Mapping of Water Quality Sites¶
Introduction¶
Overview¶
This Notebook provides some examples of combining mapping, and quantitative data display in a single graphic. The main packages used as Folium and Altair.
Motivation¶
One of my activities is to regularly take water quality measurements (as part of https://ecollaboration.org.au/community-engagement/maroochy-waterwatch/). I thought it might be interesting to display some of the data collected in a spatial context (i.e. on a map).
Disclaimer¶
The cells holding Folium maps in this Notebook do not display in Microsoft Edge. I have included image displays where needed, to show the graphic results. Chrome seems to work fine.
Implementation¶
Notebook Imports¶
In [2]:
# all imports should go here
import pandas as pd
import numpy
import folium
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from IPython.display import display
import altair as alt
import sys
import os
import subprocess
import datetime
import platform
import datetime
Notebook magic commands¶
watermark provides documentation of the Python environment.
lab_black formats the Python cells
In [3]:
%matplotlib inline
In [4]:
%load_ext watermark
In [5]:
%load_ext lab_black
Customizations for notebook¶
In [6]:
# path to saved figures
DATA_PREFIX = '../data/'
Supporting Functions¶
None
Data Load¶
To start with, we load a database of water quality site locations, and perform some data exploration.
In [7]:
site_data_path = DATA_PREFIX + 'Cleaned Site Details.xlsx'
site_df = pd.read_excel(site_data_path)
In [8]:
site_df.head()
Out[8]:
| Waterway | Site Code | Site Name | Water Body Type | latitude | longitude | |
|---|---|---|---|---|---|---|
| 0 | Cornmeal Creek | CRN700 | Duporth Ave Bridge, Maroochydore | Enclosed Coastal/Lower estuary | -26.697250 | 152.980694 |
| 1 | Cornmeal Creek | CRN800 | Horton Parade Bridge, Maroochydore | Middle Estuary | -26.653564 | 153.090566 |
| 2 | Cornmeal Creek | CRN900 | Duporth Ave Bridge, Maroochydore | Middle Estuary | -26.655556 | 153.087806 |
| 3 | Paynter Creek | DUC100 | Duck Ponds (Kolora Park), Palmwoods | Lake / Lowland Freshwater | -26.685389 | 152.959944 |
| 4 | South Maroochy River | ECH700 | Sherwell Road (Savage Property), Dulong | Lowland Freshwater | -26.626778 | 152.882611 |
We get the unique names of the waterways, and create a dict that will map waterway name to a unique index.
In [9]:
waterways = pd.unique(site_df['Waterway'])
waterway_i = {n: i for i, n in enumerate(waterways)}
How many waterways do we have?
In [10]:
len(waterways)
Out[10]:
10
We create colourmap for waterways. tab10 is a colormap for qualititive data, and by a happy coincidence, has as many colors as we have waterways.
In [11]:
cmap = plt.cm.get_cmap('tab10', len(waterways))
Mapping¶
Supporting Mapping Functions¶
We now define a function that takes the pandas dataframe holding site details, including latitude and longitude, and creates markers. Each site gets a marker that is color coded for the waterway. In order to be able to turn the site locations on/off in our map display, we create a FeatureGroup, and add markers to that. We then add the FeatureGroup to Folium map.
In [12]:
def add_site_markers(
folium_map: folium.Map,
cmap: matplotlib.colors.ListedColormap,
site_df: pd.core.frame.DataFrame,
waterway_i: dict,
) -> None:
'''
add_site_markers: add markers for water quality sites to folium map
Parameters:
folium_map: folium.Map to add markers to
cmap: colormap for waterway identification
site_df: pandas dataframe hold water quality site locations
waterway_i: dictionary giving index into waterway names from waterway name
Side Effects
folium_map update with markers
Return:
site_fg: Folium Feature Group being added
'''
SITE_MARKER_SIZE = 5
SITE_MARKER_FILL_OPACITY = 0.95
# get arrays of data to be used to build markers
lng = site_df['longitude']
lat = site_df['latitude']
site_code = site_df['Site Code']
site_name = site_df['Site Name']
waterway = site_df['Waterway']
# create a FeatureGroup so Sites can be turned off
site_fg = folium.FeatureGroup(
name='Water Quality Sites', show=True
)
# for each site, add marker to layer
# marker is translucent so zoom out will look ok
for (
marker_y,
marker_x,
marker_site_code,
marker_site_name,
marker_way,
) in zip(lat, lng, site_code, site_name, waterway):
# get R, G, B elements of color for this waterway
# by looking up index of waterway from name
# and using that index to get r,g,b from color map
# alpha value ignored
# get r, g. b, as 0.0<->1.0
r, g, b, __ = cmap(waterway_i[marker_way])
# get r, g, b as 0<->255
r, g, b = (int(r * 256), int(g * 256), int(b * 256))
# get Leaflet style color string (hex values for r, g, b)
# two hex digits right justified, zero filled
marker_color_string = f'#{r:0>2x}{g:0>2x}{b:0>2x}'
circ_mkr = folium.CircleMarker(
location=[marker_y, marker_x],
radius=SITE_MARKER_SIZE,
color=marker_color_string,
fill=True,
fill_color=marker_color_string,
fillOpacity=SITE_MARKER_FILL_OPACITY,
opacity=SITE_MARKER_FILL_OPACITY,
tooltip=f'{marker_site_code}',
popup=marker_site_name,
)
circ_mkr.add_to(site_fg)
# end for
# add unclustered markers to map
site_fg.add_to(folium_map)
return site_fg
# end add_site_markers
We create a map, select two background layers (user selectable), create the site markers FeatureGroup and add it to the map. We use the extent (bounds) of the FeatureGroup to zoom the map back to show all the site markers.
In [78]:
start_coords = [-26.52, 153.09]
folium_map = folium.Map(
tiles=None,
location=start_coords,
zoom_start=13,
width='80%',
height='80%',
control_scale=True,
)
# support two basemaps: Open Street Map will be the default
folium.TileLayer(
tiles='OpenStreetMap', name='Open Street Map', show=True
).add_to(folium_map)
folium.TileLayer(
tiles='Stamen Terrain', name='Terrain Map', show=False
).add_to(folium_map)
sites_fg = add_site_markers(
folium_map, cmap, site_df, waterway_i
)
bounds = sites_fg.get_bounds()
folium_map.fit_bounds(bounds)
# add a control to turn layers on / aff
folium.LayerControl().add_to(folium_map)
# add a title
title_html = '''
<h3 align="left" style="font-size:20px"><b>ECOllaboration Water Quality Sites</b></h3>
'''
folium_map.get_root().html.add_child(
folium.Element(title_html)
)
display(folium_map)
The Layer Control looks like:
In [75]:
from IPython.display import Image
Image('../figures/folium01.png')
Out[75]:
The previous map had one layer for all water quality sites. As an experiment, we will create a layer (FeatureGroup) for each waterway.
In [20]:
def add_ww_markers(
folium_map: folium.Map,
cmap: matplotlib.colors.ListedColormap,
site_df: pd.core.frame.DataFrame,
waterway_i: dict,
waterways: numpy.ndarray,
) -> None:
'''
add_ww_markers: add markers for water quality sites coded by waterway to folium map
Parameters:
folium_map: folium.Map to add markers to
cmap: colormap for waterway identification
site_df: pandas dataframe hold water quality site locations
waterway_i: dictionary giving index into waterway names from waterway name
waterways: np.ndarray holding strings being waterway names
Side Effects
folium_map update with markers
folium_map bounds set to show complete marker set
Return:
None
'''
SITE_MARKER_SIZE = 5
SITE_MARKER_FILL_OPACITY = 0.95
lng = site_df['longitude']
lat = site_df['latitude']
site_code = site_df['Site Code']
site_name = site_df['Site Name']
waterway = site_df['Waterway']
# create a FeatureGroup for each waterway
site_fgs = [
folium.FeatureGroup(name=name, show=True)
for name in waterways
]
# for each site, add marker to layer for that waterway
is_first = True # trigger initial map bounds processing
for (
marker_y,
marker_x,
marker_site_code,
marker_site_name,
marker_way,
) in zip(lat, lng, site_code, site_name, waterway):
# get R, G, B elements of color for this waterway
# by looking up index of waterway from name
# and using that index to get r,g,b from color map
# alpha value ignored
# get r, g. b, as 0.0<->1.0
r, g, b, __ = cmap(waterway_i[marker_way])
# get r, g, b as 0<->255
r, g, b = (int(r * 256), int(g * 256), int(b * 256))
# get Leaflet style color string (hex values for r, g, b)
# two hex digits right justified, zero filled
marker_color_string = f'#{r:0>2x}{g:0>2x}{b:0>2x}'
circ_mkr = folium.CircleMarker(
location=[marker_y, marker_x],
radius=SITE_MARKER_SIZE,
color=marker_color_string,
fill=True,
fill_color=marker_color_string,
fillOpacity=SITE_MARKER_FILL_OPACITY,
opacity=SITE_MARKER_FILL_OPACITY,
tooltip=f'{marker_site_code}',
popup=marker_site_name,
)
# add marker to the waterway group the site belongs to
# update minimum and maximum map extents (bounds)
circ_mkr.add_to(site_fgs[waterway_i[marker_way]])
if is_first:
is_first = False
bounds = site_fgs[
waterway_i[marker_way]
].get_bounds()
else:
new_bounds = site_fgs[
waterway_i[marker_way]
].get_bounds()
bounds = [
[
min(bounds[0][0], new_bounds[0][0]),
min(bounds[0][1], new_bounds[0][1]),
],
[
max(bounds[1][0], new_bounds[1][0]),
max(bounds[1][1], new_bounds[1][1]),
],
]
# end if
# end for
# add markers to map
for i in range(len(waterways)):
site_fgs[i].add_to(folium_map)
# end for
# set map extent at start
folium_map.fit_bounds(bounds)
return None
# end add_ww_markers
Now, we have a layer control with many options.
In [76]:
start_coords = [-26.52, 153.09]
folium_map = folium.Map(
tiles=None,
location=start_coords,
zoom_start=13,
width='90%',
height='100%',
control_scale=True,
)
# support two basemaps
folium.TileLayer(
tiles='OpenStreetMap', name='Open Street Map', show=True
).add_to(folium_map)
folium.TileLayer(
tiles='Stamen Terrain', name='Terrain Map', show=False
).add_to(folium_map)
add_ww_markers(
folium_map, cmap, site_df, waterway_i, waterways
)
folium.LayerControl().add_to(folium_map)
title_html = '''
<h3 align="left" style="font-size:20px"><b>ECOllaboration Water Quality Sites</b></h3>
'''
folium_map.get_root().html.add_child(
folium.Element(title_html)
)
display(folium_map)
The Layer Control now looks like as below. I suspect that I pushed past some guideline for number of layers. It would be nice to be able to color-code the LayerControl to match the Marker color, but I don't know how to do that (yet).
In [77]:
from IPython.display import Image
Image('../figures/folium02.png')
Out[77]:
Data visualization on a map¶
Altair Charts¶
Folium supports display of Altair charts, as a popup associated with a Folium marker.
We will build a simple example of an Altair Chart.
In [70]:
alt.renderers.enable('default')
source = pd.DataFrame(
{
'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
'b': [28, 55, 43, 91, 81, 53, 19, 87, 52],
}
)
# create a bar graph from data
alt.Chart(source).mark_bar().encode(x='a', y='b')
Out[70]:
We now create a Chart that shows lines joining data points. In Altair this is two charts, stacked by the + operator.
In [71]:
a = alt.Chart(source).mark_point().encode(x='a', y='b')
b = alt.Chart(source).mark_line().encode(x='a', y='b')
a + b
Out[71]:
Charts in a Popup¶
As before we create a Folium map, and add a single marker to it. This time, instead of text, we specify the JSON that results from JSON-ifying a Altair Chart.
In [81]:
start_coords = [-26.52, 153.09]
folium_map = folium.Map(
tiles=None,
location=start_coords,
zoom_start=13,
width='80%',
height='80%',
control_scale=True,
)
# support two basemaps
folium.TileLayer(
tiles='OpenStreetMap', name='Open Street Map', show=True
).add_to(folium_map)
folium.TileLayer(
tiles='Stamen Terrain', name='Terrain Map', show=False
).add_to(folium_map)
folium.LayerControl().add_to(folium_map)
my_chart = a + b
vis1 = my_chart.to_json()
circ_mkr = folium.CircleMarker(
location=start_coords,
radius=20,
color='red',
fill=True,
fill_color='red',
fillOpacity=1.0,
opacity=1.0,
tooltip='Altair Graph',
popup=folium.Popup(max_width=400).add_child(
folium.VegaLite(vis1, width=400, height=300)
),
)
circ_mkr.add_to(folium_map)
Out[81]:
<folium.vector_layers.CircleMarker at 0x23451021e10>
In [82]:
folium_map
Out[82]:
The popup display now looks like.
In [79]:
from IPython.display import Image
Image('../figures/folium03.png')
Out[79]:
Mapping plus Data Visualization¶
Data Load¶
We load the spreadsheet of water quality measurements into pandas dataframe, and do the usual data exploration.
In [115]:
data_file = 'D:\\Water2019\\Final Water Monitoring Data April 2015 - Mar 2018 - Outliers Removed & Data Analysis.xlsx'
water_df = pd.read_excel(data_file)
In [33]:
water_df.head()
Out[33]:
| Check | Site Code | Waterway | Subcatchment | Site Name | Waterbody Type | Sample Taker | Date Sample was Taken (dd-mm-yyyy) | Time | Dissolved Oxygen (mg/L) | ... | Water Surface | Flow | Surface Slick | Offensive Odour | Floating Matter | Suspended Matter | Appearance | Dead Fauna | Water Level | Data Entry Note | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Y-90 | EUD575 | Eudlo Creek | Eudlo Creek | Below Maroochydore Road at Montessori (near Eu... | Lowland Stream | Montessori Student | 2015-06-09 | 09:45:00 | 16.20 | ... | C-Choppy | M-Medium | S-Slight | N-Nil | S-Slight | S-Slight | O-Opaque | P-Present | NaN | NaN |
| 1 | Y-90 | PET160 | Petrie Creek | Petrie Creek | Jackson's Road Bridge, Hunchy (previously PET106) | Lowland Stream | Jenny Page | 2015-07-27 | 17:00:00 | 11.85 | ... | F-Flat | S-Slow | N-Nil | N-Nil | N-Nil | N-Nil | C-Clean | P-Present | S-Stable | NaN |
| 2 | Done - Added Observation | PAY104 | Paynter Creek | Paynter Creek | Lower Landershute Rd, Palmwoods | Lowland Stream | Judy Venn | 2015-10-05 | 14:50:00 | 10.95 | ... | F-Flat | N-Nil | S-Slight | N-Nil | S-Slight | S-Slight | C-Clean | N-Nil | F-Falling | NaN |
| 3 | Done - Added Observation | SMA700 | South Maroochy River | South Maroochy | Coleman's Farm Rd, Yandina | Lowland Stream | Cerran Fawns | 2015-10-11 | 16:30:00 | 7.85 | ... | F-Flat | S-Slow | N-Nil | N-Nil | S-Slight | N-Nil | C-Clean | N-Nil | S-Stable | NaN |
| 4 | Done - Added Observation | PAY206 | Paynter Creek | Paynter Creek | Palmwoods-Montville Rd, Palmwoods | Lowland Stream | Stewart Medland | 2015-10-15 | 09:00:00 | 10.24 | ... | C-Choppy | M-Medium | M-Moderate | N-Nil | S-Slight | S-Slight | M-Murky | N-Nil | R-Rising | NaN |
5 rows × 33 columns
Check that the waterway names match.
In [36]:
water_df['Waterway'].dropna().unique()
Out[36]:
array(['Eudlo Creek', 'Petrie Creek', 'Paynter Creek',
'South Maroochy River', 'North Maroochy River', 'Stumers Creek',
'Martins Creek', 'Cornmeal Creek', 'Maroochy River',
'Maroochy Estuary'], dtype=object)
Display the column names.
In [37]:
water_df.columns
Out[37]:
Index(['Check', 'Site Code', 'Waterway', 'Subcatchment', 'Site Name',
'Waterbody Type', 'Sample Taker', 'Date Sample was Taken (dd-mm-yyyy)',
'Time', 'Dissolved Oxygen (mg/L)', 'DO % (Calculated)', 'pH',
'Turbidity (NTU)', 'Conductivity (mS/cm)', 'Water Temp (oC)',
'Salinity (%)', 'Comments', 'Equipment Name & ID', 'Air Temperature',
'Current Rainfall', 'Last Rainfall', 'Wind', 'Sky', 'Water Surface',
'Flow', 'Surface Slick', 'Offensive Odour', 'Floating Matter',
'Suspended Matter', 'Appearance', 'Dead Fauna', 'Water Level',
'Data Entry Note'],
dtype='object')
Data Extract¶
We will display pH readings. First, check the datatypes of the columns of interest. We see that the Date column is the correct data type.
In [41]:
water_df[
['Waterway', 'pH', 'Date Sample was Taken (dd-mm-yyyy)']
].dtypes
Out[41]:
Waterway object pH float64 Date Sample was Taken (dd-mm-yyyy) datetime64[ns] dtype: object
In [50]:
ph_df = (
water_df[
[
'Site Code',
'Waterway',
'pH',
'Date Sample was Taken (dd-mm-yyyy)',
]
]
.dropna()
.copy()
)
In [51]:
ph_df.head()
Out[51]:
| Site Code | Waterway | pH | Date Sample was Taken (dd-mm-yyyy) | |
|---|---|---|---|---|
| 0 | EUD575 | Eudlo Creek | 7.35 | 2015-06-09 |
| 1 | PET160 | Petrie Creek | 8.43 | 2015-07-27 |
| 2 | PAY104 | Paynter Creek | 5.40 | 2015-10-05 |
| 3 | SMA700 | South Maroochy River | 8.52 | 2015-10-11 |
| 4 | PAY206 | Paynter Creek | 4.20 | 2015-10-15 |
In [52]:
ph_df.tail()
Out[52]:
| Site Code | Waterway | pH | Date Sample was Taken (dd-mm-yyyy) | |
|---|---|---|---|---|
| 1938 | PAY206 | Paynter Creek | 6.70 | 2018-03-27 |
| 1939 | NMA310 | North Maroochy River | 7.23 | 2018-03-29 |
| 1940 | TUC140 | Petrie Creek | 7.45 | 2015-06-03 |
| 1941 | TUC130 | Petrie Creek | 7.30 | 2015-06-03 |
| 1942 | SMA346 | South Maroochy River | 7.79 | 2015-10-06 |
The column name of the Date column is a little unwieldly (especially for charting), so rename it.
In [59]:
ph_df.rename(
columns={'Date Sample was Taken (dd-mm-yyyy)': 'Date'},
inplace=True,
)
ph_df.head()
Out[59]:
| Site Code | Waterway | pH | Date | |
|---|---|---|---|---|
| 0 | EUD575 | Eudlo Creek | 7.35 | 2015-06-09 |
| 1 | PET160 | Petrie Creek | 8.43 | 2015-07-27 |
| 2 | PAY104 | Paynter Creek | 5.40 | 2015-10-05 |
| 3 | SMA700 | South Maroochy River | 8.52 | 2015-10-11 |
| 4 | PAY206 | Paynter Creek | 4.20 | 2015-10-15 |
Create a sample chart for a single site.
In [68]:
a = (
alt.Chart(ph_df[ph_df['Site Code'] == 'STU100'])
.mark_point()
.encode(
alt.X('Date'),
alt.Y('pH:Q', scale=alt.Scale(zero=False)),
)
.properties(width=600, height=200)
)
b = (
alt.Chart(ph_df[ph_df['Site Code'] == 'STU100'])
.mark_line()
.encode(
alt.X('Date'),
alt.Y('pH', scale=alt.Scale(zero=False)),
)
.properties(width=600, height=200)
)
a + b
Out[68]:
In [111]:
def make_ph_strip_chart(
ph_df: pd.core.frame.DataFrame, site_code: str
) -> alt.Chart:
'''
make_ph_strip_chart: create Altair chart with point and lines overlay
Notes:
Altair default of always showing zero point on Y axis is overidden to reduce size
site_code is added to title of chart
'''
WIDTH = 300
HEIGHT = 100
a = (
alt.Chart(ph_df[ph_df['Site Code'] == site_code])
.mark_point()
.encode(
alt.X('Date'),
alt.Y('pH:Q', scale=alt.Scale(zero=False)),
)
.properties(
width=WIDTH, height=HEIGHT, title=site_code
)
)
b = (
alt.Chart(ph_df[ph_df['Site Code'] == site_code])
.mark_line()
.encode(
alt.X('Date'),
alt.Y('pH', scale=alt.Scale(zero=False)),
)
.properties(
width=WIDTH, height=HEIGHT, title=site_code
)
)
return a + b
# end make_ph_strip_chart
add_site_strip_chart¶
Adds Folium markers with Altair Chart popups to a given map
In [112]:
def add_site_strip_chart(
folium_map: folium.Map,
cmap: matplotlib.colors.ListedColormap,
site_df: pd.core.frame.DataFrame,
ph_df: pd.core.frame.DataFrame,
waterway_i: dict,
) -> None:
'''
add_site_markers: add markers for water quality sites to folium map
Parameters:
folium_map: folium.Map to add markers to
cmap: colormap for waterway identification
site_df: pandas dataframe hold water quality site locations
waterway_i: dictionary giving index into waterway names from waterway name
Side Effects
folium_map update with markers
Return:
site_fg: Folium Feature Group being added
'''
SITE_MARKER_SIZE = 5
SITE_MARKER_FILL_OPACITY = 0.95
WIDTH = 350
HEIGHT = 100
# get arrays of data to be used to build markers
lng = site_df['longitude']
lat = site_df['latitude']
site_code = site_df['Site Code']
site_name = site_df['Site Name']
waterway = site_df['Waterway']
# create a FeatureGroup so Sites can be turned off
site_fg = folium.FeatureGroup(
name='Water Quality Sites', show=True
)
# for each site, add marker to layer
# marker is translucent so zoom out will look ok
for (
marker_y,
marker_x,
marker_site_code,
marker_site_name,
marker_way,
) in zip(lat, lng, site_code, site_name, waterway):
# get R, G, B elements of color for this waterway
# by looking up index of waterway from name
# and using that index to get r,g,b from color map
# alpha value ignored
# get r, g. b, as 0.0<->1.0
r, g, b, __ = cmap(waterway_i[marker_way])
# get r, g, b as 0<->255
r, g, b = (int(r * 256), int(g * 256), int(b * 256))
# get Leaflet style color string (hex values for r, g, b)
# two hex digits right justified, zero filled
marker_color_string = f'#{r:0>2x}{g:0>2x}{b:0>2x}'
# create strip chart
chart = make_ph_strip_chart(ph_df, marker_site_code)
vis1 = chart.to_json()
circ_mkr = folium.CircleMarker(
location=[marker_y, marker_x],
radius=SITE_MARKER_SIZE,
color=marker_color_string,
fill=True,
fill_color=marker_color_string,
fillOpacity=SITE_MARKER_FILL_OPACITY,
opacity=SITE_MARKER_FILL_OPACITY,
tooltip=f'{marker_site_code}',
popup=folium.Popup(
max_width=WIDTH,
sticky=True, # sticky=True allows multiple popups to be open
).add_child(
folium.VegaLite(
vis1, width=WIDTH, height=HEIGHT
)
),
)
circ_mkr.add_to(site_fg)
# end for
# add unclustered markers to map
site_fg.add_to(folium_map)
return site_fg
# end add_site_strip_chart
Finally, the map¶
Having defined all our helper function, we create the map!
In [113]:
start_coords = [-26.52, 153.09]
folium_map = folium.Map(
tiles=None,
location=start_coords,
zoom_start=13,
width='80%',
height='80%',
control_scale=True,
)
# support two basemaps: Open Street Map will be the default
folium.TileLayer(
tiles='OpenStreetMap', name='Open Street Map', show=True
).add_to(folium_map)
folium.TileLayer(
tiles='Stamen Terrain', name='Terrain Map', show=False
).add_to(folium_map)
sites_fg = add_site_strip_chart(
folium_map, cmap, site_df, ph_df, waterway_i
)
bounds = sites_fg.get_bounds()
folium_map.fit_bounds(bounds)
# add a control to turn layers on / aff
folium.LayerControl().add_to(folium_map)
# add a title
title_html = '''
<h3 align="left" style="font-size:20px"><b>ECOllaboration Water Quality Sites</b></h3>
'''
folium_map.get_root().html.add_child(
folium.Element(title_html)
)
display(folium_map)
The popup strip chart looks like:
In [114]:
from IPython.display import Image
Image('../figures/folium05.png')
Out[114]:
Conclusions / Summary¶
This is an example of how to tag markers in a Folium map with charts produced by Altair. I remain to be convinced that this approach has general applicability, mainly because the map get cluttered very quickly with popups (I have chosen the sticky option). Maybe if there was a facility to move the chart popups to a side panel ..., but now we talking about dashboard design :) .
Reproducibility¶
Notebook version status¶
In [116]:
theNotebook = (
'2019-12-16-dc-WaterQualityMappingNotebook.ipynb'
)
In [117]:
# show info to support reproducibility
def python_env_name():
envs = subprocess.check_output(
'conda env list'
).splitlines()
# get unicode version of binary subprocess output
envu = [x.decode('ascii') for x in envs]
active_env = list(
filter(lambda s: '*' in str(s), envu)
)[0]
env_name = str(active_env).split()[0]
return env_name
# end python_env_name
print('python version : ' + sys.version)
print('python environment :', python_env_name())
print('pandas version : ' + pd.__version__)
print('current wkg dir: ' + os.getcwd())
print('Notebook name: ' + theNotebook)
print(
'Notebook run at: '
+ str(datetime.datetime.now())
+ ' local time'
)
print(
'Notebook run at: '
+ str(datetime.datetime.utcnow())
+ ' UTC'
)
print('Notebook run on: ' + platform.platform())
python version : 3.7.1 (default, Dec 10 2018, 22:54:23) [MSC v.1915 64 bit (AMD64)] python environment : ac5-py37 pandas version : 0.23.4 current wkg dir: C:\Users\donrc\Documents\JupyterNotebooks\WaterQualityMappingNotebookProject\develop Notebook name: 2019-12-16-dc-WaterQualityMappingNotebook.ipynb Notebook run at: 2019-12-22 19:37:18.873478 local time Notebook run at: 2019-12-22 09:37:18.873478 UTC Notebook run on: Windows-10-10.0.18362-SP0
In [118]:
%watermark
2019-12-22T19:37:38+10:00 CPython 3.7.1 IPython 7.2.0 compiler : MSC v.1915 64 bit (AMD64) system : Windows release : 10 machine : AMD64 processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel CPU cores : 8 interpreter: 64bit
In [119]:
%watermark -h -iv
pandas 0.23.4 matplotlib 3.0.2 folium 0.10.0 platform 1.0.8 numpy 1.15.4 seaborn 0.9.0 altair 3.1.0 host name: DESKTOP-SODFUN6