cognizant eda (1).ipynb

{
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   },
   "source": [
    "# Task 1 - Exploratory Data Analysis\n",
    "\n",
    "---\n",
    "\n",
    "## Section 1 - Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "colab": {
     "base_uri": "https://localhost:8080/"
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    "outputId": "dba929c8-ffeb-4ded-b023-6d7a05f6a080"
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "# set working directory\n",
    "import os\n",
    "os.getcwd()\n",
    "os.chdir('C:\\\\Users\\\\Dubey\\\\Downloads')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M5nmOA2Rtd2E"
   },
   "source": [
    "---\n",
    "\n",
    "## Section 2 - Data loading\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    },
    "id": "oDB-Ylm3q_wk",
    "outputId": "b2ae17b8-120e-437c-c3d4-990b6334e0e6"
   },
   "outputs": [
    {
     "data": {
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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>transaction_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>product_id</th>\n",
       "      <th>category</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total</th>\n",
       "      <th>payment_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a1c82654-c52c-45b3-8ce8-4c2a1efe63ed</td>\n",
       "      <td>2022-03-02 09:51:38</td>\n",
       "      <td>3bc6c1ea-0198-46de-9ffd-514ae3338713</td>\n",
       "      <td>fruit</td>\n",
       "      <td>gold</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2</td>\n",
       "      <td>7.98</td>\n",
       "      <td>e-wallet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>931ad550-09e8-4da6-beaa-8c9d17be9c60</td>\n",
       "      <td>2022-03-06 10:33:59</td>\n",
       "      <td>ad81b46c-bf38-41cf-9b54-5fe7f5eba93e</td>\n",
       "      <td>fruit</td>\n",
       "      <td>standard</td>\n",
       "      <td>3.99</td>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "      <td>e-wallet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ae133534-6f61-4cd6-b6b8-d1c1d8d90aea</td>\n",
       "      <td>2022-03-04 17:20:21</td>\n",
       "      <td>7c55cbd4-f306-4c04-a030-628cbe7867c1</td>\n",
       "      <td>fruit</td>\n",
       "      <td>premium</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2</td>\n",
       "      <td>0.38</td>\n",
       "      <td>e-wallet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157cebd9-aaf0-475d-8a11-7c8e0f5b76e4</td>\n",
       "      <td>2022-03-02 17:23:58</td>\n",
       "      <td>80da8348-1707-403f-8be7-9e6deeccc883</td>\n",
       "      <td>fruit</td>\n",
       "      <td>gold</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4</td>\n",
       "      <td>0.76</td>\n",
       "      <td>e-wallet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a81a6cd3-5e0c-44a2-826c-aea43e46c514</td>\n",
       "      <td>2022-03-05 14:32:43</td>\n",
       "      <td>7f5e86e6-f06f-45f6-bf44-27b095c9ad1d</td>\n",
       "      <td>fruit</td>\n",
       "      <td>basic</td>\n",
       "      <td>4.49</td>\n",
       "      <td>2</td>\n",
       "      <td>8.98</td>\n",
       "      <td>debit card</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         transaction_id            timestamp  \\\n",
       "0  a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 09:51:38   \n",
       "1  931ad550-09e8-4da6-beaa-8c9d17be9c60  2022-03-06 10:33:59   \n",
       "2  ae133534-6f61-4cd6-b6b8-d1c1d8d90aea  2022-03-04 17:20:21   \n",
       "3  157cebd9-aaf0-475d-8a11-7c8e0f5b76e4  2022-03-02 17:23:58   \n",
       "4  a81a6cd3-5e0c-44a2-826c-aea43e46c514  2022-03-05 14:32:43   \n",
       "\n",
       "                             product_id category customer_type  unit_price  \\\n",
       "0  3bc6c1ea-0198-46de-9ffd-514ae3338713    fruit          gold        3.99   \n",
       "1  ad81b46c-bf38-41cf-9b54-5fe7f5eba93e    fruit      standard        3.99   \n",
       "2  7c55cbd4-f306-4c04-a030-628cbe7867c1    fruit       premium        0.19   \n",
       "3  80da8348-1707-403f-8be7-9e6deeccc883    fruit          gold        0.19   \n",
       "4  7f5e86e6-f06f-45f6-bf44-27b095c9ad1d    fruit         basic        4.49   \n",
       "\n",
       "   quantity  total payment_type  \n",
       "0         2   7.98     e-wallet  \n",
       "1         1   3.99     e-wallet  \n",
       "2         2   0.38     e-wallet  \n",
       "3         4   0.76     e-wallet  \n",
       "4         2   8.98   debit card  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sample_sales_data.csv')\n",
    "df.drop(columns=[\"Unnamed: 0\"], inplace=True, errors='ignore')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZI-Q3zvsGWpl"
   },
   "source": [
    "Using the `.head()` method allows us to see the top 5 (5 by default) rows within the dataframe. We can use `.tail()` to see the bottom 5. If you want to see more than 5 rows, simply enter a number into the parentheses, e.g. `head(10)` or `tail(10)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qaXaaIr5Hemv"
   },
   "source": [
    "---\n",
    "\n",
    "## Section 3 - Descriptive statistics\n",
    "\n",
    "In this section, you should try to gain a description of the data, that is: what columns are present, how many null values exist and what data types exists within each column.\n",
    "\n",
    "To get you started an explanation of what the column names mean are provided below:\n",
    "\n",
    "- transaction_id = this is a unique ID that is assigned to each transaction\n",
    "- timestamp = this is the datetime at which the transaction was made\n",
    "- product_id = this is an ID that is assigned to the product that was sold. Each product has a unique ID\n",
    "- category = this is the category that the product is contained within\n",
    "- customer_type = this is the type of customer that made the transaction\n",
    "- unit_price = the price that 1 unit of this item sells for\n",
    "- quantity = the number of units sold for this product within this transaction\n",
    "- total = the total amount payable by the customer\n",
    "- payment_type = the payment method used by the customer\n",
    "\n",
    "After this, you should try to compute some descriptive statistics of the numerical columns within the dataset, such as:\n",
    "\n",
    "- mean\n",
    "- median\n",
    "- count\n",
    "- etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "sbf25LDJ7FJG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7829 entries, 0 to 7828\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   transaction_id  7829 non-null   object \n",
      " 1   timestamp       7829 non-null   object \n",
      " 2   product_id      7829 non-null   object \n",
      " 3   category        7829 non-null   object \n",
      " 4   customer_type   7829 non-null   object \n",
      " 5   unit_price      7829 non-null   float64\n",
      " 6   quantity        7829 non-null   int64  \n",
      " 7   total           7829 non-null   float64\n",
      " 8   payment_type    7829 non-null   object \n",
      "dtypes: float64(2), int64(1), object(6)\n",
      "memory usage: 550.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7829, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit_price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7829.000000</td>\n",
       "      <td>7829.000000</td>\n",
       "      <td>7829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.819480</td>\n",
       "      <td>2.501597</td>\n",
       "      <td>19.709905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.388088</td>\n",
       "      <td>1.122722</td>\n",
       "      <td>17.446680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.190000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.190000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.190000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>28.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23.990000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>95.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        unit_price     quantity        total\n",
       "count  7829.000000  7829.000000  7829.000000\n",
       "mean      7.819480     2.501597    19.709905\n",
       "std       5.388088     1.122722    17.446680\n",
       "min       0.190000     1.000000     0.190000\n",
       "25%       3.990000     1.000000     6.570000\n",
       "50%       7.190000     3.000000    14.970000\n",
       "75%      11.190000     4.000000    28.470000\n",
       "max      23.990000     4.000000    95.960000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id    0\n",
       "timestamp         0\n",
       "product_id        0\n",
       "category          0\n",
       "customer_type     0\n",
       "unit_price        0\n",
       "quantity          0\n",
       "total             0\n",
       "payment_type      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N-krPtHdHNrh"
   },
   "source": [
    "---\n",
    "\n",
    "## Section 4 - Visualisation\n",
    "\n",
    "Now that you've computed some descriptive statistics of the dataset, let's create some visualisations. You may use any package that you wish for visualisation, however, some helper functions have been provided that make use of the `seaborn` package. If you wish to use these helper functions, ensure to run the below cells that install and import `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fT9DrY9RHMrd",
    "outputId": "b792ccca-6123-4bc0-e4f5-8d5888a36450"
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in [col for col in df.columns if df[col].dtype != 'object']:\n",
    "    sns.histplot(x=col, data=df, bins=25)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kSZLUPIORJEmSpOYZjCRJkiQ1z2AkSZIkqXkGI0mSJEnNMxhJkiRJap7BSJIkSVLzDEaSJEmSmmcwkiRJktQ8g5EkSZKk5hmMJEmSJDXPYCRJkiSpeQYjSZIkSc0zGEmSJElqnsFIkiRJUvMMRpIkSZKaZzCSJEmS1DyDkSRJkqTmGYwkSZIkNc9gJEmSJKl5BiNJkiRJzTMYSZIkSWqewUiSJElS8wxGkiRJkppnMJIkSZLUvLEHoyQPTnLWhJ/rkuw3qc1Tklw7oc27x12nJEmSpHbMGfcLVtXPga0AkqwJLAaOnKLpSVW18xhLkyRJktSooYfSPR24sKouHrgOSZIkSQ0bOhjtBhwxzb7HJTk7ybFJHjbOoiRJkiS1ZbBglGRt4DnAl6fYfQZwv6p6JPDvwNdv4zgLkixKsmjJkiUjqVWSJEnS7DZkj9GOwBlVdeXkHVV1XVXd0N8/BlgryYZTHaSqFlbV/KqaP3fu3NFWLEmSJGlWGjIY7c40w+iS3CdJ+vvb0NX56zHWJkmSJKkhY5+VDiDJesB2wCsnbHsVQFUdDLwAeHWSpcCNwG5VVUPUKkmSJGn2GyQYVdXvgHtP2nbwhPsHAQeNuy5JkiRJbRp6VjpJkiRJGpzBSJIkSVLzDEaSJEmSmmcwkiRJktQ8g5EkSZKk5hmMJEmSJDXPYCRJkiSpeQYjSZIkSc0zGEmSJElqnsFIkiRJUvMMRpIkSZKaZzCSJEmS1DyDkSRJkqTmGYwkSZIkNc9gJEmSJKl5BiNJkiRJzTMYSZIkSWqewUiSJElS8wxGkiRJkppnMJIkSZLUPIORJEmSpOYZjCRJkiQ1z2AkSZIkqXkGI0mSJEnNMxhJkiRJap7BSJIkSVLzDEaSJEmSmmcwkiRJktQ8g5EkSZKk5g0WjJL8Ksm5Sc5KsmiK/UnyiSQXJDknydZD1ClJkiRp9psz8Os/taqunmbfjsAW/c9jgE/1t5IkSZI0o1bloXS7AIdV50fAPZNsPHRRkiRJkmafIYNRAd9OcnqSBVPs3wS4ZMLjS/ttfybJgiSLkixasmTJiEqVJEmSNJsNGYyeUFVb0w2Z2yfJk+7MQapqYVXNr6r5c+fOndkKJUmSJDVhsGBUVYv726uAI4FtJjVZDGw24fGm/TZJkiRJmlGDBKMk6yVZf/l9YHvgvEnNjgL26GeneyxwbVVdPuZSJUmSJDVgqFnpNgKOTLK8hi9U1beSvAqgqg4GjgF2Ai4Afg+8fKBaJUmSJM1ygwSjqroIeOQU2w+ecL+AfcZZlyRJkqQ2rcrTdUuSJEnSWBiMJEmSJDXPYCRJkiSpeQYjSZIkSc0zGEmSJElqnsFIkiRJUvMMRpIkSZKaZzCSJEmS1DyDkSRJkqTmGYwkSZIkNc9gJEmSJKl5BiNJkiRJzTMYSZIkSWqewUiSJElS8wxGkiRJkppnMJIkSZLUPIORJEmSpOYZjCRJkiQ1z2AkSZIkqXkGI0mSJEnNMxhJkiRJap7BSJIkSVLzDEaSJEmSmmcwkiRJktQ8g5EkSZKk5hmMJEmSJDXPYCRJkiSpeQYjSZIkSc0bezBKslmS7yX5SZIfJ3ndFG2ekuTaJGf1P+8ed52SJEmS2jFngNdcCryxqs5Isj5wepLjq+onk9qdVFU7D1CfJEmSpMaMvceoqi6vqjP6+9cDPwU2GXcdkiRJkrTcoNcYJZkHPAo4ZYrdj0tydpJjkzxsvJVJkiRJaskQQ+kASHI34KvAflV13aTdZwD3q6obkuwEfB3YYprjLAAWAGy++eajK1iSJEnSrDVIj1GStehC0eFV9bXJ+6vquqq6ob9/DLBWkg2nOlZVLayq+VU1f+7cuSOtW5IkSdLsNMSsdAE+A/y0qj42TZv79O1Isg1dnb8eX5WSJEmSWjLEULptgZcC5yY5q9/2DmBzgKo6GHgB8OokS4Ebgd2qqgaoVZIkSVIDxh6MquoHQG6nzUHAQeOpSJIkSVLrBp2VTpIkSZJWBQYjSZIkSc0zGEmSJElqnsFIkiRJUvMMRpIkSZKaZzCSJEmS1DyDkSRJkqTmGYwkSZIkNc9gJEmSJKl5BiNJkiRJzTMYSZIkSWqewUiSJElS8wxGkiRJkppnMJIkSZLUPIORJEmSpOYZjCRJkiQ1z2AkSZIkqXkGI0mSJEnNMxhJkiRJap7BSJIkSVLzDEaSJEmSmmcwkiRJktQ8g5EkSZKk5hmMJEmSJDXPYCRJkiSpeQYjSZIkSc0zGEmSJElqnsFIkiRJUvMGCUZJdkjy8yQXJHnbFPvvkuRL/f5TkswboExJkiRJjRh7MEqyJvBJYEfgocDuSR46qdnewG+q6oHAx4EPj7dKSZIkSS0ZosdoG+CCqrqoqm4GvgjsMqnNLsDn+vtfAZ6eJGOsUZIkSVJDhghGmwCXTHh8ab9tyjZVtRS4Frj3WKqTJEmS1Jw5QxewspIsABb0D29I8vMh61lNbQhcPXQR45Z/2XPoElrV5PuN/e30Hkib7zcgr/U9N4Bm3284sGcoTb7n9v3YSh/iflNtHCIYLQY2m/B4037bVG0uTTIHuAfw66kOVlULgYUjqLMZSRZV1fyh61AbfL9pnHy/aZx8v2ncfM/NrCGG0p0GbJHk/knWBnYDjprU5ihg+en8FwDfraoaY42SJEmSGjL2HqOqWprkNcBxwJrAIVX14yQHAIuq6ijgM8Dnk1wAXEMXniRJkiRpJAa5xqiqjgGOmbTt3RPu/wF44bjraphDETVOvt80Tr7fNE6+3zRuvudmUByhJkmSJKl1Q1xjJEmSJEmrFIORJEmSpOYZjPQXkvi+0OCSPDSJU5BKkjRCiYtQLecXYAGQ5D5J3gtQVcv8T6IhJbkbsAvwmiRbD12PJN1RE/+OJll/yFqk6SRZY/mSOEmel+TBQ9c0JIORln94/xXwN0k+BFBVZTjSEJKkqm4APg9cBLwyyUMHLkuz3PLPuyT3THL3oevR6q3/HFv+ZfN1wH5J7jFwWdJfqKplAEmeBLwGuGrYioZlMBLVOQf4LPDwJO9Zvt1wpHGbsJjzs4GtgMcDr0/y6MGK0qzXf949j24piSOS7Hl7z5GmMyEU7Qm8GPhcVV07bFXS1JJsBxwPfL6qfpNknaFrGorTdQuAJM8B9gMuAzYDTquqN/X7Ur5RNGKTzrBuC/wn8DhgG2Br4P7Av1fVj4erUrPN8vddkrWB/wAO7Xd9DXhrVX16sOK02knycGDNqjq7f3wQ8N9V9ZUkd6mqm5KsVVW3DFupWjbV97okXwW2rKot+sdrVtWtgxQ4IHuMGpVk/SQb9PfvBrweeFtVvQR4M3DvCdccGYo0UpNC0f3ohnZeWFXXVtXxwAnAg4B/SrLlgKVqlulD0dOBtwB/AE6tqu8DOwMfTPKPgxao1UaSuwKPAi5LsmG/uYD79veXh6EnJtl03PVJ8Bd/b3dJsnuSR1TV84EfJ/khQFXdmmTNQYsdgMGoQf1FoB8GnpvkXsBNwDrAWn2T84AzgRcl+fAwVaolEz6kdwc+CvwQWDvJXv3+04GfAotpfPyzZsaEa4q2BD4OzAMeBrwsyYZVdTLwPOAjSTZztk7dnqq6EfgScE/gE0keAXwOeG8/KmO9/jPuIKC5M/FaNUz4e/smupFCWwALkzylqnYFFif5Wd+2ufepH/QNqqrrgVOBJwI79l36n6U7G//wqvo98Au6s/RfHK5StSTJPwDPBP65qq4EPg08IcnBSfYGtgX+taquGLJOzQ59T9ET6YbPvbaqXkE3fPNBwPP7cHQSsGlVXbL8AmVpsknX4q4NXAecC+wDXAE8B3gn8Ml+24uq6vJx1yktl2QTYOuqeipwM3ANcDJAVb0QOC3JvOEqHM6coQvQePXTMi6rqkOT3Azs3H+on0DXY/TNJJ8F/gHYs6rOHLJezV5TjHHeGNgDOKx/fAJwDvBa4G+Al1bVJeOtUrPcr4BHAi+huw7k80mWAU8C5iRZCFwPXmup6U04A/8qui+bC/rrNXYF3gN8gO49tQ6wdlUtGahUNWr5d78Jm24GKsnhwD2A51bVzUleDHy7ql46SKGrACdfaMiEi4w3Ay7rx49uD+wJHA0cCTwWuDdwVVWdOGC5msUmjXG+x/LZmpLsTxeE5lfVLye0n/yhLt1p/dpYT6yqf+vPip5GN7HHAf3+PYBFVfWTAcvUaiTJPnR/S19aVT/vt90DWEA3ecwn+uGZ0lhN+nv7AOD6qrqqH0r3QmBBVZ2d5OXAG4AdqmrxgCUPyh6jhvShaAdgf+D8JHPoxpcuBV4O3AX4Sj/UThqJSR/Srwce2c8I9s6qem+SpcCJ/XjnC+FP6yxIM2R9YNckt1bVQUn+lu49t05VvaOqDru9A0jLJVmPbmmB3YFbk7wS2At4K10P+B/oeielsUryN3RD1P+1D+8vAe7Sjwz6PfB14OAkZwJPphvm2WwoAnuMmpLkr4FvAXsDV9J18+8C7ARsR3eh8Zuq6rKhalQ7krwaeBHwLLrJPi4B/qmqTk7yAbr348OBWx3CpJnQny29oqp+119f9BbghL7n6AHAKXRTxF9oGNd0ppnq+AC6MHQKcBLd0PSdgB2ApS1exK5h9ZdJPJduTcCr6ML7LnTX625DNwHIV+hmTbwLcEFVXTxIsasQe4wakWRjYD26LwEn9UOTPpJkc2Dnqjo8ycmGIo1KulW1H1RVn+57K+9Hd03RK+km+/gJ8B9J/rGq3pHkwKpaOmDJWs31w4b3qKr398sTvBH4TZL3L/8cBD6TZIOqek+STavqD8NWrVXZpB7vFwAbAKdU1buTHA38oroFMp8MPA1Yq6puGrBkNWjCpRPHAsvoToSv23++fSfJDcC/ACdW1Q8GLHWV46x0s9ik6Wj3B3YEdkny8glnQ6/hT2ssGIo0SrcCH0jy0j7wvBO4K10wf3ZVvbV/vHs/pOnXQxarWeFGugllNq6q39BNpbw+8MYk6/frFX0deJqhSCtiQijaD3gd3d/PT/Q94MtD0TuAf6VbIPiGoWpVm/oT3wV/nEL+v4AvALckeUO//RTgfOAh/XMyzeGaY4/RLNafLXg28Ca6btL1gC8DB/Q9SD+jm0Z0v+XtBypVs9jyD9yq+p9+uMmHk9y1qhYm+U3fZFe6EzVnAh/xC6pmQlVd3b/Hjk1ySVXtneRWuiGcH07yTbrhmm+tqksHLVartIkTwCR5NN2QyyfTXax+d2BLYM0knwF+Cbykqn48VL1q14T36auATYHLq+qT/UiN3ZJ8ATiebrKt9/fP8ftfz2uMZrEkGwFfBV5RVT/rL7zbiO4L6F/TfXj/qKr+a8Ay1Yj+DOt84Ld017m9oao+lW6Nol2A+wO7+WVCK2vCMJI1+9k3N6ZbF+uiqtq3vyD5lXRTdX/Mz0CtqHTrv9wI3Iuut+h9wNOBd9FdF/l/qurg4SpUqyYN83w8cAjd+/K9wHeq6rVJnk43hO4XdNf0/mKwgldRBqNZrB9TfzTw5v5s/VrAp+im4z68qr7St3N9Do1M32O0CXAU3VnUn/SzgB0DvKWqPpvkbnTjn68aslbNHkm2A54ALO57JzcCDgXOr6rX9m026Ic++RmoKfVfMDevqi8m2ZfupM6ZwK+BG4Cbq+oDSfaiOwP/Lj/HNKT+PXtPuu/4RydZFzgLOLaqXtd/Np7t+3RqDqWbxfo/+F+lGz9/bVWdl+TLdOvEPDvJ0VV1o18INNMmftHsz9xfSbdY65wkc6rqtCTvobvw/Yaq+jLdlwxppfU9Qv8JfAj4p/76oXcneRnw/5IcWlUvA64Fh5HoNm0AfDDJw4AHAM8H5tH1fu8MbJvkwcDjgWf7ZVPjNqmnaG/gn4CrgQuTXFRVP02yFXBxkpuq6i0DlrvKMxjNfl+iGzLy0SRnAC8AXkrXvfog4OwBa9MsNeFD+iHAr6tqSZIldLOCvZbuC+llwOHAGYMVqlljwvC5BwHr0g0T+XySY+gmYKAPR38HbAauj6Xb159xvxn4ON1Z9guTXEr3+XV34EDgHsD7quqCAUtVoyb8vX0J3fe6rYEH081Et2uSZVX1834W4vtOeyABDqVrQpK7053NeiTd8KV16c6mbldVVw5Zm2aXSRcov45uYo+TgF9W1f5JDqH7EnET3ZoKO1fVRQOVq1liQih6BvB/6cbP3wXYu+8p3wQ4EfhyVb1tyFq1ekqyC91QzFdV1Zf6bUcBC6vqm0PWpjZNcS3lKXTLYGzcb98O2J5ugeHDqur8QQteTRiMGpPkqcAHgVdWlb1FGol0i2c+H/gksA7dQpqLq+pt/fTx9wfOq6oLByxTs0iSbejOkB5FtwzB39ONs1/YX9e2KXD/qjppsCK1WkuyM/AJ4DC6azbeB+zq55jGbdLwuQ2r6ur+/veA31XVzv3jnehOjH/cJTBWjMGoMf3sTGuXqxtrBNItmHl/uuFxR1XVS/tJP+5HN+55WVW9fMgaNbskWRMo4AS6IXLbVdWv+imVn0U3Xe2/LZ/t0IkWtDL6pQW+CnwTeL093hpSuvWzngb8HDi5H/p5MnBFVT23b7NuVf1+yDpXJy7w2piqutxQpJk0cWG4qlrWnz3dE3h8kh2q6hbgQuADwB/6cC6tlAnvu3X74Zs7AD+lC+BU1enAscCVdMGJfruhSHdaVX2d7ovo6wxFGlKS3el6xt8EPAPYDqCqHgdskeSLfdMbh6lw9WSPkaQ7bVJ3/vPpzth/r6rO7s+sfgzYp6qO7b/IzumDkrTSkjwT+EdgEXAB3Zn87wLnVtWr+zZ3r6rrhqtSkmZWPzrjH+mGdN6fblKtZ1XVLUnWr6rrk8yrql8NWOZqyR4jSXfahFC0L90K8HcBvpBkQX9m9XXAF5NsVx1DkWZEkifQBe8P0M3EtFtV3Ux31vRv+4k+MBRJWt1NHJkBf5xR8yq6mV33rqrt+1C0L93MrxiK7hyn65a0UpI8gu7L6FOBBcCtwFP7mXI+lWQ34JdD1qjZYcIsTOsDc+m+AKwBPIRusg+AtekWdt1qkCIlaYZNGpmxBfAF4IfAt4Crkvw18DhgL+AlQ9U5GziUTtIdkuSvAKrqqiTPqKoTktyH7ovom6rqGUneALyebi2ZQ4erVrNNku3pZlm6gG4B16uBp1XVNf3QuicD766qpQOWKUkrbdJw9ZfQzfD6Q7oFhvejm/X1MXTXvV1P9zf33GGqnR3sMZJ0R20BHJDkVOCZ/TC5K5Lcl+5Cd4BLgB/RXfwuzYgkWwPPAb5UVScleSxwN6D6KeIPBN5mKJK0upsUiu5DNxrjeVV1QZL9gLcDH66q9yc5EKCq/jBYwbOEPUaSVsikxVv/D/AK4NlVdVy/bTPgSLpwNA94flX9bKByNUtMGD4X4HTgFmB3uuGZW9ENodsO+C3wyao6yim5Ja3OJoWi/YB96RZqPaWq9uq370s3bO7NVXXiULXONgYjSbdr0of0q4GN6LrwdwJe3k+NTL+I5uOAM6vqgqHq1ezST7SwPnAf4B3AQVX1bxP23wO4tapuMBRJmi36z74FwHvp1mRbAFxQVfv3+18JHFtV/ztclbOLwUjSCus/hPei685fnOSddGfsdwW2B+5bVQcMWKJmiQk9RY8HPkO3aPClwBOBBwLvq6p/H7JGSRqVJA8Avg78BNiDbj22RwP7AJdX1ZuHq2728hojSSskyV2BHYF3A7f0IWkOcC/gE3Rn8181XIWaTfpQtA3wfrpeyR8leSDwv3STL7w9yYbLz5xK0mxSVRcm2Z9u9s3tgW8CpwBrAXv0n39XD1njbGQwkrRCqurGJMfQzQR2CfAz4CLgMOD/AldX1TUDlqjZ5x7Ak+hmXPoRcDFdr9GFwLuATYYrTZJGo1/Atarqa/31lW+iW5rgKOAHwKlOtDAaBiNJd8RhwJnAhf30yC+mG9r0waq6cdjSNNtU1fFJngccmOSXVXVEkt8COwMHVtUvvaZI0upsms+wNapqaT8T5/eBpcD+wNKqOppuIgaNgMFI0grrz1CdlmSNJHvTraOwu6FIo1JV30iyDDi8X9xwGfCeqrq2328okrRamjSx0Tzgpqq6vA9F2wKfBl7dfw6uCZw3YLlNWGPoAiStltah+4L6oqryg1ojVVX/RTct7QOB05ZPyd0PMZGk1c6kUPQG4Gjg4CTv75vsTDcV938DVNXXquriQYptiD1Gku6wqvp9kkM9W69x6cPQH4BDklxYVV8buiZJurMmhKLHAFvTBaG1gc8nubGq3t7vn0O3HIF/b8fA6bolSauNJNvRXeN20dC1SNKd1fd4bwl8HlgE/ENV3Zpkc+DLwBlV9eoha2yRQ+kkSauNqjreUCRpdTRx+G91zgb+BdgCeGyStfrFWncDHpLkrxwyPF72GEmSJElj0s/ougVwFd1yF8+iWzz9AOBHVXVLkjlVtXTAMptkj5EkSZI0Bkn2AfYFfgM8GDiu//kc8FFgPoChaBhOviBJkiSNwPLZ5ybMQvcI4LVVdWq//x3AR6rqFUnuASwest7W2WMkSZIkzbBJi7dukWQtYFPgKROafZP++3hVfbK/xkgDMRhJkiRJM2jSOkWvAY4BPgCcDbw2yV5900cA85Lc04kWhudQOkmSJGkGTQhFz6GblvuZwPbA3YETgH9O8ijgqcDfVdVvBypVEzgrnSRJkjTDkmwCnAycUFV7JbkL8HxgM2ADYCFwbVX9esAyNYFD6SRJkqQZVlWLgf2AHZLsVlU3AV8ElgDLgGsMRasWh9JJkiRJI1BVX0tyE/DBJFTVF5McCqxXVdcPXJ4mMRhJkiRJI1JVRydZBixMsrSqvgIYilZBXmMkSZIkjViS7YALq+qioWvR1AxGkiRJkprn5AuSJEmSmmcwkiRJktQ8g5EkSZKk5hmMJEmSJDXPYCRJWqUkecfQNQAkmZfk74euQ5I0HgYjSdKqZqzBKMl0a/rNAwxGktQIg5EkaUYl2SPJOUnOTvL5JIcmecGE/Tf0txsnOTHJWUnOS/LEJB8C7tpvO7xv94Z+/3lJ9uu3zUvys/7Yv0hyeJJnJPmfJOcn2aZvt16SQ5KcmuTMJLv021+W5Kgk3wW+M82v8iHgiX0tr+9r3WrC7/GDJI9M8p7+9zy5f+1/mNDmzUlO6/893juT/86SpJk13VkySZLusCQPA94FPL6qrk5yL+Bj0zT/e+C4qnp/kjWBdavqpCSvqaqt+uM9Gng58BggwClJvg/8Bngg8EJgL+C0/nhPAJ5D1+u0K/BO4LtVtVeSewKnJjmhf/2tgS2r6ppp6nsb8Kaq2rmv5RrgZcB+SR4ErFNVZyd5LrAl8FhgPeDMJEcDDwe2ALbpaz8qyZOq6sQV/OeUJI2RPUaSpJn0NODLVXU1wG2EDujCzMuTvAd4RFVdP0WbJwBHVtXvquoG4GvAE/t9v6yqc6tqGfBj4DvVrVp+Lt0wOIDtgbclOQv4b2AdYPN+3/G3U99kXwZ2TrIWXRg7dMK+b1TVjf3v/T26MLR9/3MmcAbwELqgJElaBdljJEkataX0J+KSrAGsDVBVJyZ5EvAs4NAkH6uqw+7AcW+acH/ZhMfL+NPftwDPr6qfT3xikscAv7sjv0RV/T7J8cAuwIuAR0/cPbl5/9ofrKr/uCOvI0kahj1GkqSZ9F3ghUnuDdAPpfsVfwoRzwHW6vfdD7iyqv4T+DTd0DaAW/peGYCTgF2TrJtkPeC5/bYVdRywb5L0r/moO/Dc64H1J237NPAJ4LSq+s2E7bskWaf/vZ9C1xt2HLBXkrv1r71Jkr+6A68vSRoje4wkSTOmqn6c5P3A95PcSjeM7K3AN5KcDXyLP/XUPAV4c5JbgBuAPfrtC4FzkpxRVS9Ocihwar/v01V1ZpJ5K1jS+4B/7Y+3BvBLYOcVfO45wK193YdW1cer6vQk1wGfnaLt94ANgfdV1WXAZUn+Bji5z2U3AC8BrlrB15ckjVG64diSJOn2JLkv3bVKD+mvbaK/RuqGqvqXAUuTJK0kh9JJkrQCkuwBnAK8c3kokiTNHvYYSZKaluQRwOcnbb6pqh4zRD2SpGEYjCRJkiQ1z6F0kiRJkppnMJIkSZLUPIORJEmSpOYZjCRJkiQ1z2AkSZIkqXkGI0mSJEnN+/9FhbLbSyPk0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in [col for col in df.columns[3:] if df[col].dtype == 'object']:\n",
    "    plt.figure(figsize=(14,6))\n",
    "    plt.xticks(rotation=45)\n",
    "    sns.barplot(x=col, y='total', data=df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column transaction_id:\n",
      "a1c82654-c52c-45b3-8ce8-4c2a1efe63ed    1\n",
      "6532e258-95fd-4eb5-8c67-2bfb879a8fec    1\n",
      "6fce2af3-47a0-4755-99c9-0cefb5ab6f41    1\n",
      "6476e388-3990-471f-b415-3ee59ae18832    1\n",
      "10afe89b-c45b-49a2-b0be-dec89a4c3f80    1\n",
      "                                       ..\n",
      "a9abe5ac-99d5-4d8b-bbbd-c2a207642849    1\n",
      "6b0b23e8-412b-4665-8cc4-3e37f0d9e195    1\n",
      "711a4162-1985-4f5a-94ca-137cfacaeadf    1\n",
      "7d1e9010-dbaf-4770-a467-f31477910f7a    1\n",
      "afd70b4f-ee21-402d-8d8f-0d9e13c2bea6    1\n",
      "Name: transaction_id, Length: 7829, dtype: int64\n",
      "\n",
      "Column timestamp:\n",
      "2022-03-02 19:32:20    2\n",
      "2022-03-05 10:11:33    2\n",
      "2022-03-01 18:12:17    2\n",
      "2022-03-03 13:28:14    2\n",
      "2022-03-07 17:13:29    2\n",
      "                      ..\n",
      "2022-03-01 10:00:58    1\n",
      "2022-03-02 11:05:58    1\n",
      "2022-03-04 17:48:49    1\n",
      "2022-03-04 14:53:27    1\n",
      "2022-03-06 13:50:36    1\n",
      "Name: timestamp, Length: 7738, dtype: int64\n",
      "\n",
      "Column product_id:\n",
      "ecac012c-1dec-41d4-9ebd-56fb7166f6d9    114\n",
      "80da8348-1707-403f-8be7-9e6deeccc883    109\n",
      "0ddc2379-adba-4fb0-aa97-19fcafc738a1    108\n",
      "7c55cbd4-f306-4c04-a030-628cbe7867c1    104\n",
      "3bc6c1ea-0198-46de-9ffd-514ae3338713    101\n",
      "                                       ... \n",
      "49f7d4a9-713a-4824-b378-aebb33ff8b2f      5\n",
      "a8fab83a-16d4-4db0-a83a-f824ecd8604a      5\n",
      "c8de27d0-2c44-4b5a-b178-59c45d054ccb      5\n",
      "5adfc643-aa8e-4140-b2c3-98a946444632      5\n",
      "ec0bb9b5-45e3-4de8-963d-e92aa91a201e      3\n",
      "Name: product_id, Length: 300, dtype: int64\n",
      "\n",
      "Column category:\n",
      "fruit                    998\n",
      "vegetables               846\n",
      "packaged foods           507\n",
      "baked goods              443\n",
      "canned foods             431\n",
      "refrigerated items       425\n",
      "kitchen                  382\n",
      "meat                     382\n",
      "dairy                    375\n",
      "beverages                301\n",
      "cheese                   293\n",
      "cleaning products        292\n",
      "baking                   264\n",
      "snacks                   263\n",
      "frozen                   263\n",
      "seafood                  253\n",
      "medicine                 243\n",
      "baby products            224\n",
      "condiments and sauces    181\n",
      "personal care            177\n",
      "pets                     161\n",
      "spices and herbs         125\n",
      "Name: category, dtype: int64\n",
      "\n",
      "Column customer_type:\n",
      "non-member    1601\n",
      "standard      1595\n",
      "premium       1590\n",
      "basic         1526\n",
      "gold          1517\n",
      "Name: customer_type, dtype: int64\n",
      "\n",
      "Column payment_type:\n",
      "cash           2027\n",
      "credit card    1949\n",
      "e-wallet       1935\n",
      "debit card     1918\n",
      "Name: payment_type, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# value counts in the object type data\n",
    "def print_value_counts(df):\n",
    "  for col in df:\n",
    "    if df[col].dtype == 'object':\n",
    "      print(f'Column {col}:')\n",
    "      print(df[col].value_counts(), end='\\n\\n')\n",
    "print_value_counts(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('category')['unit_price'].mean().sort_values().plot(kind='barh')\n",
    "plt.xlabel('unit price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('category')['total'].mean().sort_values().plot(kind='barh')\n",
    "plt.xlabel('total')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id    7829\n",
       "timestamp         7738\n",
       "product_id         300\n",
       "category            22\n",
       "customer_type        5\n",
       "unit_price          64\n",
       "quantity             4\n",
       "total              256\n",
       "payment_type         4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(df.corr(),annot=True,fmt='.2f',cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oBin5kdG4iS0"
   },
   "source": [
    "---\n",
    "\n",
    "## Section 5 - Summary\n",
    "\n",
    "We have completed an initial exploratory data analysis on the sample of data provided. We should now have a solid understanding of the data. \n",
    "\n",
    "The client wants to know\n",
    "\n",
    "```\n",
    "\"How to better stock the items that they sell\"\n",
    "```\n",
    "\n",
    "From this dataset, it is impossible to answer that question. In order to make the next step on this project with the client, it is clear that:\n",
    "\n",
    "- We need more rows of data. The current sample is only from 1 store and 1 week worth of data\n",
    "- We need to frame the specific problem statement that we want to solve. The current business problem is too broad, we should narrow down the focus in order to deliver a valuable end product\n",
    "- We need more features. Based on the problem statement that we move forward with, we need more columns (features) that may help us to understand the outcome that we're solving for\n",
    "\n",
    "Insights:\n",
    "\n",
    "* Medicine and seafood are top in sales\n",
    "* Fruits and vegetables are the most frequently bought categories\n",
    "* Category column contains variety which makes it a good candidate for modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "eda",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}