def kpi_escuela(df):
streamlit_metrics.metric_row({
'Nº Alumnos':
df.shape[0],
'Nº Promociones':
len(df['Promoción'].unique()),
'% Alumnos Graduados':
round(
df['Estado de matrícula'].value_counts(normalize=True)['Graduado']
* 100, 0),
})
def getOrderBookInfo(coinName):
bidOrderBookDF = pd.DataFrame()
askOrderBookDF = pd.DataFrame()
result = requests.get('https://api1.binance.com/api/v3/depth?symbol=' +
str(coinName) + '&limit=5000')
json_data = json.loads(result.text)
for i in range(0, len(json_data['bids'])):
bidPrice = float(json_data['bids'][i][0])
Qty = float(json_data['bids'][i][1])
bidData = {'Bid': bidPrice, 'Qty': Qty}
bidOrderBookDF = bidOrderBookDF.append(bidData, ignore_index=True)
bidOrderBookDF[
'Total price'] = bidOrderBookDF['Bid'] * bidOrderBookDF['Qty']
bidOrderBookDF = bidOrderBookDF[(bidOrderBookDF['Total price'] >= 10000)]
bidOrderBookDF['Qty'] = bidOrderBookDF['Qty'].round()
bidOrderBookDF['Total price'] = bidOrderBookDF['Total price'].round()
for i in range(0, len(json_data['asks'])):
askPrice = float(json_data['asks'][i][0])
Qty = float(json_data['asks'][i][1])
askData = {'Ask': askPrice, 'Qty': Qty}
askOrderBookDF = askOrderBookDF.append(askData, ignore_index=True)
askOrderBookDF[
'Total price'] = askOrderBookDF['Ask'] * askOrderBookDF['Qty']
askOrderBookDF = askOrderBookDF[(askOrderBookDF['Total price'] >= 10000)]
askOrderBookDF['Qty'] = askOrderBookDF['Qty'].round()
askOrderBookDF['Total price'] = askOrderBookDF['Total price'].round()
col1, col2 = st.beta_columns(2)
with col1:
st.info("__Support__ - " + getBestBidPriceQty(coinName))
bidOrderBookDF = bidOrderBookDF.reset_index(drop=True)
st.dataframe(bidOrderBookDF.sort_values(by=['Bid'], ascending=False))
totalBids = bidOrderBookDF['Total price'].sum()
with col2:
st.info("__Resistance__ - " + getBestAskPriceQty(coinName))
askOrderBookDF = askOrderBookDF.reset_index(drop=True)
st.dataframe(askOrderBookDF.sort_values(by=['Ask']))
totalAsks = askOrderBookDF['Total price'].sum()
metric_row({
"Total Bids": millify(totalBids, precision=2),
"Total Asks": millify(totalAsks, precision=2)
})
def kpi_busqueda_empleo(df):
tipo = '3 Continua su búsqueda de trabajo'
a_180 = kpi_empleo(df, 'Estado 180 días post-graduación', tipo)
a_90 = kpi_empleo(df, 'Estado 90 días post-graduación', tipo)
actual = kpi_empleo(df, 'Estado actual', tipo)
streamlit_metrics.metric_row({
'% Alumnos que Continuan buscando trabajo':
actual,
'% Alumnos que Continuan buscando trabajo a 90 días de graduarse':
a_90,
'% Alumnos que Continuan buscando trabajo a 180 días de graduarse':
a_180
})
def changes24Hours(coinName):
result = requests.get(
'https://api1.binance.com/api/v3/ticker/24hr?symbol=' + str(coinName))
json_data = json.loads(result.text)
priceChange = json_data["priceChange"]
percentChange = json_data["priceChangePercent"]
lowPrice = json_data["lowPrice"]
highPrice = json_data["highPrice"]
weightedAvgPrice = json_data["weightedAvgPrice"]
volume = int((float(json_data["volume"])))
st.info("__24 hours change statistics__")
metric_row({
"Price Change": millify(priceChange, precision=2),
"Percent Change": millify(percentChange, precision=2) + "%",
"Low Price": millify(lowPrice, precision=2),
"High Price": millify(highPrice, precision=2),
"Average Price (24 hrs)": millify(weightedAvgPrice, precision=2),
"Volume (24 hrs)": millify(volume, precision=0)
})
def show_results(counts, reset_callback, unsafe_password=None):
"""Show analytics results in streamlit, asking for password if given."""
# Show header.
st.title("Analytics Dashboard")
st.markdown("""
Psst! 👀 You found a secret section generated by
[streamlit-analytics](https://github.com/jrieke/streamlit-analytics).
If you didn't mean to go here, remove `?analytics=on` from the URL.
""")
# Ask for password if one was given.
show = True
if unsafe_password is not None:
password_input = st.text_input("Enter password to show results",
type="password")
if password_input != unsafe_password:
show = False
if len(password_input) > 0:
st.write("Nope, that's not correct ☝️")
if show:
# Show traffic.
st.header("Traffic")
st.write(f"since {counts['start_time']}")
st.write(
f"""
<sup>pageview = user (re-)loads site |
script run = streamlit re-runs upon changes |
time spent = from page load to last widget interaction (summed across users)</sup>
""",
unsafe_allow_html=True,
)
st_metrics.metric_row({
"Pageviews":
counts["total_pageviews"],
"Script runs":
counts["total_script_runs"],
"Time spent":
utils.format_seconds(counts["total_time_seconds"]),
})
# Total: {counts['total_pageviews']} pageviews, {counts['total_script_runs']} script runs
# <br>
# st.header("Traffic per day")
# Plot altair chart with pageviews and script runs.
df = pd.DataFrame(counts["per_day"])
base = alt.Chart(df).encode(
x=alt.X("monthdate(days):O", axis=alt.Axis(title="", grid=True)))
line1 = base.mark_line(point=True, stroke="#5276A7").encode(
alt.Y(
"pageviews:Q",
axis=alt.Axis(
titleColor="#5276A7",
tickColor="#5276A7",
labelColor="#5276A7",
format=".0f",
tickMinStep=1,
),
scale=alt.Scale(domain=(0, df["pageviews"].max() + 1)),
))
line2 = base.mark_line(point=True, stroke="#57A44C").encode(
alt.Y(
"script_runs:Q",
axis=alt.Axis(
title="script runs",
titleColor="#57A44C",
tickColor="#57A44C",
labelColor="#57A44C",
format=".0f",
tickMinStep=1,
),
))
layer = (alt.layer(line1, line2).resolve_scale(
y="independent").configure_axis(titleFontSize=15,
labelFontSize=12,
titlePadding=10))
st.altair_chart(layer, use_container_width=True)
# Show widget interactions.
st.header("Widget interactions")
st.markdown(
"""
Find out how users interacted with your app!
<br>
Numbers indicate how often a button was clicked, how often a specific text
input was given, ...
<br>
<sub>Note: Numbers only increase if the state of the widget
changes, not every time streamlit runs the script.</sub>
""",
unsafe_allow_html=True,
)
st.write(counts["widgets"])
# Show button to reset analytics.
st.header("Danger zone")
with st.beta_expander("Here be dragons 🐲🔥"):
st.write("""
Here you can reset all analytics results.
**This will erase everything tracked so far. You will not be able to
retrieve it. This will also overwrite any results synced to Firestore.**
""")
reset_prompt = st.selectbox(
"Continue?",
[
"No idea what I'm doing here",
"I'm absolutely sure that I want to reset the results",
],
)
if reset_prompt == "I'm absolutely sure that I want to reset the results":
reset_clicked = st.button("Click here to reset")
if reset_clicked:
reset_callback()
st.write("Done! Please refresh the page.")
expander_bar2 = st.beta_expander("Click to explore raw data table")
expander_bar2.write(filtered_df)
#st.subheader('Listing prices data')
#st.write(filtered_df)
'''
## Step 2: Create KPI cards
'''
# Calculate KPIs
listings_count = len(pd.unique(filtered_df['listing_id']))
avg_listed_days = round(filtered_df['days_on_site'].mean())
# Create visuals
st.subheader('Vancouver Housing Inventory')
metric_row({
"Number of listings": listings_count,
"Average days listed on site": avg_listed_days
})
'''
## Step 3: Create graphs
'''
# Create dataframe with count of unique listings by date
df_inventory = filtered_df[['scrape_date', 'listing_id'
]].groupby(['scrape_date']).agg(['nunique'
]).reset_index()
df_inventory.columns = ['scrape_date', 'listings_count']
st.subheader('Number of listings over time')
st.line_chart(df_inventory.set_index('scrape_date'))
'''
## Step 4: Create summary tables
'''