def create_multiple_pointers_2():
"""
Uses the dataframe built in apply function.
This is faster and should be used for large datasets.
"""
airports = vds.airports()
airports = airports[:25]
map_airports_2 = folium.Map(location=[38, -98], zoom_start=4)
airports.apply(lambda row: folium.Marker(location=[row['latitude'], row['longitude']], popup=row['name'])
.add_to(map_airports_2), axis=1)
return map_airports_2
def create_multiple_pointers_1():
# fetch data using vega_datasets
airports = vds.airports()
airports = airports[:25]
# create map
map_airports = folium.Map(location=[38, -98], zoom_start=4)
for (index, row) in airports.iterrows():
try:
folium.Marker(location=[row.loc['latitude'], row.loc['longitude']], popup=row.loc['name'] + '' + row.loc['city']
+ '' + row.loc['state'],
tooltip='click!').add_to(map_airports)
except Exception:
print("ups")
return map_airports
marker_cluster = MarkerCluster(markers=(marker6, marker7, marker8, marker9))
m.add_layer(marker_cluster);
# display_map
m
# In[12]
# Multiple Marker
from ipyleaflet import Map, Marker
# install vega_datasets first from python.org python package index
from vega_datasets import data
# airports dataframe using vega_datasets
airports = data.airports()
airports = airports[:25]
# create map
airports_map = Map(center=(-6.1753942, 106.827183), zoom=8)
# plot airport locations
for (index, row) in airports.iterrows():
marker = Marker(location=[row.loc['latitude'], row.loc['longitude']],
title=row.loc['name'] + ' ' + row.loc['city'] + ' ' + row.loc['state'])
airports_map.add_layer(marker)
# display map
airports_map
# In[14]
# st.bar_chart()
# *** SCATTER PLOT ***
# example using altair
cars = vds.cars()
st.write(cars.head())
scatter = alt.Chart(cars).mark_circle().encode(
x='Weight_in_lbs', y='Miles_per_Gallon').interactive()
st.altair_chart(scatter)
# *** OTHER CHARTS AND PLOTS ***
# streamlit can display a number of other charts, images, etc.
# *** MAP ***
st.title('Map')
airports = vds.airports()[['latitude', 'longitude']][0:100]
st.map(airports)
# *** SLIDER ***
st.title('Slider')
slider = st.slider(label='slider', min_value=0, max_value=10, value=5)
st.write(slider, 'cubed is', slider * slider * slider)
# *** CHECKBOX ***
st.title('Checkbox')
fig_map = plt.figure(figsize=(12, 8))
ax_map = fig_map.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax_map.set_global()
if st.checkbox('land'): ax_map.add_feature(cfeature.LAND)
if st.checkbox('ocean'): ax_map.add_feature(cfeature.OCEAN)
if st.checkbox('coastline'): ax_map.add_feature(cfeature.COASTLINE)
def SwitchExample(argument):
from vega_datasets import data
switcher = {"Airports": data.airports(), "Cars": data.cars()}
return switcher.get(argument, "Not found!")
def load_data():
return pd.concat((data.airports() for _ in range(100)))
@st.cache
def select_rows(dataset, nrows):
return dataset.head(nrows)
@st.cache
def describe(dataset):
return dataset.describe()
rows = st.slider("Rows", min_value=100, max_value=3300 * 100, step=10000)
start_uncached = time()
dataset_uncached = pd.concat((data.airports() for _ in range(100)))
load_uncached = time()
dataset_sample_uncached = dataset_uncached.head(rows)
select_uncached = time()
describe_uncached_dataset = dataset_sample_uncached.describe()
finish_uncached = time()
benchmark_uncached = (
f"Cached. Total: {finish_uncached - start_uncached:.2f}s"
f" Load: {load_uncached - start_uncached:.2f}"
f" Select: {select_uncached - load_uncached:.2f}"
f" Describe: {finish_uncached - select_uncached:.2f}"
)
st.text(benchmark_uncached)
st.write(describe_uncached_dataset)
ax.add_feature(cfeature.BORDERS, linestyle=':')
ax.add_feature(cfeature.LAKES, alpha=0.5)
ax.add_feature(cfeature.RIVERS)
ax.add_feature(cfeature.STATES)
plt.show()
# ~ #***********************************************************************
# ~ # AFFICHAGE AEROPORTS (VEGA_DATASETS)
# ~ #***********************************************************************
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from vega_datasets import data as vds
airports = vds.airports()
airports = airports.iloc[:10]
plt.figure(figsize=(14, 14))
ax = plt.axes(projection=ccrs.PlateCarree())
# (x0, x1, y0, y1)
ax.set_extent([-130, -60, 20, 55], ccrs.PlateCarree())
ax.add_feature(cfeature.STATES)
ax.coastlines()
for i in airports.itertuples():
ax.scatter(i.longitude,
i.latitude,
color='blue',
"""
Locations of US Airports
========================
This is a layered geographic visualization that shows the positions of US
airports on a background of US states.
"""
# category: case studies
import altair as alt
from vega_datasets import data
airports = data.airports()
states = alt.topo_feature(data.us_10m.url, feature='states')
# US states background
background = alt.Chart(states).mark_geoshape(
fill='lightgray',
stroke='white'
).properties(
width=500,
height=300
).project('albersUsa')
# airport positions on background
points = alt.Chart(airports).mark_circle().encode(
longitude='longitude:Q',
latitude='latitude:Q',
size=alt.value(10),
color=alt.value('steelblue')
)
background + points
