_arq6m8vt.json', key="user")
st.write("""
# INSTAGRAM ACCOUNT INSIGHTS
""")
params = getCreds() # get creds
response = getUserInsights(params) # get insights for a user
#follower_count = response['json_data']['data'][0]['values'][0]['value']
impressions = response['json_data']['data'][0]['values'][0]['value']
profile_views = response['json_data']['data'][1]['values'][0]['value']
reach = response['json_data']['data'][2]['values'][0]['value']
)
st.header('File Uploader')
filez = st.file_uploader('Upload the file containing your whatsapp data')
if filez is not None:
whatsapp_df = copy.deepcopy(convert_file_to_df(filez))
st.header('Word Cloud Generator')
col1, col2 = st.beta_columns(2)
with col1:
want_wc = st.button(
'Click here to view the word cloud, keep in mind generating it will take some time'
)
if want_wc == True:
placeholder1 = col2.empty()
with placeholder1:
st_lottie(lottie_json, loop=True, height=50, width=50, key='a')
st.image(generate_wordcloud(whatsapp_df).to_image(),
use_column_width=True)
placeholder1.empty()
st.header('Messages sent per person')
col3, col4 = st.beta_columns(2)
with col3:
want_mpp = st.button(
'Click here to generate the number of messages sent per person')
if want_mpp == True:
placeholder2 = col4.empty()
with placeholder2:
st_lottie(lottie_json, loop=True, height=50, width=50, key='b')
st.dataframe(messages_per_person(whatsapp_df))
placeholder2.empty()
import matplotlib.pyplot as plt
import seaborn as sns
from pandas_profiling import ProfileReport
from streamlit_pandas_profiling import st_profile_report
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_penguin = load_lottieurl(
'https://assets9.lottiefiles.com/private_files/lf30_lntyk83o.json')
st_lottie(lottie_penguin, speed=1.5, width=800, height=400)
st.title("Palmer's Penguins")
st.markdown(
'Use this Streamlit app to make your own scatterplot about penguins!')
selected_x_var = st.selectbox(
'What do want the x variable to be?',
['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'])
selected_y_var = st.selectbox(
'What about the y?',
['bill_depth_mm', 'bill_length_mm', 'flipper_length_mm', 'body_mass_g'])
penguin_file = st.file_uploader('Select Your Local Penguins CSV')
if penguin_file is not None:
penguins_df = pd.read_csv(penguin_file)
return r.json()
lottie_url = "https://assets10.lottiefiles.com/temporary_files/EGBZKv.json"
lottie_json = load_lottieurl(lottie_url)
col1, col2, col3 = st.beta_columns([8, 1, 2])
with col1:
st.title('Technica Finance Dashboard')
with col2:
st.write("")
with col3:
st_lottie(lottie_json, speed=1, height=100)
def create_df():
df = pd.read_csv(CSV_TITLE)
df[['Debit', 'Credit']] = df[['Debit', 'Credit']].apply(pd.to_numeric)
df = df.fillna(0)
df['Credit/Debit'] = df['Credit'] - df['Debit']
df = df.iloc[::-1]
df['Resulting Balance'] = STARTING_BALANCE + df['Credit/Debit'].cumsum()
df['Date'] = pd.to_datetime(df['Posted Date'])
return df
def get_data(df, month):
current_balance = df['Resulting Balance'].iloc[-1]
st.title("Hello Lottie!")
st.markdown(
"""
[Lottie](https://airbnb.io/lottie) is a library that parses [Adobe After Effects](http://www.adobe.com/products/aftereffects.html) animations
exported as json with [Bodymovin](https://github.com/airbnb/lottie-web) and renders them natively on mobile and on the web!
Go look at the [awesome animations](https://lottiefiles.com/) to spice your Streamlit app!
"""
)
with st.sidebar:
st.header("Animation parameters")
speed = st.slider("Select speed", 0.1, 2.0, 1.0)
reverse = st.checkbox("Reverse direction", False)
st_lottie(lottie_streamlit, speed=speed, reverse=reverse, height=400, key="initial")
with st.sidebar:
st.markdown("---")
st.markdown(
'<h6>Made in  <img src="https://streamlit.io/images/brand/streamlit-mark-color.png" alt="Streamlit logo" height="16">  by <a href="https://twitter.com/andfanilo">@andfanilo</a></h6>',
unsafe_allow_html=True
)
st.markdown(
'<div style="margin-top: 0.75em;"><a href="https://www.buymeacoffee.com/andfanilo" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a></div>',
unsafe_allow_html=True
)
c_col1, colx, c_col2, coly = st.columns((1, 0.1, 0.25, 1))
if c_col1.button("Run some heavy computation...for 5 seconds!"):
with c_col2.empty():
import components
import utils
from validation import Vector
st.set_page_config(
page_title="Vector Validator",
layout="centered",
page_icon="🔻",
initial_sidebar_state="collapsed",
)
col1_header, col2_header = st.beta_columns([1, 6])
lottie_url = "https://assets10.lottiefiles.com/temp/lf20_YQB3X3.json"
lottie_json = utils.load_lottieurl(lottie_url)
with col1_header:
st_lottie(lottie_json, height=100, speed=1)
col2_header.write("")
col2_header.title(f"Vector Validator")
st.markdown(
"[](https://gitHub.com/chrieke/vector-validator)"
"   [](https://www.twitter.com/chrieke)"
)
st.write("")
st.markdown(
"**Validates and automatically fixes your geospatial vector data.** <br> Select the validation options, "
"upload/paste your vector data or try one of the examples.",
unsafe_allow_html=True,
)
validation_criteria = components.config()