Copy and Paste Your Assignment HereDear Top Assignment Expert Team,I am struggeling to finalize my python assignement, using Jupyter Notebook.There are no detailed requirements, it can be done based...

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Copy and Paste Your Assignment HereDear Top Assignment Expert Team,I am struggeling to finalize my python assignement, using Jupyter Notebook.There are no detailed requirements, it can be done based on some ideas. Important is to show some visualization, as started but since the data structure is obviously not correct, I cannot really continue with this task.Would be great to get a feedback from you and maybe we can talk via Skype, Whatsapp for discussing the price and details that can be provided until Tuesday midnight.Thanks & kind regardsTanjamy email is: [email protected]
Answered Same DaySep 29, 2021

Answer To: Copy and Paste Your Assignment HereDear Top Assignment Expert Team,I am struggeling to finalize my...

Kshitij answered on Oct 01 2021
132 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from pandas import Series"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['LocID', 'Location', 'VarID', 'Variant', 'Time', 'MidPeriod', 'PopMale',\n",
" 'PopFemale', 'PopTotal'],\n",
" dtype='object')\n"
]
}
],
"source": [
"df = pd.read_csv ('WPP2019_TotalPopulationBySex(1)', encoding=\"cp1252\")\n",
"df_wbv = pd.DataFrame(df)\n",
"print(df_wbv.columns)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"
LocIDLocationVarIDVariantTimeMidPeriodPopMalePopFemalePopTotal
04Afghanistan2Medium19501950.54099.2433652.8747752.117
14Afghanistan2Medium19511951.54134.7563705.3957840.151
24Afghanistan2Medium19521952.54174.4503761.5467935.996
34Afghanistan2Medium19531953.54218.3363821.3488039.684
44Afghanistan2Medium19541954.54266.4843884.8328151.316
\n",
"
"
],
"text/plain": [
" LocID Location VarID Variant Time MidPeriod PopMale PopFemale \\\n",
"0 4 Afghanistan 2 Medium 1950 1950.5 4099.243 3652.874 \n",
"1 4 Afghanistan 2 Medium 1951 1951.5 4134.756 3705.395 \n",
"2 4 Afghanistan 2 Medium 1952 1952.5 4174.450 3761.546 \n",
"3 4 Afghanistan 2 Medium 1953 1953.5 4218.336 3821.348 \n",
"4 4 Afghanistan 2 Medium 1954 1954.5 4266.484 3884.832 \n",
"\n",
" PopTotal \n",
"0 7752.117 \n",
"1 7840.151 \n",
"2 7935.996 \n",
"3 8039.684 \n",
"4 8151.316 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# View our data\n",
"df_wbv.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 422655 entries, 0 to 422654\n",
"Data columns (total 9 columns):\n",
"LocID 422655 non-null int64\n",
"Location 422655 non-null object\n",
"VarID 422655 non-null int64\n",
"Variant 422655 non-null object\n",
"Time 422655 non-null int64\n",
"MidPeriod 422655 non-null float64\n",
"PopMale 422655 non-null float64\n",
"PopFemale 422655 non-null float64\n",
"PopTotal 422655 non-null float64\n",
"dtypes: float64(4), int64(3), object(2)\n",
"memory usage: 29.0+ MB\n"
]
}
],
"source": [
"# Viewing the details of the table\n",
"df_wbv.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df_wbv.to_excel(\"output.xlsx\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Medium 43035\n",
"Constant mortality 43035\n",
"Momentum 43035\n",
"No change 43035\n",
"Zero migration 43035\n",
"Constant fertility 43035\n",
"Low 43035\n",
"Instant replacement 43035\n",
"High 43035\n",
"Median PI 8835\n",
"Lower 95 PI 8835\n",
"Upper 95 PI 8835\n",
"Lower 80 PI 8835\n",
"Name: Variant, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Exploring the data\n",
"df_wbv.Variant.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Northern America 2966\n",
"Latin America and the Caribbean 2966\n",
"Europe 2966\n",
"Republic of Korea 1483\n",
"Saint Barthélemy 1483\n",
"British Virgin Islands 1483\n",
"Cameroon 1483\n",
"Sierra Leone 1483\n",
"Hungary 1483\n",
"Paraguay 1483\n",
"India 1483\n",
"Turkmenistan 1483\n",
"United Republic of Tanzania 1483\n",
"Tajikistan 1483\n",
"Asia 1483\n",
"Micronesia 1483\n",
"Saudi Arabia 1483\n",
"Ecuador 1483\n",
"Slovakia 1483\n",
"Algeria 1483\n",
"Bermuda 1483\n",
"Timor-Leste 1483\n",
"Seychelles 1483\n",
"Russian Federation 1483\n",
"Turks and Caicos Islands 1483\n",
"Burkina Faso 1483\n",
"Eastern Asia 1483\n",
"Nicaragua 1483\n",
"Indonesia 1483\n",
"Martinique 1483\n",
" ... \n",
"Ireland 1483\n",
"Central and Southern Asia 1483\n",
"Burundi 1483\n",
"Senegal 1483\n",
"Central Asia 1483\n",
"Finland 1483\n",
"More developed regions 1483\n",
"Guatemala 1483\n",
"Australia 1483\n",
"Jordan 1483\n",
"Côte d'Ivoire 1483\n",
"French Guiana 1483\n",
"Holy See 1483\n",
"Malawi 1483\n",
"Lesotho 1483\n",
"Cyprus 1483\n",
"Mali 1483\n",
"Maldives 1483\n",
"Angola 1483\n",
"Dominica 1483\n",
"Western Europe 1483\n",
"Oman 1483\n",
"Ukraine 1483\n",
"Somalia 1483\n",
"Tunisia 1483\n",
"Southern Asia 1483\n",
"Serbia 1483\n",
"Low-income countries 1483\n",
"Canada 1483\n",
"Bolivia (Plurinational State of) 1483\n",
"Name: Location, Length: 282, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Location.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"
LocIDLocationVarIDVariantTimeMidPeriodPopMalePopFemalePopTotal
14496276Germany2Medium19501950.532283.90537682.34769966.252
14497276Germany2Medium19511951.532476.98837823.02370300.011
14498276Germany2Medium19521952.532662.84537957.43070620.275
14499276Germany2Medium19531953.532838.82038091.01970929.839
14500276Germany2Medium19541954.533005.14038228.25371233.393
\n",
"
"
],
"text/plain": [
" LocID Location VarID Variant Time MidPeriod PopMale PopFemale \\\n",
"14496 276 Germany 2 Medium 1950 1950.5 32283.905 37682.347 \n",
"14497 276 Germany 2 Medium 1951 1951.5 32476.988 37823.023 \n",
"14498 276 Germany 2 Medium 1952 1952.5 32662.845 37957.430 \n",
"14499 276 Germany 2 Medium 1953 1953.5 32838.820 38091.019 \n",
"14500 276 Germany 2 Medium 1954 1954.5 33005.140 38228.253 \n",
"\n",
" PopTotal \n",
"14496 69966.252 \n",
"14497 70300.011 \n",
"14498 70620.275 \n",
"14499 70929.839 \n",
"14500 71233.393 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_wbv[df_wbv.Location == 'Germany'].head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df = df_wbv[df_wbv.Variant == 'Medium']\n",
"years = df['Time'].unique()\n",
"germany_pop = list(df[df.Location == 'Germany']['PopTotal'])\n",
"afghanistan_pop = list(df[df.Location == 'Afghanistan']['PopTotal'])\n",
"netherlands_pop = list(df[df.Location == 'Netherlands']['PopTotal'])\n",
"hungary_pop = list(df[df.Location == 'Hungary']['PopTotal'])\n",
"new_zealand_pop = list(df[df.Location == 'New Zealand']['PopTotal'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Population comparison of some random Countries\n",
"\n",
"plt.plot(years, germany_pop, color='b', label='Germany')\n",
"plt.plot(years, afghanistan_pop, color='r', label='Afghanistan')\n",
"plt.plot(years, netherlands_pop, color='g', label='Netherlands')\n",
"plt.plot(years, hungary_pop, color='y', label='Hungary')\n",
"plt.plot(years, new_zealand_pop, color='c', label='New Zealand')\n",
"axes = plt.gca()\n",
"plt.legend(loc=\"center right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"