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Acceptance Letter - ZHAO, J. .pdf UNIVERSITY OF CALIFORNIA, IRVINE DIVISION OF CONTINUING EDUCATION BERKELEY  DAVIS  IRVINE  LOS ANGELES  MERCED  RIVERSIDE  SAN DIEGO  SANFRANCISCO  SANTA...

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Acceptance Letter - ZHAO, J. .pdf

UNIVERSITY OF CALIFORNIA, IRVINE DIVISION OF CONTINUING EDUCATION
BERKELEY  DAVIS  IRVINE  LOS ANGELES  MERCED  RIVERSIDE  SAN DIEGO  SANFRANCISCO  SANTA BARBARA  SANTA CRUZ

Tel: XXXXXXXXXX
Fax: XXXXXXXXXX
Email: XXXXXXXXXX
P.O. Box 6050
Irvine, California XXXXXXXXXX, U.S.A.
School Code: LOS214F XXXXXXXXXX
Dear Jianghong Zhao,
On behalf of the staff and instructors at UC Irvine Division of Continuing Education, I am
delighted to notify you of your acceptance into the Fall 2020 Accelerated Certificate Program
(ACP) in Innovation Management & Entrepreneurship (IME).

The first day of the program is September 21, 2020. All new students are required to attend
orientation on September 21, 2020. If you cannot a
ive on that day, please send an email to
XXXXXXXXXX.
Please review the Welcome Guide carefully as it includes very important information about your
program. You will receive an ACP Pre-a
ival Email closer to the program start date with more
detailed information about the orientation, program, and payment.
Congratulations on your acceptance and we look forward to meeting you!
Sincerely,

Beatrice Han
Registrar & Director of Student Services
University of California, Irvine
Division of Continuing Education
mailto: XXXXXXXXXX
__MACOSX/._Acceptance Letter - ZHAO, J. .pdf
geoplotlib.pptx
The geoplotlib li
ary
1
Python graphical li
aries
pandas
matplotli
seaborn
geoplotli
okeh
INTRODUCTION
Increase size of table numbers
2
Plots to visualize geospatial data
Types
dot maps
histogram maps
interactive maps
geographical plots
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3
Example 1
4
.
Cities
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5
.
Cities in Brazil
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6
.
Cities in Brazil
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7
.
Cities in Germany and Great Britain
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8
.
Cities in Germany and Great Britain
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9
                            bounding box
Cities in Germany and Great Britain
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10
.
Cities in Germany and Great Britain - interactive
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11
.
Delaunay Triangulation
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12
.
Delaunay Triangulation – triangular mesh
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13
Example 2
14
.
Seattle Area – Crime data
15
.
Seattle Area – dot plot
16
.
Seattle Area – histogram
17
- Each row in the DataFrame is a state
- A numerical column is used to give color to the state
- The higher the value the darker the state
choropleth maps
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18
__MACOSX/._geoplotlib.pptx
world_cities_pop.csv
Country,City,AccentCity,Region,Population,Latitude,Longitude
ad,aixas,Aixàs,06,, XXXXXXXXXX, XXXXXXXXXX
ad,aixirivali,Aixirivali,06,, XXXXXXXXXX,1.5
ad,aixirivall,Aixirivall,06,, XXXXXXXXXX,1.5
ad,aixirvall,Aixirvall,06,, XXXXXXXXXX,1.5
ad,aixovall,Aixovall,06,, XXXXXXXXXX, XXXXXXXXXX
ad,ando
a,Ando
a,07,,42.5, XXXXXXXXXX
ad,ando
a la vella,Ando
a la Vella,07,20430.0,42.5, XXXXXXXXXX
ad,ando
a-vieille,Ando
a-Vieille,07,,42.5, XXXXXXXXXX
ad,ando
e,Ando
e,07,,42.5, XXXXXXXXXX
ad,ando
e-la-vieille,Ando
e-la-Vieille,07,,42.5, XXXXXXXXXX
ad,ando
e-vieille,Ando
e-Vieille,07,,42.5, XXXXXXXXXX
ad,ansalonga,Ansalonga,04,, XXXXXXXXXX, XXXXXXXXXX
ad,anyos,Anyós,05,, XXXXXXXXXX, XXXXXXXXXX
ad,arans,Arans,04,, XXXXXXXXXX, XXXXXXXXXX
ad,arinsal,Arinsal,04,, XXXXXXXXXX, XXXXXXXXXX
ad,aubinya,Aubinyà,06,,42.45,1.5
ad,auvinya,Auvinya,06,,42.45,1.5
ad,bicisa
i,Biçisa
i,06,, XXXXXXXXXX, XXXXXXXXXX
ad,bixessa
i,Bixessa
i,06,, XXXXXXXXXX, XXXXXXXXXX
ad,bixisa
i,Bixisa
i,06,, XXXXXXXXXX, XXXXXXXXXX
ad,canillo,Canillo,02,3292.0, XXXXXXXXXX,1.6
ad,casas vila,Casas Vila,03,, XXXXXXXXXX, XXXXXXXXXX
ad,certers,Certers,06,, XXXXXXXXXX,1.5
ad,certes,Certés,06,, XXXXXXXXXX,1.5
ad,eixirivall,Eixirivall,06,, XXXXXXXXXX,1.5
ad,el pui,El Pui,04,,42.55, XXXXXXXXXX
ad,els bons,Els Bons,03,, XXXXXXXXXX, XXXXXXXXXX
ad,el se
at,El Se
at,04,, XXXXXXXXXX,1.55
ad,els plans,Els Plans,02,, XXXXXXXXXX, XXXXXXXXXX
ad,el tarter,El Tarter,02,, XXXXXXXXXX,1.65
ad,el tremat,El Tremat,03,,42.55, XXXXXXXXXX
ad,el vilar,El Vilar,02,, XXXXXXXXXX,1.6
ad,encamp,Encamp,03,11224.0, XXXXXXXXXX, XXXXXXXXXX
ad,engordany,Engordany,08,, XXXXXXXXXX,1.55
ad,ercs,Ercs,04,, XXXXXXXXXX,1.5
ad,ercz,Ercz,04,, XXXXXXXXXX,1.5
ad,erez,Erez,04,, XXXXXXXXXX,1.5
ad,erts,Erts,04,, XXXXXXXXXX,1.5
ad,escaldas,Escaldas,08,,42.5, XXXXXXXXXX
ad,escaldes,Escaldes,08,,42.5, XXXXXXXXXX
ad,escas,Escàs,04,,42.55, XXXXXXXXXX
ad,fontaneda,Fontaneda,06,,42.45, XXXXXXXXXX
ad,jube
i,Jube
i,06,, XXXXXXXXXX,1.5
ad,juve
i,Juve
i,06,, XXXXXXXXXX,1.5
ad,la cortinada,La Cortinada,04,, XXXXXXXXXX, XXXXXXXXXX
ad,la costa,La Costa,02,, XXXXXXXXXX, XXXXXXXXXX
ad,l'aldosa,L'Aldosa,02,, XXXXXXXXXX, XXXXXXXXXX
ad,l'aldosa,L'Aldosa,05,,42.55, XXXXXXXXXX
ad,la macana,La Maçana,04,,42.55, XXXXXXXXXX
ad,la massana,La Massana,04,7211.0,42.55, XXXXXXXXXX
ad,las escadas,Las Escadas,08,,42.5, XXXXXXXXXX
ad,la vieja,la Vieja,07,,42.5, XXXXXXXXXX
ad,les bons,Les Bons,03,, XXXXXXXXXX, XXXXXXXXXX
ad,les escaldes,Les Escaldes,08,15854.0,42.5, XXXXXXXXXX
ad,llors,Llors,04,,42.6, XXXXXXXXXX
ad,llorta,Llorta,04,,42.6, XXXXXXXXXX
ad,llorts,Llorts,04,,42.6, XXXXXXXXXX
ad,llumeneres,Llumeneres,06,, XXXXXXXXXX, XXXXXXXXXX
ad,lors,Lors,04,,42.6, XXXXXXXXXX
ad,lo se
at,Lo Se
at,04,, XXXXXXXXXX,1.55
ad,mas d'alins,Mas d'Alins,06,, XXXXXXXXXX,1.45
ad,mas de ribafeta,Mas de Ribafeta,04,, XXXXXXXXXX, XXXXXXXXXX
ad,meritxell,Meritxell,02,,42.55,1.6
ad,molleres,Molleres,03,,42.55, XXXXXXXXXX
ad,mosquera,Mosquera,03,,42.55, XXXXXXXXXX
ad,nagol,Nagol,06,, XXXXXXXXXX,1.5
ad,ordino,Ordino,05,2553.0,42.55, XXXXXXXXXX
ad,pal,Pal,04,,42.55, XXXXXXXXXX
ad,pas de la casa,Pas de la Casa,02,,42.55, XXXXXXXXXX
ad,prats,Prats,02,, XXXXXXXXXX,1.6
ad,puiol del piu,Puiol del Piu,04,, XXXXXXXXXX,1.5
ad,ransol,Ransol,02,, XXXXXXXXXX, XXXXXXXXXX
ad,sanctuaire de meritxeli,Sanctuaire de Meritxeli,02,,42.55,1.6
ad,sanctuaire de meritxell,Sanctuaire de Meritxell,02,,42.55,1.6
ad,san joan de casettas,San Joan de Casettas,02,, XXXXXXXXXX, XXXXXXXXXX
ad,san julia,San Juliá,06,, XXXXXXXXXX,1.5
ad,santa coloma,Santa Coloma,07,,42.5,1.5
ad,santa julia de loria,Santa Juliá de Loria,06,, XXXXXXXXXX,1.5
ad,sant joan de casellas,Sant Joan de Casellas,02,, XXXXXXXXXX, XXXXXXXXXX
ad,sant joan de caselles,Sant Joan de Caselles,02,, XXXXXXXXXX, XXXXXXXXXX
ad,sant julia de loria,Sant Julià de Lòria,06,8020.0, XXXXXXXXXX,1.5
ad,sant julia,Sant Juliá,06,, XXXXXXXXXX,1.5
ad,sant pere,Sant Pere,02,, XXXXXXXXXX,1.65
ad,santuari de meritxell,Santuari de Meritxell,02,,42.55,1.6
ad,segudet,Segudet,05,,42.55, XXXXXXXXXX
ad,sertes,Sertes,06,, XXXXXXXXXX,1.5
ad,sispony,Sispony,04,, XXXXXXXXXX, XXXXXXXXXX
ad,soldeu,Soldeu,02,, XXXXXXXXXX, XXXXXXXXXX
ad,sornas,Sornàs,05,, XXXXXXXXXX, XXXXXXXXXX
ad,vila,Vila,03,, XXXXXXXXXX, XXXXXXXXXX
ad,vixesa
i,Vixesa
i,06,, XXXXXXXXXX, XXXXXXXXXX
ad,xixerella,Xixerella,04,,42.55, XXXXXXXXXX
ae,abu dabi,Abu Dabi,01,, XXXXXXXXXX, XXXXXXXXXX
ae,abu dhabi,Abu Dhabi,01, XXXXXXXXXX, XXXXXXXXXX, XXXXXXXXXX
ae,abu hail,Abu Hail,03,, XXXXXXXXXX, XXXXXXXXXX
ae,abu zabi,Abu Zabi,01,, XXXXXXXXXX, XXXXXXXXXX
ae,abu zabye,Abu Zabye,01,, XXXXXXXXXX, XXXXXXXXXX
ae,abu zabyo,Abu Zabyo,01,, XXXXXXXXXX, XXXXXXXXXX
ae,ad dha
aniya,Ad Dha
aniya,05,, XXXXXXXXXX, XXXXXXXXXX
ae,adgat,Adgat,04,, XXXXXXXXXX, XXXXXXXXXX
ae,adh dhaid,Adh Dhaid,06,, XXXXXXXXXX, XXXXXXXXXX
ae,adhin,Adhin,05,, XXXXXXXXXX, XXXXXXXXXX
ae,afarah,Afarah,04,, XXXXXXXXXX, XXXXXXXXXX
ae,`ain,`Ain,01,, XXXXXXXXXX, XXXXXXXXXX
ae,ain al ghamur,Ain al Ghamur,06,, XXXXXXXXXX, XXXXXXXXXX
ae,`ain dhawahir,`Ain Dhawahir,01,, XXXXXXXXXX, XXXXXXXXXX
ae,ajima,Ajima,05,, XXXXXXXXXX, XXXXXXXXXX
ae,akamiya,Akamiya,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al abadilah,Al Abadilah,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al ain,Al Ain,01,, XXXXXXXXXX, XXXXXXXXXX
ae,al ajman,Al Ajman,02,, XXXXXXXXXX, XXXXXXXXXX
ae,al `ayn,Al `Ayn,06,, XXXXXXXXXX, XXXXXXXXXX
ae,al `ayn al ghamur,Al `Ayn al Ghamur,06,, XXXXXXXXXX, XXXXXXXXXX
ae,al badiyah,Al Badiyah,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al bithnah,Al Bithnah,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al daid,Al Daid,06,, XXXXXXXXXX, XXXXXXXXXX
ae,al-dhayd,Al-Dhayd,06,, XXXXXXXXXX, XXXXXXXXXX
ae,al dini,Al Dini,07,, XXXXXXXXXX, XXXXXXXXXX
ae,al duss,Al Duss,07,, XXXXXXXXXX,56.393
ae,al fai,Al Fai,05,, XXXXXXXXXX, XXXXXXXXXX
ae,al fiyya,Al Fiyya,01,, XXXXXXXXXX, XXXXXXXXXX
ae,al-fudjayra,Al-Fudjayra,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al fuqait,Al Fuqait,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al fuyay',Al Fuyay',01,, XXXXXXXXXX, XXXXXXXXXX
ae,al-ghil,Al-Ghil,05,, XXXXXXXXXX, XXXXXXXXXX
ae,algida,Algida,05,, XXXXXXXXXX, XXXXXXXXXX
ae,al gissemari,Al Gissemari,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al hagarein,Al Hagarein,03,, XXXXXXXXXX, XXXXXXXXXX
ae,al haira,Al Haira,06,, XXXXXXXXXX, XXXXXXXXXX
ae,al hairah,Al Hairah,06,, XXXXXXXXXX, XXXXXXXXXX
ae,alhala,Alhala,04,, XXXXXXXXXX, XXXXXXXXXX
ae,al hama'im,Al Hama'im,01,, XXXXXXXXXX, XXXXXXXXXX
ae,al hamra',Al Hamra',05,, XXXXXXXXXX, XXXXXXXXXX
ae,al hamriya,Al Hamriya,06,, XXXXXXXXXX, XXXXXXXXXX
ae,al hayir,Al Hayir,05,, XXXXXXXXXX, XXXXXXXXXX
ae,al hayl,Al Hayl,04
Answered Same Day May 28, 2021

Solution

Ishvina answered on May 29 2021
131 Votes
5/29/2020 Geospatial_Data_Visualization
file:
S:/GN/day7_Geospatial_Data_Visualization_As_6/Geospatial_Data_Visualization.html 1/13
Geospatial Data Visualization
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: #installing the li
aries
In [4]: !pip install geoplotlib
In [5]: !pip install pyglet
In [6]: import geoplotli
In [7]: #to display the maps in the jupyter notebook
from IPython.display import Image
In [8]: #reading the data
In [9]: #data is saved at the same location as the cu
ent file location
df = pd.read_csv("world_cities_pop.csv")
In [10]: #findin the unique regions in the dataset
pd.unique(df['Region'])[:88]
In [11]: #because we have mixed datatypes , so we force
#python to consider it as a character because of the mixed data types
#reading data again with modifications
df = pd.read_csv("world_cities_pop.csv" , dtype = {'Region' : np.str})
Requirement already satisfied: geoplotlib in c:\users\dell\anaconda3\lib\site-package
s (0.3.2)
Requirement already satisfied: pyglet in c:\users\dell\anaconda3\lib\site-packages
(1.5.5)
C:\Users\DELL\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3058: Dtyp
eWarning: Columns (3) have mixed types. Specify dtype option on import or set low_mem
ory=False.
interactivity=interactivity, compiler=compiler, result=result)
Out[10]: a
ay([6.0, 7.0, 4.0, 5.0, 2.0, 3.0, 8.0, 1.0, 29.0, 10.0, 24.0, 9.0,
35.0, 42.0, 11.0, 27.0, 39.0, 28.0, 26.0, 17.0, 41.0, 33.0, 30.0,
13.0, 40.0, 18.0, 23.0, 19.0, 37.0, 14.0, 32.0, 36.0, 31.0, 34.0,
38.0, nan, 0.0, 51.0, 46.0, 49.0, 43.0, 47.0, 44.0, 45.0, 50.0,
48.0, 15.0, 12.0, 20.0, 16.0, 21.0, 22.0, '20', '01', '24', '10',
'06', '05', '11', '21', '14', '15', '02', '08', '17', '22', '07',
'12', '03', '18', '13', '04', '19', '16', '23', '09', '00', '36',
'62', '68', '65', '43', '64', '46', '66', '45', '47', '27'],
dtype=object)
5/29/2020 Geospatial_Data_Visualization
file:
S:/GN/day7_Geospatial_Data_Visualization_As_6/Geospatial_Data_Visualization.html 2/13
In [12]: pd.unique(df['Region'])[:88]
In [13]: #size of the dataset
df.shape
In [14]: #checking the data types
df.dtypes
In [15]: #viewing the first 8 rows of the dataframe
df[0:8]
In [16]: #removing one of the two columns - City / AccentCity because
#they are the same
df = df.drop(['AccentCity'], axis = 1)
Out[12]: a
ay(['06', '07', '04', '05', '02', '03', '08', '01', '29', '10', '24',
'09', '35', '42', '11', '27', '39', '28', '26', '17', '41', '33',
'30', '13', '40', '18', '23', '19', '37', '14', '32', '36', '31',
'34', '38', nan, '00', '51', '46', '49', '43', '47', '44', '45',
'50', '48', '15', '12', '20', '16', '21', '22', '62', '68', '65',
'64', '66', '58', '60', '61', '71', '57', '69', '25', '53', '52',
'70', '63', '67', '55', '59', '54', '56', 'BD', '82', '83', '81',
'80', '85', '86', '84', '76', '74', '78', '75', '73', '72', '77'],
dtype=object)
Out[13]: (3173958, 7)
Out[14]: Country object
City object
AccentCity object
Region object
Population float64
Latitude float64
Longitude float64
dtype: object
Out[15]:
Country City AccentCity Region Population Latitude Longitude
0 ad aixas Aixàs 06 NaN 42.483333 1.466667
1 ad aixirivali Aixirivali 06 NaN 42.466667 1.500000
2 ad aixirivall Aixirivall 06 NaN 42.466667 1.500000
3 ad aixirvall Aixirvall 06 NaN 42.466667 1.500000
4 ad aixovall Aixovall 06 NaN 42.466667 1.483333
5 ad ando
a Ando
a 07 NaN 42.500000 1.516667
6 ad ando
a la vella Ando
a la Vella 07 20430.0 42.500000 1.516667
7 ad ando
a-vieille Ando
a-Vieille 07 NaN 42.500000 1.516667
5/29/2020 Geospatial_Data_Visualization
file:
S:/GN/day7_Geospatial_Data_Visualization_As_6/Geospatial_Data_Visualization.html 3/13
In [17]: #viewing the dataset again
df[0:4]
In [18]: #rounding upto two decimal places
ound(df.describe() , 2)
In [19]: #viewing the city with population 7
#smallest city in the dataset
df[df.Population == 7]
In [20]: #largest cities (exceeding 10 million population)
Out[17]:
Country City Region Population Latitude Longitude
0 ad aixas 06 NaN 42.483333 1.466667
1 ad aixirivali 06 NaN 42.466667 1.500000
2 ad aixirivall 06 NaN 42.466667 1.500000
3 ad aixirvall 06 NaN 42.466667 1.500000
Out[18]:
Population Latitude Longitude
count 47980.00 3173958.00 3173958.00
mean 47719.57 27.19 37.09
std 302888.72 21.95 63.22
min 7.00 -54.93 -179.98
25% 3732.00 11.63 7.30
50% 10779.00 32.50 35.28
75% 27990.50 43.72 95.70
max 31480498.00 82.48 180.00
Out[19]:
Country City Region Population Latitude Longitude
1028303 gl neriunaq 03 7.0 64.466667 -50.316667
1028477 gl tasiusaq 03 7.0 73.366667 -56.050000
1028486 gl timerliit 03 7.0 65.833333 -53.250000
2360929 ru aliskerovo 15 7.0 67.766667 167.583333
5/29/2020 Geospatial_Data_Visualization
file:
S:/GN/day7_Geospatial_Data_Visualization_As_6/Geospatial_Data_Visualization.html 4/13
In [21]: df[df.Population >...
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