diff --git a/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb b/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb
index b71be91..6d9880e 100644
--- a/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb	
+++ b/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bae75f73",
+   "id": "33ee8b59",
    "metadata": {},
    "source": [
     "#### Dataframes list:\n",
@@ -30,18 +30,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 81,
    "id": "7d5eb017",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 77,
    "id": "5829c50a",
    "metadata": {},
    "outputs": [],
@@ -50,6 +52,7 @@
     "import sqlalchemy as sa\n",
     "pd.options.display.max_rows = 10\n",
     "\n",
+    "\n",
     "# sqlalchemy is a \"toolkit\" that allows access to SQL databases through python. SQL tables can be queried and directly brought to python objects"
    ]
   },
@@ -101536,7 +101539,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "57d22638",
+   "id": "9aa02fc1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101550,7 +101553,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6de9309",
+   "id": "00081ac5",
    "metadata": {},
    "source": [
     "----"
@@ -101591,18 +101594,18 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "09157c37",
+   "id": "4483baec",
    "metadata": {},
    "outputs": [],
    "source": [
     "# removing missing date values\n",
-    "orders_df.dropna(subset=[\"order_approved_at\",\"order_delivered_carrier_date\",\"order_delivered_customer_date\"],inplace=True)"
+    "orders_df.dropna(subset=[\"order_purchase_timestamp\",\"order_approved_at\",\"order_delivered_carrier_date\",\"order_delivered_customer_date\",\"order_estimated_delivery_date\"],inplace=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "2d9dec7c",
+   "id": "c65fa567",
    "metadata": {},
    "outputs": [
     {
@@ -101738,7 +101741,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "7f084a9f",
+   "id": "33a1071c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101748,7 +101751,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "fe605f17",
+   "id": "0be07bdc",
    "metadata": {},
    "outputs": [
     {
@@ -101769,7 +101772,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "d015a01c",
+   "id": "c6a7907f",
    "metadata": {},
    "outputs": [
     {
@@ -101790,7 +101793,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "247c06b0",
+   "id": "f844408d",
    "metadata": {},
    "outputs": [
     {
@@ -101811,7 +101814,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "6e7bbe7a",
+   "id": "d5024e70",
    "metadata": {},
    "outputs": [
     {
@@ -101832,7 +101835,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "d79bc3a1",
+   "id": "3a4da828",
    "metadata": {},
    "outputs": [
     {
@@ -101853,7 +101856,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "ab23ce0f",
+   "id": "a2b49561",
    "metadata": {},
    "outputs": [
     {
@@ -101874,7 +101877,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "d2296b46",
+   "id": "1acd8906",
    "metadata": {},
    "outputs": [
     {
@@ -101895,7 +101898,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "2fe94389",
+   "id": "123c2b15",
    "metadata": {},
    "outputs": [
     {
@@ -101952,7 +101955,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "847d35ed",
+   "id": "0086572c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101964,7 +101967,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b2919f91",
+   "id": "75555832",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102136,7 +102139,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5eae60f9",
+   "id": "8cbe2e2f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102146,7 +102149,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "deb6f240",
+   "id": "964356e6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102156,13 +102159,61 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "b59c1b6b",
+   "id": "1d567014",
    "metadata": {},
    "outputs": [],
    "source": [
     "orders_df = drop_na(orders_df)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "eefe78fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orders_df.isnull().values.any().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "945bfeb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-80-72b05e94335e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstrained_layout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0max1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0max2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustomers_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'customer_state'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'rainbow_r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcustomers_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'customer_state'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "fig = plt.figure(constrained_layout=True, figsize=(13, 12))\n",
+    "ax1 = fig.add_subplot(gs[0, :])\n",
+    "ax2 = fig.add_subplot(gs[1, :])\n",
+    "sns.countplot(x=customers_df['customer_state'], ax=ax1, palette='rainbow_r')\n",
+    "total = len(customers_df['customer_state'])\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c55bb1ab",
@@ -102176,81 +102227,81 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "28f87934",
+   "id": "ef2d5c70",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trans_product_df.to_csv('trans_product_df.csv')"
+    "trans_product_df.to_csv('trans_product_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "f90dac5b",
+   "id": "c9000f67",
    "metadata": {},
    "outputs": [],
    "source": [
-    "orders_df.to_csv('orders_df.csv')"
+    "orders_df.to_csv('orders_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "f2ba3bca",
+   "id": "4743fee1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "customers_df.to_csv('customers_df.csv')"
+    "customers_df.to_csv('customers_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "a0e1692d",
+   "id": "fdcf6891",
    "metadata": {},
    "outputs": [],
    "source": [
-    "order_payments_df.to_csv('order_payments_df.csv')"
+    "order_payments_df.to_csv('order_payments_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "5834b478",
+   "id": "4498813e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "order_items_df.to_csv('order_items_df.csv')"
+    "order_items_df.to_csv('order_items_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "067fcb4d",
+   "id": "913ffcd5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "order_customer_items_paid_df.to_csv('order_customer_items_paid_df.csv')"
+    "order_customer_items_paid_df.to_csv('order_customer_items_paid_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "5c7c9f14",
+   "id": "a5e13af7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cleaned_geo.to_csv('cleaned_geo.csv')"
+    "cleaned_geo.to_csv('cleaned_geo.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "c5f22a9c",
+   "id": "a557d72a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "sellers_df.to_csv('sellers_df.csv')"
+    "sellers_df.to_csv('sellers_df.csv', index=False)"
    ]
   },
   {
diff --git a/Connect Python_MySQL-D.ipynb b/Connect Python_MySQL-D.ipynb
index b71be91..6d9880e 100644
--- a/Connect Python_MySQL-D.ipynb	
+++ b/Connect Python_MySQL-D.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bae75f73",
+   "id": "33ee8b59",
    "metadata": {},
    "source": [
     "#### Dataframes list:\n",
@@ -30,18 +30,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 81,
    "id": "7d5eb017",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 77,
    "id": "5829c50a",
    "metadata": {},
    "outputs": [],
@@ -50,6 +52,7 @@
     "import sqlalchemy as sa\n",
     "pd.options.display.max_rows = 10\n",
     "\n",
+    "\n",
     "# sqlalchemy is a \"toolkit\" that allows access to SQL databases through python. SQL tables can be queried and directly brought to python objects"
    ]
   },
@@ -101536,7 +101539,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "57d22638",
+   "id": "9aa02fc1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101550,7 +101553,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6de9309",
+   "id": "00081ac5",
    "metadata": {},
    "source": [
     "----"
@@ -101591,18 +101594,18 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "09157c37",
+   "id": "4483baec",
    "metadata": {},
    "outputs": [],
    "source": [
     "# removing missing date values\n",
-    "orders_df.dropna(subset=[\"order_approved_at\",\"order_delivered_carrier_date\",\"order_delivered_customer_date\"],inplace=True)"
+    "orders_df.dropna(subset=[\"order_purchase_timestamp\",\"order_approved_at\",\"order_delivered_carrier_date\",\"order_delivered_customer_date\",\"order_estimated_delivery_date\"],inplace=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "2d9dec7c",
+   "id": "c65fa567",
    "metadata": {},
    "outputs": [
     {
@@ -101738,7 +101741,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "7f084a9f",
+   "id": "33a1071c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101748,7 +101751,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "fe605f17",
+   "id": "0be07bdc",
    "metadata": {},
    "outputs": [
     {
@@ -101769,7 +101772,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "d015a01c",
+   "id": "c6a7907f",
    "metadata": {},
    "outputs": [
     {
@@ -101790,7 +101793,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "247c06b0",
+   "id": "f844408d",
    "metadata": {},
    "outputs": [
     {
@@ -101811,7 +101814,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "6e7bbe7a",
+   "id": "d5024e70",
    "metadata": {},
    "outputs": [
     {
@@ -101832,7 +101835,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "d79bc3a1",
+   "id": "3a4da828",
    "metadata": {},
    "outputs": [
     {
@@ -101853,7 +101856,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "ab23ce0f",
+   "id": "a2b49561",
    "metadata": {},
    "outputs": [
     {
@@ -101874,7 +101877,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "d2296b46",
+   "id": "1acd8906",
    "metadata": {},
    "outputs": [
     {
@@ -101895,7 +101898,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "2fe94389",
+   "id": "123c2b15",
    "metadata": {},
    "outputs": [
     {
@@ -101952,7 +101955,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "847d35ed",
+   "id": "0086572c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101964,7 +101967,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b2919f91",
+   "id": "75555832",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102136,7 +102139,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5eae60f9",
+   "id": "8cbe2e2f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102146,7 +102149,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "deb6f240",
+   "id": "964356e6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102156,13 +102159,61 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "b59c1b6b",
+   "id": "1d567014",
    "metadata": {},
    "outputs": [],
    "source": [
     "orders_df = drop_na(orders_df)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "eefe78fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "orders_df.isnull().values.any().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "945bfeb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-80-72b05e94335e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconstrained_layout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0max1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0max2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustomers_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'customer_state'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'rainbow_r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcustomers_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'customer_state'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "fig = plt.figure(constrained_layout=True, figsize=(13, 12))\n",
+    "ax1 = fig.add_subplot(gs[0, :])\n",
+    "ax2 = fig.add_subplot(gs[1, :])\n",
+    "sns.countplot(x=customers_df['customer_state'], ax=ax1, palette='rainbow_r')\n",
+    "total = len(customers_df['customer_state'])\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c55bb1ab",
@@ -102176,81 +102227,81 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "28f87934",
+   "id": "ef2d5c70",
    "metadata": {},
    "outputs": [],
    "source": [
-    "trans_product_df.to_csv('trans_product_df.csv')"
+    "trans_product_df.to_csv('trans_product_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "f90dac5b",
+   "id": "c9000f67",
    "metadata": {},
    "outputs": [],
    "source": [
-    "orders_df.to_csv('orders_df.csv')"
+    "orders_df.to_csv('orders_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "f2ba3bca",
+   "id": "4743fee1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "customers_df.to_csv('customers_df.csv')"
+    "customers_df.to_csv('customers_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "a0e1692d",
+   "id": "fdcf6891",
    "metadata": {},
    "outputs": [],
    "source": [
-    "order_payments_df.to_csv('order_payments_df.csv')"
+    "order_payments_df.to_csv('order_payments_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "5834b478",
+   "id": "4498813e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "order_items_df.to_csv('order_items_df.csv')"
+    "order_items_df.to_csv('order_items_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "067fcb4d",
+   "id": "913ffcd5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "order_customer_items_paid_df.to_csv('order_customer_items_paid_df.csv')"
+    "order_customer_items_paid_df.to_csv('order_customer_items_paid_df.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "5c7c9f14",
+   "id": "a5e13af7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cleaned_geo.to_csv('cleaned_geo.csv')"
+    "cleaned_geo.to_csv('cleaned_geo.csv', index=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "c5f22a9c",
+   "id": "a557d72a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "sellers_df.to_csv('sellers_df.csv')"
+    "sellers_df.to_csv('sellers_df.csv', index=False)"
    ]
   },
   {