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Day 8: Final Assignment Submit Assignment Instructions Use dataset from day 7 -world_cities_pop.csv a) Remove all countries with Population as NaN. Call your DataFrame as df. Use df.shape to find the...

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Day 8: Final Assignment
Submit Assignment
Instructions
Use dataset from day 7 -world_cities_pop.csv
a) Remove all countries with Population as NaN. Call your DataFrame as df.
Use df.shape to find the number of countries remaining in your DataFrame.
You should work with this smaller DataFrame for the following questions.
) Select a country (it can be the name of your country or any other of your choice).
For this country, find the number of cities and the population.
c) Create a dot plot showing the cities in this country. No need to adjust the bounding box.
d) Create a new DataFrame for this country with the cities with population 
more than 1e5 only. Adjust the bounding box to be as tight as possible to the country boundaries. Create a dot plot.
e) Create a Delaunay triangulation plot for this new DataFrame. Use same bounding box as in part (d).
All parts weight 20 points. 
Submit the jupyter notebook with the Python script and including your name and all plots as a single pdf labeled as:
FirstName_LastName - Final Assignment.pdf
Day 8: Content
Overview
This session is a wrap-up of the course. We will review the various roles and skills useful for Analytics.
Readings
· The reference  for this session is here
Presentation Slides
Big Data Visualization and Analytics -day 8

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Solution

Ishvina answered on May 30 2021
121 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#installing the li
aries"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: geoplotlib in c:\\users\\dell\\anaconda3\\lib\\site-packages (0.3.2)\n"
]
}
],
"source": [
"!pip install geoplotlib"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pyglet in c:\\users\\dell\\anaconda3\\lib\\site-packages (1.5.5)\n"
]
}
],
"source": [
"!pip install pyglet"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import geoplotlib"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#to display the maps in the jupyter notebook\n",
"from IPython.display import Image"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#reading the data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stde
",
"output_type": "stream",
"text": [
"C:\\Users\\DELL\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"#data is saved at the same location as the cu
ent file location\n",
"\n",
"df = pd.read_csv(\"world_cities_pop.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#because we have mixed datatypes , so we force \n",
"#python to consider it as a character because of the mixed data types\n",
"#reading data again with modifications\n",
"df = pd.read_csv(\"world_cities_pop.csv\" , dtype = {'Region' : np.str})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a
ay(['06', '07', '04', '05', '02', '03', '08', '01', '29', '10', '24',\n",
" '09', '35', '42', '11', '27', '39', '28', '26', '17', '41', '33',\n",
" '30', '13', '40', '18', '23', '19', '37', '14', '32', '36', '31',\n",
" '34', '38', nan, '00', '51', '46', '49', '43', '47', '44', '45',\n",
" '50', '48', '15', '12', '20', '16', '21', '22', '62', '68', '65',\n",
" '64', '66', '58', '60', '61', '71', '57'], dtype=object)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.unique(df['Region'])[:62]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3173958, 7)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#size of the orignal dataset\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Country object\n",
"City object\n",
"AccentCity object\n",
"Region object\n",
"Population float64\n",
"Latitude float64\n",
"Longitude float64\n",
"dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#checking the data types\n",
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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