Analytics_Report - Alessia Monfardino.ipynb

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    "# Problem statement\n",
    "\n",
    "Expanding into international markets can be a challenging and costly undertaking. This is why it's crucial to conduct an in-depth analysis of the potential markets and guarantee a profitable and sustainable expansion in the long run. A comprehensive understanding of the market characteristics, coupled with identifying countries that have high growth potential, is vital in making an informed decision on the most suitable approach for the expansion.\n",
    "\n",
    "To undertake the best decision, I analyzed the following problems: \n",
    "\n",
    "\n",
    "## Which country shows the best short-term opportunity for expansion? \n",
    "\n",
    "Identifying the country with the largest sports clothes market opportunity can help the company prioritize its expansion efforts and allocate resources accordingly.\n",
    "This problem has been approached by analyzing the online customers' pool, together with an overall study of the  economic trends. \n",
    "The steps to solving this problem included selecting and analyzing data on market size, identifying key characteristic of the market, and evaluating which State is more interesting to locate the first store. \n",
    "\n",
    "#### Which countries have the highest recorded online sales?\n",
    "\n",
    "This problem is important because it can help the company identify the countries with the highest demand for sports clothes and prioritize them as potential markets for expansion in the short-run. Indeed, it is likely that these locations are already familiar with the brand and would be easier to target. \n",
    "The problem has been approached by analyzing order volume and total online sales in different countries to identify the locations with the highest demand for our brand's products.\n",
    "\n",
    "#### What are the characteristics of the target market in the identified country?\n",
    "\n",
    "Understanding the characteristics of the target market in the identified country can help the company develop effective marketing and sales strategies and tailor exports to meet the needs of the local consumers.\n",
    "\n",
    "#### What is the most suitable penetration area in the identified country?\n",
    "    \n",
    "Evaluating the most suitable area of entry in the identified country can help the company minimize risks and maximize the chances of success for its expansion efforts. In the analysis, the identification of multiple target cities in the same geographical area has been prioritized. The data collected must be further discussed with the Financial team to evaluate taxation constraints.\n",
    "\n",
    "## Which country shows the best long-term potential for expansion?  \n",
    "\n",
    "It is crucial to identify emerging markets so that the market can be penetrated before the competition.  Indeed, even if riskier, investments in growing economies can result in greater opportunities for the future.\n",
    "Because of the long-term nature of the problem, data must be updated every 6 months to verify the consistency of the trend over time.\n",
    "Historical data for e-commerce has been analyzed to identify trends and growth patterns. Then, research was conducted to assess the potential of different countries in terms of factors such as consumer past purchases, demographics, and economic indicators.\n",
    "These included household expenditure per capita, GDP per capita, Purchasing Power Index, Net Migration and population trends.\n",
    "\n",
    "####  What are the economic characteristics of the target market in the identified country?\n",
    "\n",
    "Understanding the economic characteristics of the target market is key because it can help to tailor the marketing and pricing strategies to better meet the needs and preferences of consumers in that market. Moreover, a growing economy has high potential for increasing purchasing power, and therefore forecasts future increases in sales.  \n",
    "\n",
    "#### What are the demographic characteristics of the target market in the identified country?\n",
    "\n",
    "Because of the long-term expansion goal, it is crucial to evaluate the demographic characteristics of an emerging market. Indeed, a growing population, both from locals and immigrants, implies a larger customer pool in the future. Moreover, I considered the population between 15 and 64 years old for each country. In marketing, this group of individuals is often the primary target for businesses as they are more likely to make purchases and have a significant impact on the market."
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    "# Results"
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    "\n",
    "## The best short-term opportunity for expansion\n",
    "\n",
    "With a population of over 330 million, the United States presents a vast market for sport clothing companies to capitalize on. Additionally, the centrality of sports in American culture creates a high demand for sportswear (Statista, 2021). \n",
    "As outlined by the data, the United States boasts a prosperous economy with a high disposable income per capita, which implies that Americans possess significant buying power for high-quality sportswear, leading to a potentially profitable market for sport clothing companies. \n",
    "The sportswear industry in the United States is characterized by its innovative nature and trendsetting approach, led by well-known American sportswear brands. This culture of experimentation and willingness to embrace new ideas presents an opportunity for sport clothing companies to introduce new products and concepts to the market (Euromonitor International, 2021).\n",
    "\n",
    "\n",
    "### Countries with the highest recorded e-commerce sales\n",
    "\n",
    "Our e-commerce historical data has revealed a promising opportunity for our brand to expand its market share in the US. Despite being based in Europe, our company has already established a strong presence in the country and it has become our top shipping destination for order volume. This significant market share is a testament to the brand's success in capturing the attention and loyalty of US consumers. Taking into account the US's thriving economy and consumer culture, it is clear that the country has the potential to generate substantial revenue for our company.\n",
    "\n",
    "\n",
    "#### The Economic characteristics of the target market \n",
    "\n",
    "As outlined by the World Bank data, The United States boasts the highest GDP per capita in the world, and its consumers have the second highest purchasing power after Qatar, presenting a unique opportunity for our sportswear company to tap into a market with strong consumer spending power. Additionally, American households have the second largest expenditure per capita after Hong Kong, indicating a culture of high-quality products and a willingness to invest in premium goods. This makes the United States an attractive market for our company to introduce innovative, high-end products to a receptive and discerning customer base.\n",
    "\n",
    "#### The most suitable penetration area\n",
    "\n",
    "As observable from the graph below, California presents the highest customer pool based on our historical data. Indeed, the county is outstanding both for total sales and order frequency. \n",
    "\n",
    "\n",
    "Generally speaking, California is the largest state in terms of population and has a reputation for being a trendsetter in the fashion and sportswear industry. It has a diverse and active population that values a healthy lifestyle, which could present a significant opportunity for a sportswear company. Moreover, California has a thriving start-up and innovation culture, which can benefit a company looking to introduce new and innovative products.\n",
    "\n",
    "\n",
    "By observing the Total Sales by City in the United States, it becomes evident that Los Angeles and San Francisco are prime locations for expanding our retail operations. With these two cities filling the second and third positions respectively in terms of sales, there is a clear demand for our products in these markets. As leading fashion and lifestyle hubs, Los Angeles and San Francisco offer excellent opportunities to showcase our brand and engage with customers who prioritize quality and style. Expanding our presence in these cities would enable us to tap into a lucrative market and strengthen our brand identity, paving the way for future growth and success.\n",
    "In addition, San Diego is also in the top 10 cities for total sales, making it another attractive location for our expansion plans. With its proximity to Los Angeles, our company can quickly and easily establish a strong presence in the San Diego market, building upon our existing customer base and extending our reach to new audiences. With three of the top 10 cities for sales in the United States in close proximity, our company is well-positioned to achieve significant growth and establish ourselves as a major player in the sportswear industry.\n",
    "\n",
    "\n",
    "##### Sources:\n",
    "\n",
    "Euromonitor International. (2021). Sportswear in the US. Passport. https://www.euromonitor.com/sportswear-in-the-us/report\n",
    "\n",
    "Statista. (2021). U.S. Population by State 2020. https://www.statista.com/statistics/183635/us-population-by-state/"
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    "## The best long-term opportunity for expansion\n",
    "\n",
    "After analyzing our historical e-commerce data, we have identified Singapore as the best long-term opportunity for our European sport company's expansion efforts. Our data has indicated that Singapore has the third highest growth potential among all the countries we currently ship to, making it a lucrative market for our company.\n",
    "\n",
    "With a population of over 5 million people, Singapore has a growing middle class with a high purchasing power (Tan H., 2020), and an appetite for premium sportswear products. In addition, the country has a strong culture of fitness and healthy living, which presents a natural fit for our company's product offerings (National Library Board Singapore).\n",
    "\n",
    "Our e-commerce data has shown that there is already a significant demand for our brand in Singapore, and this demand is projected to grow in the coming years. By leveraging our existing brand recognition and tailoring our marketing efforts to the unique needs of the Singaporean market, we believe we can capture a significant market share and establish a strong foothold in the region.\n",
    "\n",
    "Furthermore, Singapore's strategic location in Southeast Asia provides us with an opportunity to expand further into the region, which is projected to be a key growth market for sportswear products in the coming years.\n",
    "\n",
    "Therefore, we recommend that our company focus its expansion efforts on Singapore as the best long-term opportunity to achieve sustained growth and profitability.\n",
    "\n",
    "<img src=\"Growth Potential.png\" width=\"50%\">\n",
    "\n",
    "\n",
    "#### The Economic characteristics of the target market\n",
    "\n",
    "By expanding to Singapore, our company can tap into a thriving and growing market with excellent growth potential, while also benefiting from the country's favorable business environment and stable economic conditions. \n",
    "Indeed, Singapore ranks 9th in the world for GDP per capita, 8th in the local purchasing power index, and 8th in household expenditure per capita. These economic factors, coupled with the favorable market conditions, make Singapore the top choice for our company's expansion strategy.\n",
    "\n",
    "#### The Demographic characteristics of the target market\n",
    "\n",
    "Singapore's demographic conditions make it an attractive location for business expansion. For starters, Singapore is the second most popular destination for net migration in Asia. This indicates that the country's population is growing, which means there is an increasing demand for goods and services.\n",
    "Moreover, Singapore's population is young and well-educated, and ranks 13th in the world for population between the ages of 15 and 64 years old. This demographic advantage means that there is a large and active workforce in Singapore, with a high level of disposable income to support the consumption of goods and services.\n",
    "\n",
    "\n",
    "Sources:\n",
    "\n",
    "Tan, H. (2020, October 14). Singapore’s middle class shrinks, but more are wealthier: Report. The Straits Times. https://www.straitstimes.com/singapore/spores-middle-class-shrinks-but-more-are-wealthier-report\n",
    "\n",
    "National Library Board Singapore. (n.d.). Fitness & wellness. https://www.nlb.gov.sg/Portals/0/Subject_Libraries/Subject%20Guide%20-%20Fitness%20%26%20Wellness.pdf"
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    "# Assumptions made"
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    "### The best short-term opportunity for expansion\n",
    "\n",
    "Our expansion strategy is backed by thorough analysis, and critical assumptions have been made to ensure an overall evaluation of the current market. While conducting an extensive evaluation of our e-commerce historical data, it was assumed that 'Customer Country' was country of origin of our buyers, and we have indeed prioritized their current location, recorded as 'Order Country'.\n",
    "\n",
    "Moreover, we have taken into account all necessary factors to streamline our expansion process. We assumed no restrictions for taxation or logistics, which enables us to take advantage of new market opportunities and provide a seamless experience for our customers. With these assumptions in mind, we are confident that our expansion plans are well-informed, strategically sound, and poised for long-term success.\n",
    "\n",
    "Lastly, I am assuming that the Supply Chain Data refers to online sales of our company, and we do not have any third party seller (Amazon, Alibaba, etc.)."
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    "### The best long-term opportunity for expansion\n",
    "\n",
    "By assuming no restrictions for taxation or logistics, we are better equipped to capitalize on emerging market opportunities and provide a seamless and efficient experience for our customers. Our database analysis focuses on identifying regions with high growth potential, increasing demand, and a stable political climate. We also assume that there are no major legal or regulatory barriers to entry in the target markets. We base our expansion plans on these assumptions, ensuring that our strategies are sound, well-informed, and well-positioned for long-term success. Additionally, we continuously monitor market conditions and adjust our plans accordingly to ensure we are maximizing our opportunities for growth."
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   "source": [
    "# Limitations"
   ]
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    "### The best short-term opportunity for expansion: California, United States\n",
    "\n",
    "Part of the research data is available until 2016, which may not accurately represent the current market dynamics. Furthermore, economic data offers a snapshot of 2020, which was heavily impacted by the COVID-19 pandemic. Therefore, it may not be a representative sample of a typical year, and the findings should be interpreted with caution. \n",
    "\n",
    "Moreover, the research does not provide an analysis of the competitive landscape in the sportswear industry. Understanding the competition is crucial for the success of any expansion strategy, and the lack of information on this topic limits the conclusions that can be drawn from the research.\n",
    "\n",
    "Lastly, the research does not provide an in-depth analysis of consumer behavior, preferences, or purchasing patterns. Understanding these factors is essential for tailoring the company's products and marketing strategy to the target market."
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    "### The best long-term opportunity for expansion: Singapore\n",
    "\n",
    "The research for a country with growth potential has some limitations that should be considered. Firstly, the analysis only focuses on historical e-commerce data and may not fully represent the market conditions in Singapore. There may be other factors or trends that have not been accounted for in the data analysis.\n",
    "\n",
    "Secondly, part of the research data only includes sales until 2016 or a snapshot of 2020, which means that the analysis does not consider the most recent market developments or trends. This could limit the generalizability of the findings and the accuracy of the projections for future market growth.\n",
    "\n",
    "Furthermore, the research only considers Singapore as a potential market for expansion, without exploring other potential markets that may have similar or better growth potential. This limits the scope of the analysis and may not provide a comprehensive picture of the best long-term opportunities for expansion.\n",
    "\n",
    "Lastly, also in this case logistics, taxes and regulations are not considered, and could potentially change the outcome of the research. "
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    "## DataCoSupplyChainDataset.csv\n",
    "\n",
    "### Reasons for use: \n",
    "The dataset was utilized to evaluate the level of demand for sports clothing in various geographical locations, including countries, states, and cities. The aim was to gain insights into the varying levels of demand across different regions, which could aid in identifying potentially lucrative markets to expand globally.\n",
    "Additionally, this dataset played a vital role in narrowing down the locations that demonstrated high potential for the sports clothing business. By analyzing the data, it was possible to identify the areas that displayed a higher demand for our brand compared to others.\n",
    "\n",
    "### Heps to answer Problem:\n",
    "- 6.1.1  Which countries have the highest recorded online sales?\n",
    "- 6.1.3.1  Total Sales by State in the United States (Top 10)\n",
    "- 6.1.4  Order Frequency by State in the United States (Top 10)\n",
    "- 6.1.5  Total Sales by City in the United States (Top 10)\n",
    "- 6.2  Which country shows the best long-term opportunity for expansion?\n",
    "\n",
    "### Data Source:\n",
    "\n",
    "The dataset included in this analysis contains information about sales data gathered from a presumably online sport clothes retailer. It can be inferred that the store operates online since orders are placed from numerous locations and the customers are frequently from a different location than the one they are shipping the product to.\n",
    "The data can be accessed through the file path: DataCoSupplyChainDataset.csv\n",
    "\n",
    "### EDA:\n",
    "Different columns have not been taken into consideration in the analysis because not consistent, therefore memory was reduced. \n",
    "In order to run a better analysis, blank cells in numerical fields have been filled with the statistical median of the column; on the other side, blank cells in text fields for the considered columns implied the elimination of the whole row. "
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       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
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       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
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       "      <th>...</th>\n",
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       "      <th>shipping date (DateOrders)</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-247.779999</td>\n",
       "      <td>309.720001</td>\n",
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       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>...</td>\n",
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       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1/17/2018 12:06</td>\n",
       "      <td>Standard Class</td>\n",
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       "      <th>3</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22.860001</td>\n",
       "      <td>304.809998</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Los Angeles</td>\n",
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       "      <td>1360</td>\n",
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       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
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       "       Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "0     DEBIT                         3                              4   \n",
       "1  TRANSFER                         5                              4   \n",
       "2      CASH                         4                              4   \n",
       "3     DEBIT                         3                              4   \n",
       "4   PAYMENT                         2                              4   \n",
       "\n",
       "   Benefit per order  Sales per customer   Delivery Status  \\\n",
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       "2        -247.779999          309.720001  Shipping on time   \n",
       "3          22.860001          304.809998  Advance shipping   \n",
       "4         134.210007          298.250000  Advance shipping   \n",
       "\n",
       "   Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "0                   0           73  Sporting Goods        Caguas  ...   \n",
       "1                   1           73  Sporting Goods        Caguas  ...   \n",
       "2                   0           73  Sporting Goods      San Jose  ...   \n",
       "3                   0           73  Sporting Goods   Los Angeles  ...   \n",
       "4                   0           73  Sporting Goods        Caguas  ...   \n",
       "\n",
       "  Order Zipcode Product Card Id Product Category Id  Product Description  \\\n",
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       "1           NaN            1360                  73                  NaN   \n",
       "2           NaN            1360                  73                  NaN   \n",
       "3           NaN            1360                  73                  NaN   \n",
       "4           NaN            1360                  73                  NaN   \n",
       "\n",
       "                                  Product Image  Product Name Product Price  \\\n",
       "0  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "1  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "2  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "3  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "4  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "\n",
       "  Product Status shipping date (DateOrders)   Shipping Mode  \n",
       "0              0             2/3/2018 22:56  Standard Class  \n",
       "1              0            1/18/2018 12:27  Standard Class  \n",
       "2              0            1/17/2018 12:06  Standard Class  \n",
       "3              0            1/16/2018 11:45  Standard Class  \n",
       "4              0            1/15/2018 11:24  Standard Class  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "Supply_Chain = pd.read_csv('DataCoSupplyChainDataset.csv', encoding='unicode_escape')\n",
    "Supply_Chain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e789219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180519, 53)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Supply_Chain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1b71f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Supply_Chain.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f23b902",
   "metadata": {},
   "outputs": [],
   "source": [
    "Supply_Chain.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e097c6-ec64-46a9-ba98-3bc402b44613",
   "metadata": {},
   "source": [
    "## Countries_and_continents_of_the_world.csv\n",
    "\n",
    "### Reasons for use:\n",
    "The code is used to create a horizontal bar chart showing the top 10 non-European countries by GDP per capita. This helps to understand where people will be willing to spend more for products and were it will be easier to scale sales easily. \n",
    "\n",
    "### Heps to answer Problem:\n",
    "- 6.1.2.1  Top 10 Non-European Countries by GDP per Capita\n",
    "- 6.2.1.1  Top 10 Non-European Countries by GDP per Capita\n",
    "\n",
    "### Data Source: \n",
    "The dataset is included in the World Bank folder. The World Bank Group compiles, collects and distributes a vast array of data sets and statistics, commonly known as World Bank data. This information covers an extensive range of topics related to economic development, including poverty, education, health, finance, infrastructure, and the environment (Source: Investopedia).\n",
    "\n",
    "### EDA:\n",
    "The analysis did not consider various columns due to inconsistencies, resulting in a reduction in memory usage.\n",
    "To improve the analysis, numerical fields with empty cells were filled with the median value of the column, while rows with empty cells in text fields for the considered columns were eliminated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a2dad47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_coldestQuart</th>\n",
       "      <th>temp_driestQuart</th>\n",
       "      <th>Agriculture</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Net migration</th>\n",
       "      <th>Coastline (coast/area ratio)</th>\n",
       "      <th>Capital</th>\n",
       "      <th>accessibility_to_cities</th>\n",
       "      <th>Population 2020</th>\n",
       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>cloudiness</th>\n",
       "      <th>geonumber</th>\n",
       "      <th>Region</th>\n",
       "      <th>Pop. Density (per sq. mi.)</th>\n",
       "      <th>temp_seasonality</th>\n",
       "      <th>Area (sq. mi.)</th>\n",
       "      <th>Geoname ID</th>\n",
       "      <th>Industry</th>\n",
       "      <th>climate_additional_description</th>\n",
       "      <th>climate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.261352</td>\n",
       "      <td>21.120329</td>\n",
       "      <td>0,38</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>23,06</td>\n",
       "      <td>0,00</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>317.715752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31056997.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.228983</td>\n",
       "      <td>22376.749753</td>\n",
       "      <td>ASIA (EX. NEAR EAST)</td>\n",
       "      <td>48,0</td>\n",
       "      <td>88.153515</td>\n",
       "      <td>647500.0</td>\n",
       "      <td>1149361.0</td>\n",
       "      <td>0,24</td>\n",
       "      <td>arid to semiarid; cold winters and hot summers</td>\n",
       "      <td>Warm Temperate/Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.101205</td>\n",
       "      <td>-1.280922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aland Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mariehamn</td>\n",
       "      <td>235.068864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27052.050057</td>\n",
       "      <td>WESTERN EUROPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.670694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>661882.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cool/Taiga/Siberian Temperate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.579896</td>\n",
       "      <td>19.573206</td>\n",
       "      <td>0,232</td>\n",
       "      <td>Albania</td>\n",
       "      <td>-4,93</td>\n",
       "      <td>1,26</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>73.830856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3581655.0</td>\n",
       "      <td>...</td>\n",
       "      <td>181.131113</td>\n",
       "      <td>23627.768091</td>\n",
       "      <td>EASTERN EUROPE</td>\n",
       "      <td>124,6</td>\n",
       "      <td>62.668515</td>\n",
       "      <td>28748.0</td>\n",
       "      <td>783754.0</td>\n",
       "      <td>0,188</td>\n",
       "      <td>mild temperate; cool, cloudy, wet winters; hot...</td>\n",
       "      <td>Temperate Grasslands/Steppe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.151553</td>\n",
       "      <td>26.921072</td>\n",
       "      <td>0,101</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>-0,39</td>\n",
       "      <td>0,04</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>1212.799818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32930091.0</td>\n",
       "      <td>...</td>\n",
       "      <td>90.674395</td>\n",
       "      <td>21247.759106</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "      <td>13,8</td>\n",
       "      <td>75.110263</td>\n",
       "      <td>2381740.0</td>\n",
       "      <td>2589581.0</td>\n",
       "      <td>0,6</td>\n",
       "      <td>arid to semiarid; mild, wet winters with hot, ...</td>\n",
       "      <td>Hot/Mid-Latitude Desert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.696020</td>\n",
       "      <td>25.859011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>-20,71</td>\n",
       "      <td>58,29</td>\n",
       "      <td>Pago Pago</td>\n",
       "      <td>323.064327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57794.0</td>\n",
       "      <td>...</td>\n",
       "      <td>256.029854</td>\n",
       "      <td>13461.092823</td>\n",
       "      <td>OCEANIA</td>\n",
       "      <td>290,4</td>\n",
       "      <td>4.975535</td>\n",
       "      <td>199.0</td>\n",
       "      <td>5880801.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tropical marine, moderated by southeast trade ...</td>\n",
       "      <td>Savana/Sudan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp_coldestQuart  temp_driestQuart Agriculture    Country Name  \\\n",
       "0          -0.261352         21.120329        0,38     Afghanistan   \n",
       "1          -4.101205         -1.280922         NaN   Aland Islands   \n",
       "2           3.579896         19.573206       0,232         Albania   \n",
       "3          13.151553         26.921072       0,101         Algeria   \n",
       "4          25.696020         25.859011         NaN  American Samoa   \n",
       "\n",
       "  Net migration Coastline (coast/area ratio)    Capital  \\\n",
       "0         23,06                         0,00      Kabul   \n",
       "1           NaN                          NaN  Mariehamn   \n",
       "2         -4,93                         1,26     Tirana   \n",
       "3         -0,39                         0,04    Algiers   \n",
       "4        -20,71                        58,29  Pago Pago   \n",
       "\n",
       "   accessibility_to_cities  Population 2020  Population  ...  cloudiness  \\\n",
       "0               317.715752              NaN  31056997.0  ...  114.228983   \n",
       "1               235.068864              NaN         NaN  ...         NaN   \n",
       "2                73.830856              NaN   3581655.0  ...  181.131113   \n",
       "3              1212.799818              NaN  32930091.0  ...   90.674395   \n",
       "4               323.064327              NaN     57794.0  ...  256.029854   \n",
       "\n",
       "      geonumber                Region Pop. Density (per sq. mi.)  \\\n",
       "0  22376.749753  ASIA (EX. NEAR EAST)                       48,0   \n",
       "1  27052.050057        WESTERN EUROPE                        NaN   \n",
       "2  23627.768091        EASTERN EUROPE                      124,6   \n",
       "3  21247.759106       NORTHERN AFRICA                       13,8   \n",
       "4  13461.092823               OCEANIA                      290,4   \n",
       "\n",
       "  temp_seasonality Area (sq. mi.) Geoname ID Industry  \\\n",
       "0        88.153515       647500.0  1149361.0     0,24   \n",
       "1        72.670694            NaN   661882.0      NaN   \n",
       "2        62.668515        28748.0   783754.0    0,188   \n",
       "3        75.110263      2381740.0  2589581.0      0,6   \n",
       "4         4.975535          199.0  5880801.0      NaN   \n",
       "\n",
       "                      climate_additional_description  \\\n",
       "0     arid to semiarid; cold winters and hot summers   \n",
       "1                                                NaN   \n",
       "2  mild temperate; cool, cloudy, wet winters; hot...   \n",
       "3  arid to semiarid; mild, wet winters with hot, ...   \n",
       "4  tropical marine, moderated by southeast trade ...   \n",
       "\n",
       "                         climate  \n",
       "0   Warm Temperate/Mediterranean  \n",
       "1  Cool/Taiga/Siberian Temperate  \n",
       "2    Temperate Grasslands/Steppe  \n",
       "3        Hot/Mid-Latitude Desert  \n",
       "4                   Savana/Sudan  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "GDP= pd.read_csv('World Bank/Countries_and_continents_of_the_world.csv')\n",
    "GDP.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "47086092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(269, 51)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3d3b1bac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_coldestQuart</th>\n",
       "      <th>temp_driestQuart</th>\n",
       "      <th>accessibility_to_cities</th>\n",
       "      <th>Population 2020</th>\n",
       "      <th>Population</th>\n",
       "      <th>temp_annual_range</th>\n",
       "      <th>elevation</th>\n",
       "      <th>temp_diurnal_range</th>\n",
       "      <th>latitude</th>\n",
       "      <th>temp_warmestQuart</th>\n",
       "      <th>...</th>\n",
       "      <th>longitude</th>\n",
       "      <th>temp_mean_annual</th>\n",
       "      <th>temp_wettestQuart</th>\n",
       "      <th>wind</th>\n",
       "      <th>GDP ($ per capita)</th>\n",
       "      <th>cloudiness</th>\n",
       "      <th>geonumber</th>\n",
       "      <th>temp_seasonality</th>\n",
       "      <th>Area (sq. mi.)</th>\n",
       "      <th>Geoname ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>236.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>2.260000e+02</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>2.260000e+02</td>\n",
       "      <td>2.490000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.343579</td>\n",
       "      <td>17.476764</td>\n",
       "      <td>913.304632</td>\n",
       "      <td>2.494231e+07</td>\n",
       "      <td>2.859019e+07</td>\n",
       "      <td>20.959832</td>\n",
       "      <td>476.801487</td>\n",
       "      <td>9.609079</td>\n",
       "      <td>15.792769</td>\n",
       "      <td>23.221114</td>\n",
       "      <td>...</td>\n",
       "      <td>15.158869</td>\n",
       "      <td>18.444641</td>\n",
       "      <td>19.434992</td>\n",
       "      <td>3.079724</td>\n",
       "      <td>9729.777778</td>\n",
       "      <td>200.430719</td>\n",
       "      <td>19058.064688</td>\n",
       "      <td>38.801726</td>\n",
       "      <td>5.904961e+05</td>\n",
       "      <td>2.385470e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.212408</td>\n",
       "      <td>11.130117</td>\n",
       "      <td>2070.077240</td>\n",
       "      <td>5.334900e+07</td>\n",
       "      <td>1.181313e+08</td>\n",
       "      <td>9.978027</td>\n",
       "      <td>605.242282</td>\n",
       "      <td>2.935235</td>\n",
       "      <td>26.735426</td>\n",
       "      <td>6.265048</td>\n",
       "      <td>...</td>\n",
       "      <td>74.158425</td>\n",
       "      <td>8.865489</td>\n",
       "      <td>8.066510</td>\n",
       "      <td>1.316086</td>\n",
       "      <td>10053.537599</td>\n",
       "      <td>56.325035</td>\n",
       "      <td>4816.014625</td>\n",
       "      <td>31.672845</td>\n",
       "      <td>1.790455e+06</td>\n",
       "      <td>1.544676e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-28.506789</td>\n",
       "      <td>-19.847401</td>\n",
       "      <td>0.969005</td>\n",
       "      <td>3.812800e+04</td>\n",
       "      <td>7.026000e+03</td>\n",
       "      <td>5.466729</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.878827</td>\n",
       "      <td>-82.862752</td>\n",
       "      <td>-3.306808</td>\n",
       "      <td>...</td>\n",
       "      <td>-178.116500</td>\n",
       "      <td>-17.213942</td>\n",
       "      <td>-12.287875</td>\n",
       "      <td>1.203084</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>50.357229</td>\n",
       "      <td>1419.704640</td>\n",
       "      <td>1.168763</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.951800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.862823</td>\n",
       "      <td>11.187788</td>\n",
       "      <td>73.434430</td>\n",
       "      <td>2.243103e+06</td>\n",
       "      <td>4.368775e+05</td>\n",
       "      <td>11.761746</td>\n",
       "      <td>110.626113</td>\n",
       "      <td>7.170131</td>\n",
       "      <td>-0.228021</td>\n",
       "      <td>19.427054</td>\n",
       "      <td>...</td>\n",
       "      <td>-36.587909</td>\n",
       "      <td>10.683758</td>\n",
       "      <td>13.086224</td>\n",
       "      <td>2.197467</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>170.878720</td>\n",
       "      <td>16233.665573</td>\n",
       "      <td>11.093533</td>\n",
       "      <td>4.407250e+03</td>\n",
       "      <td>1.168579e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.526643</td>\n",
       "      <td>22.284416</td>\n",
       "      <td>213.662956</td>\n",
       "      <td>8.075802e+06</td>\n",
       "      <td>4.724234e+06</td>\n",
       "      <td>19.903918</td>\n",
       "      <td>301.354710</td>\n",
       "      <td>9.332330</td>\n",
       "      <td>15.870032</td>\n",
       "      <td>25.234531</td>\n",
       "      <td>...</td>\n",
       "      <td>18.732207</td>\n",
       "      <td>22.232745</td>\n",
       "      <td>23.010930</td>\n",
       "      <td>2.778315</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>205.807448</td>\n",
       "      <td>19083.973900</td>\n",
       "      <td>29.912853</td>\n",
       "      <td>8.523500e+04</td>\n",
       "      <td>2.363686e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.595530</td>\n",
       "      <td>25.236814</td>\n",
       "      <td>638.757539</td>\n",
       "      <td>1.574856e+07</td>\n",
       "      <td>1.712839e+07</td>\n",
       "      <td>27.977329</td>\n",
       "      <td>597.092975</td>\n",
       "      <td>11.730448</td>\n",
       "      <td>35.937496</td>\n",
       "      <td>27.373185</td>\n",
       "      <td>...</td>\n",
       "      <td>53.847818</td>\n",
       "      <td>25.531276</td>\n",
       "      <td>25.618864</td>\n",
       "      <td>3.707561</td>\n",
       "      <td>15700.000000</td>\n",
       "      <td>237.885364</td>\n",
       "      <td>22759.294197</td>\n",
       "      <td>62.725459</td>\n",
       "      <td>4.294915e+05</td>\n",
       "      <td>3.474414e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.076407</td>\n",
       "      <td>35.217450</td>\n",
       "      <td>15531.333330</td>\n",
       "      <td>3.310027e+08</td>\n",
       "      <td>1.313974e+09</td>\n",
       "      <td>50.631191</td>\n",
       "      <td>5428.765033</td>\n",
       "      <td>16.269263</td>\n",
       "      <td>77.553604</td>\n",
       "      <td>35.217450</td>\n",
       "      <td>...</td>\n",
       "      <td>178.065032</td>\n",
       "      <td>28.256957</td>\n",
       "      <td>31.852690</td>\n",
       "      <td>11.187699</td>\n",
       "      <td>55100.000000</td>\n",
       "      <td>322.185346</td>\n",
       "      <td>30183.318992</td>\n",
       "      <td>145.221422</td>\n",
       "      <td>1.707520e+07</td>\n",
       "      <td>7.909807e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp_coldestQuart  temp_driestQuart  accessibility_to_cities  \\\n",
       "count         236.000000        236.000000               237.000000   \n",
       "mean           13.343579         17.476764               913.304632   \n",
       "std            12.212408         11.130117              2070.077240   \n",
       "min           -28.506789        -19.847401                 0.969005   \n",
       "25%             2.862823         11.187788                73.434430   \n",
       "50%            18.526643         22.284416               213.662956   \n",
       "75%            23.595530         25.236814               638.757539   \n",
       "max            28.076407         35.217450             15531.333330   \n",
       "\n",
       "       Population 2020    Population  temp_annual_range    elevation  \\\n",
       "count     4.600000e+01  2.260000e+02         236.000000   235.000000   \n",
       "mean      2.494231e+07  2.859019e+07          20.959832   476.801487   \n",
       "std       5.334900e+07  1.181313e+08           9.978027   605.242282   \n",
       "min       3.812800e+04  7.026000e+03           5.466729     0.000000   \n",
       "25%       2.243103e+06  4.368775e+05          11.761746   110.626113   \n",
       "50%       8.075802e+06  4.724234e+06          19.903918   301.354710   \n",
       "75%       1.574856e+07  1.712839e+07          27.977329   597.092975   \n",
       "max       3.310027e+08  1.313974e+09          50.631191  5428.765033   \n",
       "\n",
       "       temp_diurnal_range    latitude  temp_warmestQuart  ...   longitude  \\\n",
       "count          236.000000  269.000000         236.000000  ...  269.000000   \n",
       "mean             9.609079   15.792769          23.221114  ...   15.158869   \n",
       "std              2.935235   26.735426           6.265048  ...   74.158425   \n",
       "min              2.878827  -82.862752          -3.306808  ... -178.116500   \n",
       "25%              7.170131   -0.228021          19.427054  ...  -36.587909   \n",
       "50%              9.332330   15.870032          25.234531  ...   18.732207   \n",
       "75%             11.730448   35.937496          27.373185  ...   53.847818   \n",
       "max             16.269263   77.553604          35.217450  ...  178.065032   \n",
       "\n",
       "       temp_mean_annual  temp_wettestQuart        wind  GDP ($ per capita)  \\\n",
       "count        236.000000         236.000000  187.000000          225.000000   \n",
       "mean          18.444641          19.434992    3.079724         9729.777778   \n",
       "std            8.865489           8.066510    1.316086        10053.537599   \n",
       "min          -17.213942         -12.287875    1.203084          500.000000   \n",
       "25%           10.683758          13.086224    2.197467         1900.000000   \n",
       "50%           22.232745          23.010930    2.778315         5600.000000   \n",
       "75%           25.531276          25.618864    3.707561        15700.000000   \n",
       "max           28.256957          31.852690   11.187699        55100.000000   \n",
       "\n",
       "       cloudiness     geonumber  temp_seasonality  Area (sq. mi.)  \\\n",
       "count  233.000000    265.000000        236.000000    2.260000e+02   \n",
       "mean   200.430719  19058.064688         38.801726    5.904961e+05   \n",
       "std     56.325035   4816.014625         31.672845    1.790455e+06   \n",
       "min     50.357229   1419.704640          1.168763    2.000000e+00   \n",
       "25%    170.878720  16233.665573         11.093533    4.407250e+03   \n",
       "50%    205.807448  19083.973900         29.912853    8.523500e+04   \n",
       "75%    237.885364  22759.294197         62.725459    4.294915e+05   \n",
       "max    322.185346  30183.318992        145.221422    1.707520e+07   \n",
       "\n",
       "         Geoname ID  \n",
       "count  2.490000e+02  \n",
       "mean   2.385470e+06  \n",
       "std    1.544676e+06  \n",
       "min    4.951800e+04  \n",
       "25%    1.168579e+06  \n",
       "50%    2.363686e+06  \n",
       "75%    3.474414e+06  \n",
       "max    7.909807e+06  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bbf99a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp_coldestQuart                     float64\n",
       "temp_driestQuart                      float64\n",
       "Agriculture                            object\n",
       "Country Name                           object\n",
       "Net migration                          object\n",
       "Coastline (coast/area ratio)           object\n",
       "Capital                                object\n",
       "accessibility_to_cities               float64\n",
       "Population 2020                       float64\n",
       "Population                            float64\n",
       "Climate_short_desc                     object\n",
       "temp_annual_range                     float64\n",
       "elevation                             float64\n",
       "Phones (per 1000)                      object\n",
       "Continent                              object\n",
       "Development Status                     object\n",
       "Climate_index                          object\n",
       "Literacy (%)                           object\n",
       "temp_diurnal_range                    float64\n",
       "latitude                              float64\n",
       "temp_warmestQuart                     float64\n",
       "Birthrate                              object\n",
       "official_name_en                       object\n",
       "Service                                object\n",
       "Human Asset Index                     float64\n",
       "Country Code                           object\n",
       "Human Development Index               float64\n",
       "Infant mortality (per 1000 births)     object\n",
       "Languages                              object\n",
       "temp_min_coldestMonth                 float64\n",
       "is_independent                         object\n",
       "temp_max_warmestMonth                 float64\n",
       "longitude                             float64\n",
       "temp_mean_annual                      float64\n",
       "temp_wettestQuart                     float64\n",
       "ISO4217-currency_name                  object\n",
       "GNI per capita                         object\n",
       "wind                                  float64\n",
       "Climate_long_desc                      object\n",
       "GDP ($ per capita)                    float64\n",
       "Deathrate                              object\n",
       "cloudiness                            float64\n",
       "geonumber                             float64\n",
       "Region                                 object\n",
       "Pop. Density (per sq. mi.)             object\n",
       "temp_seasonality                      float64\n",
       "Area (sq. mi.)                        float64\n",
       "Geoname ID                            float64\n",
       "Industry                               object\n",
       "climate_additional_description         object\n",
       "climate                                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16cf8fa9",
   "metadata": {},
   "source": [
    "## Cost of living index by country 2020.csv\n",
    "\n",
    "### Reasons for use:\n",
    "\n",
    "The Local Purchasing Power (LPP) Index is a measure of the relative purchasing power of individuals in a given location. It takes into account factors such as inflation rates, exchange rates, and price levels, among other economic indicators. The LPP Index is an important metric for understanding the economic conditions and standard of living of people in different regions, as it provides a more accurate representation of their buying power.\n",
    "\n",
    "### Heps to answer Problem:\n",
    "- 6.1.2.2  Top 10 Non-European Countries by Local Purchasing Power \n",
    "- 6.2.1.2  Top 10 Non-European Countries by Local Purchasing Power Index\n",
    "\n",
    "### Data Source:\n",
    "The dataset is included in the World Bank folder. The World Bank Group compiles, collects and distributes a vast array of data sets and statistics, commonly known as World Bank data. This information covers an extensive range of topics related to economic development, including poverty, education, health, finance, infrastructure, and the environment (Source: Investopedia).\n",
    "\n",
    "The data can be accessed through the file path: Cost of living index by country 2020.csv.\n",
    "\n",
    "### EDA:\n",
    "Various columns were not included in the analysis due to inconsistencies, resulting in a reduction in memory usage.\n",
    "To improve the analysis, numerical fields with blank cells were filled with the median value of the column, while rows with blank cells in text fields for the relevant columns were removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "66dcb13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Cost of Living Index</th>\n",
       "      <th>Rent Index</th>\n",
       "      <th>Cost of Living Plus Rent Index</th>\n",
       "      <th>Groceries Index</th>\n",
       "      <th>Restaurant Price Index</th>\n",
       "      <th>Local Purchasing Power Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>122.40</td>\n",
       "      <td>50.25</td>\n",
       "      <td>87.89</td>\n",
       "      <td>120.27</td>\n",
       "      <td>123.01</td>\n",
       "      <td>119.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>101.43</td>\n",
       "      <td>36.15</td>\n",
       "      <td>70.21</td>\n",
       "      <td>91.14</td>\n",
       "      <td>109.28</td>\n",
       "      <td>88.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>100.48</td>\n",
       "      <td>46.95</td>\n",
       "      <td>74.88</td>\n",
       "      <td>86.89</td>\n",
       "      <td>113.74</td>\n",
       "      <td>79.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Japan</td>\n",
       "      <td>83.35</td>\n",
       "      <td>25.97</td>\n",
       "      <td>55.90</td>\n",
       "      <td>81.82</td>\n",
       "      <td>48.95</td>\n",
       "      <td>87.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>83.00</td>\n",
       "      <td>31.92</td>\n",
       "      <td>58.57</td>\n",
       "      <td>61.74</td>\n",
       "      <td>100.75</td>\n",
       "      <td>100.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Cost of Living Index  Rent Index  \\\n",
       "0  Switzerland                122.40       50.25   \n",
       "1       Norway                101.43       36.15   \n",
       "2      Iceland                100.48       46.95   \n",
       "3        Japan                 83.35       25.97   \n",
       "4      Denmark                 83.00       31.92   \n",
       "\n",
       "   Cost of Living Plus Rent Index  Groceries Index  Restaurant Price Index  \\\n",
       "0                           87.89           120.27                  123.01   \n",
       "1                           70.21            91.14                  109.28   \n",
       "2                           74.88            86.89                  113.74   \n",
       "3                           55.90            81.82                   48.95   \n",
       "4                           58.57            61.74                  100.75   \n",
       "\n",
       "   Local Purchasing Power Index  \n",
       "0                        119.53  \n",
       "1                         88.38  \n",
       "2                         79.44  \n",
       "3                         87.28  \n",
       "4                        100.88  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Purchasing_Power = pd.read_csv('./World Bank/countries-dataset-2020/Cost of living index by country 2020.csv')\n",
    "Purchasing_Power.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e81be896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132, 7)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Purchasing_Power.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec5a2239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of          Country  Cost of Living Index  Rent Index  \\\n",
       "0    Switzerland                122.40       50.25   \n",
       "1         Norway                101.43       36.15   \n",
       "2        Iceland                100.48       46.95   \n",
       "3          Japan                 83.35       25.97   \n",
       "4        Denmark                 83.00       31.92   \n",
       "..           ...                   ...         ...   \n",
       "127   Uzbekistan                 26.01        7.54   \n",
       "128        Syria                 25.31        5.68   \n",
       "129        India                 24.58        5.68   \n",
       "130  Afghanistan                 24.24        4.03   \n",
       "131     Pakistan                 21.98        4.59   \n",
       "\n",
       "     Cost of Living Plus Rent Index  Groceries Index  Restaurant Price Index  \\\n",
       "0                             87.89           120.27                  123.01   \n",
       "1                             70.21            91.14                  109.28   \n",
       "2                             74.88            86.89                  113.74   \n",
       "3                             55.90            81.82                   48.95   \n",
       "4                             58.57            61.74                  100.75   \n",
       "..                              ...              ...                     ...   \n",
       "127                           17.18            21.59                   20.17   \n",
       "128                           15.92            22.42                   21.38   \n",
       "129                           15.54            24.55                   17.17   \n",
       "130                           14.58            17.70                   17.38   \n",
       "131                           13.66            19.08                   16.78   \n",
       "\n",
       "     Local Purchasing Power Index  \n",
       "0                          119.53  \n",
       "1                           88.38  \n",
       "2                           79.44  \n",
       "3                           87.28  \n",
       "4                          100.88  \n",
       "..                            ...  \n",
       "127                         24.31  \n",
       "128                         11.46  \n",
       "129                         54.30  \n",
       "130                         28.20  \n",
       "131                         30.57  \n",
       "\n",
       "[132 rows x 7 columns]>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Purchasing_Power.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c76eb8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Purchasing_Power.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79da76a",
   "metadata": {},
   "source": [
    "## householdexpenditure.csv\n",
    "\n",
    "### Reasons for use:\n",
    "\n",
    "The household expenditure dataset provides insights into the spending habits and purchasing power of consumers in different countries. By analyzing the data in this file, I could understand which countries outside Europe spend the most per capita, I was able to make an informed decision about where to expand the sport clothing business.\n",
    "\n",
    "### Heps to answer Problem:\n",
    "\n",
    "6.1.2.3  Top 10 Countries by Household Expenditure per Capita\n",
    "6.2.1.3  Top 10 Countries by Household Expenditure per Capita\n",
    "\n",
    "### Data Source:\n",
    "\n",
    "The dataset is included in the World Bank folder. The World Bank Group compiles, collects and distributes a vast array of data sets and statistics, commonly known as World Bank data. This information covers an extensive range of topics related to economic development, including poverty, education, health, finance, infrastructure, and the environment (Source: Investopedia). \n",
    "\n",
    "The data can be accessed through the file path:householdexpenditure.csv\n",
    "\n",
    "### EDA:\n",
    "\n",
    "Inconsistencies across certain columns meant that not all data could be used in the analysis, ultimately leading to a reduction in memory requirements.\n",
    "To optimize the analysis, any numerical fields with blank cells were filled in with the median value of the column, whereas text fields with blank cells required the removal of the entire row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "80f7b719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>HouseholdExpenditurePerCapita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>38285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>37903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>28320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>28261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Norway</td>\n",
       "      <td>25481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  HouseholdExpenditurePerCapita\n",
       "0     Hong Kong                          38285\n",
       "1          USA                           37903\n",
       "2  Switzerland                           28320\n",
       "3   Luxembourg                           28261\n",
       "4       Norway                           25481"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Household_Expenditure = pd.read_csv('World Bank/householdexpenditure/householdexpenditure.csv')\n",
    "Household_Expenditure.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d6b612",
   "metadata": {},
   "outputs": [],
   "source": [
    "Household_Expenditure.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6a866ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "Household_Expenditure.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c431d323",
   "metadata": {},
   "outputs": [],
   "source": [
    "Household_Expenditure.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "920a8258",
   "metadata": {},
   "source": [
    "## countries of the world.csv\n",
    "\n",
    "### Reasons for use:\n",
    "\n",
    "Analyzing the Top Countries in Asia by Net Migration is helpful in understanding where to expand the business because it provides information about the countries in Asia where people are moving to and from. This information can give insights into where there may be potential markets for business.\n",
    "Moreover, Countries with high net migration rates are often experiencing economic growth and an influx of new consumers.\n",
    "\n",
    "### Heps to answer Problem:\n",
    "\n",
    "6.2.2.1  Top Countries in Asia by Net Migration\n",
    "\n",
    "### Data Source:\n",
    "\n",
    "The dataset is included in the World Bank folder. The World Bank Group compiles, collects and distributes a vast array of data sets and statistics, commonly known as World Bank data. This information covers an extensive range of topics related to economic development, including poverty, education, health, finance, infrastructure, and the environment (Source: Investopedia). \n",
    "\n",
    "The data can be accessed through the file path: countries of the world.csv\n",
    "\n",
    "### EDA:\n",
    "\n",
    "The presence of inconsistencies in some columns necessitated their exclusion from the analysis, which resulted in a decrease in memory usage. Additionally, to improve the analysis, numerical fields with blank cells were replaced with the statistical median, while rows with blank cells in text fields were eliminated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "40d90a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Area (sq. mi.)</th>\n",
       "      <th>Pop. Density (per sq. mi.)</th>\n",
       "      <th>Coastline (coast/area ratio)</th>\n",
       "      <th>Net migration</th>\n",
       "      <th>Infant mortality (per 1000 births)</th>\n",
       "      <th>GDP ($ per capita)</th>\n",
       "      <th>Literacy (%)</th>\n",
       "      <th>Phones (per 1000)</th>\n",
       "      <th>Arable (%)</th>\n",
       "      <th>Crops (%)</th>\n",
       "      <th>Other (%)</th>\n",
       "      <th>Climate</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Deathrate</th>\n",
       "      <th>Agriculture</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>ASIA (EX. NEAR EAST)</td>\n",
       "      <td>31056997</td>\n",
       "      <td>647500</td>\n",
       "      <td>48,0</td>\n",
       "      <td>0,00</td>\n",
       "      <td>23,06</td>\n",
       "      <td>163,07</td>\n",
       "      <td>700.0</td>\n",
       "      <td>36,0</td>\n",
       "      <td>3,2</td>\n",
       "      <td>12,13</td>\n",
       "      <td>0,22</td>\n",
       "      <td>87,65</td>\n",
       "      <td>1</td>\n",
       "      <td>46,6</td>\n",
       "      <td>20,34</td>\n",
       "      <td>0,38</td>\n",
       "      <td>0,24</td>\n",
       "      <td>0,38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>EASTERN EUROPE</td>\n",
       "      <td>3581655</td>\n",
       "      <td>28748</td>\n",
       "      <td>124,6</td>\n",
       "      <td>1,26</td>\n",
       "      <td>-4,93</td>\n",
       "      <td>21,52</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>86,5</td>\n",
       "      <td>71,2</td>\n",
       "      <td>21,09</td>\n",
       "      <td>4,42</td>\n",
       "      <td>74,49</td>\n",
       "      <td>3</td>\n",
       "      <td>15,11</td>\n",
       "      <td>5,22</td>\n",
       "      <td>0,232</td>\n",
       "      <td>0,188</td>\n",
       "      <td>0,579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "      <td>32930091</td>\n",
       "      <td>2381740</td>\n",
       "      <td>13,8</td>\n",
       "      <td>0,04</td>\n",
       "      <td>-0,39</td>\n",
       "      <td>31</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>70,0</td>\n",
       "      <td>78,1</td>\n",
       "      <td>3,22</td>\n",
       "      <td>0,25</td>\n",
       "      <td>96,53</td>\n",
       "      <td>1</td>\n",
       "      <td>17,14</td>\n",
       "      <td>4,61</td>\n",
       "      <td>0,101</td>\n",
       "      <td>0,6</td>\n",
       "      <td>0,298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>OCEANIA</td>\n",
       "      <td>57794</td>\n",
       "      <td>199</td>\n",
       "      <td>290,4</td>\n",
       "      <td>58,29</td>\n",
       "      <td>-20,71</td>\n",
       "      <td>9,27</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>97,0</td>\n",
       "      <td>259,5</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>22,46</td>\n",
       "      <td>3,27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>WESTERN EUROPE</td>\n",
       "      <td>71201</td>\n",
       "      <td>468</td>\n",
       "      <td>152,1</td>\n",
       "      <td>0,00</td>\n",
       "      <td>6,6</td>\n",
       "      <td>4,05</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>100,0</td>\n",
       "      <td>497,2</td>\n",
       "      <td>2,22</td>\n",
       "      <td>0</td>\n",
       "      <td>97,78</td>\n",
       "      <td>3</td>\n",
       "      <td>8,71</td>\n",
       "      <td>6,25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country                               Region  Population  \\\n",
       "0     Afghanistan         ASIA (EX. NEAR EAST)             31056997   \n",
       "1         Albania   EASTERN EUROPE                          3581655   \n",
       "2         Algeria   NORTHERN AFRICA                        32930091   \n",
       "3  American Samoa   OCEANIA                                   57794   \n",
       "4         Andorra   WESTERN EUROPE                            71201   \n",
       "\n",
       "   Area (sq. mi.) Pop. Density (per sq. mi.) Coastline (coast/area ratio)  \\\n",
       "0          647500                       48,0                         0,00   \n",
       "1           28748                      124,6                         1,26   \n",
       "2         2381740                       13,8                         0,04   \n",
       "3             199                      290,4                        58,29   \n",
       "4             468                      152,1                         0,00   \n",
       "\n",
       "  Net migration Infant mortality (per 1000 births)  GDP ($ per capita)  \\\n",
       "0         23,06                             163,07               700.0   \n",
       "1         -4,93                              21,52              4500.0   \n",
       "2         -0,39                                 31              6000.0   \n",
       "3        -20,71                               9,27              8000.0   \n",
       "4           6,6                               4,05             19000.0   \n",
       "\n",
       "  Literacy (%) Phones (per 1000) Arable (%) Crops (%) Other (%) Climate  \\\n",
       "0         36,0               3,2      12,13      0,22     87,65       1   \n",
       "1         86,5              71,2      21,09      4,42     74,49       3   \n",
       "2         70,0              78,1       3,22      0,25     96,53       1   \n",
       "3         97,0             259,5         10        15        75       2   \n",
       "4        100,0             497,2       2,22         0     97,78       3   \n",
       "\n",
       "  Birthrate Deathrate Agriculture Industry Service  \n",
       "0      46,6     20,34        0,38     0,24    0,38  \n",
       "1     15,11      5,22       0,232    0,188   0,579  \n",
       "2     17,14      4,61       0,101      0,6   0,298  \n",
       "3     22,46      3,27         NaN      NaN     NaN  \n",
       "4      8,71      6,25         NaN      NaN     NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Net_migration = pd.read_csv('World Bank/countries-of-the-world/countries of the world.csv')\n",
    "Net_migration.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d8ee27",
   "metadata": {},
   "outputs": [],
   "source": [
    "Net_migration.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "003dbc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Net_migration.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edaf4849",
   "metadata": {},
   "outputs": [],
   "source": [
    "Net_migration.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53759f2e",
   "metadata": {},
   "source": [
    "## country_population.csv\n",
    "\n",
    "### Reasons for use:\n",
    "\n",
    "This file has been used to see the demographic trend in Singapore. Indeed, a growing population might imply that the working force feels financially secure this could be a sign of wealth and a potential opportunity for your business to capture a share of this growing market. Moreover, growing population means that there will be a higher consumer pool in the future. \n",
    "\n",
    "### Heps to answer Problem:\n",
    "\n",
    "6.2.2.2  Population of Singapore over time\n",
    "\n",
    "### Data Source:\n",
    "\n",
    "The dataset is included in the World Bank folder. The World Bank Group compiles, collects and distributes a vast array of data sets and statistics, commonly known as World Bank data. This information covers an extensive range of topics related to economic development, including poverty, education, health, finance, infrastructure, and the environment (Source: Investopedia). \n",
    "\n",
    "The data can be accessed through the file path: country_population.csv\n",
    "\n",
    "### EDA:\n",
    "Different columns have not been taken into consideration in the analysis because not consistent, therefore memory was reduced. \n",
    "In order to run a better analysis, blank cells in numerical fields have been filled with the statistical median of the column; on the other side, blank cells in text fields for the considered columns implied the elimination of the whole row. The data is limited because it only includes data until 2016, so it does not provide updated trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0f12df93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>...</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>54211.0</td>\n",
       "      <td>55438.0</td>\n",
       "      <td>56225.0</td>\n",
       "      <td>56695.0</td>\n",
       "      <td>57032.0</td>\n",
       "      <td>57360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>101220.0</td>\n",
       "      <td>101353.0</td>\n",
       "      <td>101453.0</td>\n",
       "      <td>101669.0</td>\n",
       "      <td>102053.0</td>\n",
       "      <td>102577.0</td>\n",
       "      <td>103187.0</td>\n",
       "      <td>103795.0</td>\n",
       "      <td>104341.0</td>\n",
       "      <td>104822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>8996351.0</td>\n",
       "      <td>9166764.0</td>\n",
       "      <td>9345868.0</td>\n",
       "      <td>9533954.0</td>\n",
       "      <td>9731361.0</td>\n",
       "      <td>9938414.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26616792.0</td>\n",
       "      <td>27294031.0</td>\n",
       "      <td>28004331.0</td>\n",
       "      <td>28803167.0</td>\n",
       "      <td>29708599.0</td>\n",
       "      <td>30696958.0</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>32758020.0</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>34656032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>5643182.0</td>\n",
       "      <td>5753024.0</td>\n",
       "      <td>5866061.0</td>\n",
       "      <td>5980417.0</td>\n",
       "      <td>6093321.0</td>\n",
       "      <td>6203299.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20997687.0</td>\n",
       "      <td>21759420.0</td>\n",
       "      <td>22549547.0</td>\n",
       "      <td>23369131.0</td>\n",
       "      <td>24218565.0</td>\n",
       "      <td>25096150.0</td>\n",
       "      <td>25998340.0</td>\n",
       "      <td>26920466.0</td>\n",
       "      <td>27859305.0</td>\n",
       "      <td>28813463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>1608800.0</td>\n",
       "      <td>1659800.0</td>\n",
       "      <td>1711319.0</td>\n",
       "      <td>1762621.0</td>\n",
       "      <td>1814135.0</td>\n",
       "      <td>1864791.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2970017.0</td>\n",
       "      <td>2947314.0</td>\n",
       "      <td>2927519.0</td>\n",
       "      <td>2913021.0</td>\n",
       "      <td>2905195.0</td>\n",
       "      <td>2900401.0</td>\n",
       "      <td>2895092.0</td>\n",
       "      <td>2889104.0</td>\n",
       "      <td>2880703.0</td>\n",
       "      <td>2876101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>13411.0</td>\n",
       "      <td>14375.0</td>\n",
       "      <td>15370.0</td>\n",
       "      <td>16412.0</td>\n",
       "      <td>17469.0</td>\n",
       "      <td>18549.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82683.0</td>\n",
       "      <td>83861.0</td>\n",
       "      <td>84462.0</td>\n",
       "      <td>84449.0</td>\n",
       "      <td>83751.0</td>\n",
       "      <td>82431.0</td>\n",
       "      <td>80788.0</td>\n",
       "      <td>79223.0</td>\n",
       "      <td>78014.0</td>\n",
       "      <td>77281.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Name Country Code     Indicator Name Indicator Code       1960  \\\n",
       "0        Aruba          ABW  Population, total    SP.POP.TOTL    54211.0   \n",
       "1  Afghanistan          AFG  Population, total    SP.POP.TOTL  8996351.0   \n",
       "2       Angola          AGO  Population, total    SP.POP.TOTL  5643182.0   \n",
       "3      Albania          ALB  Population, total    SP.POP.TOTL  1608800.0   \n",
       "4      Andorra          AND  Population, total    SP.POP.TOTL    13411.0   \n",
       "\n",
       "        1961       1962       1963       1964       1965  ...        2007  \\\n",
       "0    55438.0    56225.0    56695.0    57032.0    57360.0  ...    101220.0   \n",
       "1  9166764.0  9345868.0  9533954.0  9731361.0  9938414.0  ...  26616792.0   \n",
       "2  5753024.0  5866061.0  5980417.0  6093321.0  6203299.0  ...  20997687.0   \n",
       "3  1659800.0  1711319.0  1762621.0  1814135.0  1864791.0  ...   2970017.0   \n",
       "4    14375.0    15370.0    16412.0    17469.0    18549.0  ...     82683.0   \n",
       "\n",
       "         2008        2009        2010        2011        2012        2013  \\\n",
       "0    101353.0    101453.0    101669.0    102053.0    102577.0    103187.0   \n",
       "1  27294031.0  28004331.0  28803167.0  29708599.0  30696958.0  31731688.0   \n",
       "2  21759420.0  22549547.0  23369131.0  24218565.0  25096150.0  25998340.0   \n",
       "3   2947314.0   2927519.0   2913021.0   2905195.0   2900401.0   2895092.0   \n",
       "4     83861.0     84462.0     84449.0     83751.0     82431.0     80788.0   \n",
       "\n",
       "         2014        2015        2016  \n",
       "0    103795.0    104341.0    104822.0  \n",
       "1  32758020.0  33736494.0  34656032.0  \n",
       "2  26920466.0  27859305.0  28813463.0  \n",
       "3   2889104.0   2880703.0   2876101.0  \n",
       "4     79223.0     78014.0     77281.0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_population = pd.read_csv('World Bank/country_population.csv')\n",
    "country_population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16e94d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_population.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f51553e",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_population.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "295c8749",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_population.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d31a46",
   "metadata": {},
   "source": [
    "## Coutries age structure.csv\n",
    "\n",
    "### Reasons for use:\n",
    "\n",
    "The file provides information about the age demographics of the country's population. This information can give insights into the size and composition of the potential market for the business.\n",
    "The age structure file has been used to identify which countries or regions have a significant population between the ages of 15 and 64. This age range is typically associated with the working-age population, which is an important market for many types of businesses, including clothing businesses.\n",
    "Moreover, countries or regions with a large population of working-age individuals may be experiencing economic growth and a rise in disposable income, which can create opportunities for businesses to expand and capture a share of the market.\n",
    "\n",
    "### Heps to answer Problem:\n",
    "\n",
    "6.2.2.3  Top 15 Countries by Percentage of Population in the Age Group 15 to 64 years\n",
    "\n",
    "### Data Source:\n",
    "\n",
    "The dataset is included in the World Bank folder. The World Bank Group compiles, collects and distributes a vast array of data sets and statistics, commonly known as World Bank data. This information covers an extensive range of topics related to economic development, including poverty, education, health, finance, infrastructure, and the environment (Source: Investopedia). \n",
    "\n",
    "The data can be accessed through the file path: Coutries age structure.csv\n",
    "\n",
    "### EDA:\n",
    "The analysis did not consider various columns due to inconsistencies, resulting in a reduction in memory usage.\n",
    "To improve the analysis, numerical fields with empty cells were filled with the median value of the column, while rows with empty cells in text fields for the considered columns were eliminated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1b018820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Age 0 to 14 Years</th>\n",
       "      <th>Age 15 to 64 Years</th>\n",
       "      <th>Age above 65 Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Japan</td>\n",
       "      <td>12.90%</td>\n",
       "      <td>60.10%</td>\n",
       "      <td>27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Italy</td>\n",
       "      <td>13.50%</td>\n",
       "      <td>63.50%</td>\n",
       "      <td>23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>13.60%</td>\n",
       "      <td>64.90%</td>\n",
       "      <td>22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>13.10%</td>\n",
       "      <td>65.50%</td>\n",
       "      <td>22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>16.40%</td>\n",
       "      <td>62.40%</td>\n",
       "      <td>21%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country Age 0 to 14 Years Age 15 to 64 Years Age above 65 Years\n",
       "0     Japan            12.90%             60.10%                27%\n",
       "1     Italy            13.50%             63.50%                23%\n",
       "2  Portugal            13.60%             64.90%                22%\n",
       "3   Germany            13.10%             65.50%                22%\n",
       "4   Finland            16.40%             62.40%                21%"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.read_csv('World Bank/countries-dataset-2020/Coutries age structure.csv')\n",
    "\n",
    "age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4741e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "age.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a5dec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "age.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea2c49fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "age.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba879eb2",
   "metadata": {},
   "source": [
    "## File selection "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f4a63af",
   "metadata": {},
   "source": [
    "As an analyst, I have reviewed several CSV files as part of the analysis for our business expansion plan. However, not all CSV files may have been used in the final analysis. Here are some potential reasons why certain CSV files may not have been used:\n",
    "\n",
    "- Data quality issues: Some CSV files may have contained incomplete or inconsistent data, or data that was not relevant to our business expansion plan. In such cases, it may have been more efficient to exclude these files from the final analysis rather than spending time trying to clean or reformat the data.\n",
    "\n",
    "- Limited relevance to business goals: Some CSV files may have contained data that was not directly relevant to our business expansion plan. While this data may have been interesting or informative, it may not have been necessary for making decisions about where to expand our business.\n",
    "\n",
    "- Redundancy with other files: In some cases, multiple CSV files contained similar or overlapping data. In such cases, it may have been more efficient to use one file as the primary source of information and exclude the others from the final analysis.\n",
    "\n",
    "- Not clear or required too many assumptions to be made in order to use the data effectively: This could include situations where the data was poorly documented or labeled, making it difficult to understand or interpret, or where the data required significant processing or normalization before it could be used in the analysis.\n",
    "\n",
    "- Issues related to data completeness: For example, where the data was missing critical information or contained gaps that would make it difficult to draw accurate conclusions, or issues related to data integrity, where the data was suspect or appeared to contain errors or anomalies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b5dcb3-8ca6-446d-a904-30b8716f5afe",
   "metadata": {},
   "source": [
    "# Problem Solving"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a19fa08-7a81-4de4-addb-f73014e73646",
   "metadata": {},
   "source": [
    "## Which country shows the best short-term opportunity for expansion? \n",
    "In order to answer this question, I developed the following questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9a635b",
   "metadata": {},
   "source": [
    "### Which countries have the highest recorded online sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbd40b1c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Estados Unidos</th>\n",
       "      <td>24840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Francia</th>\n",
       "      <td>13222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>México</th>\n",
       "      <td>13172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alemania</th>\n",
       "      <td>9564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>8497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brasil</th>\n",
       "      <td>7987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reino Unido</th>\n",
       "      <td>7302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>5758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italia</th>\n",
       "      <td>4989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>4783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Order Country\n",
       "Order Country                \n",
       "Estados Unidos          24840\n",
       "Francia                 13222\n",
       "México                  13172\n",
       "Alemania                 9564\n",
       "Australia                8497\n",
       "Brasil                   7987\n",
       "Reino Unido              7302\n",
       "China                    5758\n",
       "Italia                   4989\n",
       "India                    4783"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas DataFrame\n",
    "Supply_Chain = pd.read_csv('DataCoSupplyChainDataset.csv', encoding='unicode_escape')\n",
    "\n",
    "# Keep only the necessary columns\n",
    "Supply_Chain = Supply_Chain[['Order City', 'Order State', 'Order Country', 'order date (DateOrders)', 'Order Profit Per Order', 'Order Item Total']]\n",
    "\n",
    "# Remove rows where Order City, Order State, or Order Country are blank\n",
    "Supply_Chain = Supply_Chain.dropna(subset=['Order City', 'Order State', 'Order Country'])\n",
    "\n",
    "# Format the order date to month/day/year format and remove the time\n",
    "Supply_Chain['order date (DateOrders)'] = pd.to_datetime(Supply_Chain['order date (DateOrders)']).dt.strftime('%m/%d/%Y')\n",
    "\n",
    "# Replace blank cells in Order Profit Per Order with the statistical median of the column\n",
    "profit_median = Supply_Chain['Order Profit Per Order'].median()\n",
    "Supply_Chain['Order Profit Per Order'] = Supply_Chain['Order Profit Per Order'].replace(np.nan, profit_median).round(2)\n",
    "\n",
    "# Replace blank cells in Order Item Total with the statistical median of the column\n",
    "item_total_median = Supply_Chain['Order Item Total'].median()\n",
    "Supply_Chain['Order Item Total'] = Supply_Chain['Order Item Total'].replace(np.nan, item_total_median).round(2)\n",
    "\n",
    "# Which countries have the highest order volume? \n",
    "\n",
    "# We can answer this question by grouping the data by country and counting the number of orders in each country:\n",
    "order_volume = Supply_Chain.groupby('Order Country')['Order Country'].count().sort_values(ascending=False)[:10]\n",
    "\n",
    "colors = ['#FEE4E4', '#FECDD3', '#FDC7DE', '#E2D2F0', '#D3E0F5', '#C9DEF5', '#FEE4C4', '#E4F4C4', '#F8D2D2', '#E2D2F0']\n",
    "\n",
    "# Create a horizontal bar graph for the top 10 countries\n",
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "ax.barh(order_volume.index, order_volume.values, color=colors)\n",
    "ax.invert_yaxis()  # Invert the y-axis to show countries with highest order volume at the top\n",
    "ax.set_title('Order Volume by Country (Top 10)')\n",
    "ax.set_xlabel('Number of Orders')\n",
    "ax.set_ylabel('Country')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "pd.DataFrame(order_volume).round(2).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92d9bab",
   "metadata": {},
   "source": [
    "### What are the characteristics of the target market in the identified country?\n",
    "To have an overall view of the economic situation of the country, I analyzed:\n",
    "- Top 10 Non-European Countries by GDP per Capita\n",
    "- Top 10 Non-European Countries by Local Purchasing Power Index\n",
    "- Top 10 Countries by Household Expenditure per Capita"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d3bca1",
   "metadata": {},
   "source": [
    "#### Top 10 Non-European Countries by GDP per Capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9296c34c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "GDP = pd.read_csv('World Bank/Countries_and_continents_of_the_world.csv')\n",
    "\n",
    "# Filter out countries in Europe\n",
    "GDP = GDP.loc[GDP['Continent'] != 'Europe']\n",
    "\n",
    "# Sort the dataframe by GDP per capita in descending order\n",
    "GDP = GDP.sort_values('GDP ($ per capita)', ascending=False)\n",
    "\n",
    "# Select the top 10 countries by GDP per capita\n",
    "top_10_GDP = GDP.head(10)\n",
    "\n",
    "# Sort the top 10 data by GDP per capita in ascending order\n",
    "top_10_GDP = top_10_GDP.sort_values('GDP ($ per capita)')\n",
    "\n",
    "# Create a horizontal bar chart of the top 10 non-European countries by GDP per capita\n",
    "plt.barh(top_10_GDP['Country Name'], top_10_GDP['GDP ($ per capita)'], color=colors)\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('GDP per capita ($)')\n",
    "\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Country')\n",
    "\n",
    "plt.title('Top 10 Non-European Countries by GDP per Capita')\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5c2bed",
   "metadata": {},
   "source": [
    "#### Top 10 Non-European Countries by Local Purchasing Power Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1097ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "Purchasing_Power = pd.read_csv('World Bank/countries-dataset-2020/Cost of living index by country 2020.csv')\n",
    "\n",
    "# Filter out the columns of interest\n",
    "cols = ['Country', 'Cost of Living Index', 'Rent Index', 'Local Purchasing Power Index']\n",
    "Purchasing_Power_filtered = Purchasing_Power[cols]\n",
    "\n",
    "# Exclude the European Countries\n",
    "excluded_countries = ['Switzerland', 'Luxembourg', 'Sweden', 'Denmark', 'Germany', 'Finland', 'Netherlands','United Kingdom','Norway']\n",
    "Purchasing_Power_filtered = Purchasing_Power_filtered[~Purchasing_Power_filtered['Country'].isin(excluded_countries)]\n",
    "\n",
    "# Sort the filtered dataframe by Local Purchasing Power Index in descending order\n",
    "Purchasing_Power_filtered = Purchasing_Power_filtered.sort_values('Local Purchasing Power Index', ascending=False)\n",
    "\n",
    "colors = ['#FEE4E4', '#FECDD3', '#FDC7DE', '#E2D2F0', '#D3E0F5', '#C9DEF5', '#FEE4C4', '#E4F4C4', '#F8D2D2', '#E2D2F0']\n",
    "\n",
    "\n",
    "# Create a horizontal bar chart of the top 10 countries by Local Purchasing Power Index\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(Purchasing_Power_filtered['Country'].head(10)[::-1], Purchasing_Power_filtered['Local Purchasing Power Index'].head(10)[::-1], color=colors)\n",
    "\n",
    "# Set the x-axis label\n",
    "ax.set_xlabel('Local Purchasing Power Index')\n",
    "\n",
    "# Set the y-axis label\n",
    "ax.set_ylabel('Country')\n",
    "\n",
    "\n",
    "ax.set_title('Top 10 Non-European Countries by Local Purchasing Power Index')\n",
    "\n",
    "# Set the figure size to 6x3 inches\n",
    "fig.set_size_inches(6, 3)\n",
    "\n",
    "# Display the chart\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edcaa211",
   "metadata": {},
   "source": [
    "#### Top 10 Countries by Household Expenditure per Capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43cdf8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>HouseholdExpenditurePerCapita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>38285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>37903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>28320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>28261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Norway</td>\n",
       "      <td>25481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>24868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Canada</td>\n",
       "      <td>24704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>24703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>24679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>24147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  HouseholdExpenditurePerCapita\n",
       "0     Hong Kong                          38285\n",
       "1          USA                           37903\n",
       "2  Switzerland                           28320\n",
       "3   Luxembourg                           28261\n",
       "4       Norway                           25481\n",
       "5      Bermuda                           24868\n",
       "6       Canada                           24704\n",
       "7    Singapore                           24703\n",
       "8      Iceland                           24679\n",
       "9    Australia                           24147"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "Household_Expenditure = pd.read_csv('World Bank/householdexpenditure/householdexpenditure.csv')\n",
    "\n",
    "# Sort the dataframe by HouseholdExpenditurePerCapita in descending order\n",
    "Household_Expenditure_sorted = Household_Expenditure.sort_values('HouseholdExpenditurePerCapita', ascending=False)\n",
    "\n",
    "colors = ['#FEE4E4', '#FECDD3', '#FDC7DE', '#E2D2F0', '#D3E0F5', '#C9DEF5', '#FEE4C4', '#E4F4C4', '#F8D2D2', '#E2D2F0']\n",
    "\n",
    "# Select the top 10 rows\n",
    "Household_Expenditure_top10 = Household_Expenditure_sorted.head(10)\n",
    "\n",
    "# Create a horizontal bar chart with country names on the y-axis\n",
    "plt.barh(Household_Expenditure_top10['Country'], Household_Expenditure_top10['HouseholdExpenditurePerCapita'], color=colors)\n",
    "\n",
    "# Invert the y-axis to show the longest bars first\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Household Expenditure per Capita')\n",
    "\n",
    "plt.title('Top 10 Countries by Household Expenditure per Capita')\n",
    "\n",
    "# Set the figure size to 6x3 inches\n",
    "fig.set_size_inches(6, 3)\n",
    "\n",
    "# Display the chart\n",
    "plt.show()\n",
    "\n",
    "# Display the table\n",
    "Household_Expenditure_top10.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ddc28e4-8149-4f23-8b1b-1d26e4186ea0",
   "metadata": {},
   "source": [
    "### What is the most suitable penetration area in the identified country?\n",
    "To make a reasoned recommendation on where to expand first, I analyzed:\n",
    "- Total Sales by State in the United States (Top 10)\n",
    "- Order Frequency by State in the United States (Top 10)\n",
    "- Total Sales by City in the United States (Top 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664add61",
   "metadata": {},
   "source": [
    "#### Total Sales by State in the United States (Top 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82a57737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>884014.630189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nueva York</th>\n",
       "      <td>485336.605515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>434870.714797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pensilvania</th>\n",
       "      <td>247905.532713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>221081.772605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>216443.942449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>205233.212232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>169186.461915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carolina del Norte</th>\n",
       "      <td>110122.571316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>109756.031216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Total Sales\n",
       "Order State                      \n",
       "California          884014.630189\n",
       "Nueva York          485336.605515\n",
       "Texas               434870.714797\n",
       "Pensilvania         247905.532713\n",
       "Washington          221081.772605\n",
       "Illinois            216443.942449\n",
       "Ohio                205233.212232\n",
       "Florida             169186.461915\n",
       "Carolina del Norte  110122.571316\n",
       "Michigan            109756.031216"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Load the CSV file into a pandas DataFrame\n",
    "Supply_Chain = pd.read_csv('DataCoSupplyChainDataset.csv', encoding='unicode_escape')\n",
    "\n",
    "# Keep only the necessary columns\n",
    "Supply_Chain = Supply_Chain[['Order City', 'Order State', 'Order Country', 'order date (DateOrders)', 'Order Profit Per Order', 'Order Item Total']]\n",
    "\n",
    "# Remove rows where Order City, Order State, or Order Country are blank\n",
    "Supply_Chain = Supply_Chain.dropna(subset=['Order City', 'Order State', 'Order Country'])\n",
    "\n",
    "Supply_Chain_us = Supply_Chain[Supply_Chain['Order Country'] == 'Estados Unidos']\n",
    "\n",
    "# Group the DataFrame by Order State and calculate the total sales (i.e., sum of Order Item Total)\n",
    "sales_by_state = Supply_Chain_us.groupby('Order State')['Order Item Total'].sum().sort_values(ascending=False)[:10]\n",
    "\n",
    "# Create a pandas DataFrame with the top 10 states and their sales\n",
    "top_states_Supply_Chain_us = pd.DataFrame({'Order State': sales_by_state.index, 'Total Sales': sales_by_state.values})\n",
    "top_states_Supply_Chain_us = top_states_Supply_Chain_us.set_index('Order State')\n",
    "\n",
    "colors = ['#FEE4E4', '#FECDD3', '#FDC7DE', '#E2D2F0', '#D3E0F5', '#C9DEF5', '#FEE4C4', '#E4F4C4', '#F8D2D2', '#E2D2F0']\n",
    "\n",
    "# Create a horizontal bar chart with light orange bars\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(sales_by_state.index, sales_by_state.values, color= colors)\n",
    "ax.invert_yaxis()  # Invert the y-axis to show states with highest sales at the top\n",
    "ax.set_title('Total Sales by State in the United States (Top 10)')\n",
    "ax.set_xlabel('Total Sales')\n",
    "ax.set_ylabel('Order State')\n",
    "plt.show()\n",
    "\n",
    "top_states_Supply_Chain_us "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5140a0",
   "metadata": {},
   "source": [
    "### Order Frequency by State in the United States (Top 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39d715a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Order Frequency]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas DataFrame\n",
    "Supply_Chain_us = pd.read_csv('DataCoSupplyChainDataset.csv', encoding='unicode_escape')\n",
    "\n",
    "# Keep only the necessary columns\n",
    "Supply_Chain_us = Supply_Chain_us[['Order City', 'Order State', 'Order Country', 'order date (DateOrders)', 'Order Profit Per Order', 'Order Item Total']]\n",
    "\n",
    "# Remove rows where Order City, Order State, or Order Country are blank\n",
    "Supply_Chain_us = Supply_Chain_us.dropna(subset=['Order City', 'Order State', 'Order Country'])\n",
    "\n",
    "# Convert order date to datetime format\n",
    "Supply_Chain_us['order date (DateOrders)'] = pd.to_datetime(Supply_Chain_us['order date (DateOrders)'], format='%m/%d/%Y %H:%M')\n",
    "\n",
    "# Filter orders from United States\n",
    "us_orders = Supply_Chain_us[Supply_Chain_us['Order Country'] == 'Estados Unidos']\n",
    "\n",
    "# Group orders by state and calculate the number of unique order dates\n",
    "order_frequency = us_orders.groupby('Order State')['order date (DateOrders)'].nunique().sort_values(ascending=False)[:10]\n",
    "\n",
    "# Create a horizontal bar graph with light orange bars for the top 10 states\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(order_frequency.index, order_frequency.values, color= colors)\n",
    "ax.invert_yaxis()  # Invert the y-axis to show states with highest frequency at the top\n",
    "ax.set_title('Order Frequency by State in the United States (Top 10)')\n",
    "ax.set_xlabel('Number of Orders')\n",
    "ax.set_ylabel('State')\n",
    "plt.show()\n",
    "\n",
    "# Display the results as a dataframe\n",
    "pd.DataFrame(order_frequency, columns=['Order Frequency']).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164864b0",
   "metadata": {},
   "source": [
    "### Total Sales by City in the United States (Top 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6392cb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Item Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>392595.134493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>332631.263842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>229830.572602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia</th>\n",
       "      <td>229544.082529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <td>186824.502271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Order Item Total\n",
       "Order City                     \n",
       "New York City     392595.134493\n",
       "Los Angeles       332631.263842\n",
       "San Francisco     229830.572602\n",
       "Philadelphia      229544.082529\n",
       "Seattle           186824.502271"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the DataFrame to only include orders from the United States\n",
    "Supply_Chain_us = Supply_Chain_us[Supply_Chain_us['Order Country'] == 'Estados Unidos']\n",
    "\n",
    "# Group the DataFrame by Order City and calculate the total sales (i.e., sum of Order Item Total)\n",
    "sales_by_city = Supply_Chain_us.groupby('Order City')['Order Item Total'].sum().sort_values(ascending=False)[:10]\n",
    "\n",
    "# Create a horizontal bar chart with light orange bars\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(sales_by_city.index, sales_by_city.values, color= colors)\n",
    "ax.invert_yaxis()  # Invert the y-axis to show cities with highest sales at the top\n",
    "ax.set_title('Total Sales by City in the United States (Top 10)')\n",
    "ax.set_xlabel('Total Sales')\n",
    "ax.set_ylabel('City')\n",
    "plt.show()\n",
    "\n",
    "pd.DataFrame(sales_by_city).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da155fc",
   "metadata": {},
   "source": [
    "## Which country shows the best long-term opportunity for expansion? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10740277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Calculate total revenue and profit for each country\n",
    "Supply_Chain['Total Revenue'] = Supply_Chain['Order Item Total'] * Supply_Chain['Order Profit Per Order']\n",
    "country_revenue = Supply_Chain.groupby('Order Country')['Total Revenue'].sum()\n",
    "country_profit = Supply_Chain.groupby('Order Country')['Order Profit Per Order'].sum()\n",
    "\n",
    "# Calculate average revenue and profit per order for each country\n",
    "country_order_count = Supply_Chain.groupby('Order Country')['Order City'].nunique()\n",
    "country_avg_revenue_per_order = country_revenue / country_order_count\n",
    "country_avg_profit_per_order = country_profit / country_order_count\n",
    "\n",
    "# Calculate growth potential based on revenue and profit per order\n",
    "growth_potential = country_avg_revenue_per_order * country_avg_profit_per_order\n",
    "\n",
    "# Sort countries by growth potential in descending order\n",
    "sorted_countries = growth_potential.sort_values(ascending=False)\n",
    "\n",
    "# Create a DataFrame for the top 10 countries with the most potential for growth\n",
    "top_countries = pd.DataFrame({\n",
    "    'Country': sorted_countries.index[:10],\n",
    "    'Growth Potential': sorted_countries.round(2)[:10]\n",
    "})\n",
    "\n",
    "# Set the index of the DataFrame to the country name\n",
    "top_countries.set_index('Country', inplace=True)\n",
    "\n",
    "# Sort the DataFrame in descending order\n",
    "top_countries = top_countries.sort_values(by='Growth Potential', ascending=True)\n",
    "\n",
    "# Plot a horizontal bar chart\n",
    "ax = top_countries.plot(kind='barh', legend=None, color= colors)\n",
    "\n",
    "# Set the axis labels and title\n",
    "ax.set_xlabel('Growth Potential')\n",
    "ax.set_ylabel('Country')\n",
    "ax.set_title('Top 10 Countries with the Most Growth Potential')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d938fd",
   "metadata": {},
   "source": [
    "### What are the economic characteristics of the target market in the identified country?\n",
    "- Top 10 Non-European Countries by GDP per Capita\n",
    "- Top 10 Non-European Countries by Local Purchasing Power Index\n",
    "- Top 10 Countries by Household Expenditure per Capita"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd4a2a0",
   "metadata": {},
   "source": [
    "#### Top 10 Non-European Countries by GDP per Capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6152bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "GDP = pd.read_csv('World Bank/Countries_and_continents_of_the_world.csv')\n",
    "\n",
    "# Filter out countries in Europe\n",
    "GDP = GDP.loc[GDP['Continent'] != 'Europe']\n",
    "\n",
    "# Sort the dataframe by GDP per capita in descending order\n",
    "GDP = GDP.sort_values('GDP ($ per capita)', ascending=False)\n",
    "\n",
    "# Select the top 10 countries by GDP per capita\n",
    "top_10_GDP = GDP.head(10)\n",
    "\n",
    "# Sort the top 10 data by GDP per capita in ascending order\n",
    "top_10_GDP = top_10_GDP.sort_values('GDP ($ per capita)')\n",
    "\n",
    "# Create a horizontal bar chart of the top 10 non-European countries by GDP per capita\n",
    "plt.barh(top_10_GDP['Country Name'], top_10_GDP['GDP ($ per capita)'], color=colors)\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('GDP per capita ($)')\n",
    "\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Country')\n",
    "\n",
    "# Set the title of the chart\n",
    "plt.title('Top 10 Non-European Countries by GDP per Capita')\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59776b9c",
   "metadata": {},
   "source": [
    "#### Top 10 Non-European Countries by Local Purchasing Power Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9bc6fcba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "Purchasing_Power = pd.read_csv('World Bank/countries-dataset-2020/Cost of living index by country 2020.csv')\n",
    "\n",
    "# Filter out the columns of interest\n",
    "cols = ['Country', 'Cost of Living Index', 'Rent Index', 'Local Purchasing Power Index']\n",
    "Purchasing_Power_filtered = Purchasing_Power[cols]\n",
    "\n",
    "# Exclude the European Countries\n",
    "excluded_countries = ['Switzerland', 'Luxembourg', 'Sweden', 'Denmark', 'Germany', 'Finland', 'Netherlands','United Kingdom','Norway']\n",
    "Purchasing_Power_filtered = Purchasing_Power_filtered[~Purchasing_Power_filtered['Country'].isin(excluded_countries)]\n",
    "\n",
    "# Sort the filtered dataframe by Local Purchasing Power Index in descending order\n",
    "Purchasing_Power_filtered = Purchasing_Power_filtered.sort_values('Local Purchasing Power Index', ascending=False)\n",
    "\n",
    "colors = ['#FEE4E4', '#FECDD3', '#FDC7DE', '#E2D2F0', '#D3E0F5', '#C9DEF5', '#FEE4C4', '#E4F4C4', '#F8D2D2', '#E2D2F0']\n",
    "\n",
    "\n",
    "# Create a horizontal bar chart of the top 10 countries by Local Purchasing Power Index\n",
    "fig, ax = plt.subplots()\n",
    "ax.barh(Purchasing_Power_filtered['Country'].head(10)[::-1], Purchasing_Power_filtered['Local Purchasing Power Index'].head(10)[::-1], color=colors)\n",
    "\n",
    "# Set the x-axis label\n",
    "ax.set_xlabel('Local Purchasing Power Index')\n",
    "\n",
    "# Set the y-axis label\n",
    "ax.set_ylabel('Country')\n",
    "\n",
    "\n",
    "ax.set_title('Top 10 Non-European Countries by Local Purchasing Power Index')\n",
    "\n",
    "# Set the figure size to 6x3 inches\n",
    "fig.set_size_inches(6, 3)\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75f9ba3",
   "metadata": {},
   "source": [
    "#### Top 10 Countries by Household Expenditure per Capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4296826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>HouseholdExpenditurePerCapita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>38285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>37903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>28320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>28261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Norway</td>\n",
       "      <td>25481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>24868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Canada</td>\n",
       "      <td>24704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>24703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>24679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>24147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  HouseholdExpenditurePerCapita\n",
       "0     Hong Kong                          38285\n",
       "1          USA                           37903\n",
       "2  Switzerland                           28320\n",
       "3   Luxembourg                           28261\n",
       "4       Norway                           25481\n",
       "5      Bermuda                           24868\n",
       "6       Canada                           24704\n",
       "7    Singapore                           24703\n",
       "8      Iceland                           24679\n",
       "9    Australia                           24147"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "Household_Expenditure = pd.read_csv('World Bank/householdexpenditure/householdexpenditure.csv')\n",
    "\n",
    "# Sort the dataframe by HouseholdExpenditurePerCapita in descending order\n",
    "Household_Expenditure_sorted = Household_Expenditure.sort_values('HouseholdExpenditurePerCapita', ascending=False)\n",
    "\n",
    "colors = ['#FEE4E4', '#FECDD3', '#FDC7DE', '#E2D2F0', '#D3E0F5', '#C9DEF5', '#FEE4C4', '#E4F4C4', '#F8D2D2', '#E2D2F0']\n",
    "\n",
    "# Select the top 10 rows\n",
    "Household_Expenditure_top10 = Household_Expenditure_sorted.head(10)\n",
    "\n",
    "# Create a horizontal bar chart with country names on the y-axis\n",
    "plt.barh(Household_Expenditure_top10['Country'], Household_Expenditure_top10['HouseholdExpenditurePerCapita'], color=colors)\n",
    "\n",
    "# Invert the y-axis to show the longest bars first\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Household Expenditure per Capita')\n",
    "\n",
    "plt.title('Top 10 Countries by Household Expenditure per Capita')\n",
    "\n",
    "# Set the figure size to 6x3 inches\n",
    "fig.set_size_inches(6, 3)\n",
    "\n",
    "# Display the chart\n",
    "plt.show()\n",
    "\n",
    "# Display the table\n",
    "Household_Expenditure_top10.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0951c857",
   "metadata": {},
   "source": [
    "### What are the demographic characteristics of the target market in the identified country?\n",
    "- Top Countries in Asia by Net Migration\n",
    "- Population of Singapore over time\n",
    "- Top 15 Countries by Percentage of Population in the Age Group 15 to 64 years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d1a8e9",
   "metadata": {},
   "source": [
    "#### Top Countries in Asia by Net Migration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4d12cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "Net_migration = pd.read_csv('World Bank/countries-of-the-world/countries of the world.csv', decimal=',')\n",
    "\n",
    "# Filter out the columns of interest and drop rows with missing values\n",
    "cols = ['Country', 'Net migration', 'Region']\n",
    "Net_migration_filtered = Net_migration[cols].dropna()\n",
    "\n",
    "# Filter out rows where Region does not contain 'ASIA'\n",
    "Net_migration_filtered = Net_migration_filtered[Net_migration_filtered['Region'].str.contains('ASIA')]\n",
    "\n",
    "# Sort the filtered dataframe by Net migration in descending order\n",
    "Net_migration_filtered = Net_migration_filtered.sort_values('Net migration', ascending=False)\n",
    "\n",
    "# Create a horizontal bar chart of the top countries by Net migration\n",
    "plt.barh(Net_migration_filtered['Country'].head(10)[::-1], Net_migration_filtered['Net migration'].head(10)[::-1], color=colors)\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Net migration')\n",
    "\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Country')\n",
    "\n",
    "# Set the title of the chart\n",
    "plt.title('Top Countries in Asia by Net Migration')\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4433d092",
   "metadata": {},
   "source": [
    "#### Population of Singapore over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2be4ded3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/sj/b9tmh6xd2l7bsbmq112nld0r0000gn/T/ipykernel_1178/1876374299.py:12: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  country_population_filtered = country_population_filtered[cols].fillna(country_population_filtered.median())\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "country_population = pd.read_csv('World Bank/country_population.csv')\n",
    "\n",
    "# Filter for Singapore\n",
    "country_population_filtered = country_population[country_population['Country Name'] == 'Singapore']\n",
    "\n",
    "# Select the columns of interest and fill missing values with the column median\n",
    "cols = ['Country Name', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']\n",
    "country_population_filtered = country_population_filtered[cols].fillna(country_population_filtered.median())\n",
    "\n",
    "# Convert the columns to rows and create a line chart of population over time\n",
    "country_population_filtered = country_population_filtered.set_index('Country Name')\n",
    "country_population_filtered = country_population_filtered.T\n",
    "country_population_filtered = country_population_filtered.astype(int)\n",
    "\n",
    "country_population_filtered.plot(kind='line')\n",
    "\n",
    "# Set the title of the chart\n",
    "plt.title('Population of Singapore over Time')\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Year')\n",
    "\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Population')\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11930e69",
   "metadata": {},
   "source": [
    "#### Top 15 Countries by Percentage of Population in the Age Group 15 to 64 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7fb3aa66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the CSV file into a pandas dataframe\n",
    "age = pd.read_csv('World Bank/countries-dataset-2020/Coutries age structure.csv')\n",
    "\n",
    "# Filter out rows with missing values\n",
    "age_filtered = age.dropna()\n",
    "\n",
    "# Sort the dataframe by the percentage of population in the age group 15 to 64 years\n",
    "age_sorted = age_filtered.sort_values('Age 15 to 64 Years', ascending=True)\n",
    "\n",
    "# Select the top 15 rows\n",
    "age_top15 = age_sorted.tail(15)\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "# Create a horizontal bar chart with country names on the y-axis\n",
    "plt.barh(age_top15['Country'], age_top15['Age 15 to 64 Years'], color=colors)\n",
    "\n",
    "# Set the x-axis label and increase font size\n",
    "plt.xlabel('Percentage of population in the age group 15 to 64 years', fontsize=5)\n",
    "\n",
    "# Set the title of the chart and increase font size\n",
    "plt.title('Top 15 Countries by Percentage of Population in the Age Group 15 to 64 years', fontsize=10, color='black')\n",
    "\n",
    "# Increase font size of y-axis labels\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "# Display the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80ba1a46",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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