path: root/notebooks/basic_data_analysis.ipynb

{
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   "source": [
    "\n",
    "# 📊 Basic Data Analysis & Visualization\n",
    "\n",
    "This notebook demonstrates how to load a sample dataset, perform quick exploratory analysis, group and pivot the data, and create visualizations.\n",
    "\n",
    "We’re using a CSV file from: https://sample-files.com/downloads/data/csv/basic-data.csv\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92fdfd1",
   "metadata": {},
   "source": [
    "\n",
    "## 📦 Install Dependencies\n",
    "\n",
    "If you haven't installed the required libraries, run:\n",
    "\n",
    "```bash\n",
    "pip install pandas matplotlib seaborn\n",
    "```\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5d7308a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandas in /Users/cmc/Library/Python/3.9/lib/python/site-packages (2.2.3)\n",
      "Requirement already satisfied: matplotlib in /Users/cmc/Library/Python/3.9/lib/python/site-packages (3.9.4)\n",
      "Requirement already satisfied: seaborn in /Users/cmc/Library/Python/3.9/lib/python/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy>=1.22.4 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from pandas) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (4.58.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (11.2.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (3.2.3)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from matplotlib) (6.5.2)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /Users/cmc/Library/Python/3.9/lib/python/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.21.0)\n",
      "Requirement already satisfied: six>=1.5 in /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas) (1.15.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas matplotlib seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0632140a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set plot style\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d326b5b6",
   "metadata": {},
   "source": [
    "## 📂 Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5d5de24b",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Country</th>\n",
       "      <th>Email</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Name_1</td>\n",
       "      <td>62</td>\n",
       "      <td>Country_1</td>\n",
       "      <td>email_1@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Name_2</td>\n",
       "      <td>48</td>\n",
       "      <td>Country_2</td>\n",
       "      <td>email_2@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Name_3</td>\n",
       "      <td>61</td>\n",
       "      <td>Country_3</td>\n",
       "      <td>email_3@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Name_4</td>\n",
       "      <td>32</td>\n",
       "      <td>Country_4</td>\n",
       "      <td>email_4@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Name_5</td>\n",
       "      <td>69</td>\n",
       "      <td>Country_5</td>\n",
       "      <td>email_5@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   ID    Name  Age    Country                Email\n",
       "0   1  Name_1   62  Country_1  email_1@example.com\n",
       "1   2  Name_2   48  Country_2  email_2@example.com\n",
       "2   3  Name_3   61  Country_3  email_3@example.com\n",
       "3   4  Name_4   32  Country_4  email_4@example.com\n",
       "4   5  Name_5   69  Country_5  email_5@example.com"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset from URL\n",
    "url = \"https://sample-files.com/downloads/data/csv/basic-data.csv\"\n",
    "df = pd.read_csv(url, skiprows=1)\n",
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "# Preview the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f7c2e7",
   "metadata": {},
   "source": [
    "## 📊 Basic Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aca7309e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 5)\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Country</th>\n",
       "      <th>Email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Name_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Country_1</td>\n",
       "      <td>email_1@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.530000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.011492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.190012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID    Name         Age    Country                Email\n",
       "count   100.000000     100  100.000000        100                  100\n",
       "unique         NaN     100         NaN         10                  100\n",
       "top            NaN  Name_1         NaN  Country_1  email_1@example.com\n",
       "freq           NaN       1         NaN         10                    1\n",
       "mean     50.500000     NaN   44.530000        NaN                  NaN\n",
       "std      29.011492     NaN   15.190012        NaN                  NaN\n",
       "min       1.000000     NaN   18.000000        NaN                  NaN\n",
       "25%      25.750000     NaN   32.000000        NaN                  NaN\n",
       "50%      50.500000     NaN   43.500000        NaN                  NaN\n",
       "75%      75.250000     NaN   59.250000        NaN                  NaN\n",
       "max     100.000000     NaN   69.000000        NaN                  NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check shape and summary stats\n",
    "print(df.shape)\n",
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e775e42f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ID      Name  Age    Country                  Email\n",
      "0     1    Name_1   62  Country_1    email_1@example.com\n",
      "1     2    Name_2   48  Country_2    email_2@example.com\n",
      "2     3    Name_3   61  Country_3    email_3@example.com\n",
      "3     4    Name_4   32  Country_4    email_4@example.com\n",
      "4     5    Name_5   69  Country_5    email_5@example.com\n",
      "..  ...       ...  ...        ...                    ...\n",
      "95   96   Name_96   60  Country_6   email_96@example.com\n",
      "96   97   Name_97   26  Country_7   email_97@example.com\n",
      "97   98   Name_98   52  Country_8   email_98@example.com\n",
      "98   99   Name_99   24  Country_9   email_99@example.com\n",
      "99  100  Name_100   55  Country_0  email_100@example.com\n",
      "\n",
      "[100 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc10be72",
   "metadata": {},
   "source": [
    "## 📈 Grouping and Pivoting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f87ea8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>RecordCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Country_1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Country_2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Country_3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Country_4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country_5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "     Country  RecordCount\n",
       "0  Country_1           10\n",
       "1  Country_2           10\n",
       "2  Country_3           10\n",
       "3  Country_4           10\n",
       "4  Country_5           10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example: Group by 'Country' and count number of records\n",
    "country_counts = df[\"Country\"].value_counts().reset_index()\n",
    "country_counts.columns = [\"Country\", \"RecordCount\"]\n",
    "country_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f156d6de",
   "metadata": {},
   "source": [
    "## 📊 Visualizing Record Counts by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d644fc06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pf/tt0qdz214wn0q90g989_0r0h0000gn/T/ipykernel_79108/1394705902.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=country_counts, x='Country', y='RecordCount', palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=country_counts, x=\"Country\", y=\"RecordCount\", palette=\"viridis\")\n",
    "plt.title(\"Record Count by Country\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887b2525",
   "metadata": {},
   "source": [
    "## 📈 Pivot Table Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1e26e06b",
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Country_0</th>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_1</th>\n",
       "      <td>43.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_2</th>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_3</th>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_4</th>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_5</th>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_6</th>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_7</th>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_8</th>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country_9</th>\n",
       "      <td>47.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Age\n",
       "Country        \n",
       "Country_0  43.7\n",
       "Country_1  43.8\n",
       "Country_2  51.0\n",
       "Country_3  38.2\n",
       "Country_4  44.3\n",
       "Country_5  48.3\n",
       "Country_6  47.3\n",
       "Country_7  41.1\n",
       "Country_8  40.5\n",
       "Country_9  47.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If 'Age' exists, average age by country (example only if dataset has relevant column)\n",
    "if \"Age\" in df.columns:\n",
    "    age_pivot = df.pivot_table(index=\"Country\", values=\"Age\", aggfunc=\"mean\")\n",
    "    display(age_pivot)\n",
    "else:\n",
    "    print(\"No 'Age' column in dataset to pivot on.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9793c4e",
   "metadata": {},
   "source": [
    "\n",
    "## 🎉 Summary\n",
    "\n",
    "In this notebook, we:\n",
    "- Loaded a sample CSV dataset\n",
    "- Explored its structure\n",
    "- Grouped and counted records by country\n",
    "- Visualized results with a bar chart\n",
    "- Created a pivot table (if applicable)\n",
    "\n",
    "🖥️ Try modifying this notebook for your own datasets or audit use cases!\n",
    "    "
   ]
  }
 ],
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