
%pip install -U seaborn


%pip install geopandas


%pip install folium


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import databricks.koalas as ks


from pyspark.sql.functions import regexp_replace, regexp_extract, col


df_pd = pd.read_csv("/dbfs/FileStore/tables/tables/skum_test/ca_2020_census/California_CA.csv", header='infer')


df_pd.head()


df_sp=spark.read.format("csv").option("header","true").load("/FileStore/tables/tables/skum_test/ca_2020_census/California_CA.csv")


#df_sp.head()


#df_sp.dtypes


census_df_sp = spark.read.option("inferSchema", True).option("header", True).csv("/FileStore/tables/tables/skum_test/ca_2020_census/California_CA.csv")


#display(census_df_sp)


#display(census_df.summary())


census_df_sp.count()


len(census_df_sp.columns)


import databricks.koalas as ks

census_ks = census_df_sp.to_koalas()
census_ks.head()


import databricks.koalas as ks
data_ks = ks.read_csv("/FileStore/tables/tables/skum_test/ca_2020_census/California_CA.csv", header=0)


#display(data_ks)


#data_ks.info()


data_ks.shape


census_df_sp_sub = census_df_sp.select(["REGION","DIVISION","COUNTY","CBSA","MEMI","CSA","METDIV","AREALAND","AREAWATR","BASENAME","Name","POP100","HU100","INTPTLAT","INTPTLON","LSADC","P0010001", "P0010002", "P0010003", "P0010004", "P0010005", "P0010006", "P0010007", "P0010008", "P0010009", "P0010010", "P0010011", "P0010012", "P0010013", "P0010014", "P0010015", "P0010016", "P0010017", "P0010018", "P0010019", "P0010020", "P0010021", "P0010022", "P0010023", "P0010024", "P0010025", "P0010026", "P0010027", "P0010028", "P0010029", "P0010030", "P0010031", "P0010032", "P0010033", "P0010034", "P0010035", "P0010036", "P0010037", "P0010038", "P0010039", "P0010040", "P0010041", "P0010042", "P0010043", "P0010044", "P0010045", "P0010046", "P0010047", "P0010048", "P0010049", "P0010050", "P0010051", "P0010052", "P0010053", "P0010054", "P0010055", "P0010056", "P0010057", "P0010058", "P0010059", "P0010060", "P0010061", "P0010062", "P0010063", "P0010064", "P0010065", "P0010066", "P0010067", "P0010068", "P0010069", "P0010070", "P0010071", "P0020002", "P0020003", "H0010001", "H0010002", "H0010003", "P0050001", "P0050002", "P0050003", "P0050004", "P0050005", "P0050006", "P0050007", "P0050008", "P0050009", "P0050010"])
#display(census_df_sp_sub)


#df.colRegex("P001")


display(census_df_sp_sub.summary())


data_ks_sub = data_ks[["REGION","DIVISION","COUNTY","CBSA","MEMI","CSA","METDIV","AREALAND","AREAWATR","BASENAME","NAME","POP100","HU100","INTPTLAT","INTPTLON","LSADC","P0010001", "P0010002", "P0010003", "P0010004", "P0010005", "P0010006", "P0010007", "P0010008", "P0010009", "P0010010", "P0010011", "P0010012", "P0010013", "P0010014", "P0010015", "P0010016", "P0010017", "P0010018", "P0010019", "P0010020", "P0010021", "P0010022", "P0010023", "P0010024", "P0010025", "P0010026", "P0010027", "P0010028", "P0010029", "P0010030", "P0010031", "P0010032", "P0010033", "P0010034", "P0010035", "P0010036", "P0010037", "P0010038", "P0010039", "P0010040", "P0010041", "P0010042", "P0010043", "P0010044", "P0010045", "P0010046", "P0010047", "P0010048", "P0010049", "P0010050", "P0010051", "P0010052", "P0010053", "P0010054", "P0010055", "P0010056", "P0010057", "P0010058", "P0010059", "P0010060", "P0010061", "P0010062", "P0010063", "P0010064", "P0010065", "P0010066", "P0010067", "P0010068", "P0010069", "P0010070", "P0010071", "P0020002", "P0020003", "H0010001", "H0010002", "H0010003", "P0050001", "P0050002", "P0050003", "P0050004", "P0050005", "P0050006", "P0050007", "P0050008", "P0050009", "P0050010"]]


#data_ks_sub.dtypes


data_ks_sub.head()


data_ks_sub.describe()


# Plot NAME vs POP100

plot_df = data_ks_sub[["NAME","POP100","INTPTLAT","INTPTLON"]]
plot_df.rename(columns={"POP100":"Total Population"}, inplace=True)
plot_df.head()


plot_df.dtypes


#plot_df["NAME"].value_counts()


pd_temp = plot_df.toPandas()
pd_temp_city = pd_temp[pd_temp["NAME"].str.contains('city', regex=False)].drop_duplicates(subset=["NAME"])
pd_temp_county = pd_temp[pd_temp["NAME"].str.contains('County', regex=False)].drop_duplicates(subset=["NAME"])
pd_temp_school_district = pd_temp[pd_temp["NAME"].str.contains('School District', regex=False)].drop_duplicates(subset=["NAME"])
pd_temp_census_tract = pd_temp[pd_temp["NAME"].str.contains('Census Tract', regex=False)].drop_duplicates(subset=["NAME"])
pd_temp_block = pd_temp[pd_temp["NAME"].str.contains('Block', regex=False)].drop_duplicates(subset=["NAME"])


plt.figure(figsize=(25,10))

splot = sns.barplot(x="NAME",y="Total Population", palette="viridis", data=pd_temp_city.sample(n=50, random_state=1))
splot.axes.set_title("City population",fontsize=40)
splot.set_xlabel("Cities",fontsize=20)
splot.tick_params(labelsize=13)

for item in splot.get_xticklabels():
    item.set_rotation(90)


plt.figure(figsize=(25,10))

splot = sns.barplot(x="NAME",y="Total Population", palette="viridis", data=pd_temp_county.sample(n=100, random_state=1))
splot.axes.set_title("County population",fontsize=40)
splot.set_xlabel("Counties",fontsize=20)
splot.tick_params(labelsize=13)

for item in splot.get_xticklabels():
    item.set_rotation(90)


plt.figure(figsize=(25,10))

splot = sns.barplot(x="NAME",y="Total Population", palette="viridis", data=pd_temp_school_district.sample(n=50, random_state=1))
splot.axes.set_title("School District population",fontsize=40)
splot.set_xlabel("School Districts",fontsize=20)
splot.tick_params(labelsize=13)

for item in splot.get_xticklabels():
    item.set_rotation(90)


plt.figure(figsize=(25,10))

splot = sns.barplot(x="NAME",y="Total Population", palette="viridis", data=pd_temp_census_tract.sample(n=50, random_state=1))
splot.axes.set_title("Census Tract - population",fontsize=40)
splot.set_xlabel("Census Tracts",fontsize=20)
splot.tick_params(labelsize=13)

for item in splot.get_xticklabels():
    item.set_rotation(90)


# For a NAME, check the distribution of population by one race
# P0010001 Total:
# P0010002 Population of one race:
# P0010003 White alone
# P0010004 Black or African American alone
# P0010005 American Indian and Alaska Native alone
# P0010006 Asian alone
# P0010007 Native Hawaiian and Other Pacific Islander alone
# P0010008 Some Other Race alone
# P0020002 Hispanic or Latino

plot_df2 = data_ks_sub[["NAME","P0010003","P0010004","P0010005","P0010006","P0010007","P0010008","P0020002"]]
plot_df2.rename(columns={"P0010003":"White","P0010004":"Black or African American","P0010005":"American Indian and Alaska Native","P0010006":"Asian","P0010007":"Native Hawaiian and Other Pacific Islander","P0010008":"Some Other Race","P0020002":"Hispanic or Latino"}, inplace=True)
plot_df2.head()


pd_temp2 = plot_df2.toPandas()
pd_temp_SD = pd_temp2[pd_temp2["NAME"] == 'San Diego County']
pd_temp_SD


pd_temp_SD = pd_temp_SD.reset_index(drop=True)
pd_temp_SD


pd_temp_SD = pd_temp_SD.drop(["NAME"], axis=1)
pd_temp_SD


pd_temp_SD_T = pd_temp_SD.T
#pd_temp_SD_T = pd_temp_SD_T.rename(columns={"Population"})
pd_temp_SD_T


plt.figure(figsize=(35,10))
ax = pd_temp_SD_T.plot.bar()
plt.title("San Diego Population distribution by ethnicity")


pd_temp_city.head()


pd_temp_city.shape


pd_temp_city_summary = pd_temp_city.describe()
pd_temp_city_summary


pd_temp_city_summary.shape


max_pop_val = pd_temp_city_summary["Total Population"]["max"]
max_pop_val


pd_temp_city["Circle_radius"] = pd_temp_city["Total Population"].apply(lambda x: x / max_pop_val)
pd_temp_city.head()


pd_temp_city = pd_temp_city.drop_duplicates(subset=["NAME"])
pd_temp_city.shape


pd_temp_city["Total Population"].describe()


pd_temp_city[pd_temp_city["NAME"] == 'Santa Barbara city']


#import folium as f

# Make an empty map
#mp = f.Map(location=[37,-119], tiles="OpenStreetMap", zoom_start=6)
#mp = f.Map(location=[37,-119], control_scale=True, zoom_start=6)
#mp


import folium as f

# Make an empty map
mp = f.Map(location=[37,-119], control_scale=True, zoom_start=7)

#data_m=pd_temp_city.sample(n=500, random_state=1)
data_m=pd_temp_city
for i in range(0,data_m.shape[0]):  
  popup = """
  #City Name : <b>%s</b><br>
  #Population : <b>%s</b><br>
  """ % (data_m.iloc[i]["NAME"], 
         data_m.iloc[i]["Total Population"]) 
  f.Circle(
      location=[data_m.iloc[i]['INTPTLAT'], data_m.iloc[i]['INTPTLON']],
      tooltip=popup,
      #popup=data_m.iloc[i]['NAME'],
      radius=float(data_m.iloc[i]['Circle_radius'])*50000,
      color='blue',
      fill=True,
      #fill_color='blue'
   ).add_to(mp)
mp

#import webbrowser
#map_osm = folium.Map(location=[45.5236, -122.6750])
#mp.save('map.html')
#webbrowser.open('map.html')

#html_map = mp._repr_html_()
#displayHTML(html_map)

from IPython.core.display import HTML

HTML(mp._repr_html_())


#mp.save("/FileStore/sukumar/Ca_census_map.html")

#dbutils.fs.put("/FileStore/sukumar/Ca_census_map.html", mp)


#P0050002 Institutionalized population:
#P0050003 Correctional facilities for adults
#P0050004 Juvenile facilities
#P0050005 Nursing facilities/Skilled-nursing facilities
#P0050006 Other institutional facilities
#P0050007 Noninstitutionalized population:


df_k_means = data_ks_sub[["NAME","P0050002","P0050003","P0050004","P0050005","P0050006","P0050007"]].toPandas()
df_k_means.rename(columns={"P0050002":"Institutionalized population","P0050003":"Correctional facilities for adults","P0050004":"Juvenile facilities","P0050005":"Nursing facilities/Skilled-nursing facilities","P0050006":"Other institutional facilities","P0050007":"Noninstitutionalized population"}, inplace=True)
df_k_means = df_k_means[df_k_means["NAME"].str.contains('city', regex=False)].drop_duplicates(subset=["NAME"])
df_k_means.head()


df_k_means.shape


df_k_means.dtypes


df_k_means["Nursing facilities/Skilled-nursing facilities"].describe()


df_k_means_sub = df_k_means[(df_k_means["Nursing facilities/Skilled-nursing facilities"] < 1000) & (df_k_means["Nursing facilities/Skilled-nursing facilities"] > 0)]


plt.figure(figsize=(25,10))

splot = sns.histplot(data = df_k_means_sub, x="Nursing facilities/Skilled-nursing facilities",palette="viridis")
splot.axes.set_title("Histogram of population in Nursing facilities/Skilled-nursing facilities excluding outliers",fontsize=40)
splot.set_xlabel("Nursing facilities/Skilled-nursing facilities",fontsize=20)
splot.tick_params(labelsize=13)


X_df = df_k_means_sub[["Institutionalized population","Noninstitutionalized population"]]
X_df = X_df.dropna()
X_df.shape


X = X_df.values


from sklearn.cluster import KMeans

Error = []
for i in range(1, 12):
  kmeans = KMeans(n_clusters = i)
  kmeans.fit(X)
  Error.append(kmeans.inertia_)
  
plt.plot(range(1,12), Error)
plt.title("Elbow method")
plt.xlabel("Number of clusters")
plt.ylabel("Error")


from sklearn.cluster import KMeans

Kmean = KMeans(n_clusters=4)
y = Kmean.fit_predict(X)

plt.figsize=(25,10)
frame = plt.scatter(X[:,0], X[:,1], c=y, s=50, cmap='viridis')
frame.axes.get_xaxis().set_visible(False)

	FILEID	STUSAB	SUMLEV	LOGRECNO	GEOID	GEOCODE	REGION	DIVISION	STATE	STATENS	COUNTY	COUNTYCC	COUNTYNS	COUSUB	COUSUBCC	COUSUBNS	SUBMCD	SUBMCDCC	SUBMCDNS	ESTATE	ESTATECC	ESTATENS	CONCIT	CONCITCC	CONCITNS	PLACE	PLACECC	PLACENS	TRACT	BLKGRP	BLOCK	AIANHH	AIHHTLI	AIANHHFP	AIANHHCC	AIANHHNS	...	P0040048	P0040049	P0040050	P0040051	P0040052	P0040053	P0040054	P0040055	P0040056	P0040057	P0040059	P0040060	P0040061	P0040063	P0040064	P0040065	P0040066	P0040067	P0040068	P0040069	P0040070	P0040072	P0040073	H0010001	H0010002	H0010003	CIFSN_y.1	P0050001	P0050002	P0050003	P0050004	P0050005	P0050006	P0050007	P0050008	P0050009	P0050010
0	PLST	CA	50	2	0500000US06001	6001	4	9	6	1779778	1.0	H1	1675839.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	39	472	179	22	92	41	43	2	59	5	8	14	3	1	3	63	28	21	0	7	7	5	5	621958	591636	30322	3	53833	10130	3406	397	6218	109	43703	17463	421	25819
1	PLST	CA	50	3	0500000US06003	6003	4	9	6	1779778	3.0	H1	1675840.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1540	530	1010	3	52	1	1	0	0	0	51	0	0	51
2	PLST	CA	50	4	0500000US06005	6005	4	9	6	1779778	5.0	H1	1675841.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	2	6	0	2	0	0	0	1	0	0	0	3	0	0	0	3	0	2	1	0	0	0	0	18805	15678	3127	3	4311	4098	4045	2	51	0	213	0	0	213
3	PLST	CA	50	5	0500000US06007	6007	4	9	6	1779778	7.0	H1	1675842.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	3	19	5	1	5	1	0	0	7	0	0	0	0	0	0	8	5	3	0	0	0	0	0	90133	83268	6865	3	4941	1449	496	31	902	20	3492	2234	0	1258
4	PLST	CA	50	6	0500000US06009	6009	4	9	6	1779778	9.0	H1	1675885.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0	10	2	1	0	0	0	0	4	0	3	0	0	0	0	1	0	0	0	1	0	0	0	27422	18758	8664	3	461	311	170	40	101	0	150	0	0	150

	FILEID	STUSAB	SUMLEV	LOGRECNO	GEOID	GEOCODE	REGION	DIVISION	STATE	STATENS	COUNTY	COUNTYCC	COUNTYNS	COUSUB	COUSUBCC	COUSUBNS	SUBMCD	SUBMCDCC	SUBMCDNS	ESTATE	ESTATECC	ESTATENS	CONCIT	CONCITCC	CONCITNS	PLACE	PLACECC	PLACENS	TRACT	BLKGRP	BLOCK	AIANHH	AIHHTLI	AIANHHFP	AIANHHCC	AIANHHNS	AITS	AITSFP	AITSCC	AITSNS	TTRACT	TBLKGRP	ANRC	ANRCCC	ANRCNS	CBSA	MEMI	CSA	METDIV	NECTA	NMEMI	CNECTA	NECTADIV	CBSAPCI	NECTAPCI	UA	UATYPE	UR	CD116	CD118	CD119	CD120	CD121	SLDU18	SLDU22	SLDU24	SLDU26	SLDU28	SLDL18	SLDL22	SLDL24	SLDL26	SLDL28	VTD	VTDI	ZCTA	SDELM	SDSEC	SDUNI	PUMA	AREALAND	AREAWATR	BASENAME	NAME	FUNCSTAT	GCUNI	POP100	HU100	INTPTLAT	INTPTLON	LSADC	PARTFLAG	UGA	CIFSN_y97	P0010001	P0010002	P0010003	P0010004	P0010005	P0010006	P0010007	P0010008	P0010009	P0010010	P0010011	P0010012	P0010013	P0010014	P0010015	P0010016	P0010017	P0010018	P0010019	P0010020	P0010021	P0010022	P0010023	P0010024	P0010025	P0010026	P0010027	P0010028	P0010029	P0010030	P0010031	P0010032	P0010033	P0010034	P0010035	P0010036	P0010037	P0010038	P0010039	P0010040	P0010041	P0010042	P0010043	P0010044	P0010045	P0010046	P0010047	P0010048	P0010049	P0010050	P0010051	P0010052	P0010053	P0010054	P0010055	P0010056	P0010057	P0010058	P0010059	P0010061	P0010062	P0010063	P0010064	P0010065	P0010066	P0010067	P0010068	P0010070	P0010071	P0020001	P0020002	P0020003	P0020004	P0020005	P0020006	P0020007	P0020008	P0020009	P0020010	P0020011	P0020012	P0020013	P0020014	P0020015	P0020016	P0020017	P0020018	P0020019	P0020020	P0020021	P0020022	P0020023	P0020024	P0020025	P0020026	P0020027	P0020028	P0020029	P0020030	P0020031	P0020032	P0020033	P0020034	P0020035	P0020036	P0020037	P0020038	P0020039	P0020040	P0020041	P0020042	P0020043	P0020044	P0020045	P0020046	P0020047	P0020048	P0020049	P0020050	P0020051	P0020052	P0020053	P0020054	P0020055	P0020056	P0020057	P0020059	P0020060	P0020061	P0020063	P0020064	P0020065	P0020066	P0020067	P0020068	P0020069	P0020070	P0020072	P0020073	CIFSN_x242	P0030001	P0030002	P0030003	P0030004	P0030005	P0030006	P0030007	P0030008	P0030009	P0030010	P0030011	P0030012	P0030013	P0030014	P0030015	P0030016	P0030017	P0030018	P0030019	P0030020	P0030021	P0030022	P0030023	P0030024	P0030025	P0030026	P0030027	P0030028	P0030029	P0030030	P0030031	P0030032	P0030033	P0030034	P0030035	P0030036	P0030037	P0030038	P0030039	P0030040	P0030041	P0030042	P0030043	P0030044	P0030045	P0030046	P0030047	P0030048	P0030049	P0030050	P0030051	P0030052	P0030053	P0030054	P0030055	P0030056	P0030057	P0030058	P0030059	P0030061	P0030062	P0030063	P0030064	P0030065	P0030066	P0030067	P0030068	P0030070	P0030071	P0040001	P0040002	P0040003	P0040004	P0040005	P0040006	P0040007	P0040008	P0040009	P0040010	P0040011	P0040012	P0040013	P0040014	P0040015	P0040016	P0040017	P0040018	P0040019	P0040020	P0040021	P0040022	P0040023	P0040024	P0040025	P0040026	P0040027	P0040028	P0040029	P0040030	P0040031	P0040032	P0040033	P0040034	P0040035	P0040036	P0040037	P0040038	P0040039	P0040040	P0040041	P0040042	P0040043	P0040044	P0040045	P0040046	P0040048	P0040049	P0040050	P0040051	P0040052	P0040053	P0040054	P0040055	P0040056	P0040057	P0040059	P0040060	P0040061	P0040063	P0040064	P0040065	P0040066	P0040067	P0040068	P0040069	P0040070	P0040072	P0040073	H0010001	H0010002	H0010003	CIFSN_y390	P0050001	P0050002	P0050003	P0050004	P0050005	P0050006	P0050007	P0050008	P0050009	P0050010
0	PLST	CA	50	2	0500000US06001	06001	4	9	6	1779778	1.0	H1	1675839.0	NaN	None	NaN	None	None	None	None	None	None	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	None	NaN	None	NaN	NaN	NaN	None	NaN	None	None	NaN	NaN	NaN	41860.0	1.0	488.0	36084.0	NaN	NaN	NaN	NaN	None	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	None	None	None	NaN	NaN	NaN	None	1910017353	216902808	Alameda	Alameda County	A	None	1682353	621958	37.647139	-121.912488	06	None	NaN	1	1682353	1491537	523836	164879	19659	545261	14123	223779	190816	171703	13579	10768	39888	2293	81119	2682	4125	640	4182	707	151	3685	3684	3285	915	16711	2460	1652	154	1551	1066	137	4502	1704	2121	206	176	34	231	205	194	30	64	77	20	127	2112	404	49	819	110	159	22	124	257	21	105	25	8	6	3	262	95	129	14	12	12	28	28	1682353	393749	1288604	1200067	472277	159499	4131	540511	13209	10440	88537	80792	11936	7033	37058	1921	9261	2221	3799	566	1376	452	92	59	3343	1125	550	6898	1803	1394	116	417	710	92	67	1444	315	15	105	17	71	157	72	13	25	7	3	55	750	300	27	120	99	71	2	78	5	27	14	3	1	3	92	48	23	7	7	7	5	5	2	1338388	1208791	448581	134525	14919	434901	10921	164944	129597	117917	8444	8557	21834	1557	61432	2112	2214	361	2852	426	119	2807	2882	1654	666	10250	1748	665	75	860	585	87	3459	1075	930	108	100	20	172	100	97	4	40	40	9	76	1246	229	28	585	41	81	5	75	105	12	46	25	7	4	3	159	51	85	7	7	9	25	25	1338388	286178	1052210	994680	410177	131210	3319	431949	10355	7670	57530	52710	7762	5969	20670	1375	7303	1834	2101	332	1126	276	83	49	2660	709	461	4280	1369	575	64	279	415	60	58	942	179	4	66	8	63	84	43	1	24	7	39	472	179	22	92	41	43	2	59	5	8	14	3	1	3	63	28	21	0	7	7	5	5	621958	591636	30322	3	53833	10130	3406	397	6218	109	43703	17463	421	25819
1	PLST	CA	50	3	0500000US06003	06003	4	9	6	1779778	3.0	H1	1675840.0	NaN	None	NaN	None	None	None	None	None	None	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	None	NaN	None	NaN	NaN	NaN	None	NaN	None	None	NaN	NaN	NaN	99999.0	9.0	999.0	99999.0	NaN	NaN	NaN	NaN	None	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	None	None	None	NaN	NaN	NaN	None	1912292607	12557304	Alpine	Alpine County	A	None	1204	1540	38.621783	-119.798352	06	None	NaN	1	1204	1085	814	10	236	12	0	13	119	89	5	10	6	1	50	5	2	1	0	1	1	3	2	2	0	20	2	0	4	0	0	2	3	0	4	1	0	0	1	1	0	1	0	1	0	0	9	2	0	0	0	1	0	0	1	0	5	0	0	0	0	1	0	0	0	1	0	0	0	1204	84	1120	1044	801	10	214	12	0	7	76	56	5	10	4	1	25	5	0	1	0	1	0	1	2	1	0	15	2	0	3	0	0	2	0	0	4	1	0	0	1	1	0	0	0	1	0	0	4	2	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	2	1009	941	736	9	174	11	0	11	68	54	5	1	2	1	40	0	2	0	0	0	1	1	0	1	0	8	1	0	1	0	0	0	2	0	3	0	0	0	1	0	0	0	0	0	0	0	6	0	0	0	0	1	0	0	0	0	5	0	0	0	0	0	0	0	0	0	0	0	0	1009	62	947	915	726	9	162	11	0	7	32	26	5	1	0	1	18	0	0	0	0	0	0	0	0	1	0	5	1	0	0	0	0	0	0	0	3	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1540	530	1010	3	52	1	1	0	0	0	51	0	0	51
2	PLST	CA	50	4	0500000US06005	06005	4	9	6	1779778	5.0	H1	1675841.0	NaN	None	NaN	None	None	None	None	None	None	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	None	NaN	None	NaN	NaN	NaN	None	NaN	None	None	NaN	NaN	NaN	99999.0	9.0	999.0	99999.0	NaN	NaN	NaN	NaN	None	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	None	None	None	NaN	NaN	NaN	None	1539965777	29438413	Amador	Amador County	A	None	40474	18805	38.443549	-120.653858	06	None	NaN	1	40474	36596	31104	1236	757	582	82	2835	3878	3591	163	1123	307	59	1804	20	5	0	6	18	2	15	59	6	4	261	28	7	4	12	34	11	87	26	19	5	2	1	1	8	1	7	0	0	0	8	17	0	2	4	1	4	1	0	0	0	0	5	0	0	0	9	5	3	1	0	0	0	0	40474	6014	34460	32393	29725	1215	577	554	73	249	2067	1948	139	977	263	52	417	13	5	0	2	9	2	9	59	1	0	107	24	4	0	6	31	7	2	22	0	0	0	0	0	8	1	0	0	0	0	2	9	0	2	0	1	0	1	0	0	0	5	0	0	0	3	0	2	1	0	0	0	0	2	34042	31277	26465	1193	578	498	60	2483	2765	2617	99	831	185	42	1353	9	0	0	2	16	0	15	59	6	0	133	13	1	0	6	15	7	56	12	10	0	2	0	1	5	1	0	0	0	0	4	10	0	2	4	0	0	1	0	0	0	0	3	0	0	0	5	1	3	1	0	0	0	0	34042	4550	29492	27953	25572	1184	450	478	53	216	1539	1471	87	755	160	40	343	9	0	0	1	7	0	9	59	1	0	59	13	1	0	6	12	7	0	12	0	0	0	0	0	5	1	0	0	0	2	6	0	2	0	0	0	1	0	0	0	3	0	0	0	3	0	2	1	0	0	0	0	18805	15678	3127	3	4311	4098	4045	2	51	0	213	0	0	213
3	PLST	CA	50	5	0500000US06007	06007	4	9	6	1779778	7.0	H1	1675842.0	NaN	None	NaN	None	None	None	None	None	None	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	None	NaN	None	NaN	NaN	NaN	None	NaN	None	None	NaN	NaN	NaN	17020.0	1.0	999.0	99999.0	NaN	NaN	NaN	NaN	None	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	None	None	None	NaN	NaN	NaN	None	4238488698	105260548	Butte	Butte County	A	None	211632	90133	39.665336	-121.603209	06	None	NaN	1	211632	186947	149557	3644	4492	10533	573	18148	24685	22849	1764	6709	2396	388	10402	177	93	24	157	69	22	374	172	84	18	1652	345	97	16	124	162	61	480	182	87	21	16	1	21	4	7	0	5	16	1	6	156	30	7	41	1	6	7	29	23	2	7	0	2	0	1	28	12	11	2	3	0	0	0	211632	40112	171520	158046	139651	3320	3050	10333	508	1184	13474	12581	1537	5837	2174	348	2129	127	80	22	55	50	21	21	154	25	1	824	263	87	16	37	122	45	31	170	15	2	6	0	16	4	2	0	5	0	0	3	55	23	2	10	1	0	0	16	3	0	0	0	0	0	14	5	6	0	3	0	0	0	2	168339	151623	123122	2773	3324	7963	464	13977	16716	15688	852	4862	1444	259	7450	105	56	13	112	41	19	270	132	65	8	921	181	50	1	59	56	26	325	107	50	18	5	0	16	4	4	0	5	8	0	6	86	7	6	19	1	6	7	20	13	2	3	0	2	0	0	21	12	8	1	0	0	0	0	168339	28425	139914	130683	116628	2606	2317	7828	416	888	9231	8761	776	4390	1334	234	1637	79	50	11	39	31	18	18	120	23	1	443	144	44	1	24	49	26	25	100	4	2	0	0	12	4	0	0	5	0	3	19	5	1	5	1	0	0	7	0	0	0	0	0	0	8	5	3	0	0	0	0	0	90133	83268	6865	3	4941	1449	496	31	902	20	3492	2234	0	1258
4	PLST	CA	50	6	0500000US06009	06009	4	9	6	1779778	9.0	H1	1675885.0	NaN	None	NaN	None	None	None	None	None	None	NaN	NaN	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	None	NaN	None	NaN	NaN	NaN	None	NaN	None	None	NaN	NaN	NaN	99999.0	9.0	999.0	99999.0	NaN	NaN	NaN	NaN	None	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	NaN	None	None	None	None	None	None	None	NaN	NaN	NaN	None	2641837359	43789489	Calaveras	Calaveras County	A	None	45292	27422	38.191068	-120.554106	06	None	NaN	1	45292	40264	36315	364	747	743	100	1995	5028	4706	195	1513	428	110	2285	13	13	0	24	6	6	43	39	28	3	285	15	8	2	28	25	24	104	39	21	0	8	0	2	0	0	0	0	5	0	4	31	6	1	4	0	7	0	4	0	6	3	0	0	0	0	5	3	0	0	1	1	1	1	45292	5865	39427	36548	34668	334	497	706	75	268	2879	2748	182	1370	385	109	618	13	5	0	2	0	6	5	39	11	3	112	15	6	0	10	25	8	8	36	1	0	0	0	0	0	0	0	0	3	0	0	15	3	1	4	0	0	0	4	0	3	0	0	0	0	4	3	0	0	1	0	0	0	2	37337	33724	30591	278	613	652	82	1508	3613	3411	105	1163	237	80	1694	11	5	0	22	1	6	36	35	13	3	184	11	6	2	20	18	15	67	24	14	0	0	0	0	0	0	0	0	5	0	2	16	5	1	0	0	0	0	4	0	3	3	0	0	0	0	2	0	0	0	1	1	0	0	37337	4153	33184	31086	29518	254	422	621	66	205	2098	2006	101	1058	221	80	474	11	5	0	2	0	6	5	35	5	3	81	11	6	0	10	18	4	8	21	0	0	0	0	0	0	0	0	0	3	0	10	2	1	0	0	0	0	4	0	3	0	0	0	0	1	0	0	0	1	0	0	0	27422	18758	8664	3	461	311	170	40	101	0	150	0	0	150

summary	REGION	DIVISION	COUNTY	CBSA	MEMI	CSA	METDIV	AREALAND	AREAWATR	BASENAME	Name	POP100	HU100	INTPTLAT	INTPTLON	LSADC	P0010001	P0010002	P0010003	P0010004	P0010005	P0010006	P0010007	P0010008	P0010009	P0010010	P0010011	P0010012	P0010013	P0010014	P0010015	P0010016	P0010017	P0010018	P0010019	P0010020	P0010021	P0010022	P0010023	P0010024	P0010025	P0010026	P0010027	P0010028	P0010029	P0010030	P0010031	P0010032	P0010033	P0010034	P0010035	P0010036	P0010037	P0010038	P0010039	P0010040	P0010041	P0010042	P0010043	P0010044	P0010045	P0010046	P0010047	P0010048	P0010049	P0010050	P0010051	P0010052	P0010053	P0010054	P0010055	P0010056	P0010057	P0010058	P0010059	P0010060	P0010061	P0010062	P0010063	P0010064	P0010065	P0010066	P0010067	P0010068	P0010069	P0010070	P0010071	P0020002	P0020003	H0010001	H0010002	H0010003	P0050001	P0050002	P0050003	P0050004	P0050005	P0050006	P0050007	P0050008	P0050009	P0050010
count	669171	669171	650290	650290	650290	650290	650290	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171	669171
mean	4.0	9.0	56.366931676636575	38679.26071752603	1.3898522197788679	554.2995555828937	76237.78717956605	2.8677515498694655E7	1445348.4535836133	1832.536732234136	null	3059.615147697674	1113.5215647420464	36.032115277906854	-119.64066830452249	26.733684030450775	3059.615147697674	2613.714258388364	1258.4589409881778	173.5204469410659	48.9568824709977	472.2799613252816	12.19083612409982	648.3071905387412	445.9008893093096	416.4643730825155	19.981495013979984	29.66331176933848	48.420317078893135	3.5101820013120713	280.72336816747884	2.8957964406706207	4.014609120837574	0.6792180175171967	6.895657462741212	1.020704722709143	0.19860244989696207	8.816177030983113	3.9774721259588355	4.740206912732321	0.9272547674660139	26.405721407532603	3.201568806777341	1.8671311219404307	0.2581283408874563	2.5513657943933614	1.7074604249138112	0.2554354567068806	9.24852093112224	2.400646471529699	3.273394991713628	0.328717173936109	0.2000086674407588	0.04289635982431994	0.2618583291864112	0.20435733168353082	0.20147466043806442	0.04687889941434999	0.07420076482692764	0.113359664420604	0.024413789599369965	0.14390342677731102	2.6784095545084887	0.503422891906553	0.0758356234803959	0.9203895566305175	0.15077909831717154	0.2116663752613308	0.02595001875454854	0.19549860947351275	0.33209598144569924	0.03252681302686458	0.16080941941596394	0.020153294150523558	0.021743321213860134	0.005952140783148104	0.01316703802167159	0.008419372626727697	0.31766768135498996	0.103903187675497	0.1549753351534959	0.018213580684159954	0.016043731721787108	0.02205265918576866	0.002479186934281372	0.034717583397965546	0.034717583397965546	1206.5007987494976	1853.1143489481763	1113.5215647420464	1042.8784271882673	70.64313755377923	70.68672282570523	26.4018389918272	15.296785126671658	0.6814057991156222	9.684245133157294	0.7394029328826264	44.284883833878034	17.845792779424094	4.347791521150797	22.091299533303147
stddev	0.0	0.0	28.31755854283744	15294.999450959238	1.623580576183806	270.1235591357882	33581.03897460754	7.881045914630078E8	4.494612437568705E7	1362.64644400938	null	66405.47319871612	24431.340821414025	2.3066087112499662	2.0902436730725986	25.362129522066663	66405.47319871612	56853.01741716403	26526.04242970642	4595.227009682515	1060.2947009505854	11095.468936577807	278.1856483724344	15286.754026573077	9606.180301680255	8971.494547229793	448.8439672083964	590.8450733585147	1074.9128948779255	72.65506501955869	6136.860344046799	71.65700168239906	94.854026160174	15.98677244280636	179.10509565816344	22.166134660665143	4.2246339388363285	206.5055051043879	87.47833887109377	106.04737114758841	21.221856045450323	573.1525791990103	72.15967177677882	42.057610874354104	5.747666701614405	57.86506363067629	35.35640895983642	5.169246161049264	204.17508670962263	51.408998717980424	70.47563280844962	6.91416990943495	4.636601663755835	1.102240632199946	6.325829834166733	4.855388638690167	4.731520760612667	1.1410024230780134	1.5805620556340394	2.4675008146077446	0.5354034287121976	3.1674736799695964	59.43995607021075	11.468646987613933	1.7201859377688478	21.449689415311948	3.4206981519379043	4.873442687524608	0.5832214962803234	4.109139259114911	7.215087980828405	0.6897207574776267	3.441005165520258	0.5021498570588202	0.49869988729761555	0.17870930429585002	0.2965270764180188	0.20318667124143872	7.078996390196105	2.3648623549130847	3.568688830888468	0.3975957995646648	0.3813182936499723	0.5296301026671097	0.0831706430900629	0.8453477335540394	0.8453477335540394	27432.786453061643	40094.052441055195	24431.340821414025	22903.924030413393	1604.306770712246	1662.1753646137315	577.9366182088413	372.60041352603315	16.466467763815917	227.47278374446967	25.182709662978187	1168.0248620705302	555.4863597123475	221.16130607469864	568.7430289189296
min	4	9	1.0	12540.0	1.0	260.0	11244.0	0	0	0	ABC Unified School District	0	0	32.5395511	-124.4136928	00	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
25%	4	9	37.0	31080.0	1.0	348.0	36084.0	16661	0	1007.0	null	7	2	34.0052005	-121.6376844	0.0	7	5	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	2	1	0	0	0	0	0	0	0	0	0	0	0
50%	4	9	59.0	40140.0	1.0	472.0	99999.0	51601	0	2001.0	null	61	21	35.4357468	-119.2696632	22.0	61	50	23	0	0	2	0	5	9	8	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	15	33	21	19	0	0	0	0	0	0	0	0	0	0	0
75%	4	9	75.0	41860.0	1.0	999.0	99999.0	638110	0	3000.0	null	246	93	37.866253799999996	-117.930457	57.0	246	213	105	10	4	29	1	48	36	34	2	3	4	0	24	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	96	148	93	87	7	0	0	0	0	0	0	0	0	0	0
max	4	9	115.0	99999.0	9.0	999.0	99999.0	282055294521	15719629492	Zayante	Zayante CDP	28573858	10571847	42.0074191	-114.13926399999998	OT	28573858	24485003	11576460	1799185	447548	4474283	115115	6072412	4088855	3811637	194177	262089	459478	31625	2532955	29671	40474	6789	69354	9699	1847	82008	37940	44752	8779	247939	31244	18189	2467	24478	15573	2273	85549	22286	29988	2983	1983	455	2648	2083	1976	475	682	1045	211	1351	25817	5001	746	8969	1473	2086	243	1780	3058	293	1507	209	206	64	105	77	3120	1043	1529	164	153	208	23	342	342	11128716	17445142	10571847	9866472	705375	696051	234511	126683	6408	93493	7927	461540	197511	39597	226824

	REGION	DIVISION	COUNTY	CBSA	MEMI	CSA	METDIV	AREALAND	AREAWATR	BASENAME	NAME	POP100	HU100	INTPTLAT	INTPTLON	LSADC	P0010001	P0010002	P0010003	P0010004	P0010005	P0010006	P0010007	P0010008	P0010009	P0010010	P0010011	P0010012	P0010013	P0010014	P0010015	P0010016	P0010017	P0010018	P0010019	P0010020	P0010021	P0010022	P0010023	P0010024	P0010025	P0010026	P0010027	P0010028	P0010029	P0010030	P0010031	P0010032	P0010033	P0010034	P0010035	P0010036	P0010037	P0010038	P0010039	P0010040	P0010041	P0010042	P0010043	P0010044	P0010045	P0010046	P0010047	P0010048	P0010049	P0010050	P0010051	P0010052	P0010053	P0010054	P0010055	P0010056	P0010057	P0010058	P0010059	P0010061	P0010062	P0010063	P0010064	P0010065	P0010066	P0010067	P0010068	P0010070	P0010071	P0020002	P0020003	H0010001	H0010002	H0010003	P0050001	P0050002	P0050003	P0050004	P0050005	P0050006	P0050007	P0050008	P0050009	P0050010
0	4	9	1.0	41860.0	1.0	488.0	36084.0	1910017353	216902808	Alameda	Alameda County	1682353	621958	37.647139	-121.912488	06	1682353	1491537	523836	164879	19659	545261	14123	223779	190816	171703	13579	10768	39888	2293	81119	2682	4125	640	4182	707	151	3685	3684	3285	915	16711	2460	1652	154	1551	1066	137	4502	1704	2121	206	176	34	231	205	194	30	64	77	20	127	2112	404	49	819	110	159	22	124	257	21	105	25	8	6	3	262	95	129	14	12	12	28	28	393749	1288604	621958	591636	30322	53833	10130	3406	397	6218	109	43703	17463	421	25819
1	4	9	3.0	99999.0	9.0	999.0	99999.0	1912292607	12557304	Alpine	Alpine County	1204	1540	38.621783	-119.798352	06	1204	1085	814	10	236	12	0	13	119	89	5	10	6	1	50	5	2	1	0	1	1	3	2	2	0	20	2	0	4	0	0	2	3	0	4	1	0	0	1	1	0	1	0	1	0	0	9	2	0	0	0	1	0	0	1	0	5	0	0	0	0	1	0	0	0	1	0	0	0	84	1120	1540	530	1010	52	1	1	0	0	0	51	0	0	51
2	4	9	5.0	99999.0	9.0	999.0	99999.0	1539965777	29438413	Amador	Amador County	40474	18805	38.443549	-120.653858	06	40474	36596	31104	1236	757	582	82	2835	3878	3591	163	1123	307	59	1804	20	5	0	6	18	2	15	59	6	4	261	28	7	4	12	34	11	87	26	19	5	2	1	1	8	1	7	0	0	0	8	17	0	2	4	1	4	1	0	0	0	0	5	0	0	0	9	5	3	1	0	0	0	0	6014	34460	18805	15678	3127	4311	4098	4045	2	51	0	213	0	0	213
3	4	9	7.0	17020.0	1.0	999.0	99999.0	4238488698	105260548	Butte	Butte County	211632	90133	39.665336	-121.603209	06	211632	186947	149557	3644	4492	10533	573	18148	24685	22849	1764	6709	2396	388	10402	177	93	24	157	69	22	374	172	84	18	1652	345	97	16	124	162	61	480	182	87	21	16	1	21	4	7	0	5	16	1	6	156	30	7	41	1	6	7	29	23	2	7	0	2	0	1	28	12	11	2	3	0	0	0	40112	171520	90133	83268	6865	4941	1449	496	31	902	20	3492	2234	0	1258
4	4	9	9.0	99999.0	9.0	999.0	99999.0	2641837359	43789489	Calaveras	Calaveras County	45292	27422	38.191068	-120.554106	06	45292	40264	36315	364	747	743	100	1995	5028	4706	195	1513	428	110	2285	13	13	0	24	6	6	43	39	28	3	285	15	8	2	28	25	24	104	39	21	0	8	0	2	0	0	0	0	5	0	4	31	6	1	4	0	7	0	4	0	6	3	0	0	0	0	5	3	0	0	1	1	1	1	5865	39427	27422	18758	8664	461	311	170	40	101	0	150	0	0	150

	REGION	DIVISION	COUNTY	CBSA	MEMI	CSA	METDIV	AREALAND	AREAWATR	POP100	HU100	INTPTLAT	INTPTLON	P0010001	P0010002	P0010003	P0010004	P0010005	P0010006	P0010007	P0010008	P0010009	P0010010	P0010011	P0010012	P0010013	P0010014	P0010015	P0010016	P0010017	P0010018	P0010019	P0010020	P0010021	P0010022	P0010023	P0010024	P0010025	P0010026	P0010027	P0010028	P0010029	P0010030	P0010031	P0010032	P0010033	P0010034	P0010035	P0010036	P0010037	P0010038	P0010039	P0010040	P0010041	P0010042	P0010043	P0010044	P0010045	P0010046	P0010047	P0010048	P0010049	P0010050	P0010051	P0010052	P0010053	P0010054	P0010055	P0010056	P0010057	P0010058	P0010059	P0010060	P0010061	P0010062	P0010063	P0010064	P0010065	P0010066	P0010067	P0010068	P0010069	P0010070	P0010071	P0020002	P0020003	H0010001	H0010002	H0010003	P0050001	P0050002	P0050003	P0050004	P0050005	P0050006	P0050007	P0050008	P0050009	P0050010
count	669171.0	669171.0	650290.000000	650290.000000	650290.000000	650290.000000	650290.000000	6.691710e+05	6.691710e+05	6.691710e+05	6.691710e+05	669171.000000	669171.000000	6.691710e+05	6.691710e+05	6.691710e+05	6.691710e+05	669171.000000	6.691710e+05	669171.000000	6.691710e+05	6.691710e+05	6.691710e+05	669171.000000	669171.000000	669171.000000	669171.000000	6.691710e+05	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	6.691710e+05	6.691710e+05	6.691710e+05	6.691710e+05	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000	669171.000000
mean	4.0	9.0	56.366932	38679.260718	1.389852	554.299556	76237.787180	2.867752e+07	1.445348e+06	3.059615e+03	1.113522e+03	36.032115	-119.640668	3.059615e+03	2.613714e+03	1.258459e+03	1.735204e+02	48.956882	4.722800e+02	12.190836	6.483072e+02	4.459009e+02	4.164644e+02	19.981495	29.663312	48.420317	3.510182	2.807234e+02	2.895796	4.014609	0.679218	6.895657	1.020705	0.198602	8.816177	3.977472	4.740207	0.927255	26.405721	3.201569	1.867131	0.258128	2.551366	1.707460	0.255435	9.248521	2.400646	3.273395	0.328717	0.200009	0.042896	0.261858	0.204357	0.201475	0.046879	0.074201	0.113360	0.024414	0.143903	2.678410	0.503423	0.075836	0.920390	0.150779	0.211666	0.025950	0.195499	0.332096	0.032527	0.160809	0.020153	0.021743	0.005952	0.013167	0.008419	0.317668	0.103903	0.154975	0.018214	0.016044	0.022053	0.002479	0.034718	0.034718	1.206501e+03	1.853114e+03	1.113522e+03	1.042878e+03	70.643138	70.686723	26.401839	15.296785	0.681406	9.684245	0.739403	44.284884	17.845793	4.347792	22.091300
std	0.0	0.0	28.317559	15294.999451	1.623581	270.123559	33581.038975	7.881046e+08	4.494612e+07	6.640547e+04	2.443134e+04	2.306609	2.090244	6.640547e+04	5.685302e+04	2.652604e+04	4.595227e+03	1060.294701	1.109547e+04	278.185648	1.528675e+04	9.606180e+03	8.971495e+03	448.843967	590.845073	1074.912895	72.655065	6.136860e+03	71.657002	94.854026	15.986772	179.105096	22.166135	4.224634	206.505505	87.478339	106.047371	21.221856	573.152579	72.159672	42.057611	5.747667	57.865064	35.356409	5.169246	204.175087	51.408999	70.475633	6.914170	4.636602	1.102241	6.325830	4.855389	4.731521	1.141002	1.580562	2.467501	0.535403	3.167474	59.439956	11.468647	1.720186	21.449689	3.420698	4.873443	0.583221	4.109139	7.215088	0.689721	3.441005	0.502150	0.498700	0.178709	0.296527	0.203187	7.078996	2.364862	3.568689	0.397596	0.381318	0.529630	0.083171	0.845348	0.845348	2.743279e+04	4.009405e+04	2.443134e+04	2.290392e+04	1604.306771	1662.175365	577.936618	372.600414	16.466468	227.472784	25.182710	1168.024862	555.486360	221.161306	568.743029
min	4.0	9.0	1.000000	12540.000000	1.000000	260.000000	11244.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	32.539551	-124.413693	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	4.0	9.0	37.000000	31080.000000	1.000000	348.000000	36084.000000	1.665900e+04	0.000000e+00	7.000000e+00	2.000000e+00	34.005323	-121.637698	7.000000e+00	5.000000e+00	1.000000e+00	0.000000e+00	0.000000	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	2.000000e+00	2.000000e+00	1.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	4.0	9.0	59.000000	40140.000000	1.000000	472.000000	99999.000000	5.159000e+04	0.000000e+00	6.100000e+01	2.100000e+01	35.434519	-119.269756	6.100000e+01	5.000000e+01	2.300000e+01	0.000000e+00	0.000000	2.000000e+00	0.000000	5.000000e+00	9.000000e+00	8.000000e+00	0.000000	0.000000	0.000000	0.000000	4.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.500000e+01	3.300000e+01	2.100000e+01	1.900000e+01	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	4.0	9.0	75.000000	41860.000000	1.000000	999.000000	99999.000000	6.380330e+05	0.000000e+00	2.460000e+02	9.300000e+01	37.866010	-117.930126	2.460000e+02	2.130000e+02	1.050000e+02	1.000000e+01	4.000000	2.900000e+01	1.000000	4.800000e+01	3.600000e+01	3.400000e+01	2.000000	3.000000	4.000000	0.000000	2.400000e+01	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	9.600000e+01	1.480000e+02	9.300000e+01	8.700000e+01	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	4.0	9.0	115.000000	99999.000000	9.000000	999.000000	99999.000000	2.820553e+11	1.571963e+10	2.857386e+07	1.057185e+07	42.007419	-114.139264	2.857386e+07	2.448500e+07	1.157646e+07	1.799185e+06	447548.000000	4.474283e+06	115115.000000	6.072412e+06	4.088855e+06	3.811637e+06	194177.000000	262089.000000	459478.000000	31625.000000	2.532955e+06	29671.000000	40474.000000	6789.000000	69354.000000	9699.000000	1847.000000	82008.000000	37940.000000	44752.000000	8779.000000	247939.000000	31244.000000	18189.000000	2467.000000	24478.000000	15573.000000	2273.000000	85549.000000	22286.000000	29988.000000	2983.000000	1983.000000	455.000000	2648.000000	2083.000000	1976.000000	475.000000	682.000000	1045.000000	211.000000	1351.000000	25817.000000	5001.000000	746.000000	8969.000000	1473.000000	2086.000000	243.000000	1780.000000	3058.000000	293.000000	1507.000000	209.000000	206.000000	64.000000	105.000000	77.000000	3120.000000	1043.000000	1529.000000	164.000000	153.000000	208.000000	23.000000	342.000000	342.000000	1.112872e+07	1.744514e+07	1.057185e+07	9.866472e+06	705375.000000	696051.000000	234511.000000	126683.000000	6408.000000	93493.000000	7927.000000	461540.000000	197511.000000	39597.000000	226824.000000

CI Challenge 2021 - Q4¶

California 2020 Census data profiling¶

Install packages¶

Import libraries¶

Read csv file with pandas¶

Read csv file with spark¶

Read csv file with Spark using inferSchema option¶

Convert Spark dataframe to Koalas dataframe to perform Pandas like operations¶

Read csv file with Koalas¶

Time to read 721 MB csv file during one of the runs¶

Selecting 104 columns for analysis¶

Subsetting from Spark dataframe¶

Subsetting from Koalas dataframe¶

Continuing with Koalas dataframe¶

Plot population be city / county / school district / census tract¶

Plot population distribution by ethnicity in San Diego County¶

Use Geopandas to show population bubbles on a Map¶

Institutionalized and non institutionalized population¶

K Means clustering of institutionalized and non institutionalized population¶

	0
White	1633129
Black or African American	155813
American Indian and Alaska Native	40968
Asian	410752
Native Hawaiian and Other Pacific Islander	15286
Some Other Race	520994
Hispanic or Latino	1119629

	NAME	Total Population	INTPTLAT	INTPTLON
36816	Adelanto city	38046	34.580902	-117.439458
36818	Agoura Hills city	20299	34.148942	-118.763930
36823	Alameda city	78280	37.741911	-122.259914
36825	Albany city	20271	37.890650	-122.318116
36828	Alhambra city	82868	34.083571	-118.136444

	Total Population	INTPTLAT	INTPTLON
count	6.750000e+02	675.000000	675.000000
mean	6.075501e+04	35.913016	-119.678155
std	1.791500e+05	2.252842	2.139734
min	0.000000e+00	32.569140	-124.261239
25%	7.344000e+03	33.930746	-121.826875
50%	2.703800e+04	35.194361	-119.318750
75%	6.898100e+04	37.830509	-117.869322
max	3.898747e+06	41.964965	-114.593545

	NAME	Institutionalized population	Correctional facilities for adults	Juvenile facilities	Nursing facilities/Skilled-nursing facilities	Other institutional facilities	Noninstitutionalized population
36816	Adelanto city	2674	2674	0	0	0	77
36818	Agoura Hills city	148	0	0	148	0	0
36823	Alameda city	916	0	0	916	0	614
36825	Albany city	10	0	0	10	0	1385
36828	Alhambra city	691	21	37	547	86	212