FinalprojectinvolvingUSwaterquality_More_query_practice_working on maps.py

#%%
## Goals: To compare different areas and contaminant levels, illustrate areas, contaminant levels over time.
# compare number of tests year to year
# Maany metrics 

## To start a good thing would be the number of stations from year to year and the number of tests

import pandas as pd
import folium
import re
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
import numpy as np
from matplotlib.text import OffsetFrom
import matplotlib.pyplot as plt
from datetime import datetime 
import altair as alt
import geopandas 

#%%
# My data set is not supposed to be super huge but you know what. I will use a tiny bit for my project
#https://catalog.data.gov/dataset/water-quality-data-0de37


field_results = pd.read_csv(r"C:\Users\amcfa\gitfiles\Projects\MastersWork\FundamentalssofDataVisualizzations\Water quality data\field_results.csv", low_memory=False)
period_of_record = pd.read_csv(r"C:\Users\amcfa\gitfiles\Projects\MastersWork\FundamentalssofDataVisualizzations\Water quality data\period_of_record.csv",low_memory=False)
stations =pd.read_csv(r"C:\Users\amcfa\gitfiles\Projects\MastersWork\FundamentalssofDataVisualizzations\Water quality data\stations.csv",low_memory=False)
lab_results = pd.read_csv(r"C:\Users\amcfa\gitfiles\Projects\MastersWork\FundamentalssofDataVisualizzations\Water quality data\lab_results.csv",low_memory=False)

#%%
lab_results.dtypes
lab_results
#%%

a = lab_results[['latitude','longitude','station_number','sample_date','parameter','result','reporting_limit', 'units']]
b = stations[['latitude','longitude', 'station_number']]
c = field_results[['latitude','longitude','station_number','full_station_name','sample_date','parameter','fdr_result', 'fdr_reporting_limit','uns_name']]
d = period_of_record[['latitude','longitude','station_number','sample_date_max','sample_date_min']]


#%%

#%%
# Replacing all "" with ''

b = b.replace('"', '')
c = c.replace('"', '')
d = d.replace('"', '')

#%%
# Drop all 0 values

#%%

#%%
## So this seemed to work actually to change the Dtype to datetime64
e = a.dtypes['sample_date'] = pd.to_datetime(a['sample_date'])
f = a.assign(sample_date=e)
#%% 
## Finally date time 64. Oh my gosh 
f.dtypes
1#%%
f
# %% 
# Drop all 0 values for this new column 
def filter_rows_by_values(df, col, values):
    return f[~f[col].isin(values)]
f = filter_rows_by_values(f,"result",[0])
#%%
f.dtypes

## Now all 0 values are gone 

#%%
## Now this actually works to query the dates and to make the data less overwhelming I will just stick to the last year
g = f[(f['sample_date'] > '2022') & (f['sample_date'] <= '2023')]
#%%
g
#%%

## and now we have unique stations for this year, 180 total
len(g.station_number.unique())
#%% 
## Going to try to use station names and the squeeze method in order to obtain unique station names
## Maybe interesting data would be how many station names there have been over time?
#%%
## So now that how many unique stations were sample in a calendar year 
# I can look at some data.
## How many unique tests were done
len(g.parameter.unique())

## so there were a specific number of tests

# %%
h = f[(f['sample_date'] > '2021') & (f['sample_date'] <= '2022')]
#%%
h
# %%
len(h.station_number.unique())
# %%
len(h.parameter.unique())
# %%
# %%
i = f[(f['sample_date'] > '2020-01-01') & (f['sample_date'] <= '2021-01-01')]
#%%
i
# %%
len(i.station_number.unique())
# %%
len(i.parameter.unique())
# %%
#####################################################
#%%
j = f[(f['sample_date'] > '2019-01-01') & (f['sample_date'] <= '2020-01-01')]
#%%
j
# %%
len(j.station_number.unique())
# %%
len(j.parameter.unique())
# %%
k = f[(f['sample_date'] > '2018-01-01') & (f['sample_date'] <= '2019-01-01')]
#%%
k
# %%
len(k.station_number.unique())
# %%
len(k.parameter.unique())
# %%
l = f[(f['sample_date'] > '2017') & (f['sample_date'] <= '2018')]
#%%
l
# %%
len(l.station_number.unique())
# %%
len(l.parameter.unique())
# %%
m = f[(f['sample_date'] > '2016') & (f['sample_date'] <= '2017')]
#%%
m
# %%
len(m.station_number.unique())
# %%
len(m.parameter.unique())
# %%
# %%
n = f[(f['sample_date'] > '2015') & (f['sample_date'] <= '2016')]
#%%
n
# %%
len(n.station_number.unique())

# %%
len(n.parameter.unique())
# %%

# %%
y = ((len(i.parameter.unique()),len(j.parameter.unique()),len(k.parameter.unique()),len(l.parameter.unique()),len(m.parameter.unique()),len(n.parameter.unique())))
y
#%%
plt.plot(['2015-2016'],[150],'ro')
plt.show()
# %%
x = ['2015-2016','2016-2017','2017-2018','2018-2019','2019-2020','2020-2021']
y = ((len(i.parameter.unique()),len(j.parameter.unique()),len(k.parameter.unique()),len(l.parameter.unique()),len(m.parameter.unique()),len(n.parameter.unique())))
y
# %%
fig1 = plt.plot(x,y,'ro')
fig1.show()
#%%
x2 =['2015-2016','2016-2017','2017-2018','2018-2019','2019-2020','2020-2021'] 
y2 = ((len(i.station_number.unique()),len(j.station_number.unique()),len(k.station_number.unique()),len(l.station_number.unique()),len(m.station_number.unique()),len(n.station_number.unique())))
# %%
fig1, ax = plt.subplots(1)
ax.plot(x, y, 'bs')
ax.plot(x, y2,'ro')
# %%

#%%
len(f.station_number.unique())
f.dtypes
# %%
## So lets build a space for this data to live
# there are 46,000 Different station numbers that were used during the course of this 56 year or so study
# %%
len(f.station_number.unique())
len(f.station_number.unique())


# %%
start_date = f['sample_date'].min()
end_date = f['sample_date'].max()
# %%
start_date
#%%
end_date


# %%
# so uh make a list of 120 empty spaces for all the years to live because there are just a lot of them
## freq 'A means at the end of the year
years = pd.date_range(start=pd.datetime(1903,1,1), periods=120, freq='Y')

# cool to know
# %%
years
# %%
# %%
m = f[(f['sample_date'] > '2016') & (f['sample_date'] <= '2017')]
# %%

# %%

np.dtype('datetime64[ns]') == np.dtype('<M8[ns]')
# %%

# %%
df = pd.DataFrame(years)
df.rename({0: 'years'}, axis=1, inplace=True)
# %%
df['unique_stations'] = " "
# %%
df['yearly_samples'] = " "
# %%
# %%
## after all that effort. A df with blank columns is made
df
# %%

# %%

# %%
df
lst = ""
counter = 0
step = 0
# %%

            
#%%
a = (f.station_number.unique())
b = (f.sample_date.unique())
c = (n.parameter.unique())
        
abc =  pd.DataFrame(a)

# %%
abc
abc['yearly_samples'] = " "
# %%
# %%
for sample in abc[0]:
    if sample in f['station_number'] and f['sample_date'] not in abc:
        print('sample_date')
        abc['yearly_samples'] = abc['yearly_samples']+f['sample_date']
        abc +1
        f + 1
    else:
        break
        
        
# %%
abc= set(abc)
# %%
sample_dates = set()
for sample in abc:
    if sample in f['station_number']:
        if f['sample_date'] not in sample_dates:
            sample_dates.add(f['sample_date'])
            f = f+1 
# %%
abc
# %%
isinstance(f,pd.DataFrame)
# %%
##Trying to troubleshoot how to iterate over my df still. I dont want to have to go manually year by year.
# %%
for sample in abc:
    if sample in f['station_number']:
        f = f
        print(f)
# %%
k = df['years']
m = f[(f['sample_date'] > k)]
# %%
k
# %%

d = {}
for station in f['station_number']:
    d.add((f.sample_date.unique()))
# %%
ad =(f.groupby(['station_number','sample_date'])['sample_date'].count())
# %%

#%%
abc =  pd.DataFrame(ad)
#%%
abc
# %%
abc.rename({'sample_date': 'samples'}, axis=1, inplace=True)
#%%
abc
# %%


## Maybe for years this will help. Back to the draawing board monday 
# Need to see how I can sort this better it is final project after all
start_index = df[df['datetime']=='20100927'].index[0]
days_to_test = 30

for offset in days_to_test:
    fn(df.iloc[start_index:start_index+offset])
# %%
aa = f[(f['sample_date'] > '1903') & (f['sample_date'] <= '1913')]
ab = f[(f['sample_date'] > '1914') & (f['sample_date'] <= '1923')]
ac = f[(f['sample_date'] > '1924') & (f['sample_date'] <= '1933')]
ad = f[(f['sample_date'] > '1934') & (f['sample_date'] <= '1943')]
ae = f[(f['sample_date'] > '1944') & (f['sample_date'] <= '1953')]
af = f[(f['sample_date'] > '1954') & (f['sample_date'] <= '1963')]
ag = f[(f['sample_date'] > '1964') & (f['sample_date'] <= '1973')]
ah = f[(f['sample_date'] > '1974') & (f['sample_date'] <= '1983')]
ai = f[(f['sample_date'] > '1984') & (f['sample_date'] <= '1993')]
aj = f[(f['sample_date'] > '1994') & (f['sample_date'] <= '2003')]
ak = f[(f['sample_date'] > '2004') & (f['sample_date'] <= '2013')]
al = f[(f['sample_date'] > '2014') & (f['sample_date'] <= '2023')]

# %%
aa
# %%
x = ['1903-1913','1914-1923','1924-1933','1934-1943','1944-1953','1954-1963','1964-1973','1974-1983','1984-1993','1994-2003','2004-2013','2014-2023']
y = ((len(aa.station_number.unique()),len(ab.station_number.unique()),
      len(ac.station_number.unique()),len(ad.station_number.unique()),
      len(ae.station_number.unique()),len(af.station_number.unique()),
      len(ag.station_number.unique()),len(ah.station_number.unique()),
      len(ai.station_number.unique()),len(aj.station_number.unique()),
      len(ak.station_number.unique()),len(al.station_number.unique()),
      ))
y
# %%
# Finally not squished looking

plt.figure(figsize=(15,10))
plt.plot(x, y, 'bs')
plt.show()
# %%
xx = ['1903-1913','1914-1923','1924-1933','1934-1943','1944-1953','1954-1963','1964-1973','1974-1983','1984-1993','1994-2003','2004-2013','2014-2023']
yy = ((len(aa.parameter.unique()),len(ab.parameter.unique()),
      len(ac.parameter.unique()),len(ad.parameter.unique()),
      len(ae.parameter.unique()),len(af.parameter.unique()),
      len(ag.parameter.unique()),len(ah.parameter.unique()),
      len(ai.parameter.unique()),len(aj.parameter.unique()),
      len(ak.parameter.unique()),len(al.parameter.unique()),
      ))

# %%
plt.figure(figsize=(15,10))
plt.plot(xx, yy, 'bs')
plt.show()
# %%
ag
# %%
ag = f[(f['sample_date'] > '1963') & (f['sample_date'] <= '1973')]
# %%
x2 =['1963'] 
y2 = ((len(i.station_number.unique()),len(j.station_number.unique()),len(k.station_number.unique()),len(l.station_number.unique()),len(m.station_number.unique()),len(n.station_number.unique())))
# %%
g = f[(f['sample_date'] > '1963') & (f['sample_date'] <= '1973')]
# %%
g['sample_date'].count()
# %%
g['result'].plot(x="sample_date", y="result")
# %%
## There were a lot of results in 1960's and it seems in the beginning

# %%
aaa = f[(f['sample_date'] > '1963') & (f['sample_date'] <= '1964')]
aab = f[(f['sample_date'] > '1964') & (f['sample_date'] <= '1965')]
aac = f[(f['sample_date'] > '1965') & (f['sample_date'] <= '1966')]
aad = f[(f['sample_date'] > '1966') & (f['sample_date'] <= '1967')]
# %%
aaa['sample_date'].count()
#%%
print(aaa)
## So this is where the really big spike was apperently
aab['sample_date'].count()
# %%
aac['sample_date'].count()
# %%
aad['sample_date'].count()
#%%
aad.info()
# %%
## looks like you can actually sclice a Df based on years as integers.


# %%
aa = f[(f['sample_date'] > '1903') & (f['sample_date'] <= '1913')]
ab = f[(f['sample_date'] > '1914') & (f['sample_date'] <= '1923')]
ac = f[(f['sample_date'] > '1924') & (f['sample_date'] <= '1933')]
ad = f[(f['sample_date'] > '1934') & (f['sample_date'] <= '1943')]
ae = f[(f['sample_date'] > '1944') & (f['sample_date'] <= '1953')]
af = f[(f['sample_date'] > '1954') & (f['sample_date'] <= '1963')]
ag = f[(f['sample_date'] > '1964') & (f['sample_date'] <= '1973')]
ah = f[(f['sample_date'] > '1974') & (f['sample_date'] <= '1983')]
ai = f[(f['sample_date'] > '1984') & (f['sample_date'] <= '1993')]
aj = f[(f['sample_date'] > '1994') & (f['sample_date'] <= '2003')]
ak = f[(f['sample_date'] > '2004') & (f['sample_date'] <= '2013')]
al = f[(f['sample_date'] > '2014') & (f['sample_date'] <= '2023')]

# %%
aa
# %%
x = ['1903-1913','1914-1923','1924-1933','1934-1943','1944-1953','1954-1963','1964-1973','1974-1983','1984-1993','1994-2003','2004-2013','2014-2023']
y = ((len(aa.station_number.unique()),len(ab.station_number.unique()),
      len(ac.station_number.unique()),len(ad.station_number.unique()),
      len(ae.station_number.unique()),len(af.station_number.unique()),
      len(ag.station_number.unique()),len(ah.station_number.unique()),
      len(ai.station_number.unique()),len(aj.station_number.unique()),
      len(ak.station_number.unique()),len(al.station_number.unique()),
      ))
y
fig, ax = plt.subplots()
ax = (ax.plt.figure(figsize=(15,10)),
      plt.plot(x, y, 'rs'),
      plt.xlabel('Decades'),
      plt.ylabel('station numbers'),
      plt.title("Unique sample stations by decade"))
ax2 = ax.twinx()
ax2.plot(xx,yy,color = 'blue') 
fig.tight_layout()
plt.show()


# %%
plt.plot(x,y,'ro',xx,yy,'k')
plt.show()

# %%

## Very basic Data so far trying to visualize it better
fig, ax1 = plt.subplots() # Setting subplots
ax2 = ax1.twinx() # twinned Axes
ax1.plot(x, y, 'rs') # First set of data
ax2.plot(xx, yy, 'bo') # Second set of data

ax1.set_xlabel('Decades')
ax1.set_ylabel('Number os stations', color='g')
ax2.set_ylabel('Different tests', color='b')
fig.set_figheight(15)
fig.set_figwidth(15)
plt.show
# %%

# while this is cool we do not want multiple axes on one plot 
fig, ax1 = plt.subplots() # Setting subplots
ax2 = ax1.twinx() # twinned Axes
ax1.bar(x, y,width=1) # First set of data
ax2.plot(xx, yy, 'bo') # Second set of data

ax1.set_xlabel('Decades')
ax1.set_ylabel('Number os stations', color='g')
ax2.set_ylabel('Different tests', color='b')
fig.set_figheight(15)
fig.set_figwidth(15)
plt.show
# %%
plt.figure(figsize=(15,10)),
plt.plot(x, y, 'rs'),
plt.xlabel('Decades'),
plt.ylabel('station numbers'),
plt.title("Unique sample stations by decade")
plt.show()


# %%
plt.figure(figsize=(15,10)),
plt.plot(xx, yy, 'rs'),
plt.xlabel('Decades'),
plt.ylabel('different tests'),
plt.title("Unique tests by decade")
plt.show()
# %%

## Lets see the coordinates of where stations were, were they clustered or spread out?
xyx = ['1903-1913','1914-1923','1924-1933','1934-1943','1944-1953','1954-1963','1964-1973','1974-1983','1984-1993','1994-2003','2004-2013','2014-2023']
yxx = (((aa.latitude.count()),(ab.latitude.count()),
      (ac.latitude.count()),(ad.latitude.count()),
      (ae.latitude.count()),(af.latitude.count()),
      (ag.latitude.count()),(ah.latitude.count()),
      (ai.latitude.count()),(aj.latitude.count()),
      (ak.latitude.count()),(al.latitude.count()),
      ))
y
# %%
plt.figure(figsize=(15,10)),
plt.plot(xyx, yxx, 'rs'),
plt.xlabel('Decades'),
plt.ylabel('different tests'),
plt.title("Unique tests by decade")
plt.show()
# %%
plt.scatter(x=f['latitude'], y=f['longitude'])
plt.show()
# %%

m = folium.Map(location = [37.8073,-121.5617])  
m    
## Now where is that?

folium.Marker([37.8073,-121.5617], popup="01S04E32C001M").add_to(m)
# Now to add one lonely child
m.add_child(folium.ClickForMarker(popup="Waypoint"))

folium.Marker([38.4749,-121.5883], popup="B9D82851352").add_to(m)
# Now to add one lonely child
m.add_child(folium.ClickForMarker(popup="Waypoint"))
# %%
## Goiing to look over maps and how to visualize them best continuing on


f['latitude']

# %%
df2 = f.sort_values(['latitude', 'longitude'])
# %%
df2
# %%
## What I am trying to do now. Get where the most frequent placements are of stations

df3 = df2.dropna()
# %%
df3 = df3.round(decimals=2)
df4 = -df3['longitude']
# %%
df3
df4 = df3.assign(longitude=df4)

#%%

#%%
df4
# %%
for i in range(len(aa)):
    folium.Circle(
        location=[aa.iloc[i]['latitude'], aa.iloc[i]['longitude']],
        radius=10,
    ).add_to(m)

# Same as before, we save it to file
m.save('circle_map.html')
# %%
df3['Status'] = 'Unique'

# %%

# %%
df4  = df3.groupby(["latitude", "longitude"]).size()
# %%
df4
# %%
m = folium.Map(location = [32.54,-117.04])  
m 
#%%
folium.Marker([32.54,-117.05], popup="01S04E32C001M").add_to(m)
# Now to add one lonely child
m.add_child(folium.ClickForMarker(popup="Waypoint"))


folium.Marker([32.54,-117.04], popup="01S04E32C001M").add_to(m)
# Now to add one lonely child
m.add_child(folium.ClickForMarker(popup="Waypoint"))
m
import json
import requests
import pandas as pd
import numpy as np
import plotly.express as px
import folium
from folium.plugins import MarkerCluster

#%%
m = folium.Map()
m.save("footprint.html")


# %%
import folium

m = folium.Map(location=(32.54,-117.04), tiles="cartodb positron")
m.save("footprint.html")
# %%
m
# %%
import folium
#https://gis.data.ca.gov/datasets/8713ced9b78a4abb97dc130a691a8695_0/explore?location=37.850970%2C-115.891382%2C6.42/ for the data thank you!
us_counties = (
    "C:/Users/amcfa/Downloads/California_County_Boundaries.geojson"
)
#%%
us_counties
#%%
m = folium.Map(location=(30, 10), zoom_start=3, tiles="cartodb positron")
folium.GeoJson(us_counties).add_to(m)

m.save("footprint.html")
# %%
m
# %%
m = folium.Map(location=(32.54,-117.04), zoom_start=3, tiles="cartodb positron")
folium.Choropleth(
    geo_data=us_counties,
    data=stations,
    columns=["latitude", "longitude"],
    key_on="feature.properties.COUNTY_NAME",
).add_to(m)

m.save("footprint.html")
# %%
m
# %%
stations
# %%
period_of_record
# %%