Acceptance Letter - ZHAO, J. .pdf UNIVERSITY OF CALIFORNIA, IRVINE DIVISION OF CONTINUING EDUCATION...

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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

day-7-archive-qjqv1001.zip geoplotlib-hpe1tse1-ubk3corc.pptx day-7-hwjye1ip-lbofwusk.docx day-7-1g3nic3g-qqjl2kvb.docx

Ishvina · Accepted Answer

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 libraries
In [4]: !pip install geoplotlib 
In [5]: !pip install pyglet 
In [6]: import geoplotlib
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 current 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]: array([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]: array(['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 andorra Andorra 07 NaN 42.500000 1.516667
6 ad andorra la vella Andorra la Vella 07 20430.0 42.500000 1.516667
7 ad andorra-vieille Andorra-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
round(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.

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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