def data_load(data):
# ----------------------
# Load
# ----------------------
# Define Map
def updateMap(data, limit1, limit2, limit3, limit4, condition,
construction):
houses = data[(data['sqft_living'] >= limit1)
& (data['bathrooms'] >= limit2) &
(data['price'] <= limit3) &
(data['sqft_basement'] <= limit4) &
(data['condition'] >= condition) &
(data['yr_built'] <= construction)][[
'id', 'lat', 'long', 'sqft_living', 'bathrooms',
'price', 'sqft_basement', 'condition', 'yr_built'
]]
# plot map
st.title('House Rocket Map')
is_check = st.checkbox('Display Map')
living_room_min = int(data['sqft_living'].min())
living_room_max = int(data['sqft_living'].max())
bedroom_min = int(data['bedrooms'].min())
bedroom_max = int(data['bathrooms'].max())
price_min = int(data['price'].min())
price_max = int(data['price'].max())
basement_size_min = int(data['sqft_basement'].min())
basement_size_max = int(data['sqft_basement'].max())
house_condition_min = int(data['condition'].min())
house_condition_max = int(data['condition'].max())
year_built_min = int(data['yr_built'].min())
year_built_max = int(data['yr_built'].max())
# waterview = str(data['waterfront'])
living_room = st.slider('Living room size',
min_value=living_room_min,
max_value=living_room_max,
value=6000,
step=100)
bedroom = st.slider('Number of bedrooms',
min_value=bedroom_min,
max_value=bedroom_max,
value=3,
step=1)
price = st.slider('House price',
min_value=price_min,
max_value=price_max,
value=1000000,
step=5000)
basement_size = st.slider('Basement size',
min_value=basement_size_min,
max_value=basement_size_max,
value=1000,
step=10)
house_condition = st.slider('House condition',
min_value=house_condition_min,
max_value=house_condition_max,
value=3,
step=1)
year_built = st.slider('Year built',
min_value=year_built_min,
max_value=year_built_max,
value=2000,
step=1)
waterview = st.selectbox('Water View', options=['Yes', 'No'], index=0)
if is_check:
# select rows
houses = data[(data['sqft_living'] < living_room)
& (data['bedrooms'] < bedroom) & (data['price'] < price)
& (data['sqft_basement'] < basement_size) &
(data['condition'] <= house_condition) &
(data['yr_built'] <= year_built) &
(data['waterfront'] == waterview)][[
'id', 'lat', 'long', 'price'
]]
# st.dataframe(houses)
fig = px.scatter_mapbox(
houses,
lat='lat',
lon='long',
# color = 'bathrooms',
size='price',
color_continuous_scale=px.colors.cyclical.IceFire,
size_max=15,
zoom=10)
fig.update_layout(mapbox_style='open-street-map')
fig.update_layout(height=600,
margin={
'r': 0,
't': 0,
'l': 0,
'b': 0,
})
st.plotly_chart(fig)
def update_app_ui2(month_value, day_value, region_value, country_value,
state_value, city_value, attack_value, year_value):
if (region_value != None):
df1 = pd.DataFrame()
dfnew = pd.DataFrame()
n = [x for x in range(year_value[0], year_value[1] + 1)]
for i in n:
df2 = pd.DataFrame(df[df['iyear'] == i])
df1 = df1.append(df2)
df3 = pd.DataFrame(df[df['region_txt'] == (region_value)])
df1 = df1.merge(df3)
dfnew = df1
print(df1)
if (country_value != None):
dfnew = df1
dfc = pd.DataFrame(df[df['country_txt'] == (country_value)])
df1 = df1.merge(dfc)
if (month_value != None):
dfnew = df1
dfm = pd.DataFrame(df[df['imonth'].isin(month_value)])
df1 = df1.merge(dfm)
if (day_value != None):
dfnew = df1
dfd = pd.DataFrame(df[df['iday'].isin(day_value)])
df1 = df1.merge(dfd)
if (state_value != None):
dfnew = df1
dfs = pd.DataFrame(df[df['provstate'].isin(state_value)])
df1 = df1.merge(dfs)
if (city_value != None):
dfnew = df1
dfy = pd.DataFrame(df[df['city'].isin(city_value)])
df1 = df1.merge(dfy)
if (attack_value != None):
dfnew = df1
dfa = pd.DataFrame(
df[df['attacktype1_txt'].isin(attack_value)])
df1 = df1.merge(dfa)
if df1.empty:
value = 'No Results Found'
fig = px.scatter_mapbox(dfnew,
lat=dfnew["latitude"],
lon=dfnew["longitude"],
hover_name=dfnew["city"],
hover_data=[
'region_txt', 'country_txt', 'provstate',
'city', 'attacktype1_txt', 'nkill',
'imonth', 'iday'
],
color='attacktype1_txt',
zoom=2,
height=500)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
else:
value = 'Results Found'
print('the df1 in month is', df1)
fig = px.scatter_mapbox(df1,
lat=df1["latitude"],
lon=df1["longitude"],
hover_name=df1["city"],
hover_data=[
'region_txt', 'country_txt', 'provstate',
'city', 'attacktype1_txt', 'nkill',
'imonth', 'iday'
],
color='attacktype1_txt',
zoom=2,
height=500)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig, value
def update_app_ui(
Tabs,
subtabs2,
month_value,
date_value,
region_value,
country_value,
state_value,
city_value,
attack_value,
year_value,
chart_dp_value,
search,
chart_year_value,
):
fig = None
if (Tabs == "Map"):
print("Data Type of month value = ", str(type(month_value)))
print("Data of month value = ", month_value)
print("Data Type of Day value = ", str(type(date_value)))
print("Data of Day value = ", date_value)
print("Data Type of region value = ", str(type(region_value)))
print("Data of region value = ", region_value)
print("Data Type of country value = ", str(type(country_value)))
print("Data of country value = ", country_value)
print("Data Type of state value = ", str(type(state_value)))
print("Data of state value = ", state_value)
print("Data Type of city value = ", str(type(city_value)))
print("Data of city value = ", city_value)
print("Data Type of Attack value = ", str(type(attack_value)))
print("Data of Attack value = ", attack_value)
print("Data Type of year value = ", str(type(year_value)))
print("Data of year value = ", year_value)
# year_filter
year_range = range(year_value[0], year_value[1] + 1)
new_df = df[df["iyear"].isin(year_range)]
# month_filter
if month_value == [] or month_value is None:
pass
else:
if date_value == [] or date_value is None:
new_df = new_df[new_df["imonth"].isin(month_value)]
else:
new_df = new_df[new_df["imonth"].isin(month_value)
& (new_df["iday"].isin(date_value))]
# region, country, state, city filter
if region_value == [] or region_value is None:
pass
else:
if country_value == [] or country_value is None:
new_df = new_df[new_df["region_txt"].isin(region_value)]
else:
if state_value == [] or state_value is None:
new_df = new_df[
(new_df["region_txt"].isin(region_value))
& (new_df["country_txt"].isin(country_value))]
else:
if city_value == [] or city_value is None:
new_df = new_df[
(new_df["region_txt"].isin(region_value))
& (new_df["country_txt"].isin(country_value)) &
(new_df["provstate"].isin(state_value))]
else:
new_df = new_df[
(new_df["region_txt"].isin(region_value))
& (new_df["country_txt"].isin(country_value)) &
(new_df["provstate"].isin(state_value)) &
(new_df["city"].isin(city_value))]
if attack_value == [] or attack_value is None:
pass
else:
new_df = new_df[new_df["attacktype1_txt"].isin(attack_value)]
if new_df.shape[0]:
pass
else:
new_df = pd.DataFrame(columns=[
'iyear', 'imonth', 'iday', 'country_txt', 'region_txt',
'provstate', 'city', 'latitude', 'longitude',
'attacktype1_txt', 'nkill'
])
new_df.loc[0] = [
0, 0, 0, None, None, None, None, None, None, None, None
]
mapFigure = px.scatter_mapbox(new_df,
lat="latitude",
lon="longitude",
color="attacktype1_txt",
hover_name="city",
hover_data=[
"region_txt", "country_txt",
"provstate", "city",
"attacktype1_txt", "nkill", "iyear",
"imonth", "iday"
],
zoom=1)
mapFigure.update_layout(
mapbox_style="stamen-toner",
autosize=True,
margin=dict(l=0, r=0, t=25, b=20),
)
fig = mapFigure
elif (Tabs == "Chart"):
fig = None
chart_df = None
year_range_chart = range(chart_year_value[0], chart_year_value[1] + 1)
chart_df = df[df["iyear"].isin(year_range_chart)]
if subtabs2 == "WorldChart":
pass
elif subtabs2 == "IndiaChart":
chart_df = chart_df[(chart_df["region_txt"] == "South Asia")
& (chart_df["country_txt"] == "India")]
if chart_dp_value is not None:
if search is not None:
chart_df = chart_df.groupby(
"iyear")[chart_dp_value].value_counts().reset_index(
name="count")
chart_df = chart_df[chart_df[chart_dp_value].str.contains(
search, case=False)]
else:
chart_df = chart_df.groupby(
"iyear")[chart_dp_value].value_counts().reset_index(
name="count")
else:
raise PreventUpdate
if chart_df.shape[0]:
pass
else:
chart_df = pd.DataFrame(columns=['iyear', 'count', chart_dp_value])
chart_df.loc[0] = [0, 0, "No data"]
fig = px.area(chart_df, x="iyear", y="count", color=chart_dp_value)
return dcc.Graph(figure=fig)
#Using MapBox (via Plotly plugin) for city-focused geo visuals. Default Plotly maps are only available at more macro levels (globe, country)
MBToken = 'pk.eyJ1Ijoic2NvaGVuZGUiLCJhIjoiY2szemMxczZoMXJhajNrcGRsM3cxdGdibiJ9.2oazpPgLvgJGF9EBOYa9Wg'
px.set_mapbox_access_token(MBToken)
specText = 'Below is a map of all schools marked as "Specialized", broken up by borough (color). The size of the circle \
denotes enrollment size. The four largest circles are the oldest specialized schools: Stuyvesant, Bronx Science and Brooklyn Tech, \
which specialize in STEM; and Laguardia, which specializes in the arts). These are also the oldest specialized schools. The \
smaller circles are relative newcomers to the specialized admissions process, and show that an expansion of the system is \
already underway'
specFig = px.scatter_mapbox(specSchools.dropna(),
lat="latitude",
lon="longitude",
color="borough",
size="total_students",
text="school_name",
hover_name="school_name",
hover_data=["neighborhood"],
size_max=15,
zoom=9)
intText = 'Taking a look at the breakdown in popular interests in non-specialized schools. Science/Tech takes up a lot of \
space, but there is a lot of interest in Humanities. As of now, there are no schools which specialize in social sciences.'
intFig = go.Figure(
data=[go.Pie(labels=dfg3['interests'], values=dfg3['count'])])
distText = 'Scatter showing min distance from any specialized school using color gradient, and number of specialized test \
takers as marker size. The large, dark circles indicate a school with a large number of students interested in attending \
one of the specialized schools, but that are situated inconveniently far away.'
"primary": "dimgrey",
"background": "black",
"font": {
"color": 'white',
'size': 17
},
},
)
#Resende Bubble Map
resende_bubble = px.scatter_mapbox(
df_resende_bairro,
lat='lat',
lon='long',
hover_data=['Número de casos', 'Número de mortes'],
hover_name='Bairro',
zoom=12.2,
size='Número de casos',
height=559 + 25,
size_max=30,
center=dict(lat=-22.470, lon=-44.46))
resende_bubble.update_layout(
mapbox_style='dark',
mapbox_accesstoken=
'pk.eyJ1IjoibWF0aGV1c2dhbGhhbm8iLCJhIjoiY2s5YWozZmZyMjQyazNkcGdqZXlzajFraSJ9.mQwhayrKhADgig-Y1MKwSA',
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
#Train model
count = CountVectorizer()
countVec = count.fit(postmatesBOW['BagOfWords'].values.astype('U'))
count_matrix = countVec.transform(
postmatesBOW['BagOfWords'].values.astype('U'))
#Plot map figure
fig = px.scatter_mapbox(
postmates_grouped,
lat="Latitude",
lon="Longitude",
size="scale",
hover_name="Name",
hover_data={
"scale": False,
"Favorites": True,
"Latitude": False,
"Longitude": False
},
color="Category",
zoom=10,
height=800,
template="plotly_dark",
)
fig.update_layout(mapbox_style="dark", mapbox_accesstoken=token)
fig.update_traces(marker={'sizemin': 4})
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.update_layout(uirevision=True)
# ------------------------------------------------------------------------------
# App layout
def update_output(n_clicks, location, start_date, start_time, end_date,
end_time):
if n_clicks is None:
raise PreventUpdate
else:
# change format of user input info
start_date = start_date.encode('utf-8')
end_date = end_date.encode('utf-8')
start_time = str(start_time)
end_time = str(end_time)
lat_long_str = get_coordinates(location)
dist = str(dist)
# combine the query for parking
query_from_user = """SELECT * FROM parking_hourly WHERE ST_DWithin(geoloc, ST_GeomFromText('"""\
+ "Point(" \
+ query_from_user1 + lat_long_str + ")'"\
+ start_date + "' AS date) AND CAST('"\
+ end_date + "' AS date) "\
+ "AND parking_hourly.time BETWEEN CAST('"\
+ start_time + "' AS int) AND CAST('"\
+ end_time + "' AS int) ORDER BY id, date, time ASC;"
# combine the query for collisions
col_info = "SELECT * FROM collisions WHERE ST_DWithin(geoloc, ST_GeomFromText" + """(' """\
+ "Point(" + lat_long_str + ")', 4326), 500) AND collisions.date BETWEEN CAST('"\
+ start_date + "' AS date) AND CAST('" + end_date + "' AS date)" \
+ "ORDER BY date, time ASC"
conn = psycopg2.connect(host = host, \
port=port, \
user = user, \
dbname = db_name, \
password = password, \
sslmode='require')
parking = pd.read_sql_query(query_from_user, conn)
parking['dot_size'] = parking['total_space'] * 50
collisions = pd.read_sql_query(col_info, conn)
# show error information if nothing returned from the query
if len(parking) == 0:
fig = px.scatter_mapbox(parking, lat="lat", lon="long",color_discrete_sequence=["#1e2c63"], \
size= "dot_size", zoom=15, height=600, hover_name="id", hover_data=["total_space"])
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.add_trace(
go.Scattermapbox(
mode= "markers+text", \
lat=collisions['lat'],\
lon=collisions['long'],\
marker=go.scattermapbox.Marker(size=20),\
text = ['Hit Parked Cars: {}'.format(collisions['hit_parked_car'][i]) for i in range(len(collisions))],\
hoverinfo = 'text'
)
)
return '', 'You\'ve entered value that no information found from the database!', dash.no_update, dash.no_update
raise PreventUpdate
# update the map per submit
fig = px.scatter_mapbox(parking, \
lat="lat", \
lon="long",\
color_discrete_sequence=["#1e2c63"],\
size= "dot_size",
zoom=15, \
height=600, \
hover_name="id", \
hover_data=["total_space"]
)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.add_trace(
go.Scattermapbox(
mode= "markers+text", \
lat=collisions['lat'],\
lon=collisions['long'],\
marker=go.scattermapbox.Marker(size=20),\
text = ['Hit Parked Cars: {}'.format(collisions['hit_parked_car'][i]) for i in range(len(collisions))], \
hoverinfo = 'text'
)
)
fig.update_layout(showlegend=False)
park_id = parking['id'].unique()
return 'Here is the result:', '', fig, {
'data': [
dict(x=parking[parking['id'] == park_id[i]]["date"],
y=parking[parking['id'] == park_id[i]]["occupanc_rate"],
mode='plot',
text=parking[parking['id'] == park_id[i]]['id'],
name=park_id[i]) for i in range(len(park_id))
],
'layout':
dict(xaxis={'title': 'Date'},
yaxis={'title': 'Parking Occupancy Rate'},
legend={i: park_id[i]},
hovermode='closest',
plot_bgcolor=colors['background'],
paper_bgcolor=colors['background'],
font={'color': colors['sub_title']})
}
def update_map_ui(tab, month, day, region, country, provstate, city,
attacktype, year, chart_dp_value, search_value,
subtabs2_value):
df1 = df
print('worldmap')
figure = None
if tab == 'tab-1':
year_range = range(year[0], year[1] + 1)
df1 = df1[df1['iyear'].isin(year_range)]
if (month is None or month == []):
pass
else:
df1 = df1[df1['imonth'].isin(month)]
if (day is None or day == []):
pass
else:
df1 = df1[df1['iday'].isin(day)]
if (region is None or region == []):
pass
else:
df1 = df[df['region_txt'].isin(region)]
if (country is None or country == []):
pass
else:
df1 = df1[df1['country_txt'].isin(country)]
if (provstate is None or provstate == []):
pass
else:
df1 = df1[df1['provstate'].isin(provstate)]
if (city is None or city == []):
pass
else:
df1 = df1[df1['city'].isin(city)]
if (attacktype is None or attacktype == []):
pass
else:
df1 = df1[df1['attacktype1_txt'].isin(attacktype)]
if (df1.shape[0]):
pass
else:
df1 = pd.DataFrame(columns=[
'iyear', 'imonth', 'iday', 'region_txt', 'country_txt',
'provstate', 'city', 'latitude', 'longitude', 'nkill',
'attacktype1_txt'
])
df1.loc[0] = [
0, 0, 0, None, None, None, None, None, None, None, None
]
figure = go.Figure()
figure = px.scatter_mapbox(df1,
lat='latitude',
lon='longitude',
color='attacktype1_txt',
hover_data=[
'region_txt', 'country_txt',
'provstate', 'city', 'attacktype1_txt',
'nkill', 'iyear'
],
zoom=1,
title='World Map Plot')
figure.update_layout(mapbox_style='open-street-map',
autosize=True,
margin=dict(l=0, r=0, t=25, b=20))
elif tab == 'Chart':
print(subtabs2_value)
chart_df = df
if subtabs2_value == "tab-1":
if chart_dp_value is not None:
if search_value is not None:
chart_df = df.groupby(
"iyear")[chart_dp_value].value_counts().reset_index(
name="count")
chart_df = chart_df[chart_df[chart_dp_value].str.contains(
search_value, case=False)]
else:
chart_df = df.groupby(
"iyear")[chart_dp_value].value_counts().reset_index(
name="count")
else:
raise PreventUpdate
elif subtabs2_value == 'tab-2':
if chart_dp_value is not None:
chart_df = chart_df[chart_df['region_txt'] == 'South Asia']
chart_df = chart_df[chart_df['country_txt'] == 'India']
chart_df = chart_df.groupby(
"iyear")[chart_dp_value].value_counts().reset_index(
name="count")
if search_value is not None:
chart_df = chart_df[chart_df[chart_dp_value].str.contains(
search_value, case=False)]
else:
raise PreventUpdate
title = 'Area Chart-Incidents Per Year Grouped By:'
inci = ''
for item in chart_dropdown_values:
if (item.get('value') == chart_dp_value):
inci = item.get('label')
title = title + inci
chartFigure = px.area(chart_df,
x="iyear",
y="count",
color=chart_dp_value,
title=title)
figure = chartFigure
else:
return None
return figure
colors = cm.rainbow(np.linspace(0, 1, 10))
plt.scatter(df.Latitude, df.Longitude, color=colors[df.cluster])
plt.scatter(centroids[:, 0],
centroids[:, 1],
color='purple',
marker='*',
label='centroid')
plt.xlabel('latitude')
plt.ylabel('longitude')
plt.legend()
fig = px.scatter_mapbox(df,
lat="Latitude",
lon="Longitude",
zoom=10,
color="cluster",
color_continuous_scale=px.colors.qualitative.G10,
height=500,
opacity=0.05)
fig.add_trace(
go.Scattermapbox(lat=centroids[:, 0],
lon=centroids[:, 1],
mode='markers',
marker=go.scattermapbox.Marker(
size=10,
color='rgb(255, 0, 0)',
)))
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()
def update_app_ui(Tabs,month_value,date_value,region_value,country_value,state_value,city_value,attack_value,year_value,
chart_year_selector, chart_dp_value, search,chartsubtabs):
fig = None
if Tabs == "tab1":
print('Data Type of month value:',str(type(month_value)))
print('Data Type of date value:',str(type(date_value)))
print('Data Type of region value:',str(type(region_value)))
print('Data Type of country value:',str(type(country_value)))
print('Data Type of state value:',str(type(state_value)))
print('Data Type of city value:',str(type(city_value)))
print('Data Type of Attack value:',str(type(attack_value)))
print('Data Type of year value:',str(type(year_value)))
# year filter
year_range = range(year_value[0], year_value[1]+1)
df1 = df[df["iyear"].isin(year_range)]
# month filter
if month_value==[] or month_value is None:
pass
else:
if date_value==[] or date_value is None:
df1 = df1[df1["imonth"].isin(month_value)]
else:
df1 = df1[df1["imonth"].isin(month_value)
& (df1["iday"].isin(date_value))]
# region, country, state, city filter
if region_value==[] or region_value is None:
pass
else:
if country_value==[] or country_value is None :
df1 = df1[df1["region_txt"].isin(region_value)]
else:
if state_value == [] or state_value is None:
df1 = df1[(df1["region_txt"].isin(region_value))&
(df1["country_txt"].isin(country_value))]
else:
if city_value == [] or city_value is None:
df1 = df1[(df1["region_txt"].isin(region_value))&
(df1["country_txt"].isin(country_value)) &
(df1["provstate"].isin(state_value))]
else:
df1 = df1[(df1["region_txt"].isin(region_value))&
(df1["country_txt"].isin(country_value)) &
(df1["provstate"].isin(state_value))&
(df1["city"].isin(city_value))]
#attack type filter
if attack_value == [] or attack_value is None:
pass
else:
df1 = df1[df1["attacktype1_txt"].isin(attack_value)]
mapfigure = go.Figure()
if df1.shape[0]:
pass
else:
df1 = pd.DataFrame(columns = ['iyear','imonth','iday','country_txt','region_txt','provstate',
'city','latitude','longitude','attacktype1_txt','nkill'])
df1.loc[0] = [0, 0 ,0, None, None, None, None, None, None, None, None]
mapFigure = px.scatter_mapbox(df1,lat="latitude",
lon="longitude",
color="attacktype1_txt",
hover_name="city",
hover_data=["region_txt","country_txt","provstate","city","attacktype1_txt","nkill","iyear","imonth","iday"],
zoom=1)
mapFigure.update_layout(mapbox_style="open-street-map",
autosize=True,
margin=dict(l=0,r=0,t=25,b=20),
)
fig = mapFigure
elif Tabs=="chart":
fig = None
year_range_c = range(chart_year_selector[0], chart_year_selector[1]+1)
chart_df = df[df["iyear"].isin(year_range_c)]
if chartsubtabs == "tab4":
pass
elif chartsubtabs == "tab5":
chart_df = chart_df[(chart_df["region_txt"]=="South Asia")
&(chart_df["country_txt"]=="India")]
if chart_dp_value is not None and chart_df.shape[0]:
if search is not None:
chart_df = chart_df.groupby("iyear")[chart_dp_value].value_counts().reset_index(name = "count")
chart_df = chart_df[chart_df[chart_dp_value].str.contains(search, case=False)]
else:
chart_df = chart_df.groupby("iyear")[chart_dp_value].value_counts().reset_index(name="count")
if chart_df.shape[0]:
pass
else:
chart_df = pd.DataFrame(columns = ['iyear', 'count', chart_dp_value])
chart_df.loc[0] = [0, 0,"No data"]
chartfigure = px.area(chart_df, x="iyear", y ="count", color = chart_dp_value)
fig = chartfigure
return dcc.Graph(figure = fig)
'There are **%d** donors along with you in **2020**. ' %
len(donor['Donor'].unique()) +
'They are as kind as you. Thanks to your warm-hearted donate, our world is getting better.'
+
"A person's power may be limited. But when the power of many kind people is connected together, it is enough to have an impact on the world"
)
st.write(
'Start your journey in **2021** with us **RIGHT NOW** by clicking the following link:'
)
st.write('http://url.com')
fig = px.scatter_mapbox(
donor,
lat='Latitude',
lon='Longitude',
hover_name="Donor",
hover_data=['Date planted', 'Family', 'Genus', 'Species'],
zoom=17,
color='label',
title='Here is <b>your</b> tree!')
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "l": 0, "b": 0})
fig.update_layout(width=1000, height=600)
st.write(fig)
if select_don == "Donor's trees":
fig = px.scatter_mapbox(
donor,
lat='Latitude',
lon='Longitude',
if fit:
stdbscan_fit = ST_DBSCAN(eps1,
eps2,
np.round(np.log(len(np_acled_ame))),
metric='haversine')
stdbscan_fit.fit(np_acled_ame)
df_acled_ame['cluster_fit_' + str(eps1) + '_' +
str(eps2)] = stdbscan_fit.labels
fig = px.scatter_mapbox(
df_acled_ame,
lat="latitude",
lon="longitude",
animation_frame='cluster_fit_' + str(eps1) + '_' + str(eps2),
zoom=4,
# center=cent,
#color_continuous_scale=px.colors.cyclical.IceFire,
hover_data=[
'cluster_fit_' + str(eps1) + '_' + str(eps2), 'latitude',
'longitude', 'event_date', 'country', 'actor1', 'actor2'
])
fig.update_layout(mapbox_style="open-street-map")
fig.write_html('maps/fig_' + str(eps1) + '_' + str(eps2) + '.html')
end = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(
end,
'Finish ST-DBSCAN ',
'eps1:',
eps1,
'eps2',
eps2,
title='Common Road Features for Accidents in Houston',
orientation='h')
fig14.update_traces(textposition='outside')
fig14.update_layout(uniformtext_minsize=8,
uniformtext_mode='hide',
title_x=0.5,
height=500)
# GRAPH 15
# ACCIDENT - PRONE SPOTS IN TOP BUSY STREETS IN US
token = 'pk.eyJ1IjoiYmlsaDEiLCJhIjoiY2tpazR5MzM0MDZpYTJ0cW00YmM5MGFicyJ9.x-qdyysjpp-CGnICYerTkw'
# px.set_mapbox_access_token(token)
fig15 = px.scatter_mapbox(mapbox,
lat="lat",
lon="long",
color="Street",
color_continuous_scale=px.colors.cyclical,
zoom=3,
height=600,
hover_data=['City', 'Street', 'lat', 'long'])
fig15.update_layout(mapbox_style="dark", mapbox_accesstoken=token)
fig15.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
app.layout = html.Div([
navbar,
dbc.Container([
dbc.Row([dbc.Col(html.H1("USA Accidents Analysis"),
className="mb-2")]),
dbc.Row([
dbc.Col(html.H6(
children='Visualising accident trends across the United States'
),
# # pprint(df5)
df_append_1 = df.append(df1)
# pprint(df_append_1)
df_append_2 = df_append_1.append(df2)
# pprint(df_append_2)
df_append_3 = df_append_2.append(df3)
# pprint(df_append_3)
df_append_4 = df_append_3.append(df4)
# pprint(df_append_4)
df_append_5 = df_append_4.append(df5)
pprint(df_append_5)
fig = px.scatter_mapbox(df_append_5, lat="lat", lon="lng", color="rating", size="user_ratings_total", color_continuous_scale=px.colors.cyclical.IceFire, size_max=30, zoom=15, hover_data=[
"name",
"price_level",
"vicinity"
])
fig.show()
# # df_append_5.to_csv("data2.csv")
def map_each_hospital(features_train,
target_train=None,
quantile=1.0,
quantile_direction='bottom',
labels={},
midpoint=None,
save_file=None,
make_plot=True):
'''
Map out the locations of hospitals, with marker sizes and colors
reflecting the size of the target variable. Map is interactive.
Parameters
----------
features_train: pandas DataFrame of all features (training data only)
target_train: pandas Series of target values (training data only)
quantile: float in range [0.0, 1.0]. Indicates what top % of
the target data you want displayed (locations outside of
the defined quantile will not be plotted). A value of 1.0
(the default) results in all data being plotted. Values less than 0.5
are assumed to mean "show me the top X%", whereas values greater
than 0.5 are assumed to mean "show me the bottom (1-X)%". Ignored if
target_column is None. Use 1.0 if you want to see all data.
quantile_direction: str. Either 'top' or 'bottom'. Indicates if user
wants to see the "top X%" quantile or the "bottom X%"
quantile. Use 'bottom' if you want to see all data.
labels: dict with str keys and str values. By default, column names are
used in the figure for axis titles, legend entries and hovers. This
parameter allows this to be overridden. The keys of this dict should
correspond to column names, and the values should correspond to the
desired label to be displayed (e.g. {target_column: 'Heart Attacks'})
midpoint: float. If set, indicates the value of target_train that you
want as the midpoint of the color scale used
save_file: str. Should provide a filepath for the interactive HTML
to be saved. If None, file is not saved.
make_plot: bool. If True, automatically plots the resultant figure
Returns
-------
Plots an interactive (plotly-driven) map of all locations with
marker sizes and color corresponding to the target variable values. Also returns a plotly go.Figure object
'''
# Pass in my public mapbox token for contextual mapping
px.set_mapbox_access_token(open("secure_keys/public.mapbox_token").read())
data = features_train.copy()
# Setup figure title
if quantile == 1.0:
title = "All Data"
elif quantile_direction == 'top':
title = f"Top {int(quantile * 100)}%"
else:
title = f"Bottom {int(quantile * 100)}%"
# Combine features and target, if target is specified
if target_train is not None:
target_column = target_train.name
# Combine target and features
data[target_column] = target_train
# 'top' -> at or above
if quantile_direction == 'top':
print(f"Only showing values at or above \
{data.quantile(1-quantile)[target_column]}")
top_target_quantile = data[data[target_column] >= \
data.quantile(1-quantile)[target_column]]
# Assume when a large quantile is given, users wants "this % and lower"
elif quantile_direction == 'bottom':
print(f"Only showing values at or below \
{data.quantile(quantile)[target_column]}")
top_target_quantile = data[data[target_column]\
<= data.quantile(quantile)[target_column]]
else:
raise ValueError(
"Invalid value for quantile_direction. Use 'top' or 'bottom'.")
# Map only top X% of target value with color scale midpoint at median of whole dataset
if midpoint is None: midpoint = target_train.quantile(0.5)
fig = px.scatter_mapbox(
top_target_quantile,
lat="Latitude",
lon="Longitude",
color=target_column,
size=target_column,
size_max=7,
zoom=2,
opacity=0.25,
color_continuous_scale=px.colors.diverging.Portland,
color_continuous_midpoint=midpoint,
labels=labels,
title=title)
# No target data provided
else:
fig = px.scatter_mapbox(data,
lat="Latitude",
lon="Longitude",
zoom=2,
labels=labels,
title=title,
opacity=0.25)
if save_file:
fig.write_html(save_file)
if make_plot: fig.show()
return fig
st.markdown("----------")
####Visualization 4######
expander3 = st.beta_expander("Click for More Information", expanded=False)
px.set_mapbox_access_token(
"pk.eyJ1IjoiZ2VlbWFuMTIwOSIsImEiOiJja291aTI5bjAwbDZ6Mm9sbGVxb292c3NiIn0.eKZKp_JepI5fBsYRa2cdzw"
)
fig5 = px.scatter_mapbox(dfinal,
lat="latitude",
lon="longitude",
animation_frame="DATA_YEAR",
animation_group="BIAS_DESC",
color="BIAS_DESC",
hover_name="BIAS_DESC",
size="VICTIM_COUNT",
hover_data=["target_loc"],
color_continuous_scale=px.colors.cyclical.IceFire,
size_max=35,
zoom=3,
width=1500)
fig5.update_layout(
title=
'Hate Crimes per year<br>(Hover for City,State/Bias Description/Victim Count)',
autosize=True,
geo_scope='usa',
)
st.plotly_chart(fig5)
r="frequency",
theta="direction",
color="strength",
template="plotly_dark",
color_discrete_sequence=px.colors.sequential.Plasma[-2::-1],
)
fig.write_html(os.path.join(dir_name, "bar_polar.html"))
# #### Maps
carshare = px.data.carshare()
fig = px.scatter_mapbox(
carshare,
lat="centroid_lat",
lon="centroid_lon",
color="peak_hour",
size="car_hours",
color_continuous_scale=px.colors.cyclical.IceFire,
size_max=15,
zoom=10,
)
fig.write_html(os.path.join(dir_name, "scatter_mapbox.html"))
carshare = px.data.carshare()
fig = px.line_mapbox(carshare,
lat="centroid_lat",
lon="centroid_lon",
color="peak_hour")
fig.write_html(os.path.join(dir_name, "line_mapbox.html"))
sample_geojson = {
"type":
h1_pos_diario / h1_tot_diario, h2_pos_diario / h2_tot_diario,
super1_pos_diario / super1_tot_diario,
super2_pos_diario / super2_tot_diario, t1_pos_diario / t1_tot_diario,
t2_pos_diario / t2_tot_diario, t3_pos_diario / t3_tot_diario,
t4_pos_diario / t4_tot_diario, uni_pos_diario / uni_tot_diario
]
})
#BubbleMap DIARIO:
mapa = px.scatter_mapbox(city_diario,
lat="latitude",
lon="longitude",
hover_name="edificio",
hover_data=["edificio", "ia"],
color="pcr_positivo",
size_max=25,
zoom=12,
height=600,
size=city_diario["pcr_positivo"],
color_continuous_scale=[(0.00, "green"),
(0.5, "green"), (0.5, "red"),
(1.00, "red")],
labels={"pcr_psitivo": "Casos positivos"})
mapa.update_layout(mapbox_style="open-street-map", height=450)
mapa.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0}, autosize=True)
# Datos ACUMULADOS
# Eliminamos duplicados id ACUMULADO
cc = cc[cc.pcr == 'Positivo'].drop_duplicates(subset='id', keep="first")
c1 = c1[c1.pcr == 'Positivo'].drop_duplicates(subset='id', keep="first")
parking['dot_size'] = parking['total_space'] * 50
park_id = parking['id'].unique()
print(park_id)
collisions = pd.read_sql_query(col_info, conn)
collisions['dot_size'] = collisions['veh_count'] * 120
available_indicators = [
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
]
print(collisions.head(3))
print(len(collisions))
# add parking map to the website
fig = px.scatter_mapbox(parking, lat="lat", lon="long",color_discrete_sequence=["#1e2c63"], \
size= "dot_size", zoom=15, height=450)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
# add collision info
fig.add_trace(
go.Scattermapbox(
mode= "markers+text", \
lat=collisions['lat'],\
lon=collisions['long'],\
marker=go.scattermapbox.Marker(size=20),\
text = ['Hit Parked Cars: {}'.format(collisions['hit_parked_car'][i]) for i in range(len(collisions))], \
hoverinfo = 'text'
)
)
def update_figures(option):
today = datetime.datetime.now(tz=tz.gettz('Europe/Helsinki'))
date_ = today.date().isoformat()
hour_ = today.time().hour
minute_ = today.time().minute
update_time_fi = f"Aineisto on peräisin Helsingin Sanomien avoimesta rajapinnasta." \
f" Päivitetty {date_} klo {hour_}.{minute_:02}."
update_time_en = f"Data from API by Helsingin Sanomat." \
f" Updated {date_} {hour_:02}:{minute_:02}."
if option == 'total':
size_col = 'active'
data, all_data = get_data(
START_DATE,
END_DATE,
cumulative=True,
)
elif option == 'deaths':
size_col = 'deaths'
data, all_data = get_data(
'2020-03-20',
END_DATE,
cumulative=True,
)
else:
size_col = 'confirmed'
data, all_data = get_data(
START_DATE,
END_DATE,
cumulative=False,
)
fig1 = px.scatter_mapbox(
data,
lat='lat',
lon='lon',
color='shp',
hover_name='shp',
hover_data='confirmed deaths recovered'.split(),
size=size_col,
animation_frame='pvm',
center={
'lat': 63.4,
'lon': 26.0
},
zoom=4,
size_max=36,
height=600,
width=None,
opacity=OPACITY,
)
fig1.update_layout(mapbox_style="carto-darkmatter")
fig2 = px.bar(
data,
x='pvm',
y=size_col,
color='shp',
hover_name='shp',
height=520,
width=None,
opacity=OPACITY,
)
fig3 = px.line(
all_data,
x='pvm',
y=size_col,
height=520,
width=None,
)
if option == 'confirmed':
fig3_title = 'Koko maa | All Finland'
else:
fig3.update_layout(yaxis_type='log')
fig3_title = 'Koko maa, logaritminen asteikko | All Finland, logarithmic scale'
return fig1, fig2, fig3, fig3_title, update_time_fi, update_time_en
colors='red',
how='spline',
mode='lines',
item='Maize',
title='Maize Production in Somalia from 2014 - 2018')
fig3 = px.scatter_mapbox(dd.total_element('production', 'Maize'),
lat='Lat',
lon='Lon',
color='Total',
hover_name='Area',
size='Total',
hover_data={
'Lat': False,
'Lon': False
},
labels={'Area': 'Element'},
color_continuous_scale=colors,
zoom=1.4,
width=1900,
height=700,
template='presentation',
center=None,
mapbox_style='light',
title='Global Maize Production in Tonnes(2014-2018)')
fig4 = data[data['Element'] == 'Production'].iplot(
asFigure=True,
kind='pie',
labels='Area',
values='Y2018',
def plotly_relieve(year, hazard):
df = wildfires_data()
clustering = wildfires_clustering()
# I put in a new dataframe the species and the labels so I can figure out which label has each specie
df['labels'] = clustering['labels']
values = labels_based_effect(df, df['SIZE_HA'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['LATITUDE']
longitude = df['LONGITUDE']
text = "date:" + df['YEAR'].astype(str) + "-" + df['MONTH'].astype(
str) + " - HA affected:" + df['SIZE_HA'].astype(
str) + " - Province: " + df['SRC_AGENCY'].astype(str)
if hazard == "Tornadoes":
df = tornadoes_data()
clustering = tornadoes_clustering()
df['labels'] = clustering['labels']
df['effect'] = df['FUJITA'].astype(int) + df['HUMAN_FATAL'].astype(
int) + df['HUMAN_INJ']
values = labels_based_effect(df, df['effect'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YYYY_LOCAL'] == year)] # I get the year I want
latitude = df['START_LAT_N']
longitude = df['START_LON_W']
text = "date" + df['YYYY_LOCAL'].astype(
str) + " - " + df['MM_LOCAL'].astype(
str) + " - Place: " + df['NEAR_CMMTY'].astype(str)
if hazard == "Earthquakes":
df = earthquakes_data()
clustering = earthquakes_clustering()
df['labels'] = clustering['labels']
values = labels_based_effect(df, df['depth'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['latitude']
longitude = df['longitude']
text = "date:" + df['date'] + "- depth:" + df['depth'].astype(
str) + "- magnitude:" + df['magnitude'].astype(
str) + "- place:" + df['place'].astype(str)
if hazard == "Floods":
df = floods_data()
clustering = floods_clustering()
df['labels'] = clustering['labels']
df['effect'] = df['FATALITIES'] + df['INJURED / INFECTED'] + df[
'EVACUATED']
values = labels_based_effect(df, df['effect'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['LATITUDE']
longitude = df['LONGITUDE']
text = "date: " + df['EVENT'].astype(
str) + "- injured & fatalities & evacuated:" + df['effect'].astype(
str) + " - place:" + df['PLACE'].astype(str)
if hazard == "Hurricanes":
df = hurricanes_data()
clustering = hurricanes_clustering()
df['labels'] = clustering['labels']
df['effect'] = df['FATALITIES'] + df['INJURED / INFECTED'] + df[
'EVACUATED']
values = labels_based_effect(df, df['effect'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['LATITUDE']
longitude = df['LONGITUDE']
text = "date: " + df['EVENT'].astype(
str) + "- injured & fatalities & evacuated:" + df['effect'].astype(
str) + " - place:" + df['PLACE'].astype(str)
fig = px.scatter_mapbox(df,
lat=latitude,
lon=longitude,
hover_name=text,
color='colors',
zoom=3)
fig.update_layout(
mapbox_style="white-bg",
mapbox_layers=[{
"below":
'traces',
"sourcetype":
"raster",
"sourceattribution":
"United States Geological Survey",
"source": [
"https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/tile/{z}/{y}/{x}"
]
}, {
"sourcetype":
"raster",
"sourceattribution":
"Government of Canada",
"source": [
"https://geo.weather.gc.ca/geomet/?"
"SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&BBOX={bbox-epsg-3857}&CRS=EPSG:3857"
"&WIDTH=1000&HEIGHT=1000&LAYERS=RADAR_1KM_RDBR&TILED=true&FORMAT=image/png"
],
}])
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
# "vicinity4": data_0["results"][3]["vicinity"],
# "vicinity5": data_0["results"][4]["vicinity"]
}
restaurant_list = []
data_0 = extract_data_0(
requests.get(url2,
params={
"location": lat_lng,
"radius": 1000,
"type": "restaurant",
"key": api_key
}).json())
restaurant_list.append(data_0)
# pprint(restaurant_list)
# df = pd.DataFrame(restaurant_list).drop_duplicates()
# pprint(df)
df = pd.DataFrame(restaurant_list)
pprint(df)
fig = px.scatter_mapbox(df, lat="lat", lon="lng")
fig.show()
# # df_append_4.to_csv("data2.csv")
def plotly_states(year, hazard):
df = wildfires_data()
clustering = wildfires_clustering()
# I put in a new dataframe the species and the labels so I can figure out which label has each specie
df['labels'] = clustering['labels']
values = labels_based_effect(df, df['SIZE_HA'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['LATITUDE']
longitude = df['LONGITUDE']
text = "date:" + df['YEAR'].astype(str) + "-" + df['MONTH'].astype(
str) + " - HA affected:" + df['SIZE_HA'].astype(
str) + " - Province: " + df['SRC_AGENCY'].astype(str)
if hazard == "Tornadoes":
df = tornadoes_data()
clustering = tornadoes_clustering()
df['labels'] = clustering['labels']
df['effect'] = df['FUJITA'].astype(int) + df['HUMAN_FATAL'].astype(
int) + df['HUMAN_INJ']
values = labels_based_effect(df, df['effect'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YYYY_LOCAL'] == year)] # I get the year I want
latitude = df['START_LAT_N']
longitude = df['START_LON_W']
text = "date" + df['YYYY_LOCAL'].astype(
str) + " - " + df['MM_LOCAL'].astype(
str) + " - Place: " + df['NEAR_CMMTY'].astype(str)
if hazard == "Earthquakes":
df = earthquakes_data()
clustering = earthquakes_clustering()
df['labels'] = clustering['labels']
values = labels_based_effect(df, df['depth'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['latitude']
longitude = df['longitude']
text = "date:" + df['date'] + "- depth:" + df['depth'].astype(
str) + "- magnitude:" + df['magnitude'].astype(
str) + "- place:" + df['place'].astype(str)
if hazard == "Floods":
df = floods_data()
clustering = floods_clustering()
df['labels'] = clustering['labels']
df['effect'] = df['FATALITIES'] + df['INJURED / INFECTED'] + df[
'EVACUATED']
values = labels_based_effect(df, df['effect'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['LATITUDE']
longitude = df['LONGITUDE']
text = "date: " + df['EVENT'].astype(
str) + " - injured & fatalities & evacuated:" + df[
'effect'].astype(str) + " - place:" + df['PLACE'].astype(str)
if hazard == "Hurricanes":
df = hurricanes_data()
clustering = hurricanes_clustering()
df['labels'] = clustering['labels']
df['effect'] = df['FATALITIES'] + df['INJURED / INFECTED'] + df[
'EVACUATED']
values = labels_based_effect(df, df['effect'])
df['colors'] = df['labels'].apply(lambda x: labeling(x, values))
df = df[(df['YEAR'] == year)] # I get the year I want
latitude = df['LATITUDE']
longitude = df['LONGITUDE']
text = "date: " + df['EVENT'].astype(
str) + "- injured & fatalities & evacuated:" + df['effect'].astype(
str) + " - place:" + df['PLACE'].astype(str)
fig = px.scatter_mapbox(df,
lat=latitude,
lon=longitude,
hover_name=text,
color='colors',
zoom=3)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
def update_app_ui(month_value, day_value, region_value, country_value,
state_value, city_value, attack_value, year_value):
global dfnew
dfnew = df # dfnew so that if we unselect option, it must show the previous result
if (year_value != None):
global df1
n = [x for x in range(year_value[0], year_value[1] + 1)]
df1 = pd.DataFrame()
print(n)
for i in n:
df2 = pd.DataFrame(df[df['iyear'] == i])
df1 = df1.append(df2)
dfnew = df1
if (month_value != None):
dfnew = df1 #before changing df1 value, i need to store the values in dfnew so if we unselect we get the previous values
df1 = pd.DataFrame(df[df['imonth'].isin(month_value)])
if (day_value != None):
dfnew = df1
dfd = pd.DataFrame(df[df['iday'].isin(day_value)])
df1 = df1.merge(dfd)
print('df1 is', df1)
if (region_value != None):
dfnew = df1
df3 = pd.DataFrame(df[df['region_txt'].isin(region_value)])
df1 = df1.merge(df3)
print(df1)
if (country_value != None):
dfnew = df1
dfc = pd.DataFrame(df[df['country_txt'].isin(country_value)])
df1 = df1.merge(dfc)
if (state_value != None):
dfnew = df1
dfs = pd.DataFrame(df[df['provstate'].isin(state_value)])
df1 = df1.merge(dfs)
if (city_value != None):
dfnew = df1
dfy = pd.DataFrame(df[df['city'].isin(city_value)])
df1 = df1.merge(dfy)
if (attack_value != None):
dfnew = df1
dfa = pd.DataFrame(df[df['attacktype1_txt'].isin(attack_value)])
df1 = df1.merge(dfa)
if (attack_value != None):
dfnew = df1
dfa = pd.DataFrame(df[df['attacktype1_txt'].isin(attack_value)])
df1 = df1.merge(dfa)
if df1.empty:
value = 'Results Found'
fig = px.scatter_mapbox(dfnew,
lat=dfnew["latitude"],
lon=dfnew["longitude"],
hover_name=dfnew["city"],
hover_data=[
'region_txt', 'country_txt', 'provstate',
'city', 'attacktype1_txt', 'nkill',
'imonth', 'iday'
],
color='attacktype1_txt',
zoom=2,
height=500)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
else:
value = 'Results Found'
print(df1)
fig = px.scatter_mapbox(df1,
lat=df1["latitude"],
lon=df1["longitude"],
hover_name=df1["city"],
hover_data=[
'region_txt', 'country_txt', 'provstate',
'city', 'attacktype1_txt', 'nkill',
'imonth', 'iday'
],
color='attacktype1_txt',
zoom=2,
height=500)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig, value
# We calculate the approximate center of all the points to set the initial position of our map
c_lat = (df["latitude"].max() + df["latitude"].min()) / 2
c_lon = (df["longitude"].max() + df["longitude"].min()) / 2
# Since we have defined 3 posibilities of maps on the dropbox, we will have three different
# cases we will have to code. These should ideally have been modulated into other files,
# but we've decised to keep it into a single file to simplify for this workshop.
# The first map is a simple scatter over the world
if map_type == "Global distribution":
# We create a map with the points of our dataframe
my_map = px.scatter_mapbox(df,
lat="latitude",
lon="longitude",
hover_name="city",
zoom=1,
center={
"lat": c_lat,
"lon": c_lon
})
# We use open-street-map because it needs no token
my_map.update_layout(mapbox_style="open-street-map")
# This sets the padding for the map within it's space
my_map.update_layout(margin={"r": 10, "t": 0, "l": 0, "b": 0})
# The second option is a choropleth map, therefore we will need the geojson file with the
# polygons that draw the borders of the countries
if map_type == "By country":
with open('data/countries.geojson', 'r') as data:
countries = json.load(data)
# We also group the data by country so we can get a count of entries for each
##########################################################################
##########################################################################
kmeans = KMeans(n_clusters=3, init='k-means++')
kmeans.fit(X3[X2.columns[0:3]]) # Compute k-means clustering.
X3['cluster_label'] = kmeans.fit_predict(X3[X2.columns[0:3]])
centers = kmeans.cluster_centers_ # Coordinates of cluster centers.
labels = kmeans.predict(X3[X2.columns[0:3]]) # Labels of each point
##########################################################################
##########################################################################
fig = go.Figure()
fig = px.scatter_mapbox(X3,
lat="lat",
lon="lon",
color="cluster_label",
zoom=5,
size="Deaths",
animation_frame="date",
height=750,
hover_name="Province/State")
fig.update_layout(mapbox_style="stamen-terrain")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
########### Initiate the app
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
server = app.server
# app.layout = html.Div([
#
# ], style={'display' : 'flex', 'flex-direction' : 'column', 'align-items':'center'})
ds.iloc[:,0]
ds.loc[:,'id']
cols=[True, False,False, False,False, False,False, False,False, False,False,
False,False, False,False, False,False, False,False,False, False,False]
ds.loc[:,cols]
#19. Salve um arquivo .csv com somente as colunas do item 10.
idd=ds['id']
idd.to_csv('new.csv',index=False)
import plotly.express as px
data_map=ds[['id','lat','long','price','condition']]
mapa=px.scatter_mapbox(data_map, lat='lat', lon='long', hover_name='id',
hover_data=['price'],
color='condition',
zoom=9,
size='price',
height=300)
mapa.update_layout(mapbox_style='open-street-map')
mapa.update_layout(height=600,margin={'r':0,'t':0,'l':0, 'b':0})
mapa.show()
mapa.write_html('mapa_house_rocket.html')
def initial_map():
ts_confirmed, ts_death, ts_recovered = get_data_from_postgres()
date = ts_recovered.columns[-2]
zoom = 2
filtered_ts_confirmed = ts_confirmed
filtered_ts_death = ts_death
filtered_ts_recovered = ts_recovered
px.set_mapbox_access_token(mapbox_access_token)
## Rename columns to prettify hover data
temp_deaths_df = filtered_ts_death.rename(columns = {date:'Deaths', 'Lat':'Latitude', 'Long':'Longitude'})
temp_recovered_df = filtered_ts_recovered.rename(columns = {date:'Recovered', 'Lat':'Latitude', 'Long':'Longitude'})
temp_confirmed_df = filtered_ts_confirmed.rename(columns = {date:'Confirmed', 'Lat':'Latitude', 'Long':'Longitude'})
#Some data from JHU appears as -1 unsure why
temp_recovered_df[temp_recovered_df['Recovered'] < 0] = 0
temp_confirmed_df[temp_confirmed_df['Confirmed'] < 0] = 0
temp_deaths_df[temp_deaths_df['Deaths'] < 0] = 0
#assumes order of countries from datasets are the same
temp_all = temp_confirmed_df[['City/Country', 'Confirmed','Latitude','Longitude']]
temp_all.insert(2,'Deaths', temp_deaths_df[['Deaths']])
temp_all.insert(2,'Recovered',temp_recovered_df[['Recovered']])
fig = px.scatter_mapbox(temp_all, lat="Latitude", lon="Longitude", color="Deaths", size="Confirmed",
#color_continuous_scale=px.colors.diverging.Picnic,
size_max=50, hover_name="City/Country",
#hover_data=["Confirmed", "Recovered", "Deaths"],
hover_data=["Confirmed", "Deaths"],
)
fig.update_traces(hoverinfo='text', marker=dict(sizemin=2),showlegend=False)
fig.update(
layout=dict(title=dict(x=0.5), paper_bgcolor=colors['background'] )
)
fig.update_layout(
autosize=True,
height=750,
#width=1500,
font={
'family': 'Courier New, monospace',
'size': 18,
'color': 'white'
},
hovermode='closest',
legend=dict(
font={'color':colors['background']},
),
mapbox=dict(
accesstoken=mapbox_access_token,
bearing=0,
style="dark",
# center=dict(
# lat=56,
# lon=324
# ),
pitch=0,
zoom=zoom
),
)
return fig
import dash_html_components as html
import pandas as pd
import numpy as np
import plotly.express as px
from dash.dependencies import Input, Output
df = pd.read_csv('finals.csv')
app = dash.Dash(__name__)
server = app.server
fig = px.scatter_mapbox(df,
lat="latitude",
lon="longitude",
hover_name="location",
hover_data=["Severity Type"],
color_discrete_sequence=["red"],
zoom=3,
height=500)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
#all_options = {
# 'One Location': [
# dcc.Input(id='my-id', value='initial value', type='text'),
# html.Div(id='my-div')],
# 'Many Locations': [
# dcc.Input(id='my-id', value='initial value', type='text'),
# html.Div(id='my-div')]
#}
