{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ[\"OPENAI_API_KEY\"] = \"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\"\n", "os.environ[\"OPENAI_ORGANIZATION\"] = \"org-lnKDQT1GAiB9UWYLNPsvnKJz\"\n", "os.environ[\"sql_dialect\"] = \"mysql+pymysql://4HgsvrQ16HUnrUW.root:vlgyZWUwh4JIV511@gateway01.eu-central-1.prod.aws.tidbcloud.com:4000/thelook_ecommerce?ssl_ca%2Fetc%2Fssl%2Fcerts%2Fca-certificates.crt&ssl_verify_identity=true\"\n", "%load_ext jupytee" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "### The table description\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6f90dc24048b4df083617a64ccc9ecd8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Description', 'accessor': 'descriptio…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### The table relationship\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**distribution_centers** \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "products: The distribution_center_id in the products table refers to the id in the distribution_centers table, indicating which distribution center the product is located in. \n", "inventory_items: The product_distribution_center_id in the inventory_items table refers to the id in the distribution_centers table, indicating which distribution center the inventory item is located in." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**events** \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "users: The user_id in the events table refers to the id in the users table, indicating which user performed the event." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**inventory_items** \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "products: The product_id in the inventory_items table refers to the id in the products table, indicating which product the inventory item is. \n", "order_items: The id in the inventory_items table refers to the inventory_item_id in the order_items table, indicating which inventory item is part of the order." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**order_items** \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "orders: The order_id in the order_items table refers to the order_id in the orders table, indicating which order the item belongs to. \n", "users: The user_id in the order_items table refers to the id in the users table, indicating which user placed the order. \n", "products: The product_id in the order_items table refers to the id in the products table, indicating which product the order item is." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**orders** \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "users: The user_id in the orders table refers to the id in the users table, indicating which user placed the order." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**products** \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "order_items: The id in the products table refers to the product_id in the order_items table, indicating which product is part of the order." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**users** \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "events: The id in the users table refers to the user_id in the events table, indicating which user performed the event. \n", "orders: The id in the users table refers to the user_id in the orders table, indicating which user placed the order. \n", "order_items: The id in the users table refers to the user_id in the order_items table, indicating which user placed the order for the item." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### The relevant domain knowledge about data source\n", " The domain it belongs to is E-commerce. The reason is that The given data sources contain information about users, products, orders, inventory items, distribution centers, and user events, which are all related to online shopping and e-commerce.\n", " \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### The domain knowledge about E-commerce\n", " \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "**Domain Definition** \n", "E-commerce, or electronic commerce, refers to the buying and selling of goods or services using the internet, and the transfer of money and data to execute these transactions. It involves various activities such as online shopping, electronic payments, online auctions, and internet banking. \n", " \n", "**User behavior** \n", "Understanding user behavior on the website, such as browsing patterns, traffic sources, and event types, can help improve the user experience and increase conversion rates. \n", " \n", "**Inventory management** \n", "Efficient inventory management is crucial for e-commerce businesses to ensure that products are available for customers, minimize storage costs, and optimize the supply chain. \n", " \n", "**Order processing** \n", "Order processing involves receiving, tracking, and fulfilling customer orders. It includes various stages such as order creation, payment processing, shipping, and delivery. \n", " \n", "**Product assortment** \n", "A diverse product assortment is essential for attracting and retaining customers. E-commerce businesses should offer a wide range of products across different categories, brands, and price points. \n", " \n", "**Distribution centers** \n", "Distribution centers play a vital role in e-commerce logistics, as they store products and facilitate their shipment to customers. Efficient distribution center management can help reduce shipping times and costs. \n", " \n", "**Customer segmentation** \n", "Segmenting customers based on their demographics, preferences, and behavior can help e-commerce businesses tailor their marketing strategies and improve customer satisfaction. \n", " \n", "**Return management** \n", "Handling product returns is an important aspect of e-commerce, as it affects customer satisfaction and trust. Efficient return management can help minimize costs and improve the overall shopping experience. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%explore_db" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "### Column description for table: distribution_centers" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3522576815414b2eab854293c6b86b43", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Type', 'accessor': 'type'}, {'header'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column statistics for table: distribution_centers" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idnamelatitudelongitude
count10.000001010.00000010.000000
uniqueNaN10NaNNaN
topNaNMemphis TNNaNNaN
freqNaN1NaNNaN
mean5.50000NaN34.679310-87.938550
std3.02765NaN4.57266612.722916
min1.00000NaN29.760400-118.250000
25%3.25000NaN31.024975-90.042800
50%5.50000NaN33.416650-87.863900
75%7.75000NaN38.741850-80.229150
max10.00000NaN41.836900-73.783400
\n", "
" ], "text/plain": [ " id name latitude longitude\n", "count 10.00000 10 10.000000 10.000000\n", "unique NaN 10 NaN NaN\n", "top NaN Memphis TN NaN NaN\n", "freq NaN 1 NaN NaN\n", "mean 5.50000 NaN 34.679310 -87.938550\n", "std 3.02765 NaN 4.572666 12.722916\n", "min 1.00000 NaN 29.760400 -118.250000\n", "25% 3.25000 NaN 31.024975 -90.042800\n", "50% 5.50000 NaN 33.416650 -87.863900\n", "75% 7.75000 NaN 38.741850 -80.229150\n", "max 10.00000 NaN 41.836900 -73.783400" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Sample data for table: distribution_centers" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idnamelatitudelongitude
01Memphis TN35.1174-89.9711
12Chicago IL41.8369-87.6847
23Houston TX29.7604-95.3698
34Los Angeles CA34.0500-118.2500
45New Orleans LA29.9500-90.0667
56Port Authority of New Yor40.6340-73.7834
67Philadelphia PA39.9500-75.1667
78Mobile AL30.6944-88.0431
89Charleston SC32.7833-79.9333
910Savannah GA32.0167-81.1167
\n", "
" ], "text/plain": [ " id name latitude longitude\n", "0 1 Memphis TN 35.1174 -89.9711\n", "1 2 Chicago IL 41.8369 -87.6847\n", "2 3 Houston TX 29.7604 -95.3698\n", "3 4 Los Angeles CA 34.0500 -118.2500\n", "4 5 New Orleans LA 29.9500 -90.0667\n", "5 6 Port Authority of New Yor 40.6340 -73.7834\n", "6 7 Philadelphia PA 39.9500 -75.1667\n", "7 8 Mobile AL 30.6944 -88.0431\n", "8 9 Charleston SC 32.7833 -79.9333\n", "9 10 Savannah GA 32.0167 -81.1167" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column value histgram for table: distribution_centers" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column description for table: events" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eb05a94739ae45f982fbb666e505a9ed", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Type', 'accessor': 'type'}, {'header'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column statistics for table: events" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
iduser_idsequence_numbersession_idcreated_atip_addresscitystatepostal_codebrowsertraffic_sourceurievent_type
count2.000000e+02200.000000200.000000200200200200200200200200200200
uniqueNaNNaNNaN200NaN20012281785511
topNaNNaNNaNaecd0141-0640-4232-b409-27c92b2e725fNaN64.226.187.3LondonEngland59ChromeEmail/cartcart
freqNaNNaNNaN1NaN12315999080200200
mean1.105768e+0629809.4500004.215000NaN2021-09-27 20:09:19.735000320NaNNaNNaNNaNNaNNaNNaNNaN
min4.446000e+030.0000002.000000NaN2019-01-16 10:30:00NaNNaNNaNNaNNaNNaNNaNNaN
25%5.063065e+050.0000003.000000NaN2020-10-17 16:13:51.750000128NaNNaNNaNNaNNaNNaNNaNNaN
50%1.009394e+0619021.0000003.000000NaN2021-12-07 05:31:05.500000NaNNaNNaNNaNNaNNaNNaNNaN
75%1.719915e+0657864.0000006.000000NaN2022-10-18 09:56:52NaNNaNNaNNaNNaNNaNNaNNaN
max2.405152e+0699631.00000012.000000NaN2023-05-04 09:29:39NaNNaNNaNNaNNaNNaNNaNNaN
std6.881609e+0532829.8226522.318546NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " id user_id sequence_number \n", "count 2.000000e+02 200.000000 200.000000 \\\n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 1.105768e+06 29809.450000 4.215000 \n", "min 4.446000e+03 0.000000 2.000000 \n", "25% 5.063065e+05 0.000000 3.000000 \n", "50% 1.009394e+06 19021.000000 3.000000 \n", "75% 1.719915e+06 57864.000000 6.000000 \n", "max 2.405152e+06 99631.000000 12.000000 \n", "std 6.881609e+05 32829.822652 2.318546 \n", "\n", " session_id created_at \n", "count 200 200 \\\n", "unique 200 NaN \n", "top aecd0141-0640-4232-b409-27c92b2e725f NaN \n", "freq 1 NaN \n", "mean NaN 2021-09-27 20:09:19.735000320 \n", "min NaN 2019-01-16 10:30:00 \n", "25% NaN 2020-10-17 16:13:51.750000128 \n", "50% NaN 2021-12-07 05:31:05.500000 \n", "75% NaN 2022-10-18 09:56:52 \n", "max NaN 2023-05-04 09:29:39 \n", "std NaN NaN \n", "\n", " ip_address city state postal_code browser traffic_source \n", "count 200 200 200 200 200 200 \\\n", "unique 200 122 8 178 5 5 \n", "top 64.226.187.3 London England 59 Chrome Email \n", "freq 1 23 159 9 90 80 \n", "mean NaN NaN NaN NaN NaN NaN \n", "min NaN NaN NaN NaN NaN NaN \n", "25% NaN NaN NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN NaN NaN \n", "max NaN NaN NaN NaN NaN NaN \n", "std NaN NaN NaN NaN NaN NaN \n", "\n", " uri event_type \n", "count 200 200 \n", "unique 1 1 \n", "top /cart cart \n", "freq 200 200 \n", "mean NaN NaN \n", "min NaN NaN \n", "25% NaN NaN \n", "50% NaN NaN \n", "75% NaN NaN \n", "max NaN NaN \n", "std NaN NaN " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Sample data for table: events" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
iduser_idsequence_numbersession_idcreated_atip_addresscitystatepostal_codebrowsertraffic_sourceurievent_type
0441006340466aecd0141-0640-4232-b409-27c92b2e725f2022-04-11 23:03:1264.226.187.3BogatyniaDolnośląskie59FirefoxFacebook/cartcart
11215283934974ba44471d-76d4-4427-acce-51acd9cf6f5f2022-11-08 11:52:4689.21.201.248BogatyniaDolnośląskie59SafariEmail/cartcart
2608074802411732069-e694-4c80-96a1-68f7a231673d2021-07-13 09:01:09206.58.188.116BogatyniaDolnośląskie59ChromeEmail/cartcart
3181181403738a441f-b340-40e5-b818-007bcf4fb77c2020-07-24 17:24:00214.183.161.186BogatyniaDolnośląskie59FirefoxFacebook/cartcart
413863000395220c4c-7743-404c-9c69-a17fa493f5952020-06-26 05:51:006.13.68.17BogatyniaDolnośląskie59FirefoxAdwords/cartcart
5214936003a89b60e9-dc00-49bc-b24c-501ca1f24de72019-12-03 12:14:0034.29.236.125BogatyniaDolnośląskie59ChromeYouTube/cartcart
659290645600680dca3c3-d514-4190-b63c-ae983c17a19c2021-03-17 06:00:18120.48.153.35BogatyniaDolnośląskie59ChromeEmail/cartcart
7184406141959fb0bf8cb-00ae-42a6-92c4-5a1fcf485ea02023-02-22 09:07:0655.228.226.65BogatyniaDolnośląskie59FirefoxFacebook/cartcart
84353683359064eb1cb37-8f07-4b55-b18a-8d41d96df39b2023-04-22 13:54:3862.19.28.99BogatyniaDolnośląskie59IEEmail/cartcart
961057482061c56153e-211e-47ea-9231-5546665615172023-04-04 13:24:4786.81.117.6Gorzów WielkopolskiLubuskie66SafariFacebook/cartcart
\n", "
" ], "text/plain": [ " id user_id sequence_number session_id \n", "0 441006 34046 6 aecd0141-0640-4232-b409-27c92b2e725f \\\n", "1 1215283 93497 4 ba44471d-76d4-4427-acce-51acd9cf6f5f \n", "2 60807 4802 4 11732069-e694-4c80-96a1-68f7a231673d \n", "3 1811814 0 3 738a441f-b340-40e5-b818-007bcf4fb77c \n", "4 1386300 0 3 95220c4c-7743-404c-9c69-a17fa493f595 \n", "5 2149360 0 3 a89b60e9-dc00-49bc-b24c-501ca1f24de7 \n", "6 592906 45600 6 80dca3c3-d514-4190-b63c-ae983c17a19c \n", "7 184406 14195 9 fb0bf8cb-00ae-42a6-92c4-5a1fcf485ea0 \n", "8 435368 33590 6 4eb1cb37-8f07-4b55-b18a-8d41d96df39b \n", "9 61057 4820 6 1c56153e-211e-47ea-9231-554666561517 \n", "\n", " created_at ip_address city state \n", "0 2022-04-11 23:03:12 64.226.187.3 Bogatynia Dolnośląskie \\\n", "1 2022-11-08 11:52:46 89.21.201.248 Bogatynia Dolnośląskie \n", "2 2021-07-13 09:01:09 206.58.188.116 Bogatynia Dolnośląskie \n", "3 2020-07-24 17:24:00 214.183.161.186 Bogatynia Dolnośląskie \n", "4 2020-06-26 05:51:00 6.13.68.17 Bogatynia Dolnośląskie \n", "5 2019-12-03 12:14:00 34.29.236.125 Bogatynia Dolnośląskie \n", "6 2021-03-17 06:00:18 120.48.153.35 Bogatynia Dolnośląskie \n", "7 2023-02-22 09:07:06 55.228.226.65 Bogatynia Dolnośląskie \n", "8 2023-04-22 13:54:38 62.19.28.99 Bogatynia Dolnośląskie \n", "9 2023-04-04 13:24:47 86.81.117.6 Gorzów Wielkopolski Lubuskie \n", "\n", " postal_code browser traffic_source uri event_type \n", "0 59 Firefox Facebook /cart cart \n", "1 59 Safari Email /cart cart \n", "2 59 Chrome Email /cart cart \n", "3 59 Firefox Facebook /cart cart \n", "4 59 Firefox Adwords /cart cart \n", "5 59 Chrome YouTube /cart cart \n", "6 59 Chrome Email /cart cart \n", "7 59 Firefox Facebook /cart cart \n", "8 59 IE Email /cart cart \n", "9 66 Safari Facebook /cart cart " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column value histgram for table: events" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column description for table: inventory_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "400a96c0fa8b46a190ef27c6cce07fd9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Type', 'accessor': 'type'}, {'header'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column statistics for table: inventory_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idproduct_idcreated_atsold_atcostproduct_categoryproduct_nameproduct_brandproduct_retail_priceproduct_departmentproduct_skuproduct_distribution_center_id
count200.000000200.000000200200200.000000200200200200.000000200200200.0
uniqueNaNNaNNaN69NaN11221NaN222NaN
topNaNNaNNaN0000-00-00 00:00:00NaNTops & TeesAdidas Men's Hiking Hooded 1Side Fleece JacketNaNMen9F62C81D7B48E146423F79C9E151510DNaN
freqNaNNaNNaN132NaN3817200NaN12617NaN
mean253339.38000018108.5600002021-09-25 19:45:57.340000NaN18.022842NaNNaNNaN36.556650NaNNaN3.0
min8475.000000755.0000002019-10-22 15:29:32NaN1.641900NaNNaNNaN4.210000NaNNaN3.0
25%131374.25000011843.0000002020-12-05 20:13:53.500000NaN12.476399NaNNaNNaN22.480000NaNNaN3.0
50%270172.00000021207.0000002021-10-10 12:01:10NaN15.042550NaNNaNNaN28.990000NaNNaN3.0
75%370899.75000025135.0000002022-07-12 09:38:30.750000128NaN19.872000NaNNaNNaN45.000000NaNNaN3.0
max487304.00000027543.0000002023-04-12 00:23:00NaN39.488050NaNNaNNaN89.950000NaNNaN3.0
std139836.1340617899.756399NaNNaN10.003971NaNNaNNaN21.831001NaNNaN0.0
\n", "
" ], "text/plain": [ " id product_id created_at \n", "count 200.000000 200.000000 200 \\\n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 253339.380000 18108.560000 2021-09-25 19:45:57.340000 \n", "min 8475.000000 755.000000 2019-10-22 15:29:32 \n", "25% 131374.250000 11843.000000 2020-12-05 20:13:53.500000 \n", "50% 270172.000000 21207.000000 2021-10-10 12:01:10 \n", "75% 370899.750000 25135.000000 2022-07-12 09:38:30.750000128 \n", "max 487304.000000 27543.000000 2023-04-12 00:23:00 \n", "std 139836.134061 7899.756399 NaN \n", "\n", " sold_at cost product_category \n", "count 200 200.000000 200 \\\n", "unique 69 NaN 11 \n", "top 0000-00-00 00:00:00 NaN Tops & Tees \n", "freq 132 NaN 38 \n", "mean NaN 18.022842 NaN \n", "min NaN 1.641900 NaN \n", "25% NaN 12.476399 NaN \n", "50% NaN 15.042550 NaN \n", "75% NaN 19.872000 NaN \n", "max NaN 39.488050 NaN \n", "std NaN 10.003971 NaN \n", "\n", " product_name product_brand \n", "count 200 200 \\\n", "unique 22 1 \n", "top Adidas Men's Hiking Hooded 1Side Fleece Jacket \n", "freq 17 200 \n", "mean NaN NaN \n", "min NaN NaN \n", "25% NaN NaN \n", "50% NaN NaN \n", "75% NaN NaN \n", "max NaN NaN \n", "std NaN NaN \n", "\n", " product_retail_price product_department \n", "count 200.000000 200 \\\n", "unique NaN 2 \n", "top NaN Men \n", "freq NaN 126 \n", "mean 36.556650 NaN \n", "min 4.210000 NaN \n", "25% 22.480000 NaN \n", "50% 28.990000 NaN \n", "75% 45.000000 NaN \n", "max 89.950000 NaN \n", "std 21.831001 NaN \n", "\n", " product_sku product_distribution_center_id \n", "count 200 200.0 \n", "unique 22 NaN \n", "top 9F62C81D7B48E146423F79C9E151510D NaN \n", "freq 17 NaN \n", "mean NaN 3.0 \n", "min NaN 3.0 \n", "25% NaN 3.0 \n", "50% NaN 3.0 \n", "75% NaN 3.0 \n", "max NaN 3.0 \n", "std NaN 0.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Sample data for table: inventory_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idproduct_idcreated_atsold_atcostproduct_categoryproduct_nameproduct_brandproduct_retail_priceproduct_departmentproduct_skuproduct_distribution_center_id
049514168982023-02-16 12:16:022023-02-24 09:59:0213.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
1143943168982022-12-14 16:44:312023-02-05 00:31:3113.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
2143945168982022-01-08 15:27:000000-00-00 00:00:0013.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
3250872168982021-09-19 14:30:000000-00-00 00:00:0013.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
4251021168982022-07-25 11:06:000000-00-00 00:00:0013.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
5318341168982020-06-05 01:31:000000-00-00 00:00:0013.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
6369166168982022-05-18 18:00:000000-00-00 00:00:0013.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
7412415168982020-06-22 12:57:312020-08-05 03:11:3113.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
8433257168982020-11-18 07:10:302020-12-27 11:20:3013.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
9433259168982022-03-06 00:32:000000-00-00 00:00:0013.925Tops & TeesQuiksilver Waterman Men's On The Rise25.0Men22811EE19846217512507785E74D12CC3
\n", "
" ], "text/plain": [ " id product_id created_at sold_at cost \n", "0 49514 16898 2023-02-16 12:16:02 2023-02-24 09:59:02 13.925 \\\n", "1 143943 16898 2022-12-14 16:44:31 2023-02-05 00:31:31 13.925 \n", "2 143945 16898 2022-01-08 15:27:00 0000-00-00 00:00:00 13.925 \n", "3 250872 16898 2021-09-19 14:30:00 0000-00-00 00:00:00 13.925 \n", "4 251021 16898 2022-07-25 11:06:00 0000-00-00 00:00:00 13.925 \n", "5 318341 16898 2020-06-05 01:31:00 0000-00-00 00:00:00 13.925 \n", "6 369166 16898 2022-05-18 18:00:00 0000-00-00 00:00:00 13.925 \n", "7 412415 16898 2020-06-22 12:57:31 2020-08-05 03:11:31 13.925 \n", "8 433257 16898 2020-11-18 07:10:30 2020-12-27 11:20:30 13.925 \n", "9 433259 16898 2022-03-06 00:32:00 0000-00-00 00:00:00 13.925 \n", "\n", " product_category product_name product_brand \n", "0 Tops & Tees Quiksilver Waterman Men's On The Rise \\\n", "1 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "2 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "3 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "4 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "5 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "6 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "7 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "8 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "9 Tops & Tees Quiksilver Waterman Men's On The Rise \n", "\n", " product_retail_price product_department product_sku \n", "0 25.0 Men 22811EE19846217512507785E74D12CC \\\n", "1 25.0 Men 22811EE19846217512507785E74D12CC \n", "2 25.0 Men 22811EE19846217512507785E74D12CC \n", "3 25.0 Men 22811EE19846217512507785E74D12CC \n", "4 25.0 Men 22811EE19846217512507785E74D12CC \n", "5 25.0 Men 22811EE19846217512507785E74D12CC \n", "6 25.0 Men 22811EE19846217512507785E74D12CC \n", "7 25.0 Men 22811EE19846217512507785E74D12CC \n", "8 25.0 Men 22811EE19846217512507785E74D12CC \n", "9 25.0 Men 22811EE19846217512507785E74D12CC \n", "\n", " product_distribution_center_id \n", "0 3 \n", "1 3 \n", "2 3 \n", "3 3 \n", "4 3 \n", "5 3 \n", "6 3 \n", "7 3 \n", "8 3 \n", "9 3 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column value histgram for table: inventory_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column description for table: order_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d2147fe04081405eaa5a237a8b86c474", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Type', 'accessor': 'type'}, {'header'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column statistics for table: order_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idorder_iduser_idproduct_idinventory_item_idstatuscreated_atshipped_atdelivered_atreturned_atsale_price
count200.000000200.000000200.000000200.000000200.000000200200200200200200.000000
uniqueNaNNaNNaNNaNNaN5NaN1537715NaN
topNaNNaNNaNNaNNaNShippedNaN0000-00-00 00:00:000000-00-00 00:00:000000-00-00 00:00:00NaN
freqNaNNaNNaNNaNNaN76NaN48124186NaN
mean92582.30500063826.28500051149.45000017004.125000249835.620000NaN2022-04-17 16:12:06.904999680NaNNaNNaN4.882500
min666.000000475.000000387.0000005955.0000001798.000000NaN2019-02-15 13:32:13NaNNaNNaN2.500000
25%42692.75000029480.25000023486.00000012539.000000115124.500000NaN2021-07-31 15:08:48.249999872NaNNaNNaN3.687500
50%94562.00000065272.00000052282.00000014248.000000255219.500000NaN2022-09-23 04:13:35.500000NaNNaNNaN5.000000
75%139713.75000096237.75000077177.25000024856.000000377140.750000NaN2023-02-11 06:31:04.750000128NaNNaNNaN6.000000
max180518.000000124227.00000099582.00000028992.000000486978.000000NaN2023-05-08 02:47:19NaNNaNNaN6.000000
std52767.52104136297.53769229130.7301767214.627486142431.511106NaNNaNNaNNaNNaN1.069186
\n", "
" ], "text/plain": [ " id order_id user_id product_id \n", "count 200.000000 200.000000 200.000000 200.000000 \\\n", "unique NaN NaN NaN NaN \n", "top NaN NaN NaN NaN \n", "freq NaN NaN NaN NaN \n", "mean 92582.305000 63826.285000 51149.450000 17004.125000 \n", "min 666.000000 475.000000 387.000000 5955.000000 \n", "25% 42692.750000 29480.250000 23486.000000 12539.000000 \n", "50% 94562.000000 65272.000000 52282.000000 14248.000000 \n", "75% 139713.750000 96237.750000 77177.250000 24856.000000 \n", "max 180518.000000 124227.000000 99582.000000 28992.000000 \n", "std 52767.521041 36297.537692 29130.730176 7214.627486 \n", "\n", " inventory_item_id status created_at \n", "count 200.000000 200 200 \\\n", "unique NaN 5 NaN \n", "top NaN Shipped NaN \n", "freq NaN 76 NaN \n", "mean 249835.620000 NaN 2022-04-17 16:12:06.904999680 \n", "min 1798.000000 NaN 2019-02-15 13:32:13 \n", "25% 115124.500000 NaN 2021-07-31 15:08:48.249999872 \n", "50% 255219.500000 NaN 2022-09-23 04:13:35.500000 \n", "75% 377140.750000 NaN 2023-02-11 06:31:04.750000128 \n", "max 486978.000000 NaN 2023-05-08 02:47:19 \n", "std 142431.511106 NaN NaN \n", "\n", " shipped_at delivered_at returned_at \n", "count 200 200 200 \\\n", "unique 153 77 15 \n", "top 0000-00-00 00:00:00 0000-00-00 00:00:00 0000-00-00 00:00:00 \n", "freq 48 124 186 \n", "mean NaN NaN NaN \n", "min NaN NaN NaN \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max NaN NaN NaN \n", "std NaN NaN NaN \n", "\n", " sale_price \n", "count 200.000000 \n", "unique NaN \n", "top NaN \n", "freq NaN \n", "mean 4.882500 \n", "min 2.500000 \n", "25% 3.687500 \n", "50% 5.000000 \n", "75% 6.000000 \n", "max 6.000000 \n", "std 1.069186 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Sample data for table: order_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idorder_iduser_idproduct_idinventory_item_idstatuscreated_atshipped_atdelivered_atreturned_atsale_price
053478369302959713606144288Complete2020-06-25 23:59:582020-06-25 03:49:002020-06-27 18:51:000000-00-00 00:00:002.50
153236367622946313606143629Cancelled2021-10-02 16:02:010000-00-00 00:00:000000-00-00 00:00:000000-00-00 00:00:002.50
21768101217039754813606476971Processing2023-03-02 06:08:560000-00-00 00:00:000000-00-00 00:00:000000-00-00 00:00:002.50
398006676475421428951264524Shipped2022-07-07 14:46:162022-07-06 22:36:000000-00-00 00:00:000000-00-00 00:00:003.00
42322716083128032895162589Complete2023-04-16 23:17:222023-04-17 11:17:002023-04-21 10:44:000000-00-00 00:00:003.00
5112788778136242028951304506Returned2023-04-01 00:52:292023-04-02 17:21:002023-04-05 17:24:002023-04-08 00:16:003.00
62912820168160722895178391Cancelled2022-03-04 07:57:220000-00-00 00:00:000000-00-00 00:00:000000-00-00 00:00:003.00
7170221180094072855745862Shipped2021-01-17 02:00:262021-01-17 07:14:000000-00-00 00:00:000000-00-00 00:00:003.25
81582761089628740628557427144Shipped2022-02-13 01:29:152022-02-11 03:01:000000-00-00 00:00:000000-00-00 00:00:003.25
91730161191109548428557466729Complete2023-01-16 05:42:542023-01-17 21:25:002023-01-19 10:23:000000-00-00 00:00:003.25
\n", "
" ], "text/plain": [ " id order_id user_id product_id inventory_item_id status \n", "0 53478 36930 29597 13606 144288 Complete \\\n", "1 53236 36762 29463 13606 143629 Cancelled \n", "2 176810 121703 97548 13606 476971 Processing \n", "3 98006 67647 54214 28951 264524 Shipped \n", "4 23227 16083 12803 28951 62589 Complete \n", "5 112788 77813 62420 28951 304506 Returned \n", "6 29128 20168 16072 28951 78391 Cancelled \n", "7 17022 11800 9407 28557 45862 Shipped \n", "8 158276 108962 87406 28557 427144 Shipped \n", "9 173016 119110 95484 28557 466729 Complete \n", "\n", " created_at shipped_at delivered_at \n", "0 2020-06-25 23:59:58 2020-06-25 03:49:00 2020-06-27 18:51:00 \\\n", "1 2021-10-02 16:02:01 0000-00-00 00:00:00 0000-00-00 00:00:00 \n", "2 2023-03-02 06:08:56 0000-00-00 00:00:00 0000-00-00 00:00:00 \n", "3 2022-07-07 14:46:16 2022-07-06 22:36:00 0000-00-00 00:00:00 \n", "4 2023-04-16 23:17:22 2023-04-17 11:17:00 2023-04-21 10:44:00 \n", "5 2023-04-01 00:52:29 2023-04-02 17:21:00 2023-04-05 17:24:00 \n", "6 2022-03-04 07:57:22 0000-00-00 00:00:00 0000-00-00 00:00:00 \n", "7 2021-01-17 02:00:26 2021-01-17 07:14:00 0000-00-00 00:00:00 \n", "8 2022-02-13 01:29:15 2022-02-11 03:01:00 0000-00-00 00:00:00 \n", "9 2023-01-16 05:42:54 2023-01-17 21:25:00 2023-01-19 10:23:00 \n", "\n", " returned_at sale_price \n", "0 0000-00-00 00:00:00 2.50 \n", "1 0000-00-00 00:00:00 2.50 \n", "2 0000-00-00 00:00:00 2.50 \n", "3 0000-00-00 00:00:00 3.00 \n", "4 0000-00-00 00:00:00 3.00 \n", "5 2023-04-08 00:16:00 3.00 \n", "6 0000-00-00 00:00:00 3.00 \n", "7 0000-00-00 00:00:00 3.25 \n", "8 0000-00-00 00:00:00 3.25 \n", "9 0000-00-00 00:00:00 3.25 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column value histgram for table: order_items" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column description for table: orders" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "78126a4bb69e49098d084e84f87ed2dc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Type', 'accessor': 'type'}, {'header'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column statistics for table: orders" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
order_iduser_idstatusgendercreated_atreturned_atshipped_atdelivered_atnum_of_item
count200.000000200.000000200200200200200200200.000000
uniqueNaNNaN11NaN1NaN1NaN
topNaNNaNShippedFNaN0000-00-00 00:00:00NaN0000-00-00 00:00:00NaN
freqNaNNaN200200NaN200NaN200NaN
mean671.325000548.125000NaNNaN2022-04-25 15:16:14.805000192NaN2022-04-27 04:03:27.704999936NaN1.340000
min1.0000001.000000NaNNaN2019-06-01 10:42:00NaN2019-06-01 19:40:00NaN1.000000
25%321.750000267.750000NaNNaN2021-10-17 06:41:00NaN2021-10-18 15:55:00NaN1.000000
50%700.000000573.000000NaNNaN2022-07-30 19:14:00NaN2022-08-02 01:50:30NaN1.000000
75%1026.750000835.250000NaNNaN2023-02-06 14:49:15NaN2023-02-08 01:44:45NaN1.000000
max1301.0000001050.000000NaNNaN2023-05-05 06:17:44NaN2023-05-07 17:55:44NaN4.000000
std392.193496315.719055NaNNaNNaNNaNNaNNaN0.690495
\n", "
" ], "text/plain": [ " order_id user_id status gender \n", "count 200.000000 200.000000 200 200 \\\n", "unique NaN NaN 1 1 \n", "top NaN NaN Shipped F \n", "freq NaN NaN 200 200 \n", "mean 671.325000 548.125000 NaN NaN \n", "min 1.000000 1.000000 NaN NaN \n", "25% 321.750000 267.750000 NaN NaN \n", "50% 700.000000 573.000000 NaN NaN \n", "75% 1026.750000 835.250000 NaN NaN \n", "max 1301.000000 1050.000000 NaN NaN \n", "std 392.193496 315.719055 NaN NaN \n", "\n", " created_at returned_at \n", "count 200 200 \\\n", "unique NaN 1 \n", "top NaN 0000-00-00 00:00:00 \n", "freq NaN 200 \n", "mean 2022-04-25 15:16:14.805000192 NaN \n", "min 2019-06-01 10:42:00 NaN \n", "25% 2021-10-17 06:41:00 NaN \n", "50% 2022-07-30 19:14:00 NaN \n", "75% 2023-02-06 14:49:15 NaN \n", "max 2023-05-05 06:17:44 NaN \n", "std NaN NaN \n", "\n", " shipped_at delivered_at num_of_item \n", "count 200 200 200.000000 \n", "unique NaN 1 NaN \n", "top NaN 0000-00-00 00:00:00 NaN \n", "freq NaN 200 NaN \n", "mean 2022-04-27 04:03:27.704999936 NaN 1.340000 \n", "min 2019-06-01 19:40:00 NaN 1.000000 \n", "25% 2021-10-18 15:55:00 NaN 1.000000 \n", "50% 2022-08-02 01:50:30 NaN 1.000000 \n", "75% 2023-02-08 01:44:45 NaN 1.000000 \n", "max 2023-05-07 17:55:44 NaN 4.000000 \n", "std NaN NaN 0.690495 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Sample data for table: orders" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
order_iduser_idstatusgendercreated_atreturned_atshipped_atdelivered_atnum_of_item
011ShippedF2022-08-23 06:35:000000-00-00 00:00:002022-08-25 07:44:000000-00-00 00:00:001
1118ShippedF2023-02-07 06:23:000000-00-00 00:00:002023-02-07 16:09:000000-00-00 00:00:002
22521ShippedF2022-10-01 08:03:000000-00-00 00:00:002022-10-01 12:13:000000-00-00 00:00:002
32622ShippedF2023-01-16 07:22:000000-00-00 00:00:002023-01-16 07:42:000000-00-00 00:00:001
42927ShippedF2022-01-22 07:04:000000-00-00 00:00:002022-01-23 16:40:000000-00-00 00:00:001
53534ShippedF2023-01-30 10:23:000000-00-00 00:00:002023-02-01 15:10:000000-00-00 00:00:001
64140ShippedF2021-04-22 01:45:000000-00-00 00:00:002021-04-22 09:25:000000-00-00 00:00:001
74642ShippedF2023-01-16 14:31:000000-00-00 00:00:002023-01-18 17:02:000000-00-00 00:00:002
85348ShippedF2023-05-03 11:01:000000-00-00 00:00:002023-05-05 19:03:000000-00-00 00:00:001
95651ShippedF2023-03-15 12:18:000000-00-00 00:00:002023-03-18 04:42:000000-00-00 00:00:001
\n", "
" ], "text/plain": [ " order_id user_id status gender created_at returned_at \n", "0 1 1 Shipped F 2022-08-23 06:35:00 0000-00-00 00:00:00 \\\n", "1 11 8 Shipped F 2023-02-07 06:23:00 0000-00-00 00:00:00 \n", "2 25 21 Shipped F 2022-10-01 08:03:00 0000-00-00 00:00:00 \n", "3 26 22 Shipped F 2023-01-16 07:22:00 0000-00-00 00:00:00 \n", "4 29 27 Shipped F 2022-01-22 07:04:00 0000-00-00 00:00:00 \n", "5 35 34 Shipped F 2023-01-30 10:23:00 0000-00-00 00:00:00 \n", "6 41 40 Shipped F 2021-04-22 01:45:00 0000-00-00 00:00:00 \n", "7 46 42 Shipped F 2023-01-16 14:31:00 0000-00-00 00:00:00 \n", "8 53 48 Shipped F 2023-05-03 11:01:00 0000-00-00 00:00:00 \n", "9 56 51 Shipped F 2023-03-15 12:18:00 0000-00-00 00:00:00 \n", "\n", " shipped_at delivered_at num_of_item \n", "0 2022-08-25 07:44:00 0000-00-00 00:00:00 1 \n", "1 2023-02-07 16:09:00 0000-00-00 00:00:00 2 \n", "2 2022-10-01 12:13:00 0000-00-00 00:00:00 2 \n", "3 2023-01-16 07:42:00 0000-00-00 00:00:00 1 \n", "4 2022-01-23 16:40:00 0000-00-00 00:00:00 1 \n", "5 2023-02-01 15:10:00 0000-00-00 00:00:00 1 \n", "6 2021-04-22 09:25:00 0000-00-00 00:00:00 1 \n", "7 2023-01-18 17:02:00 0000-00-00 00:00:00 2 \n", "8 2023-05-05 19:03:00 0000-00-00 00:00:00 1 \n", "9 2023-03-18 04:42:00 0000-00-00 00:00:00 1 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column value histgram for table: orders" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column description for table: products" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8ca47ad9cc3a4288a6a8222b2e997f55", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Type', 'accessor': 'type'}, {'header'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column statistics for table: products" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idcostcategorynamebrandretail_pricedepartmentskudistribution_center_id
count200.000000200.000000200200200200.000000200200200.00000
uniqueNaNNaN119555NaN1200NaN
topNaNNaNSwimNew Men's Sexy Center Patch Bikini Swimsuit 3G...HurleyNaNMenB23C5765E165D83AA924FA8F13C05F25NaN
freqNaNNaN200344NaN2001NaN
mean27858.61000030.877874NaNNaNNaN51.097600NaNNaN1.50000
std244.52494816.370366NaNNaNNaN26.595876NaNNaN0.72292
min27420.0000007.275600NaNNaNNaN12.900000NaNNaN1.00000
25%27641.75000020.536239NaNNaNNaN35.000000NaNNaN1.00000
50%27851.50000028.928500NaNNaNNaN47.975000NaNNaN1.00000
75%28066.25000037.133850NaNNaNNaN61.000000NaNNaN2.00000
max28288.000000119.394035NaNNaNNaN199.990000NaNNaN3.00000
\n", "
" ], "text/plain": [ " id cost category \n", "count 200.000000 200.000000 200 \\\n", "unique NaN NaN 1 \n", "top NaN NaN Swim \n", "freq NaN NaN 200 \n", "mean 27858.610000 30.877874 NaN \n", "std 244.524948 16.370366 NaN \n", "min 27420.000000 7.275600 NaN \n", "25% 27641.750000 20.536239 NaN \n", "50% 27851.500000 28.928500 NaN \n", "75% 28066.250000 37.133850 NaN \n", "max 28288.000000 119.394035 NaN \n", "\n", " name brand \n", "count 200 200 \\\n", "unique 195 55 \n", "top New Men's Sexy Center Patch Bikini Swimsuit 3G... Hurley \n", "freq 3 44 \n", "mean NaN NaN \n", "std NaN NaN \n", "min NaN NaN \n", "25% NaN NaN \n", "50% NaN NaN \n", "75% NaN NaN \n", "max NaN NaN \n", "\n", " retail_price department sku \n", "count 200.000000 200 200 \\\n", "unique NaN 1 200 \n", "top NaN Men B23C5765E165D83AA924FA8F13C05F25 \n", "freq NaN 200 1 \n", "mean 51.097600 NaN NaN \n", "std 26.595876 NaN NaN \n", "min 12.900000 NaN NaN \n", "25% 35.000000 NaN NaN \n", "50% 47.975000 NaN NaN \n", "75% 61.000000 NaN NaN \n", "max 199.990000 NaN NaN \n", "\n", " distribution_center_id \n", "count 200.00000 \n", "unique NaN \n", "top NaN \n", "freq NaN \n", "mean 1.50000 \n", "std 0.72292 \n", "min 1.00000 \n", "25% 1.00000 \n", "50% 1.00000 \n", "75% 2.00000 \n", "max 3.00000 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Sample data for table: products" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idcostcategorynamebrandretail_pricedepartmentskudistribution_center_id
02756992.652565Swim2XU Men's Swimmers Compression Long Sleeve Top2XU150.41MenB23C5765E165D83AA924FA8F13C05F251
12744524.719662SwimTYR Sport Men's Square Leg Short Swim SuitTYR38.99Men2AB7D3B23574C3DEA2BD278AFD0939AB1
22745715.897600SwimTYR Sport Men's Solid Durafast Jammer Swim SuitTYR27.60Men8F831227B0EB6C6D09A05555313659331
32746617.850000SwimTYR Sport Men's Swim Short/Resistance Short Sw...TYR30.00Men67317D6DCC4CB778AEB9219565F5456B1
42748129.408000SwimTYR Alliance Team Splice JammerTYR45.95Men213C888198806EF1A0E2BBF2F4855C6C1
52748715.655590SwimTYR Sport Men's 4-Inch Nylon Trainer-A Swim SuitTYR26.49Men978F39314267ADC0E1C50DB2615B467C1
62751022.571750SwimTYR Sport Men's Solid Jammer Swim SuitTYR39.95Men4ECBB790F241666326D31F799EB85D1E1
72752922.824000SwimTYR Sport Men's Poly Mesh Trainer Swim SuitTYR36.00MenC386CBA5332D11385672EE52D036E8C11
82753724.353910SwimTYR Sport Men's Alliance Durafast Splice Squar...TYR39.99MenD012C46243D7E2391B64B221DFCC18E31
92755219.317550SwimTYR Sport Men's Solid Racer Swim SuitTYR33.95Men2AF9B1A840B4ECD522FE1CDA88C8385E1
\n", "
" ], "text/plain": [ " id cost category \n", "0 27569 92.652565 Swim \\\n", "1 27445 24.719662 Swim \n", "2 27457 15.897600 Swim \n", "3 27466 17.850000 Swim \n", "4 27481 29.408000 Swim \n", "5 27487 15.655590 Swim \n", "6 27510 22.571750 Swim \n", "7 27529 22.824000 Swim \n", "8 27537 24.353910 Swim \n", "9 27552 19.317550 Swim \n", "\n", " name brand retail_price \n", "0 2XU Men's Swimmers Compression Long Sleeve Top 2XU 150.41 \\\n", "1 TYR Sport Men's Square Leg Short Swim Suit TYR 38.99 \n", "2 TYR Sport Men's Solid Durafast Jammer Swim Suit TYR 27.60 \n", "3 TYR Sport Men's Swim Short/Resistance Short Sw... TYR 30.00 \n", "4 TYR Alliance Team Splice Jammer TYR 45.95 \n", "5 TYR Sport Men's 4-Inch Nylon Trainer-A Swim Suit TYR 26.49 \n", "6 TYR Sport Men's Solid Jammer Swim Suit TYR 39.95 \n", "7 TYR Sport Men's Poly Mesh Trainer Swim Suit TYR 36.00 \n", "8 TYR Sport Men's Alliance Durafast Splice Squar... TYR 39.99 \n", "9 TYR Sport Men's Solid Racer Swim Suit TYR 33.95 \n", "\n", " department sku distribution_center_id \n", "0 Men B23C5765E165D83AA924FA8F13C05F25 1 \n", "1 Men 2AB7D3B23574C3DEA2BD278AFD0939AB 1 \n", "2 Men 8F831227B0EB6C6D09A0555531365933 1 \n", "3 Men 67317D6DCC4CB778AEB9219565F5456B 1 \n", "4 Men 213C888198806EF1A0E2BBF2F4855C6C 1 \n", "5 Men 978F39314267ADC0E1C50DB2615B467C 1 \n", "6 Men 4ECBB790F241666326D31F799EB85D1E 1 \n", "7 Men C386CBA5332D11385672EE52D036E8C1 1 \n", "8 Men D012C46243D7E2391B64B221DFCC18E3 1 \n", "9 Men 2AF9B1A840B4ECD522FE1CDA88C8385E 1 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column value histgram for table: products" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column description for table: users" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2c166872fcf74834a0c94e66b15ef881", "version_major": 2, "version_minor": 0 }, "text/plain": [ "TableWidget(columns=[{'header': 'Name', 'accessor': 'name'}, {'header': 'Type', 'accessor': 'type'}, {'header'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column statistics for table: users" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idfirst_namelast_nameemailagegenderstatestreet_addresspostal_codecitycountrylatitudelongitudetraffic_sourcecreated_at
count200.000000200200200200.000000200200200200200200200.000000200.000000200200
uniqueNaN136148200NaN2520049393NaNNaN5NaN
topNaNBrianSmithangelanichols@example.comNaNFIowa5069 Gray Garden Suite 93469980-000Des MoinesUnited StatesNaNNaNSearchNaN
freqNaN6101NaN10511511314152NaNNaN131NaN
mean50421.375000NaNNaNNaN40.855000NaNNaNNaNNaNNaNNaN32.463391-71.080253NaN2021-04-07 12:12:04.364999936
min787.000000NaNNaNNaN12.000000NaNNaNNaNNaNNaNNaN-9.945568-96.325386NaN2019-01-06 08:57:00
25%24560.750000NaNNaNNaN26.750000NaNNaNNaNNaNNaNNaN40.022990-93.190410NaN2020-01-11 21:14:15
50%52219.500000NaNNaNNaN40.000000NaNNaNNaNNaNNaNNaN41.448532-90.693850NaN2021-05-23 05:46:00
75%74545.250000NaNNaNNaN56.000000NaNNaNNaNNaNNaNNaN41.985725-82.467880NaN2022-05-12 08:46:30
max99340.000000NaNNaNNaN70.000000NaNNaNNaNNaNNaNNaN43.148760137.557880NaN2023-05-03 14:46:38
std28050.497334NaNNaNNaN16.932143NaNNaNNaNNaNNaNNaN18.87900557.843891NaNNaN
\n", "
" ], "text/plain": [ " id first_name last_name email \n", "count 200.000000 200 200 200 \\\n", "unique NaN 136 148 200 \n", "top NaN Brian Smith angelanichols@example.com \n", "freq NaN 6 10 1 \n", "mean 50421.375000 NaN NaN NaN \n", "min 787.000000 NaN NaN NaN \n", "25% 24560.750000 NaN NaN NaN \n", "50% 52219.500000 NaN NaN NaN \n", "75% 74545.250000 NaN NaN NaN \n", "max 99340.000000 NaN NaN NaN \n", "std 28050.497334 NaN NaN NaN \n", "\n", " age gender state street_address postal_code \n", "count 200.000000 200 200 200 200 \\\n", "unique NaN 2 5 200 49 \n", "top NaN F Iowa 5069 Gray Garden Suite 934 69980-000 \n", "freq NaN 105 115 1 13 \n", "mean 40.855000 NaN NaN NaN NaN \n", "min 12.000000 NaN NaN NaN NaN \n", "25% 26.750000 NaN NaN NaN NaN \n", "50% 40.000000 NaN NaN NaN NaN \n", "75% 56.000000 NaN NaN NaN NaN \n", "max 70.000000 NaN NaN NaN NaN \n", "std 16.932143 NaN NaN NaN NaN \n", "\n", " city country latitude longitude traffic_source \n", "count 200 200 200.000000 200.000000 200 \\\n", "unique 39 3 NaN NaN 5 \n", "top Des Moines United States NaN NaN Search \n", "freq 14 152 NaN NaN 131 \n", "mean NaN NaN 32.463391 -71.080253 NaN \n", "min NaN NaN -9.945568 -96.325386 NaN \n", "25% NaN NaN 40.022990 -93.190410 NaN \n", "50% NaN NaN 41.448532 -90.693850 NaN \n", "75% NaN NaN 41.985725 -82.467880 NaN \n", "max NaN NaN 43.148760 137.557880 NaN \n", "std NaN NaN 18.879005 57.843891 NaN \n", "\n", " created_at \n", "count 200 \n", "unique NaN \n", "top NaN \n", "freq NaN \n", "mean 2021-04-07 12:12:04.364999936 \n", "min 2019-01-06 08:57:00 \n", "25% 2020-01-11 21:14:15 \n", "50% 2021-05-23 05:46:00 \n", "75% 2022-05-12 08:46:30 \n", "max 2023-05-03 14:46:38 \n", "std NaN " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Sample data for table: users" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idfirst_namelast_nameemailagegenderstatestreet_addresspostal_codecitycountrylatitudelongitudetraffic_sourcecreated_at
017478AngelaNicholsangelanichols@example.com29FAcre5069 Gray Garden Suite 93469917-400Rio BrancoBrasil-9.945568-67.83561Display2023-02-26 17:23:00
127987NathanSmithnathansmith@example.com35MAcre419 Wilson Walk69917-400Rio BrancoBrasil-9.945568-67.83561Search2021-12-21 03:33:00
239395JaimeSanchezjaimesanchez@example.net58MAcre87798 Dale Bypass Suite 95469917-400Rio BrancoBrasil-9.945568-67.83561Organic2020-08-25 16:23:00
344090MichaelReidmichaelreid@example.net41MAcre9698 Jones Run Suite 46769917-400Rio BrancoBrasil-9.945568-67.83561Organic2020-07-22 18:46:00
448973DavidNguyendavidnguyen@example.com42MAcre0025 Norris Camp Apt. 93169917-400Rio BrancoBrasil-9.945568-67.83561Search2020-06-29 07:06:00
557044BradleyWrightbradleywright@example.org58MAcre119 Gonzalez Rest Suite 62369917-400Rio BrancoBrasil-9.945568-67.83561Search2021-08-14 15:09:00
610681TracyClarktracyclark@example.net70FAcre675 Nathan Summit69940-000Sena MadureiraBrasil-9.857325-69.43706Search2023-01-25 15:42:00
716340EmilyDavisemilydavis@example.net16FAcre0593 Steven Ferry69940-000Sena MadureiraBrasil-9.857325-69.43706Search2022-09-30 14:54:00
827782ChristopherWardchristopherward@example.org30MAcre192 Davis Lights69940-000Sena MadureiraBrasil-9.857325-69.43706Search2019-09-16 15:09:00
929322KatrinaDavenportkatrinadavenport@example.org39FAcre934 Michael Gardens69940-000Sena MadureiraBrasil-9.857325-69.43706Search2022-06-01 16:27:00
\n", "
" ], "text/plain": [ " id first_name last_name email age gender \n", "0 17478 Angela Nichols angelanichols@example.com 29 F \\\n", "1 27987 Nathan Smith nathansmith@example.com 35 M \n", "2 39395 Jaime Sanchez jaimesanchez@example.net 58 M \n", "3 44090 Michael Reid michaelreid@example.net 41 M \n", "4 48973 David Nguyen davidnguyen@example.com 42 M \n", "5 57044 Bradley Wright bradleywright@example.org 58 M \n", "6 10681 Tracy Clark tracyclark@example.net 70 F \n", "7 16340 Emily Davis emilydavis@example.net 16 F \n", "8 27782 Christopher Ward christopherward@example.org 30 M \n", "9 29322 Katrina Davenport katrinadavenport@example.org 39 F \n", "\n", " state street_address postal_code city country \n", "0 Acre 5069 Gray Garden Suite 934 69917-400 Rio Branco Brasil \\\n", "1 Acre 419 Wilson Walk 69917-400 Rio Branco Brasil \n", "2 Acre 87798 Dale Bypass Suite 954 69917-400 Rio Branco Brasil \n", "3 Acre 9698 Jones Run Suite 467 69917-400 Rio Branco Brasil \n", "4 Acre 0025 Norris Camp Apt. 931 69917-400 Rio Branco Brasil \n", "5 Acre 119 Gonzalez Rest Suite 623 69917-400 Rio Branco Brasil \n", "6 Acre 675 Nathan Summit 69940-000 Sena Madureira Brasil \n", "7 Acre 0593 Steven Ferry 69940-000 Sena Madureira Brasil \n", "8 Acre 192 Davis Lights 69940-000 Sena Madureira Brasil \n", "9 Acre 934 Michael Gardens 69940-000 Sena Madureira Brasil \n", "\n", " latitude longitude traffic_source created_at \n", "0 -9.945568 -67.83561 Display 2023-02-26 17:23:00 \n", "1 -9.945568 -67.83561 Search 2021-12-21 03:33:00 \n", "2 -9.945568 -67.83561 Organic 2020-08-25 16:23:00 \n", "3 -9.945568 -67.83561 Organic 2020-07-22 18:46:00 \n", "4 -9.945568 -67.83561 Search 2020-06-29 07:06:00 \n", "5 -9.945568 -67.83561 Search 2021-08-14 15:09:00 \n", "6 -9.857325 -69.43706 Search 2023-01-25 15:42:00 \n", "7 -9.857325 -69.43706 Search 2022-09-30 14:54:00 \n", "8 -9.857325 -69.43706 Search 2019-09-16 15:09:00 \n", "9 -9.857325 -69.43706 Search 2022-06-01 16:27:00 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Column value histgram for table: users" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+kAAAL3CAYAAAD2lgQgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACUQUlEQVR4nOzdfXxT9f3//2da0rSFplAueiEFKiLXiIJCBQWhtCKijG4O5xw4plMLE/pRtE6UAlpkmzAdF+rPwdxEB1NwKkIrCgwFJlUUUCsgiAotXrUBKiE25/cH32aEtrRpk+akfdxvN25wznmfk2depMl59ZycYzEMwxAAAAAAAAi6sGAHAAAAAAAAp9GkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMhYPny5bJYLDp48OA5x3Xp0kWTJk1qlEwAAKD5mDRpkrp06VLruIMHD8pisWj58uUBzwQ0VTTpAAAAAACYBE06EAJuvvlm/fDDD+rcuXOwowAAgGbo6aefVlFRUbBjAM1Ci2AHAFC78PBwhYeHBzsGAABoQtxut06dOqXIyMhax1qt1kZIBEDiSDoQEs7+TrphGJo7d646duyo6OhoXXXVVdqzZ09wQwIAgDqr6Tves2bNksVi8UwXFBRo6NChat26tVq1aqXu3bvr/vvv91rH6XTqoYce0gUXXCCbzabk5GTNmDFDTqfTa5zFYtGUKVP03HPPqXfv3rLZbFq3bl2985aWlmrSpEmKjY1V69atNXHiRJWWltZpewBqxpF0IAQ9+OCDmjt3rq655hpdc801eu+995Senq5Tp04FOxoAAPCTPXv26Nprr1W/fv00e/Zs2Ww27du3T2+//bZnjNvt1nXXXactW7botttuU8+ePbVr1y4tWLBAn376qdasWeO1zTfffFMrV67UlClT1K5duzpdDK46hmHo+uuv15YtW3T77berZ8+eWr16tSZOnNiAZwxAokkHQs7XX3+t+fPna8yYMXrllVc8v23//e9/r0ceeSTI6QAAgL8UFBTo1KlTev3119WuXbtqx6xYsUJvvPGGNm3apKFDh3rm9+nTR7fffrveeecdXX755Z75RUVF2rVrl3r16tWgbP/+97+1efNmzZ8/X/fcc48k6Y477tBVV13VoO0C4HR3IOS88cYbOnXqlKZOnep1Oty0adOCFwoAAPhd69atJUkvv/yy3G53tWNWrVqlnj17qkePHvrmm288f0aMGCFJeuutt7zGDxs2rMENuiStXbtWLVq00B133OGZFx4erqlTpzZ420BzR5MOhJjPP/9cktStWzev+e3bt1ebNm2CEQkAAATAz3/+cw0ZMkS/+c1vFB8frwkTJmjlypVeDfvevXu1Z88etW/f3uvPhRdeKEk6evSo1zZTUlL8ku3zzz9XYmKiWrVq5TW/e/fuftk+0JxxujsAAADQyM48G+5MFRUVnn9HRUVp8+bNeuutt/Taa69p3bp1+uc//6kRI0YoPz9f4eHhcrvd6tu3rx577LFqt5ecnOw1HRUV5b8nASAgaNKBEFN5r/S9e/fq/PPP98z/+uuv9f333wcrFgAA8EGbNm2qvRJ65RlzlcLCwjRy5EiNHDlSjz32mB555BH9/ve/11tvvaW0tDR17dpVH3zwgUaOHFlj4x8InTt31oYNG3T8+HGvo+ncSx1oOE53B0JMWlqarFarnnjiCRmG4Zm/cOHC4IUCAAA+6dq1q8rKyvThhx965h05ckSrV6/2TH/33XdV1uvfv78keW6vdsMNN+irr77S008/XWXsDz/8oBMnTvg5+WnXXHONfvzxRy1ZssQzr6KiQk888URAHg9oTjiSDoSY9u3b6+6771ZeXp6uvfZaXXPNNXr//ffPeeVXAABgLhMmTNC9996rn/zkJ/rd736n8vJyLVmyRBdeeKHee+89SdLs2bO1efNmjRkzRp07d9bRo0e1ePFidezY0XMl95tvvlkrV67U7bffrrfeektDhgxRRUWFPvnkE61cuVLr16/XwIED/Z5/7NixGjJkiO677z4dPHhQvXr10ksvvaSysjK/PxbQ3NCkAyFo7ty5ioyM1NKlS/XWW29p0KBBys/P15gxY4IdDQAA1EHbtm21evVqZWdna8aMGUpJSVFeXp727t3radKvu+46HTx4UH/961/1zTffqF27dho2bJhyc3MVGxsr6fTp8GvWrNGCBQv07LPPavXq1YqOjtb555+vu+66y3MBOX8LCwvTv//9b02bNk3/+Mc/ZLFYdN111+lPf/qTLr744oA8JtBcWIwzz5cFAAAAAABBw3fSAQAAAAAwCU53BwAAAJqp7777TqdOnapxeXh4uNq3b9+IiQBwujsAAADQTA0fPlybNm2qcXnnzp118ODBxgsEgCYdAAAAaK4KCwv1/fff17g8KipKQ4YMacREAGjSAQAAAAAwCS4cBwAAAACASZjuwnFut1uHDx9WTEyMLBZLsOMAAEKYYRg6duyYkpKSFBbG76WbIvYbAAD+YKZ9BtM16YcPH1ZycnKwYwAAmpAvvvhCHTt2DHYMBAD7DQAAfzLDPoPpmvSYmBhJp4tjt9vrvR2Xy6X8/Hylp6fLarX6K16TRs18Q718R818Q718d3bNHA6HkpOTPZ8taHrYb2hc1KluqFPdUKe6oU6180eNzLTPYLomvfJUNbvd3uAP2+joaNntdl7MdUTNfEO9fEfNfEO9fFdTzTgNuuliv6FxUae6oU51Q53qhjrVzp81MsM+A1/QAwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJmnQAAAAAAEyCJh0AAAAAAJOgSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk2gR7ADNTZf7Xgt2hCoOzhsT7AgAAJhCn1nr5aywBDuGB5/RAND8cCQdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJmnQAAAAAAEyCJh0AAAAAAJOgSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJn5r0vLw8XXrppYqJiVGHDh00btw4FRUVeY0ZPny4LBaL15/bb7/dr6EBAAAAAGiKfGrSN23apKysLG3btk0FBQVyuVxKT0/XiRMnvMbdeuutOnLkiOfP/Pnz/RoaAAAAAICmqIUvg9etW+c1vXz5cnXo0EGFhYW68sorPfOjo6OVkJDgn4QAAAAAADQTDfpOellZmSQpLi7Oa/5zzz2ndu3aqU+fPsrJyVF5eXlDHgYAAAAAgGbBpyPpZ3K73Zo2bZqGDBmiPn36eOb/4he/UOfOnZWUlKQPP/xQ9957r4qKivTSSy9Vux2n0ymn0+mZdjgckiSXyyWXy1XfeJ51G7KNQLCFG8GOUMXZtTJbzcyKevmOmvmGevmO9zMAABDq6t2kZ2Vlaffu3dqyZYvX/Ntuu83z7759+yoxMVEjR47U/v371bVr1yrbycvLU25ubpX5+fn5io6Orm88j4KCggZvw5/mXxbsBFWtXbvWa9psNTM76uU7auYb6uW7yppxJhcAAAg19WrSp0yZoldffVWbN29Wx44dzzl20KBBkqR9+/ZV26Tn5OQoOzvbM+1wOJScnKz09HTZ7fb6xJN0+uhJQUGBRo0aJavVWu/t+FufWeuDHaGK3bMyJJm3ZmZFvXxHzXxDvXx3ds0qz84CAAAIFT416YZhaOrUqVq9erU2btyolJSUWtfZuXOnJCkxMbHa5TabTTabrcp8q9Xql51Sf23HX5wVlmBHqOLs+pitZmZHvXxHzXxDvXxXWTPqBgAAQo1PTXpWVpZWrFihl19+WTExMSouLpYkxcbGKioqSvv379eKFSt0zTXXqG3btvrwww81ffp0XXnllerXr19AngAAAAAAAE2FT036kiVLJEnDhw/3mr9s2TJNmjRJEREReuONN7Rw4UKdOHFCycnJyszM1AMPPOC3wAAAAAAANFU+n+5+LsnJydq0aVODAgEAAAAA0Fw16D7pAAAAAADAf2jSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJmnQAABAQFRUVmjlzplJSUhQVFaWuXbtqzpw5Xrd0NQxDDz74oBITExUVFaW0tDTt3bs3iKkBAAgumnQAABAQjz76qJYsWaK//OUv+vjjj/Xoo49q/vz5euKJJzxj5s+fr8cff1xLly7V9u3b1bJlS2VkZOjkyZNBTA4AQPC0CHYAAADQNL3zzju6/vrrNWbMGElSly5d9Pzzz+u///2vpNNH0RcuXKgHHnhA119/vSTp2WefVXx8vNasWaMJEyYELTsAAMFCkw4AAALi8ssv11NPPaVPP/1UF154oT744ANt2bJFjz32mCTpwIEDKi4uVlpammed2NhYDRo0SFu3bq22SXc6nXI6nZ5ph8MhSXK5XHK5XPXOWrmuLcyoZWTjashzCoTKPGbLZTbUqW6oU91Qp9r5o0Zmqi9NOgAACIj77rtPDodDPXr0UHh4uCoqKvTwww/rpptukiQVFxdLkuLj473Wi4+P9yw7W15ennJzc6vMz8/PV3R0dIMzzxnobvA2/Gnt2rXBjlCtgoKCYEcICdSpbqhT3VCn2jWkRuXl5X5M0jA06QAAICBWrlyp5557TitWrFDv3r21c+dOTZs2TUlJSZo4cWK9tpmTk6Ps7GzPtMPhUHJystLT02W32+ud1eVyqaCgQDN3hMnpttR7O/62e1ZGsCN4qazTqFGjZLVagx3HtKhT3VCnuqFOtfNHjSrPzDIDmnQAABAQ99xzj+677z7Paet9+/bV559/rry8PE2cOFEJCQmSpJKSEiUmJnrWKykpUf/+/avdps1mk81mqzLfarX6ZefV6bbIWWGeJt2sO+T+qndTR53qhjrVDXWqXUNqZKbacnV3AAAQEOXl5QoL897VCA8Pl9t9+pTylJQUJSQkaMOGDZ7lDodD27dvV2pqaqNmBQDALDiSDgAAAmLs2LF6+OGH1alTJ/Xu3Vvvv/++HnvsMf3617+WJFksFk2bNk1z585Vt27dlJKSopkzZyopKUnjxo0LbngAAIKEJh2m1OW+14IdoYqD88YEOwIAhJQnnnhCM2fO1J133qmjR48qKSlJv/3tb/Xggw96xsyYMUMnTpzQbbfdptLSUg0dOlTr1q1TZGRkEJMDABA8NOkAACAgYmJitHDhQi1cuLDGMRaLRbNnz9bs2bMbLxgAACbGd9IBAAAAADAJmnQAAAAAAEyCJh0AAAAAAJOgSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQAAAAAwiRbBDhBofWatl7PCEuwYptblvtckSbZwQ/Mvo2YAAAAAECwcSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkWgQ7AAAEUpf7Xgt2hCoOzhsT7AgAAAAwKY6kAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJiET016Xl6eLr30UsXExKhDhw4aN26cioqKvMacPHlSWVlZatu2rVq1aqXMzEyVlJT4NTQAAAAAAE2RT036pk2blJWVpW3btqmgoEAul0vp6ek6ceKEZ8z06dP1yiuvaNWqVdq0aZMOHz6s8ePH+z04AAAAAABNTQtfBq9bt85revny5erQoYMKCwt15ZVXqqysTM8884xWrFihESNGSJKWLVumnj17atu2bRo8eLD/kgMAAAAA0MT41KSfraysTJIUFxcnSSosLJTL5VJaWppnTI8ePdSpUydt3bq12ibd6XTK6XR6ph0OhyTJ5XLJ5XLVO1vlurYwo97baG4qa0XNqnf267FyuiGv0+YmGDWzhZvv9VzX589rzHdn14zaAQCAUFPvJt3tdmvatGkaMmSI+vTpI0kqLi5WRESEWrdu7TU2Pj5excXF1W4nLy9Pubm5Vebn5+crOjq6vvE85gx0N3gbzQ01q97atWurnV9QUNDISUJfY9Zs/mWN9lB1VtNrqSa8xnxXWbPy8vIgJwEAAPBNvZv0rKws7d69W1u2bGlQgJycHGVnZ3umHQ6HkpOTlZ6eLrvdXu/tulwuFRQUaOaOMDndlgZlbC5sYYbmDHRTsxrsnpXhNV35Ghs1apSsVmuQUoWWYNSsz6z1jfI4vjj7tVQTXmO+O7tmlWdnAQAAhIp6NelTpkzRq6++qs2bN6tjx46e+QkJCTp16pRKS0u9jqaXlJQoISGh2m3ZbDbZbLYq861Wq192Sp1ui5wVNJy+oGbVq+n16K/XanPSmDUz42vZ1+fOa8x3lTWjbgAAINT4dHV3wzA0ZcoUrV69Wm+++aZSUlK8lg8YMEBWq1UbNmzwzCsqKtKhQ4eUmprqn8QAAAAAADRRPh1Jz8rK0ooVK/Tyyy8rJibG8z3z2NhYRUVFKTY2VpMnT1Z2drbi4uJkt9s1depUpaamcmV3AAAAAABq4VOTvmTJEknS8OHDveYvW7ZMkyZNkiQtWLBAYWFhyszMlNPpVEZGhhYvXuyXsAAAAAAANGU+NemGUfutjCIjI7Vo0SItWrSo3qEAAAAAAGiOfPpOOgAAAAAACByadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJHy6cBzQnHW57zWvaVu4ofmXSX1mrZezwhKkVOZycN6YYEcAAAAAQhpH0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJmnQAABAwX331lX75y1+qbdu2ioqKUt++fbVjxw7PcsMw9OCDDyoxMVFRUVFKS0vT3r17g5gYAIDgokkHAAAB8f3332vIkCGyWq16/fXX9dFHH+lPf/qT2rRp4xkzf/58Pf7441q6dKm2b9+uli1bKiMjQydPngxicgAAgqdFsAMAAICm6dFHH1VycrKWLVvmmZeSkuL5t2EYWrhwoR544AFdf/31kqRnn31W8fHxWrNmjSZMmNDomQEACDaadAAAEBD//ve/lZGRoZ/97GfatGmTzjvvPN1555269dZbJUkHDhxQcXGx0tLSPOvExsZq0KBB2rp1a7VNutPplNPp9Ew7HA5JksvlksvlqnfWynVtYUa9txEIDXlOgVCZx2y5zIY61Q11qhvqVDt/1MhM9aVJBwAAAfHZZ59pyZIlys7O1v333693331Xv/vd7xQREaGJEyequLhYkhQfH++1Xnx8vGfZ2fLy8pSbm1tlfn5+vqKjoxucec5Ad4O34U9r164NdoRqFRQUBDtCSKBOdUOd6oY61a4hNSovL/djkoahSQcAAAHhdrs1cOBAPfLII5Kkiy++WLt379bSpUs1ceLEem0zJydH2dnZnmmHw6Hk5GSlp6fLbrfXO6vL5VJBQYFm7giT022p93b8bfesjGBH8FJZp1GjRslqtQY7jmlRp7qhTnVDnWrnjxpVnpllBjTpAAAgIBITE9WrVy+veT179tSLL74oSUpISJAklZSUKDEx0TOmpKRE/fv3r3abNptNNputynyr1eqXnVen2yJnhXmadLPukPur3k0ddaob6lQ31Kl2DamRmWrL1d0BAEBADBkyREVFRV7zPv30U3Xu3FnS6YvIJSQkaMOGDZ7lDodD27dvV2pqaqNmBQDALDiSDgAAAmL69Om6/PLL9cgjj+iGG27Qf//7Xz311FN66qmnJEkWi0XTpk3T3Llz1a1bN6WkpGjmzJlKSkrSuHHjghseAIAgoUkHAAABcemll2r16tXKycnR7NmzlZKSooULF+qmm27yjJkxY4ZOnDih2267TaWlpRo6dKjWrVunyMjIICYHACB4aNIBAEDAXHvttbr22mtrXG6xWDR79mzNnj27EVMBAGBeNOkAAHW577VgR/BycN6YYEcAAAAICi4cBwAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBItgh0AQNPR5b7XzrncFm5o/mVSn1nr5aywNFIqAAAAIHRwJB0AAAAAAJOgSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk/C5Sd+8ebPGjh2rpKQkWSwWrVmzxmv5pEmTZLFYvP5cffXV/soLAAAAAECT5XOTfuLECV100UVatGhRjWOuvvpqHTlyxPPn+eefb1BIAAAAAACaA5/vkz569GiNHj36nGNsNpsSEhLqHQoAAAAAgObI5ya9LjZu3KgOHTqoTZs2GjFihObOnau2bdtWO9bpdMrpdHqmHQ6HJMnlcsnlctU7Q+W6tjCj3ttobiprRc3qhnr5jpqdVtf3tspxDXkvrCtbuLn+T+r7nM+uWWPUDgAAwJ/83qRfffXVGj9+vFJSUrR//37df//9Gj16tLZu3arw8PAq4/Py8pSbm1tlfn5+vqKjoxucZ85Ad4O30dxQM99QL98195qtXbvWp/EFBQUBSvI/8y8L+EP4xNcana2yZuXl5f6IAwAA0Gj83qRPmDDB8+++ffuqX79+6tq1qzZu3KiRI0dWGZ+Tk6Ps7GzPtMPhUHJystLT02W32+udw+VyqaCgQDN3hMnpttR7O82JLczQnIFualZH1Mt31Oy03bMy6jSu8n1s1KhRslqtAc3UZ9b6gG7fV3Wt0dnOrlnl2VkAAAChIiCnu5/p/PPPV7t27bRv375qm3SbzSabzVZlvtVq9ctOqdNtkbOi+TYD9UHNfEO9fNfca+bre5u/3g/PxWz/Hw19vpU1C3TdAAAA/C3g90n/8ssv9e233yoxMTHQDwUAAAAAQEjz+Uj68ePHtW/fPs/0gQMHtHPnTsXFxSkuLk65ubnKzMxUQkKC9u/frxkzZuiCCy5QRkb9Tl0EAAAAAKC58LlJ37Fjh6666irPdOX3ySdOnKglS5boww8/1N/+9jeVlpYqKSlJ6enpmjNnTrWntAMAAAAAgP/xuUkfPny4DKPmW/WsX2+uiw8BAAAAABAqAv6ddAAAAAAAUDc06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASfh84TgAQMN0ue+1Oo2zhRuaf5nUZ9Z6OSssAU4FAAAAM+BIOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAICAmzdvniwWi6ZNm+aZd/LkSWVlZalt27Zq1aqVMjMzVVJSEryQAACYAE06AAAIqHfffVdPPvmk+vXr5zV/+vTpeuWVV7Rq1Spt2rRJhw8f1vjx44OUEgAAc6BJBwAAAXP8+HHddNNNevrpp9WmTRvP/LKyMj3zzDN67LHHNGLECA0YMEDLli3TO++8o23btgUxMQAAwUWTDgAAAiYrK0tjxoxRWlqa1/zCwkK5XC6v+T169FCnTp20devWxo4JAIBptAh2AAAA0DS98MILeu+99/Tuu+9WWVZcXKyIiAi1bt3aa358fLyKi4tr3KbT6ZTT6fRMOxwOSZLL5ZLL5ap31sp1bWFGvbcRCA15ToFQmcdsucyGOtUNdaob6lQ7f9TITPWlSQcAAH73xRdf6K677lJBQYEiIyP9tt28vDzl5uZWmZ+fn6/o6OgGb3/OQHeDt+FPa9euDXaEahUUFAQ7QkigTnVDneqGOtWuITUqLy/3Y5KGoUkHAAB+V1hYqKNHj+qSSy7xzKuoqNDmzZv1l7/8RevXr9epU6dUWlrqdTS9pKRECQkJNW43JydH2dnZnmmHw6Hk5GSlp6fLbrfXO6/L5VJBQYFm7giT022p93b8bfesjGBH8FJZp1GjRslqtQY7jmlRp7qhTnVDnWrnjxpVnpllBjTpAADA70aOHKldu3Z5zbvlllvUo0cP3XvvvUpOTpbVatWGDRuUmZkpSSoqKtKhQ4eUmppa43ZtNptsNluV+Var1S87r063Rc4K8zTpZt0h91e9mzrqVDfUqW6oU+0aUiMz1ZYmHQAA+F1MTIz69OnjNa9ly5Zq27atZ/7kyZOVnZ2tuLg42e12TZ06VampqRo8eHAwIgMAYAo06QAAICgWLFigsLAwZWZmyul0KiMjQ4sXLw52LAAAgoomHQAANIqNGzd6TUdGRmrRokVatGhRcAIBAGBC3CcdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJmnQAAAAAAEyCJh0AAAAAAJOgSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATMLnJn3z5s0aO3askpKSZLFYtGbNGq/lhmHowQcfVGJioqKiopSWlqa9e/f6Ky8AAAAAAE2Wz036iRMndNFFF2nRokXVLp8/f74ef/xxLV26VNu3b1fLli2VkZGhkydPNjgsAAAAAABNWQtfVxg9erRGjx5d7TLDMLRw4UI98MADuv766yVJzz77rOLj47VmzRpNmDChYWkBAAAAAGjCfG7Sz+XAgQMqLi5WWlqaZ15sbKwGDRqkrVu3VtukO51OOZ1Oz7TD4ZAkuVwuuVyuemepXNcWZtR7G81NZa2oWd1QL99RM98053rV9/2/cr2z/wYAAAgVfm3Si4uLJUnx8fFe8+Pj4z3LzpaXl6fc3Nwq8/Pz8xUdHd3gTHMGuhu8jeaGmvmGevmOmvmmOdZr7dq1DVq/oKBAklReXu6POAAAAI3Gr016feTk5Cg7O9sz7XA4lJycrPT0dNnt9npv1+VyqaCgQDN3hMnptvgjapNnCzM0Z6CbmtUR9fIdNfNNc67X7lkZ9Vqv8r1/1KhRslqtnrOzAAAAQoVfm/SEhARJUklJiRITEz3zS0pK1L9//2rXsdlsstlsVeZbrVZZrdYGZ3K6LXJWNK+d24aiZr6hXr6jZr5pjvVq6Pt/5WeIPz5HAAAAGpNf75OekpKihIQEbdiwwTPP4XBo+/btSk1N9edDAQAAAADQ5Ph8JP348ePat2+fZ/rAgQPauXOn4uLi1KlTJ02bNk1z585Vt27dlJKSopkzZyopKUnjxo3zZ24AAAAAAJocn5v0HTt26KqrrvJMV36ffOLEiVq+fLlmzJihEydO6LbbblNpaamGDh2qdevWKTIy0n+pAQAAAABognxu0ocPHy7DqPl2QBaLRbNnz9bs2bMbFAwAAAAAgObGr99JBwAAAAAA9UeTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAIiLy9Pl156qWJiYtShQweNGzdORUVFXmNOnjyprKwstW3bVq1atVJmZqZKSkqClBgAgOCjSQcAAAGxadMmZWVladu2bSooKJDL5VJ6erpOnDjhGTN9+nS98sorWrVqlTZt2qTDhw9r/PjxQUwNAEBwtQh2AAAA0DStW7fOa3r58uXq0KGDCgsLdeWVV6qsrEzPPPOMVqxYoREjRkiSli1bpp49e2rbtm0aPHhwMGIDABBUNOkAAKBRlJWVSZLi4uIkSYWFhXK5XEpLS/OM6dGjhzp16qStW7dW26Q7nU45nU7PtMPhkCS5XC65XK56Z6tc1xZm1HsbgdCQ5xQIlXnMlstsqFPdUKe6oU6180eNzFRfmnQAABBwbrdb06ZN05AhQ9SnTx9JUnFxsSIiItS6dWuvsfHx8SouLq52O3l5ecrNza0yPz8/X9HR0Q3OOWegu8Hb8Ke1a9cGO0K1CgoKgh0hJFCnuqFOdUOdateQGpWXl/sxScPQpAMAgIDLysrS7t27tWXLlgZtJycnR9nZ2Z5ph8Oh5ORkpaeny26313u7LpdLBQUFmrkjTE63pUEZ/Wn3rIxgR/BSWadRo0bJarUGO45pUae6oU51Q51q548aVZ6ZZQY06QAAIKCmTJmiV199VZs3b1bHjh098xMSEnTq1CmVlpZ6HU0vKSlRQkJCtduy2Wyy2WxV5lutVr/svDrdFjkrzNOkm3WH3F/1buqoU91Qp7qhTrVrSI3MVFuu7g4AAALCMAxNmTJFq1ev1ptvvqmUlBSv5QMGDJDVatWGDRs884qKinTo0CGlpqY2dlwAAEyBI+kAACAgsrKytGLFCr388suKiYnxfM88NjZWUVFRio2N1eTJk5Wdna24uDjZ7XZNnTpVqampXNkdANBs+f1I+qxZs2SxWLz+9OjRw98PAwAATG7JkiUqKyvT8OHDlZiY6Pnzz3/+0zNmwYIFuvbaa5WZmakrr7xSCQkJeumll4KYGgCA4ArIkfTevXvrjTfe+N+DtOCAPQAAzY1h1H47s8jISC1atEiLFi1qhEQAAJhfQLrnFi1a1HjBFwAAAAAAUL2ANOl79+5VUlKSIiMjlZqaqry8PHXq1KnasU6nU06n0zNdeel7l8vll5vR28Jq/y0+TqusFTWrG+rlO2rmm+Zcr/q+/1eud/bfAAAAocLvTfqgQYO0fPlyde/eXUeOHFFubq6uuOIK7d69WzExMVXG5+XlKTc3t8r8/Px8RUdHNzjPnIHuBm+juaFmvqFevqNmvmmO9Vq7dm2D1i8oKJAklZeX+yMOAABAo/F7kz569GjPv/v166dBgwapc+fOWrlypSZPnlxlfE5OjrKzsz3TDodDycnJSk9Pl91ur3eOyhvaz9wRJqfbPPc7NTNbmKE5A93UrI6ol++omW+ac712z8qo13qV7/2jRo2S1Wr1nJ0FoGnrct9rwY7gZe+c9GBHABDCAn5Ft9atW+vCCy/Uvn37ql1us9lks9mqzG/IjejP5HRb5KxoXju3DUXNfEO9fEfNfNMc69XQ9//KzxB/fI4AAAA0Jr/fgu1sx48f1/79+5WYmBjohwIAAAAAIKT5vUm/++67tWnTJh08eFDvvPOOfvKTnyg8PFw33nijvx8KAAAAAIAmxe+nu3/55Ze68cYb9e2336p9+/YaOnSotm3bpvbt2/v7oQAAAAAAaFL83qS/8MIL/t4kAAAAAADNQsC/kw4AAAAAAOqGJh0AAAAAAJOgSQcAAAAAwCQCfp90AAAAAAgFXe57LdgRvBycNybYERAEHEkHAAAAAMAkaNIBAAAAADAJTncHAACAT/rMWi9nhSXYMRDi+sxar/mX8XoCzsaRdAAAAAAATIImHQAAAAAAk+B0dwAAAMCPzHgaN1cJB0IHR9IBAAAAADAJmnQAAAAAAEyC090BAAAAAHXS5b7Xgh2hir1z0oMdwa84kg4AAAAAgEnQpAMAAAAAYBKc7g4AAAAAJtTlvtdkCzdMd7cABBZH0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk+A76QAAACZltlsdVX43FqHHbK8lSbKFBzsBYE4cSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJmnQAAAAAAEyCJh0AAAAAAJOgSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkaNIBAAAAADCJgDXpixYtUpcuXRQZGalBgwbpv//9b6AeCgAAhDD2GQAA+J+ANOn//Oc/lZ2drYceekjvvfeeLrroImVkZOjo0aOBeDgAABCi2GcAAMBbQJr0xx57TLfeeqtuueUW9erVS0uXLlV0dLT++te/BuLhAABAiGKfAQAAb35v0k+dOqXCwkKlpaX970HCwpSWlqatW7f6++EAAECIYp8BAICqWvh7g998840qKioUHx/vNT8+Pl6ffPJJlfFOp1NOp9MzXVZWJkn67rvv5HK56p3D5XKpvLxcLVxhqnBb6r2d5qSF21B5uZua1RH18h01801zrte3335br/Uq3/u//fZbWa1WHTt2TJJkGIY/48FPfN1nkNhvCLbm/L7kC+pUN9SpbqhT7b799luvz//6MNM+g9+bdF/l5eUpNze3yvyUlJQgpMEvgh0gxFAv31Ez3zTXerX7k3+3d+zYMcXGxvp3owgK9huCr7m+L/mKOtUNdaob6nRuiX7cbzDDPoPfm/R27dopPDxcJSUlXvNLSkqUkJBQZXxOTo6ys7M90263W999953atm0ri6X+vylyOBxKTk7WF198IbvdXu/tNCfUzDfUy3fUzDfUy3dn18wwDB07dkxJSUnBjoZq+LrPILHfEGzUqW6oU91Qp7qhTrXzR43MtM/g9yY9IiJCAwYM0IYNGzRu3DhJpz9AN2zYoClTplQZb7PZZLPZvOa1bt3ab3nsdjsvZh9RM99QL99RM99QL9+dWbNg/zYcNfN1n0Fiv8EsqFPdUKe6oU51Q51q19AamWWfISCnu2dnZ2vixIkaOHCgLrvsMi1cuFAnTpzQLbfcEoiHAwAAIYp9BgAAvAWkSf/5z3+ur7/+Wg8++KCKi4vVv39/rVu3rsqFYQAAQPPGPgMAAN4CduG4KVOm1HiqWmOw2Wx66KGHqpwSh5pRM99QL99RM99QL99Rs9AU7H0GiddOXVGnuqFOdUOd6oY61a6p1chimOEa8wAAAAAAQGHBDgAAAAAAAE6jSQcAAAAAwCRo0gEAAAAAMImQbtIXLVqkLl26KDIyUoMGDdJ///vfc45ftWqVevToocjISPXt21dr165tpKTm4UvNli9fLovF4vUnMjKyEdMG1+bNmzV27FglJSXJYrFozZo1ta6zceNGXXLJJbLZbLrgggu0fPnygOc0C1/rtXHjxiqvL4vFouLi4sYJHGR5eXm69NJLFRMTow4dOmjcuHEqKiqqdb3m/D5Wn5o19/cx1J2v+xShoi4/NydPnlRWVpbatm2rVq1aKTMzUyUlJV5jDh06pDFjxig6OlodOnTQPffcox9//NFrTF0+A0OlzvPmzZPFYtG0adM886jTaV999ZV++ctfqm3btoqKilLfvn21Y8cOz3LDMPTggw8qMTFRUVFRSktL0969e7228d133+mmm26S3W5X69atNXnyZB0/ftxrzIcffqgrrrhCkZGRSk5O1vz586tkMetnYkVFhWbOnKmUlBRFRUWpa9eumjNnjs68FFhzrFNt+4pmqkldsgSUEaJeeOEFIyIiwvjrX/9q7Nmzx7j11luN1q1bGyUlJdWOf/vtt43w8HBj/vz5xkcffWQ88MADhtVqNXbt2tXIyYPH15otW7bMsNvtxpEjRzx/iouLGzl18Kxdu9b4/e9/b7z00kuGJGP16tXnHP/ZZ58Z0dHRRnZ2tvHRRx8ZTzzxhBEeHm6sW7eucQIHma/1euuttwxJRlFRkddrrKKionECB1lGRoaxbNkyY/fu3cbOnTuNa665xujUqZNx/PjxGtdp7u9j9alZc38fQ934+vkYSuryc3P77bcbycnJxoYNG4wdO3YYgwcPNi6//HLP8h9//NHo06ePkZaWZrz//vvG2rVrjXbt2hk5OTmeMXX5DAyVOv/3v/81unTpYvTr18+46667PPOpk2F89913RufOnY1JkyYZ27dvNz777DNj/fr1xr59+zxj5s2bZ8TGxhpr1qwxPvjgA+O6664zUlJSjB9++MEz5uqrrzYuuugiY9u2bcZ//vMf44ILLjBuvPFGz/KysjIjPj7euOmmm4zdu3cbzz//vBEVFWU8+eSTnjFm/kx8+OGHjbZt2xqvvvqqceDAAWPVqlVGq1atjD//+c+eMc2xTrXtK5qpJnXJEkgh26RfdtllRlZWlme6oqLCSEpKMvLy8qodf8MNNxhjxozxmjdo0CDjt7/9bUBzmomvNVu2bJkRGxvbSOnMrS5N54wZM4zevXt7zfv5z39uZGRkBDCZOfnSpH///feNksnsjh49akgyNm3aVOMY3se81aVmvI+hLnz9fAxlZ//clJaWGlar1Vi1apVnzMcff2xIMrZu3WoYxukd67CwMK9fcC1ZssSw2+2G0+k0DKNun4GhUOdjx44Z3bp1MwoKCoxhw4Z5mnTqdNq9995rDB06tMblbrfbSEhIMP7whz945pWWlho2m814/vnnDcMwjI8++siQZLz77rueMa+//rphsViMr776yjAMw1i8eLHRpk0bT90qH7t79+6eaTN/Jo4ZM8b49a9/7TVv/Pjxxk033WQYBnUyjKr7imaqSV2yBFpInu5+6tQpFRYWKi0tzTMvLCxMaWlp2rp1a7XrbN261Wu8JGVkZNQ4vqmpT80k6fjx4+rcubOSk5N1/fXXa8+ePY0RNyQ199dYffXv31+JiYkaNWqU3n777WDHCZqysjJJUlxcXI1jeI15q0vNJN7HcG71/XwMVWf/3BQWFsrlcnk9/x49eqhTp06e579161b17dtX8fHxnjEZGRlyOByen6fa3p9Cpc5ZWVkaM2ZMledCnU7797//rYEDB+pnP/uZOnTooIsvvlhPP/20Z/mBAwdUXFzslT82NlaDBg3yqlPr1q01cOBAz5i0tDSFhYVp+/btnjFXXnmlIiIiPGMyMjJUVFSk77//3jPGrJ+Jl19+uTZs2KBPP/1UkvTBBx9oy5YtGj16tCTqVB0z1aQuWQItJJv0b775RhUVFV5vgpIUHx9f4/dZi4uLfRrf1NSnZt27d9df//pXvfzyy/rHP/4ht9utyy+/XF9++WVjRA45Nb3GHA6HfvjhhyClMq/ExEQtXbpUL774ol588UUlJydr+PDheu+994IdrdG53W5NmzZNQ4YMUZ8+fWoc19zfx85U15rxPoba1OfzMVRV93NTXFysiIgItW7d2mvsmc+/pveeymXnGlP5GRgKdX7hhRf03nvvKS8vr8oy6nTaZ599piVLlqhbt25av3697rjjDv3ud7/T3/72N0n/e57nyl9cXKwOHTp4LW/RooXi4uL8Uksz1Om+++7ThAkT1KNHD1mtVl188cWaNm2abrrpJknUqTpmqkldsgRai0Z5FISk1NRUpaameqYvv/xy9ezZU08++aTmzJkTxGRoCrp3767u3bt7pi+//HLt379fCxYs0N///vcgJmt8WVlZ2r17t7Zs2RLsKCGjrjXjfQz4H95ravbFF1/orrvuUkFBAReXPAe3262BAwfqkUcekSRdfPHF2r17t5YuXaqJEycGOZ15rFy5Us8995xWrFih3r17a+fOnZo2bZqSkpKoE+okJI+kt2vXTuHh4VWuqFlSUqKEhIRq10lISPBpfFNTn5qdrfI3gfv27QtExJBX02vMbrcrKioqSKlCy2WXXdbsXl9TpkzRq6++qrfeeksdO3Y859jm/j5WyZeanY33MZzNH5+PoaCmn5uEhASdOnVKpaWlXuPPfP41vfdULjvXmMrPQLPXubCwUEePHtUll1yiFi1aqEWLFtq0aZMef/xxtWjRQvHx8dRJp8+C69Wrl9e8nj176tChQ5L+9zzPlT8hIUFHjx71Wv7jjz/qu+++80stzVCne+65x3M0vW/fvrr55ps1ffp0z1ka1KkqM9WkLlkCLSSb9IiICA0YMEAbNmzwzHO73dqwYYPXEZMzpaameo2XpIKCghrHNzX1qdnZKioqtGvXLiUmJgYqZkhr7q8xf9i5c2ezeX0ZhqEpU6Zo9erVevPNN5WSklLrOs39NVafmp2N9zGczR+fj2ZW28/NgAEDZLVavZ5/UVGRDh065Hn+qamp2rVrl9fOcUFBgex2u6dhq+39yex1HjlypHbt2qWdO3d6/gwcOFA33XST59/USRoyZEiVW/h9+umn6ty5syQpJSVFCQkJXvkdDoe2b9/uVafS0lIVFhZ6xrz55ptyu90aNGiQZ8zmzZvlcrk8YwoKCtS9e3e1adPGM8asn4nl5eUKC/Nus8LDw+V2uyVRp+qYqSZ1yRJwjXJ5ugB44YUXDJvNZixfvtz46KOPjNtuu81o3bq154qaN998s3Hfffd5xr/99ttGixYtjD/+8Y/Gxx9/bDz00EOmuU1DY/G1Zrm5ucb69euN/fv3G4WFhcaECROMyMhIY8+ePcF6Co3q2LFjxvvvv2+8//77hiTjscceM95//33j888/NwzDMO677z7j5ptv9oyvvK3KPffcY3z88cfGokWLmtUt2Hyt14IFC4w1a9YYe/fuNXbt2mXcddddRlhYmPHGG28E6yk0qjvuuMOIjY01Nm7c6HV7sPLycs8Y3se81admzf19DHVT2+djKKvLz83tt99udOrUyXjzzTeNHTt2GKmpqUZqaqpneeWtxdLT042dO3ca69atM9q3b1/trcXO9RkYanU+8+ruhkGdDOP07elatGhhPPzww8bevXuN5557zoiOjjb+8Y9/eMbMmzfPaN26tfHyyy8bH374oXH99ddXexutiy++2Ni+fbuxZcsWo1u3bl630SotLTXi4+ONm2++2di9e7fxwgsvGNHR0VVuo2XWz8SJEyca5513nucWbC+99JLRrl07Y8aMGZ4xzbFOte0rmqkmdckSSCHbpBuGYTzxxBNGp06djIiICOOyyy4ztm3b5lk2bNgwY+LEiV7jV65caVx44YVGRESE0bt3b+O1115r5MTB50vNpk2b5hkbHx9vXHPNNcZ7770XhNTBUXmLsLP/VNZo4sSJxrBhw6qs079/fyMiIsI4//zzjWXLljV67mDxtV6PPvqo0bVrVyMyMtKIi4szhg8fbrz55pvBCR8E1dVKktdrhvcxb/WpWXN/H0PdnevzMZTV5efmhx9+MO68806jTZs2RnR0tPGTn/zEOHLkiNd2Dh48aIwePdqIiooy2rVrZ/zf//2f4XK5vMbU5TMwlOp8dpNOnU575ZVXjD59+hg2m83o0aOH8dRTT3ktd7vdxsyZM434+HjDZrMZI0eONIqKirzGfPvtt8aNN95otGrVyrDb7cYtt9xiHDt2zGvMBx98YAwdOtSw2WzGeeedZ8ybN69KFrN+JjocDuOuu+4yOnXqZERGRhrnn3++8fvf/97rtmDNsU617SuaqSZ1yRJIFsMwjMAeqwcAAAAAAHURkt9JBwAAAACgKaJJBwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkaNIBAAAAADAJmnQAAAAAAEyCJh0AEDSbN2/W2LFjlZSUJIvFojVr1vi8DcMw9Mc//lEXXnihbDabzjvvPD388MP+DwsAANAIWgQ7AACg+Tpx4oQuuugi/frXv9b48ePrtY277rpL+fn5+uMf/6i+ffvqu+++03fffefnpAAAAI3DYhiGEewQAABYLBatXr1a48aN88xzOp36/e9/r+eff16lpaXq06ePHn30UQ0fPlyS9PHHH6tfv37avXu3unfvHpzgAAAAfsTp7gAA05oyZYq2bt2qF154QR9++KF+9rOf6eqrr9bevXslSa+88orOP/98vfrqq0pJSVGXLl30m9/8hiPpAAAgZNGkAwBM6dChQ1q2bJlWrVqlK664Ql27dtXdd9+toUOHatmyZZKkzz77TJ9//rlWrVqlZ599VsuXL1dhYaF++tOfBjk9AABA/fCddACAKe3atUsVFRW68MILveY7nU61bdtWkuR2u+V0OvXss896xj3zzDMaMGCAioqKOAUeAACEHJp0AIApHT9+XOHh4SosLFR4eLjXslatWkmSEhMT1aJFC69GvmfPnpJOH4mnSQcAAKGGJh0AYEoXX3yxKioqdPToUV1xxRXVjhkyZIh+/PFH7d+/X127dpUkffrpp5Kkzp07N1pWAAAAf+Hq7gCAoDl+/Lj27dsn6XRT/thjj+mqq65SXFycOnXqpF/+8pd6++239ac//UkXX3yxvv76a23YsEH9+vXTmDFj5Ha7demll6pVq1ZauHCh3G63srKyZLfblZ+fH+RnBwAA4DuadABA0GzcuFFXXXVVlfkTJ07U8uXL5XK5NHfuXD377LP66quv1K5dOw0ePFi5ubnq27evJOnw4cOaOnWq8vPz1bJlS40ePVp/+tOfFBcX19hPBwAAoMFo0gEAAAAAMAluwQYAAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDiDohg8frj59+gQ7BgAAqMHw4cM1fPjwYMcAmgWadAAAAKAJeOeddzRr1iyVlpYGO4pPVqxYoYULFwY7BmAaNOkAAABAE/DOO+8oNzeXJh0IcTTpAJoFt9utkydPBjsGAAA6ceJEsCMAMDGadJjOsWPHNG3aNHXp0kU2m00dOnTQqFGj9N5773nGbN++XVdffbViY2MVHR2tYcOG6e23366yrS1btujSSy9VZGSkunbtqieffFKzZs2SxWLxjDl48KAsFouWL19eZX2LxaJZs2Z5zfvqq6/061//WvHx8bLZbOrdu7f++te/eo3ZuHGjLBaLVq5cqYcfflgdO3ZUZGSkRo4cqX379lV5nO3bt+uaa65RmzZt1LJlS/Xr109//vOfvcZ88skn+ulPf6q4uDhFRkZq4MCB+ve//12XklZ5TlOmTNGaNWvUp08fz3NYt26d17hJkyapS5cuVdY/u35nbnPVqlXq1auXoqKilJqaql27dkmSnnzySV1wwQWKjIzU8OHDdfDgwWqzFRYW6vLLL1dUVJRSUlK0dOnSKmOcTqceeughXXDBBbLZbEpOTtaMGTPkdDqrzfTcc8+pd+/estlsVZ4jAAD+8NVXX2ny5MlKSkqSzWZTSkqK7rjjDp06dUrLly+XxWLRpk2bdOedd6pDhw7q2LGjZ93XX39dV1xxhVq2bKmYmBiNGTNGe/bs8dr+hx9+qEmTJun8889XZGSkEhIS9Otf/1rffvutZ8ysWbN0zz33SJJSUlJksVhksVi8PnP/8Y9/aMCAAYqKilJcXJwmTJigL774osrzeeqpp9S1a1dFRUXpsssu03/+85961eXll1/WmDFjPHXp2rWr5syZo4qKCs+Y4cOH67XXXtPnn3/uyVzd/gfQnLQIdgDgbLfffrv+9a9/acqUKerVq5e+/fZbbdmyRR9//LEuueQSvfnmmxo9erQGDBighx56SGFhYVq2bJlGjBih//znP7rsssskSbt27VJ6errat2+vWbNm6ccff9RDDz2k+Pj4emcrKSnR4MGDPQ1g+/bt9frrr2vy5MlyOByaNm2a1/h58+YpLCxMd999t8rKyjR//nzddNNN2r59u2dMQUGBrr32WiUmJuquu+5SQkKCPv74Y7366qu66667JEl79uzRkCFDdN555+m+++5Ty5YttXLlSo0bN04vvviifvKTn/j0PLZs2aKXXnpJd955p2JiYvT4448rMzNThw4dUtu2betVm//85z/697//raysLElSXl6err32Ws2YMUOLFy/WnXfeqe+//17z58/Xr3/9a7355pte63///fe65pprdMMNN+jGG2/UypUrdccddygiIkK//vWvJZ0+Gn7ddddpy5Ytuu2229SzZ0/t2rVLCxYs0Keffqo1a9Z4bfPNN9/UypUrNWXKFLVr144PfQCA3x0+fFiXXXaZSktLddttt6lHjx766quv9K9//Uvl5eWecXfeeafat2+vBx980HMk/e9//7smTpyojIwMPfrooyovL9eSJUs0dOhQvf/++57PrYKCAn322We65ZZblJCQoD179uipp57Snj17tG3bNlksFo0fP16ffvqpnn/+eS1YsEDt2rWTJLVv316S9PDDD2vmzJm64YYb9Jvf/EZff/21nnjiCV155ZV6//331bp1a0nSM888o9/+9re6/PLLNW3aNH322We67rrrFBcXp+TkZJ9qs3z5crVq1UrZ2dlq1aqV3nzzTT344INyOBz6wx/+IEn6/e9/r7KyMn355ZdasGCBJKlVq1b1/v8AmgQDMJnY2FgjKyur2mVut9vo1q2bkZGRYbjdbs/88vJyIyUlxRg1apRn3rhx44zIyEjj888/98z76KOPjPDwcOPMl/6BAwcMScayZcuqPJ4k46GHHvJMT5482UhMTDS++eYbr3ETJkwwYmNjjfLycsMwDOOtt94yJBk9e/Y0nE6nZ9yf//xnQ5Kxa9cuwzAM48cffzRSUlKMzp07G99//32V51pp5MiRRt++fY2TJ096Lb/88suNbt26VVurmkgyIiIijH379nnmffDBB4Yk44knnvDMmzhxotG5c+cq6z/00EPG2W8dkgybzWYcOHDAM+/JJ580JBkJCQmGw+HwzM/JyTEkeY0dNmyYIcn405/+5JnndDqN/v37Gx06dDBOnTplGIZh/P3vfzfCwsKM//znP16Pv3TpUkOS8fbbb3tlCgsLM/bs2VO3wgAAUA+/+tWvjLCwMOPdd9+tssztdhvLli0zJBlDhw41fvzxR8+yY8eOGa1btzZuvfVWr3WKi4uN2NhYr/mV+xdnev755w1JxubNmz3z/vCHP1T5jDUMwzh48KARHh5uPPzww17zd+3aZbRo0cIz/9SpU0aHDh2M/v37e+2/PPXUU4YkY9iwYbUX5AzV5f7tb39rREdHe+3TjBkzptp9DqC54nR3mE7r1q21fft2HT58uMqynTt3au/evfrFL36hb7/9Vt98842++eYbnThxQiNHjtTmzZvldrtVUVGh9evXa9y4cerUqZNn/Z49eyojI6NeuQzD0IsvvqixY8fKMAzPY3/zzTfKyMhQWVmZ1yn5knTLLbcoIiLCM33FFVdIkj777DNJ0vvvv68DBw5o2rRpnt9gV6o8pfy7777Tm2++qRtuuEHHjh3zPOa3336rjIwM7d27V1999ZVPzyUtLU1du3b1TPfr1092u92Tqz5GjhzpdaR60KBBkqTMzEzFxMRUmX/2Y7Vo0UK//e1vPdMRERH67W9/q6NHj6qwsFCStGrVKvXs2VM9evTwqv+IESMkSW+99ZbXNocNG6ZevXrV+zkBAHAubrdba9as0dixYzVw4MAqy8/8etitt96q8PBwz3RBQYFKS0t14403en2mhYeHa9CgQV6faVFRUZ5/nzx5Ut98840GDx4sSVX2Parz0ksvye1264YbbvB6rISEBHXr1s3zWDt27NDRo0d1++23e+2/TJo0SbGxsT5Upmruyn2YK664QuXl5frkk0983h7QXHC6O0xn/vz5mjhxopKTkzVgwABdc801+tWvfqXzzz9fe/fulSRNnDixxvXLysrkdDr1ww8/qFu3blWWd+/eXWvXrvU519dff63S0lI99dRTeuqpp6odc/ToUa/pM39BIElt2rSRdPrUbknav3+/JJ3zHuH79u2TYRiaOXOmZs6cWePjnnfeeXV7ItXkqsxWmas+zt5m5Yf52afGVc4/+7GSkpLUsmVLr3kXXnihpNPXDRg8eLD27t2rjz/+2HPq3tnOrn9KSoqPzwIAgLr7+uuv5XA4zvk5Xunsz6TKfZrKXzSfzW63e/793XffKTc3Vy+88EKVz7qysrJaH3vv3r0yDKPa/SJJslqtkqTPP/9ckqqMs1qtOv/882t9nLPt2bNHDzzwgN588005HA6fcwPNFU06TOeGG27QFVdcodWrVys/P19/+MMf9Oijj3p+CyxJf/jDH9S/f/9q12/VqlWVi4idy9kXQat05kVNJHke+5e//GWNvyTo16+f1/SZvzE/k2EYdc5X+bh33313jWcBXHDBBXXeXl1z1bUutW3THzWo5Ha71bdvXz322GPVLj/7FwJn/gYfAIBgOvszqfLz/e9//7sSEhKqjG/R4n+76TfccIPeeecd3XPPPerfv79atWolt9utq6++2rOdc3G73bJYLHr99der/VwOxHfAS0tLNWzYMNntds2ePVtdu3ZVZGSk3nvvPd177711yg00VzTpMKXExETdeeeduvPOO3X06FFdcsklevjhhz0XFLHb7UpLS6tx/fbt2ysqKsrzW+ozFRUVeU1XHt0++56ilb9NPnObMTExqqioOOdj+6LylPPdu3fXuM3K31xbrVa/PW5dtGnTptr7rJ5dF385fPiwTpw44XU0/dNPP5Ukz2n0Xbt21QcffKCRI0fW+EsEAAAaS/v27WW327V7926f163cB+jQocM5P9+///57bdiwQbm5uXrwwQc986vbx6nps7Fr164yDEMpKSmes9Sq07lzZ8+2zzzC73K5dODAAV100UXnflJn2Lhxo7799lu99NJLuvLKKz3zDxw4UOfcQHPFd9JhKhUVFVVOf+rQoYOSkpLkdDo1YMAAde3aVX/84x91/PjxKut//fXXkk4fvc3IyNCaNWt06NAhz/KPP/5Y69ev91rHbrerXbt22rx5s9f8xYsXe02Hh4crMzNTL774YrUfxpWP7YtLLrlEKSkpWrhwYZWGuPJIc4cOHTR8+HA9+eSTOnLkiF8ety66du2qsrIyffjhh555R44c0erVqwPyeD/++KOefPJJz/SpU6f05JNPqn379howYICk00cSvvrqKz399NNV1v/hhx+47ywAoFGFhYVp3LhxeuWVV7Rjx44qy8911lhGRobsdrseeeQRuVyuKsvP3KepblsLFy6ssk7lL7rP3qcYP368wsPDlZubW2U7hmF4buU2cOBAtW/fXkuXLtWpU6c8Y5YvX17tL+7Ppbrcp06dqrJ/VZmb09+B/+FIOkzl2LFj6tixo37605/qoosuUqtWrfTGG2/o3Xff1Z/+9CeFhYXp//v//j+NHj1avXv31i233KLzzjtPX331ld566y3Z7Xa98sorkqTc3FytW7dOV1xxhe688079+OOPeuKJJ9S7d2+vxlOSfvOb32jevHn6zW9+o4EDB2rz5s2eo7hnmjdvnt566y0NGjRIt956q3r16qXvvvtO7733nt544w199913Pj3fsLAwLVmyRGPHjlX//v11yy23KDExUZ988on27Nnj+YXCokWLNHToUPXt21e33nqrzj//fJWUlGjr1q368ssv9cEHH9Sz4jWbMGGC7r33Xv3kJz/R7373O89tYS688MI6XaTGV0lJSXr00Ud18OBBXXjhhfrnP/+pnTt36qmnnvJ8V+7mm2/WypUrdfvtt+utt97SkCFDVFFRoU8++UQrV67U+vXrq71wDwAAgfLII48oPz9fw4YN89we9MiRI1q1apW2bNlS43p2u11LlizRzTffrEsuuUQTJkxQ+/btdejQIb322msaMmSI/vKXv8hut+vKK6/U/Pnz5XK5dN555yk/P7/aI9KVv9T+/e9/rwkTJshqtWrs2LHq2rWr5s6dq5ycHB08eFDjxo1TTEyMDhw4oNWrV+u2227T3XffLavVqrlz5+q3v/2tRowYoZ///Oc6cOCAli1b5vN30i+//HK1adNGEydO1O9+9ztZLBb9/e9/r/YXFwMGDNA///lPZWdn69JLL1WrVq00duxYnx4PaFKCcEV5oEZOp9O45557jIsuusiIiYkxWrZsaVx00UXG4sWLvca9//77xvjx4422bdsaNpvN6Ny5s3HDDTcYGzZs8Bq3adMmY8CAAUZERIRx/vnnG0uXLq32FmLl5eXG5MmTjdjYWCMmJsa44YYbjKNHj1a5BZthGEZJSYmRlZVlJCcnG1ar1UhISDBGjhxpPPXUU54xlbdgW7Vqlde6Nd3ubcuWLcaoUaM8z7lfv35et0MzDMPYv3+/8atf/cpISEgwrFarcd555xnXXnut8a9//cuXEhuSqr3FXefOnY2JEyd6zcvPzzf69OljREREGN27dzf+8Y9/1HgLtrO3Wflc//CHP3jNr642w4YNM3r37m3s2LHDSE1NNSIjI43OnTsbf/nLX6rkPHXqlPHoo48avXv3Nmw2m9GmTRtjwIABRm5urlFWVlbr8wQAwN8+//xz41e/+pXRvn17w2azGeeff76RlZVlOJ1Ozy3YqrtFm2Gc/lzMyMgwYmNjjcjISKNr167GpEmTjB07dnjGfPnll8ZPfvITo3Xr1kZsbKzxs5/9zDh8+HC1+ylz5swxzjvvPCMsLKzK7dhefPFFY+jQoUbLli2Nli1bGj169DCysrKMoqIir20sXrzYSElJMWw2mzFw4EBj8+bNxrBhw3y+Bdvbb79tDB482IiKijKSkpKMGTNmGOvXrzckGW+99ZZn3PHjx41f/OIXRuvWrQ1J3I4NzZ7FMOpx9SYghM2aNava070AAAAAINj4TjoAAAAAACbBd9KBJqK4uPicy6Oiojz3KAcAAKivr7/+usZbskpSRESE4uLiGjER0LTQpANNRGJi4jmXT5w4UcuXL2+cMAAAoMm69NJLz3lL1mHDhmnjxo2NFwhoYvhOOtBEvPHGG+dcnpSUpF69ejVSGgAA0FS9/fbb+uGHH2pc3qZNG8+V5gH4jiYdAAAAAACT4MJxAAAAAACYhOm+k+52u3X48GHFxMTIYrEEOw4AIIQZhqFjx44pKSlJYWH8XropYr8BAOAPZtpn8LlJ/+qrr3Tvvffq9ddfV3l5uS644AItW7ZMAwcOlHT6yT300EN6+umnVVpaqiFDhmjJkiXq1q1bnbZ/+PBhJScn+xoLAIAaffHFF+rYsWOwYyAA2G8AAPiTGfYZfGrSv//+ew0ZMkRXXXWVXn/9dbVv31579+5VmzZtPGPmz5+vxx9/XH/729+UkpKimTNnKiMjQx999JEiIyNrfYyYmBhJp4tjt9t9fDreXC6X8vPzlZ6eLqvV2qBtNVXUqG6oU+2oUd1Qp9r5s0YOh0PJycmezxY0PQ3ZbwiVn8dQySmRNVBCJWuo5KwUKnlDJacUOlmry2mmfQafmvRHH31UycnJWrZsmWdeSkqK59+GYWjhwoV64IEHdP3110uSnn32WcXHx2vNmjWaMGFCrY9Reaqa3W73S5MeHR0tu91u6hdJMFGjuqFOtaNGdUOdaheIGnEadNPVkP2GUPl5DJWcElkDJVSyhkrOSqGSN1RySqGT9Vw5zbDP4FOT/u9//1sZGRn62c9+pk2bNum8887TnXfeqVtvvVWSdODAARUXFystLc2zTmxsrAYNGqStW7dW26Q7nU45nU7PtMPhkHS6cC6Xq15PqlLl+g3dTlNGjeqGOtWOGtUNdaqdP2tEnQEAQKjxqUn/7LPPtGTJEmVnZ+v+++/Xu+++q9/97neKiIjQxIkTVVxcLEmKj4/3Wi8+Pt6z7Gx5eXnKzc2tMj8/P1/R0dG+xKtRQUGBX7bTlFGjuqFOtaNGdUOdauePGpWXl/shCQAAQOPxqUl3u90aOHCgHnnkEUnSxRdfrN27d2vp0qWaOHFivQLk5OQoOzvbM135XYD09HS/nO5eUFCgUaNGmfp0i2CiRnVDnWpHjeqGOtXOnzWqPDsLAAAgVPjUpCcmJqpXr15e83r27KkXX3xRkpSQkCBJKikpUWJiomdMSUmJ+vfvX+02bTabbDZblflWq9VvO7D+3FZTRY3qhjrVjhrVDXWqnT9qRI0BAECo8ekGcEOGDFFRUZHXvE8//VSdO3eWdPoicgkJCdqwYYNnucPh0Pbt25WamuqHuAAAAAAANF0+HUmfPn26Lr/8cj3yyCO64YYb9N///ldPPfWUnnrqKUmnr4Q3bdo0zZ07V926dfPcgi0pKUnjxo0LRH4AAAAAAJoMn5r0Sy+9VKtXr1ZOTo5mz56tlJQULVy4UDfddJNnzIwZM3TixAnddtttKi0t1dChQ7Vu3bo63SMdAAAAAIDmzKfT3SXp2muv1a5du3Ty5El9/PHHntuvVbJYLJo9e7aKi4t18uRJvfHGG7rwwgv9FhgAAISeefPmec64q3Ty5EllZWWpbdu2atWqlTIzM1VSUhK8kAAAmIDPTToAAIAv3n33XT355JPq16+f1/zp06frlVde0apVq7Rp0yYdPnxY48ePD1JKAADMgSYdAAAEzPHjx3XTTTfp6aefVps2bTzzy8rK9Mwzz+ixxx7TiBEjNGDAAC1btkzvvPOOtm3bFsTEAAAEl0/fSQcAAPBFVlaWxowZo7S0NM2dO9czv7CwUC6XS2lpaZ55PXr0UKdOnbR161YNHjy42u05nU45nU7PtMPhkCS5XC65XC6fslWO93W9xhYqOSWyBkqoZA2VnJVCJW+o5JRCJ2t1Oc2UmSa9kXW577VgR/BiCzc0/7JgpwAANEUvvPCC3nvvPb377rtVlhUXFysiIkKtW7f2mh8fH6/i4uIat5mXl6fc3Nwq8/Pz8xUdHV2vnAUFBfVar7GFSk6JrIESKllDJWelUMkbKjml0Ml6Zs7y8vIgJvFGkw4AAPzuiy++0F133aWCggK/3uElJydH2dnZnmmHw6Hk5GSlp6fLbrf7tC2Xy6WCggKNGjVKVqvVbxn9LVRySmQNlFDJGio5K4VKXrPl7DNrfY3LbGGG5gx0a+aOMDndlkbLtHtWhk/jq6tp5ZlZZkCTDgAA/K6wsFBHjx7VJZdc4plXUVGhzZs36y9/+YvWr1+vU6dOqbS01OtoeklJiRISEmrcrs1mk81mqzLfarXWe+e1Ies2plDJKZE1UEIla6jkrBQqec2S01lRe/PtdFvqNM5f/PH+b4baVqJJBwAAfjdy5Ejt2rXLa94tt9yiHj166N5771VycrKsVqs2bNigzMxMSVJRUZEOHTqk1NTUYEQGAMAUaNIBAIDfxcTEqE+fPl7zWrZsqbZt23rmT548WdnZ2YqLi5PdbtfUqVOVmppa40XjAABoDmjSAQBAUCxYsEBhYWHKzMyU0+lURkaGFi9eHOxYAAAEFU06AABoFBs3bvSajoyM1KJFi7Ro0aLgBAIAwITCgh0AAAAAAACcRpMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAACYsmSJerXr5/sdrvsdrtSU1P1+uuve5YPHz5cFovF68/tt98exMQAAARfi2AHAAAATVPHjh01b948devWTYZh6G9/+5uuv/56vf/+++rdu7ck6dZbb9Xs2bM960RHRwcrLgAApkCTDgAAAmLs2LFe0w8//LCWLFmibdu2eZr06OhoJSQkBCMeAACm5FOTPmvWLOXm5nrN6969uz755BNJ0smTJ/V///d/euGFF+R0OpWRkaHFixcrPj7ef4kBAEDIqaio0KpVq3TixAmlpqZ65j/33HP6xz/+oYSEBI0dO1YzZ84859F0p9Mpp9PpmXY4HJIkl8sll8vlU6bK8b6u19hCJadE1kAJlayhkrNSqOQ1W05buFHzsjDD6+/G4o/3f7PUV6rHkfTevXvrjTfe+N8GWvxvE9OnT9drr72mVatWKTY2VlOmTNH48eP19ttv+yctAAAIKbt27VJqaqpOnjypVq1aafXq1erVq5ck6Re/+IU6d+6spKQkffjhh7r33ntVVFSkl156qcbt5eXlVTlgIEn5+fn1PlW+oKCgXus1tlDJKZE1UEIla6jkrBQqec2Sc/5ltY+ZM9Ad+CBnWLt2bb3WO7Om5eXl/orTYD436S1atKj2tLSysjI988wzWrFihUaMGCFJWrZsmXr27Klt27Zp8ODBDU8LAABCSvfu3bVz506VlZXpX//6lyZOnKhNmzapV69euu222zzj+vbtq8TERI0cOVL79+9X165dq91eTk6OsrOzPdMOh0PJyclKT0+X3W73KZvL5VJBQYFGjRolq9VavyfYCEIlp0TWQAmVrKGSs1Ko5DVbzj6z1te4zBZmaM5At2buCJPTbWm0TLtnZfg0vrqaVp6ZZQY+N+l79+5VUlKSIiMjlZqaqry8PHXq1EmFhYVyuVxKS0vzjO3Ro4c6deqkrVu30qQDANAMRURE6IILLpAkDRgwQO+++67+/Oc/68knn6wydtCgQZKkffv21dik22w22Wy2KvOtVmu9d14bsm5jCpWcElkDJVSyhkrOSqGS1yw5nRW1N99Ot6VO4/zFH+//ZqhtJZ+a9EGDBmn58uXq3r27jhw5otzcXF1xxRXavXu3iouLFRERodatW3utEx8fr+Li4hq36c/vlp3NbN/fkM79HY5gqPy+iJlqZEZmfC2ZDTWqG+pUO3/WiDqbj9vt9vrcP9POnTslSYmJiY2YCAAAc/GpSR89erTn3/369dOgQYPUuXNnrVy5UlFRUfUKEIjvlp3NLN/fkOr2HY5gMFONzIw61Y4a1Q11qp0/amSm75c1Rzk5ORo9erQ6deqkY8eOacWKFdq4caPWr1+v/fv3a8WKFbrmmmvUtm1bffjhh5o+fbquvPJK9evXL9jRAQAImgbdgq1169a68MILtW/fPo0aNUqnTp1SaWmp19H0kpKSc95axZ/fLTub2b6/IZ37OxzBUPm9ETPVyIzM+FoyG2pUN9Spdv6skZm+X9YcHT16VL/61a905MgRxcbGql+/flq/fr1GjRqlL774Qm+88YYWLlyoEydOKDk5WZmZmXrggQeCHRsAgKBqUJN+/Phx7d+/XzfffLMGDBggq9WqDRs2KDMzU5JUVFSkQ4cOed1q5WyB+G5ZILfVUI353QxfmKlGZkadakeN6oY61c4fNaLGwfXMM8/UuCw5OVmbNm1qxDQAAIQGn5r0u+++W2PHjlXnzp11+PBhPfTQQwoPD9eNN96o2NhYTZ48WdnZ2YqLi5PdbtfUqVOVmprKReMAAAAAAKgDn5r0L7/8UjfeeKO+/fZbtW/fXkOHDtW2bdvUvn17SdKCBQsUFhamzMxMOZ1OZWRkaPHixQEJDgAAAABAU+NTk/7CCy+cc3lkZKQWLVqkRYsWNSgUAAAAAADNUViwAwAAAAAAgNNo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJHy6ujsAAAAAoHF0ue812cINzb9M6jNrvZwVlmBHQiPgSDoAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACbRItgBGkOfWevlrLAEOwYAAAAAAOfEkXQAAAAAAEyCJh0AAAAAAJOgSQcAAAGxZMkS9evXT3a7XXa7XampqXr99dc9y0+ePKmsrCy1bdtWrVq1UmZmpkpKSoKYGACA4KNJBwAAAdGxY0fNmzdPhYWF2rFjh0aMGKHrr79ee/bskSRNnz5dr7zyilatWqVNmzbp8OHDGj9+fJBTAwAQXM3iwnEAAKDxjR071mv64Ycf1pIlS7Rt2zZ17NhRzzzzjFasWKERI0ZIkpYtW6aePXtq27ZtGjx4cDAiAwAQdDTpAAAg4CoqKrRq1SqdOHFCqampKiwslMvlUlpammdMjx491KlTJ23durXGJt3pdMrpdHqmHQ6HJMnlcsnlcvmUqXK8r+s1tlDJKZE1UEIla6jkrBQKeW3hhmxhxul//7+/zSxYWf3x/m+m1wFNOgAACJhdu3YpNTVVJ0+eVKtWrbR69Wr16tVLO3fuVEREhFq3bu01Pj4+XsXFxTVuLy8vT7m5uVXm5+fnKzo6ul4ZCwoK6rVeYwuVnBJZAyVUsoZKzkpmzjv/sv/9e85Ad/CC+Kixs65du7Ze6535f19eXu6vOA1Gkw4AAAKme/fu2rlzp8rKyvSvf/1LEydO1KZNm+q9vZycHGVnZ3umHQ6HkpOTlZ6eLrvd7tO2XC6XCgoKNGrUKFmt1npnCrRQySmRNVBCJWuo5KwUCnn7zFovW5ihOQPdmrkjTE63JdiRzilYWXfPyvBpfHX/95VnZpkBTToAAAiYiIgIXXDBBZKkAQMG6N1339Wf//xn/fznP9epU6dUWlrqdTS9pKRECQkJNW7PZrPJZrNVmW+1Wuu9k92QdRtTqOSUyBoooZI1VHJWMnNeZ8X/Gl2n2+I1bWaNndUf7/9meg1wdXcAANBo3G63nE6nBgwYIKvVqg0bNniWFRUV6dChQ0pNTQ1iQgAAgosj6QAAICBycnI0evRoderUSceOHdOKFSu0ceNGrV+/XrGxsZo8ebKys7MVFxcnu92uqVOnKjU1lSu7AwCaNZp0AAAQEEePHtWvfvUrHTlyRLGxserXr5/Wr1+vUaNGSZIWLFigsLAwZWZmyul0KiMjQ4sXLw5yagAAgosmHQAABMQzzzxzzuWRkZFatGiRFi1a1EiJAAAwP5p0SDp95UgzXYji4LwxwY4AAAAAAI2OC8cBAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBINatLnzZsni8WiadOmeeadPHlSWVlZatu2rVq1aqXMzEyVlJQ0NCcAAAAAAE1evZv0d999V08++aT69evnNX/69Ol65ZVXtGrVKm3atEmHDx/W+PHjGxwUAAAAAICmrl5N+vHjx3XTTTfp6aefVps2bTzzy8rK9Mwzz+ixxx7TiBEjNGDAAC1btkzvvPOOtm3b5rfQAAAAAAA0RS3qs1JWVpbGjBmjtLQ0zZ071zO/sLBQLpdLaWlpnnk9evRQp06dtHXrVg0ePLjKtpxOp5xOp2fa4XBIklwul1wuV33ieVSubwszGrSdpqyyNmarUUP/7/2tMo/ZcpkJNaob6lQ7f9aIOgMAgFDjc5P+wgsv6L333tO7775bZVlxcbEiIiLUunVrr/nx8fEqLi6udnt5eXnKzc2tMj8/P1/R0dG+xqvWnIFuv2ynKTNbjdauXRvsCNUqKCgIdgTTo0Z1Q51q548alZeX+yEJAABA4/GpSf/iiy901113qaCgQJGRkX4JkJOTo+zsbM+0w+FQcnKy0tPTZbfbG7Rtl8ulgoICzdwRJqfb0tCoTZItzNCcgW7T1Wj3rIxgR/BS+VoaNWqUrFZrsOOYEjWqG+pUO3/WqPLsLAAAgFDhU5NeWFioo0eP6pJLLvHMq6io0ObNm/WXv/xF69ev16lTp1RaWup1NL2kpEQJCQnVbtNms8lms1WZb7Va/bYD63Rb5KwwTwNqRmarkVmbF3++LpsqalQ31Kl2/qgRNQYAAKHGpyZ95MiR2rVrl9e8W265RT169NC9996r5ORkWa1WbdiwQZmZmZKkoqIiHTp0SKmpqf5LDQAAAABAE+RTkx4TE6M+ffp4zWvZsqXatm3rmT958mRlZ2crLi5OdrtdU6dOVWpqarUXjQMAAAAAAP9Tr6u7n8uCBQsUFhamzMxMOZ1OZWRkaPHixf5+GAAAAAAAmpwGN+kbN270mo6MjNSiRYu0aNGihm4aAAAAAIBmJSzYAQAAAAAAwGk06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAACIi8vT5deeqliYmLUoUMHjRs3TkVFRV5jhg8fLovF4vXn9ttvD1JiAACCjyYdAAAExKZNm5SVlaVt27apoKBALpdL6enpOnHihNe4W2+9VUeOHPH8mT9/fpASAwAQfC2CHQAAADRN69at85pevny5OnTooMLCQl155ZWe+dHR0UpISGjseAAAmBJH0gEAQKMoKyuTJMXFxXnNf+6559SuXTv16dNHOTk5Ki8vD0Y8AABMgSPpAAAg4Nxut6ZNm6YhQ4aoT58+nvm/+MUv1LlzZyUlJenDDz/Uvffeq6KiIr300kvVbsfpdMrpdHqmHQ6HJMnlcsnlcvmUqXK8r+s1tlDJKZE1UEIla6jkrBQKeW3hhmxhxul//7+/zSxYWf3x/m+m1wFNOgAACLisrCzt3r1bW7Zs8Zp/2223ef7dt29fJSYmauTIkdq/f7+6du1aZTt5eXnKzc2tMj8/P1/R0dH1ylZQUFCv9RpbqOSUyBoooZI1VHJWMnPe+Zf9799zBrqDF8RHjZ117dq19VrvzP97M53FRZMOAAACasqUKXr11Ve1efNmdezY8ZxjBw0aJEnat29ftU16Tk6OsrOzPdMOh0PJyclKT0+X3W73KZfL5VJBQYFGjRolq9Xq07qNKVRySmQNlFDJ6mvOPrPWN0KqmtnCDM0Z6NbMHWFyui2SpN2zMoKa6Wx9Zq2vNqdZBSurr/9v1b1WK8/MMgOadAAAEBCGYWjq1KlavXq1Nm7cqJSUlFrX2blzpyQpMTGx2uU2m002m63KfKvVWu/mpSHrNqZQySmRNVBCJWtdczorzNFwOt0WTxaz1ffMGp2Z0+waO6s/3v/N9H9Pkw4AAAIiKytLK1as0Msvv6yYmBgVFxdLkmJjYxUVFaX9+/drxYoVuuaaa9S2bVt9+OGHmj59uq688kr169cvyOkBAAgOmnQAABAQS5YskSQNHz7ca/6yZcs0adIkRURE6I033tDChQt14sQJJScnKzMzUw888EAQ0gIAYA406QAAICAM49xX901OTtamTZsaKQ0AAKGB+6QDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASbTwZfCSJUu0ZMkSHTx4UJLUu3dvPfjggxo9erQk6eTJk/q///s/vfDCC3I6ncrIyNDixYsVHx/v9+BAMPSZtV7OCkuwY3gcnDcm2BEAAAAA+JFPR9I7duyoefPmqbCwUDt27NCIESN0/fXXa8+ePZKk6dOn65VXXtGqVau0adMmHT58WOPHjw9IcAAAAAAAmhqfjqSPHTvWa/rhhx/WkiVLtG3bNnXs2FHPPPOMVqxYoREjRkiSli1bpp49e2rbtm0aPHiw/1IDAAAAANAE1fs76RUVFXrhhRd04sQJpaamqrCwUC6XS2lpaZ4xPXr0UKdOnbR161a/hAUAAAAAoCnz6Ui6JO3atUupqak6efKkWrVqpdWrV6tXr17auXOnIiIi1Lp1a6/x8fHxKi4urnF7TqdTTqfTM+1wOCRJLpdLLpfL13heKte3hRkN2k5TVlkbs9Woof/3/mbW15KZ6lSZxUyZzIg61c6fNaLOAAAg1PjcpHfv3l07d+5UWVmZ/vWvf2nixInatGlTvQPk5eUpNze3yvz8/HxFR0fXe7tnmjPQ7ZftNGVmq9HatWuDHaFa1Kl2BQUFwY4QEqhT7fxRo/Lycj8kQX3l5eXppZde0ieffKKoqChdfvnlevTRR9W9e3fPGC46CwCAN5+b9IiICF1wwQWSpAEDBujdd9/Vn//8Z/385z/XqVOnVFpa6nU0vaSkRAkJCTVuLycnR9nZ2Z5ph8Oh5ORkpaeny263+xrPi8vlUkFBgWbuCJPTbZ4rcpuJLczQnIFu09Vo96yMYEfwYtbXkpnqVFmjUaNGyWq1BjuOaVGn2vmzRpVnZyE4Nm3apKysLF166aX68ccfdf/99ys9PV0fffSRWrZsKen0RWdfe+01rVq1SrGxsZoyZYrGjx+vt99+O8jpAQAIDp+b9LO53W45nU4NGDBAVqtVGzZsUGZmpiSpqKhIhw4dUmpqao3r22w22Wy2KvOtVqvfdmCdboupbptlRmarkVmbF+pUO3/+7DZl1Kl2/qgRNQ6udevWeU0vX75cHTp0UGFhoa688kqVlZVx0VkAAM7iU5Oek5Oj0aNHq1OnTjp27JhWrFihjRs3av369YqNjdXkyZOVnZ2tuLg42e12TZ06VampqXzIAgAAlZWVSZLi4uIkqdaLzrL/AABojnxq0o8ePapf/epXOnLkiGJjY9WvXz+tX79eo0aNkiQtWLBAYWFhyszM9PpeGQAAaN7cbremTZumIUOGqE+fPpKk4uJiny86688LzobKhRxDJadE1kAJlay+5rSFB/eCvNVdQNlsNbaFG6a90HN1gpXVH+//Zvq/96lJf+aZZ865PDIyUosWLdKiRYsaFAoAADQtWVlZ2r17t7Zs2dKg7QTigrOhciHHUMkpkTVQQiVrXXPOvyzAQerozAsDm+2ivGfWyGwXMD6Xxs5a3/+3M1+rZrrYbIO/kw4AAHAuU6ZM0auvvqrNmzerY8eOnvkJCQk+X3TWnxecDZULOYZKTomsgeKPrH1mrfdzqqrMekHimlSX10wX5ZVO/7+FUl2DldXX/7fqfqbMdLFZmnQAABAQhmFo6tSpWr16tTZu3KiUlBSv5fW56GwgLjgbKhdyDJWcElkDpSFZG/PCt2a70G5tzsxrttfCmXUMpbo2dlZ/vP+b6f+eJh0AAAREVlaWVqxYoZdfflkxMTGe75nHxsYqKiqKi84CAFANmnQAABAQS5YskSQNHz7ca/6yZcs0adIkSVx0FgCAs9GkAwCAgDCM2q/uy0VnAQDwFhbsAAAAAAAA4DSadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQAAAAAwCZp0AAAAAABMgiYdAAAAAACToEkHAAAAAMAkWgQ7AAAAAACYQZf7Xgt2BIAj6QAAAAAAmAVNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmwS3YAAAAENLqc9ssW7ih+ZdJfWatl7PC4tc8B+eN8ev2ADQvHEkHAAAAAMAkaNIBAAAAADAJmnQAAAAAAEyCJh0AAAAAAJOgSQcAAAAAwCRo0gEAAAAAMAmadAAAAAAATIImHQAAAAAAk6BJBwAAAADAJGjSAQBAQGzevFljx45VUlKSLBaL1qxZ47V80qRJslgsXn+uvvrq4IQFAMAkaNIBAEBAnDhxQhdddJEWLVpU45irr75aR44c8fx5/vnnGzEhAADm0yLYAQAAQNM0evRojR49+pxjbDabEhISGikRAADmx5F0AAAQNBs3blSHDh3UvXt33XHHHfr222+DHQkAgKDiSDoAAAiKq6++WuPHj1dKSor279+v+++/X6NHj9bWrVsVHh5e7TpOp1NOp9Mz7XA4JEkul0sul8unx68c7+t6jS1UckrBy2oLN3xfJ8zw+tuf/P38/VHX+tTI58cIYE0DIVTyhkpOKXhZ/fH+b6b3WJp0AAAQFBMmTPD8u2/fvurXr5+6du2qjRs3auTIkdWuk5eXp9zc3Crz8/PzFR0dXa8cBQUF9VqvsYVKTqnxs86/rP7rzhno9l+Q/2ft2rV+36bUsLo2pEa+CkRNAylU8oZKTqnxs9b3Z+7Mn6ny8nJ/xWkwmnQAAGAK559/vtq1a6d9+/bV2KTn5OQoOzvbM+1wOJScnKz09HTZ7XafHs/lcqmgoECjRo2S1WptUPZACpWcUvCy9pm13ud1bGGG5gx0a+aOMDndlgCk8p9QyRoqOSuFSt5QySkFL+vuWRk+ja/uvaryzCwzoEkHAACm8OWXX+rbb79VYmJijWNsNptsNluV+Vartd5NYUPWbUyhklNq/KzOivo3A063pUHrN6ZQyRoqOSuFSt5QySk1flZ/vP+b6f2VJh1Ak9blvteCHcGLLdxo1FMOgWA6fvy49u3b55k+cOCAdu7cqbi4OMXFxSk3N1eZmZlKSEjQ/v37NWPGDF1wwQXKyPDtiAgAAE2JT1d3z8vL06WXXqqYmBh16NBB48aNU1FRkdeYkydPKisrS23btlWrVq2UmZmpkpISv4YGAADmt2PHDl188cW6+OKLJUnZ2dm6+OKL9eCDDyo8PFwffvihrrvuOl144YWaPHmyBgwYoP/85z/VHikHAKC58OlI+qZNm5SVlaVLL71UP/74o+6//36lp6fro48+UsuWLSVJ06dP12uvvaZVq1YpNjZWU6ZM0fjx4/X2228H5AkAAABzGj58uAyj5iv8rl/v+/eIAQBo6nxq0tetW+c1vXz5cnXo0EGFhYW68sorVVZWpmeeeUYrVqzQiBEjJEnLli1Tz549tW3bNg0ePNh/yQEAAAAAaGIa9J30srIySVJcXJwkqbCwUC6XS2lpaZ4xPXr0UKdOnbR169Zqm3R/3u/0bJXrh8I9BYPFrPddNNN9CiXzvpbMVCez3se3Me4L64vK15DZ6mQm/nwtUWcAABBq6t2ku91uTZs2TUOGDFGfPn0kScXFxYqIiFDr1q29xsbHx6u4uLja7QTifqdnC6V7CgaL2WoUqPuLNhR1qp3Z7uNr1ou0ma1OZuSPGpnpnqcAAAB1Ue8mPSsrS7t379aWLVsaFMCf9zs9W+X970LhnoLBYtb7Lvp6r8NAM+tryUx1Mut9fOtz79xAqvyZM1udzMSfryUz3fMUAACgLurVpE+ZMkWvvvqqNm/erI4dO3rmJyQk6NSpUyotLfU6ml5SUqKEhIRqtxWI+52eLZTuKRgsZquRWZsX6lQ7s93H10z/X2cyW53MyB81osYAACDU+HQLNsMwNGXKFK1evVpvvvmmUlJSvJYPGDBAVqtVGzZs8MwrKirSoUOHlJqa6p/EAAAAAAA0UT4dSc/KytKKFSv08ssvKyYmxvM989jYWEVFRSk2NlaTJ09Wdna24uLiZLfbNXXqVKWmpnJldwAAAAAAauFTk75kyRJJp+97eqZly5Zp0qRJkqQFCxYoLCxMmZmZcjqdysjI0OLFi/0SFgAAAACApsynJt0war+VUWRkpBYtWqRFixbVOxQAAADMqct9r9W4zBZuaP5lpy/aadZrggCA2fn0nXQAAAAAABA4NOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmIRPt2ADAABA4+ly32vc1gwAmhmOpAMAAAAAYBI06QAAAAAAmARNOgAAAAAAJkGTDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAACAgNm/erLFjxyopKUkWi0Vr1qzxWm4Yhh588EElJiYqKipKaWlp2rt3b3DCAgBgEjTpAAAgIE6cOKGLLrpIixYtqnb5/Pnz9fjjj2vp0qXavn27WrZsqYyMDJ08ebKRkwIAYB4tgh0AAAA0TaNHj9bo0aOrXWYYhhYuXKgHHnhA119/vSTp2WefVXx8vNasWaMJEyY0ZlQAAEyDI+kAAKDRHThwQMXFxUpLS/PMi42N1aBBg7R169YgJgMAILg4kg4AABpdcXGxJCk+Pt5rfnx8vGdZdZxOp5xOp2fa4XBIklwul1wul08ZKsf7ul5jsoUbsoUZp//9//42M7IGRqhkDZWclUIlb6jklIKX1R/v/2b6LKBJBwAAISMvL0+5ublV5ufn5ys6Orpe2ywoKGhorICZf9n//j1noDt4QXxE1sAIlayhkrNSqOQNlZxS42ddu3ZtvdY78/2/vLzcX3EajCYdAAA0uoSEBElSSUmJEhMTPfNLSkrUv3//GtfLyclRdna2Z9rhcCg5OVnp6emy2+0+ZXC5XCooKNCoUaNktVp9ewKNpM+s9bKFGZoz0K2ZO8LkdFuCHemcyBoYoZI1VHJWCpW8oZJTCl7W3bMyfBpf3ft/5ZlZZkCTDgAAGl1KSooSEhK0YcMGT1PucDi0fft23XHHHTWuZ7PZZLPZqsy3Wq31brQbsm6gOSv+t5PrdFu8ps2MrIERKllDJWelUMkbKjmlxs/qj/d/M30O0KQDAICAOH78uPbt2+eZPnDggHbu3Km4uDh16tRJ06ZN09y5c9WtWzelpKRo5syZSkpK0rhx44IXGgCAIKNJBwAAAbFjxw5dddVVnunK09QnTpyo5cuXa8aMGTpx4oRuu+02lZaWaujQoVq3bp0iIyODFRkAgKCjSQcAAAExfPhwGUbNV/i1WCyaPXu2Zs+e3YipAAAwN+6TDgAAAACASdCkAwAAAABgEjTpAAAAAACYBE06AAAAAAAmQZMOAAAAAIBJ0KQDAAAAAGASNOkAAAAAAJgETToAAAAAACZBkw4AAAAAgEnQpAMAAAAAYBI06QAAAAAAmITPTfrmzZs1duxYJSUlyWKxaM2aNV7LDcPQgw8+qMTEREVFRSktLU179+71V14AAAAAAJosn5v0EydO6KKLLtKiRYuqXT5//nw9/vjjWrp0qbZv366WLVsqIyNDJ0+ebHBYAAAAAACasha+rjB69GiNHj262mWGYWjhwoV64IEHdP3110uSnn32WcXHx2vNmjWaMGFCw9ICAAAAANCE+fU76QcOHFBxcbHS0tI882JjYzVo0CBt3brVnw8FAAAAAECT4/OR9HMpLi6WJMXHx3vNj4+P9yw7m9PplNPp9Ew7HA5JksvlksvlalCeyvVtYUaDttOUVdbGbDVq6P+9v5n1tWSmOlVmMVMmSbKFm+v/rPI1ZLY6mYk/X0vUGb7oct9rwY4AAIB/m/T6yMvLU25ubpX5+fn5io6O9stjzBno9st2mjKz1Wjt2rXBjlAt6lS7goKCYEfwMv+yYCeontnqZEb+qFF5ebkfkgAAADQevzbpCQkJkqSSkhIlJiZ65peUlKh///7VrpOTk6Ps7GzPtMPhUHJystLT02W32xuUx+VyqaCgQDN3hMnptjRoW02VLczQnIFu09Vo96yMYEfwYtbXkpnqVFmjUaNGyWq1BjuOR59Z64MdwUvlz5zZ6mQm/nwtVZ6dBQAAECr82qSnpKQoISFBGzZs8DTlDodD27dv1x133FHtOjabTTabrcp8q9Xqtx1Yp9siZ4V5GiszMluNzNq8UKfa+fNn1x/M9P91JrPVyYz8USNqDAAAQo3PTfrx48e1b98+z/SBAwe0c+dOxcXFqVOnTpo2bZrmzp2rbt26KSUlRTNnzlRSUpLGjRvnz9wAAAAAADQ5PjfpO3bs0FVXXeWZrjxVfeLEiVq+fLlmzJihEydO6LbbblNpaamGDh2qdevWKTIy0n+pAQAAAABognxu0ocPHy7DqPlqyRaLRbNnz9bs2bMbFAwAAAAAgObGr/dJBwAAAAAA9UeTDgAAAACASdCkAwAAAABgEjTpAAAgaGbNmiWLxeL1p0ePHsGOBQBA0Pj1PukAAAC+6t27t9544w3PdIsW7J4AAJovPgUBAEBQtWjRQgkJCcGOAQCAKdCkAwCAoNq7d6+SkpIUGRmp1NRU5eXlqVOnTtWOdTqdcjqdnmmHwyFJcrlccrlcPj1u5fjKv23hNd9iNphsYYbX32ZG1sAIlayhkrNSqOQNlZxS8LI29P2/PtsIJJp0AAAQNIMGDdLy5cvVvXt3HTlyRLm5ubriiiu0e/duxcTEVBmfl5en3NzcKvPz8/MVHR1drwwFBQWSpPmX1Wv1RjNnoDvYEeqMrIERKllDJWelUMkbKjmlxs+6du3aeq1X+f4vSeXl5f6K02A06QAAIGhGjx7t+Xe/fv00aNAgde7cWStXrtTkyZOrjM/JyVF2drZn2uFwKDk5Wenp6bLb7T49tsvlUkFBgUaNGiWr1ao+s9bX/4kEkC3M0JyBbs3cESan2xLsOOdE1sAIlayhkrNSqOQNlZxS8LLunpXh0/iz3/+l/52ZZQY06QDw/7d371FR3OcbwJ+FhYVFECXcvGMqB28VlXoBU+PRSBITjWm9EI0cjZooHKW2aqwSSttINOZSI4KkjdoGr6lN1F+jxxAV4zXCWjUhaiPS2AAmGC6iAmHf3x+e3brsLhdZdmb1+Zyzf+zsMPP48p2Z77u7jESkGv7+/ggPD8e///1vm6/rdDrodDqr5R4eHuaJVkuZframXt2T3xqjRvUZTZi1bbhKVlfJaeIqeV0lJ+D8rK09/7dmG22B/wUbERERqcaNGzfw9ddfIzQ0VOkoREREimCTTkRERIr5zW9+g8OHD+PKlSs4duwYJk6cCHd3d8TFxSkdjYiISBH8ujsREREp5urVq4iLi0NZWRkCAwMxYsQInDhxAoGBgUpHIyIiUgSbdCIiIlLMtm3blI5ARESkKvy6OxEREREREZFKsEknIiIiIiIiUgk26UREREREREQqwSadiIiIiIiISCXYpBMRERERERGpBJt0IiIiIiIiIpVgk05ERERERESkEmzSiYiIiIiIiFSCTToRERERERGRSrBJJyIiIiIiIlIJNulEREREREREKsEmnYiIiIiIiEgl2KQTERERERERqQSbdCIiIiIiIiKV0CodgIiIlNfj5f9TOoKZzl2weojSKYiIiIiUwU/SiYiIiIiIiFSCTToRERERERGRSrBJJyIiIiIiIlIJNulEREREREREKsEmnYiIiIiIiEgl2KQTERERERERqQSbdCIiIiIiIiKVYJNOREREREREpBJs0omIiIiIiIhUgk06ERERERERkUq0WZOenp6OHj16wMvLC0OHDsWpU6faaldERETkwjhnICIi+p82adK3b9+ORYsWISUlBfn5+RgwYABiY2Nx7dq1ttgdERERuSjOGYiIiCy1SZP+5ptvYs6cOZg5cyb69OmDzMxM6PV6vPfee22xOyIiInJRnDMQERFZ0jp6g7W1tcjLy8OyZcvMy9zc3DBmzBgcP37cav2amhrU1NSYn1dUVAAArl+/jrq6ulZlqaurw82bN6Gtc0O9UdOqbd2vtEbBzZtG1dWorKxM6QgW1DqW1FQnU43Kysrg4eGhdBwz7Y/VSkewYDrmWCf7HFmjqqoqAICIOCIaOVhL5wyAY+cNDc9bajoO7qbWa7UtzNo2XCWrq+Q0cZW8rpITUC5rS+fEtuatapozOLxJ//7771FfX4/g4GCL5cHBwfjqq6+s1k9LS0NqaqrV8rCwMEdHIzueUzqADQ+9oXQC18A6uSY1HnNq4+gaVVVVoX379g7eKrVWS+cMwIM7b3Cl8waztg1XyeoqOU1cJa+r5ASUyerIObEa5gwOb9JbatmyZVi0aJH5udFoxPXr1xEQEACNpnXvvlRWVqJr16745ptv4Ofn19qo9yXWqHlYp6axRs3DOjXNkTUSEVRVVaFTp04OSkdKc+S8wVWOR1fJCTBrW3GVrK6S08RV8rpKTsB1strKqaY5g8Ob9Iceegju7u4oLS21WF5aWoqQkBCr9XU6HXQ6ncUyf39/h2by8/NT9SBRA9aoeVinprFGzcM6Nc1RNVL63XCyr6VzBqBt5g2ucjy6Sk6AWduKq2R1lZwmrpLXVXICrpO1YU61zBkcfuM4T09PDB48GDk5OeZlRqMROTk5GD58uKN3R0RERC6KcwYiIiJrbfJ190WLFiE+Ph5RUVEYMmQI3n77bVRXV2PmzJltsTsiIiJyUZwzEBERWWqTJn3KlCn47rvv8Morr6CkpASRkZHYt2+f1Y1h2ppOp0NKSorV1+Lof1ij5mGdmsYaNQ/r1DTW6MGi5JzBVcaaq+QEmLWtuEpWV8lp4ip5XSUn4DpZ1Z5TI2q4xzwREREREREROf5v0omIiIiIiIjo3rBJJyIiIiIiIlIJNulEREREREREKsEmnYiIiIiIiEgl7rsmPS0tDT/72c/g6+uLoKAgPPPMM7hw4YLSsVTvtddeg0ajQVJSktJRVOW///0vpk+fjoCAAHh7e6N///44ffq00rFUpb6+HsnJyQgLC4O3tzcefvhh/OEPf8CDfk/K3NxcPP300+jUqRM0Gg0+/PBDi9dFBK+88gpCQ0Ph7e2NMWPG4NKlS8qEVUhjNaqrq8PSpUvRv39/+Pj4oFOnTpgxYwa+/fZb5QKTUzTnOn779m0kJCQgICAA7dq1wy9+8QuUlpZarLNgwQIMHjwYOp0OkZGRNve1Y8cOREZGQq/Xo3v37nj99deblXHnzp0IDAyEm5sb3N3d4e/vb5Fz165dGDt2LDp27AiNRgN/f3/FckZERMDLywshISEIDw+3qKtGo7H58PHxsZn3X//6F+Li4tC1a1d4e3ujd+/e+NOf/mS130OHDmHQoEHQ6XT4yU9+gk2bNjWZ1XRO9PX1hZubG7RaLQICAizq+uqrryI6Ohre3t7Q6XR2f//OyBkaGgoPDw/4+fmhXbt25pr+9a9/tVtXvV5/T1mLi4vx3HPPITw8HG5ubi2aq6Wnp6NHjx7QarVo164dfHx8LI6rrKwsPProo/Dz84NGo8Hs2bNbVde2ypqTk2O3rl5eXveUddeuXXjssccQGBgIPz8/DB8+HPv3728yp7PHamtzOnOsfvbZZ4iJiTHPmyMiIvDWW2+1OuuFCxfMNdXr9Wjfvn2j1wBnZbU1fzt06JDdun7++edNbt/kvmvSDx8+jISEBJw4cQIHDhxAXV0dxo4di+rqaqWjqdbnn3+ODRs24Kc//anSUVTlhx9+QExMDDw8PPDxxx/jyy+/xBtvvIEOHTooHU1VVq1ahYyMDKxbtw4FBQVYtWoVVq9ejXfeeUfpaIqqrq7GgAEDkJ6ebvP11atXY+3atcjMzMTJkyfh4+OD2NhY3L5928lJldNYjW7evIn8/HwkJycjPz8fu3btwoULFzB+/HgFkpIzNec6/qtf/Qp79uzBzp07cfjwYXz77bd49tlnrbY1a9YsTJkyxeZ+Pv74Y0ybNg0vvfQSzp8/j/Xr1+Ott97CunXrGs137NgxxMXFoWPHjvjjH/+IOXPmoLq6GtevXzfnrK6uxogRI8zX1dWrVyuW84UXXoDBYICPjw8uX76Mv/3tb+a6du7cGV9//TWKi4tRXFyMRx99FACwYcMGm3nz8vIQFBSE999/H1988QWWL1+OZcuWWWQpLCzEuHHjMGrUKJw5cwZJSUmYPXt2k02F6ZzYq1cvpKam4pFHHoGXlxdqamrMda2trcWkSZMQHh6Ouro6u79/Z+TMzMzEkCFD8PDDD8Pf3x979+5FXV0dkpOTLWo6Y8YM+Pj4ICQkBLm5ufeUtaamBoGBgVixYgUGDBjQaL67bd++HYsWLUJKSgqGDRuGqKgouLu7Y/v27ebj6ocffsDjjz+O3/72twCAffv2taqubZV11qxZVnVt3749vLy8cODAgXvKmpubi8ceewz//Oc/kZeXh1GjRuHpp5+GwWBoNKuzx2prczpzrPr4+CAxMRG5ubkoKCjAihUrsGLFCmRlZbUq69ixY3Hjxg1MmjQJ8+bNw+3btxu9Bjgrq635W3R0tLmmpsfs2bMRFhaGqKioRrdtQe5z165dEwBy+PBhpaOoUlVVlfTq1UsOHDggI0eOlIULFyodSTWWLl0qI0aMUDqG6o0bN05mzZplsezZZ5+VadOmKZRIfQDIP/7xD/Nzo9EoISEh8vrrr5uXlZeXi06nk61btyqQUHkNa2TLqVOnBIAUFRU5JxSpQsPreHl5uXh4eMjOnTvN6xQUFAgAOX78uNXPp6SkyIABA6yWx8XFyS9/+UuLZWvXrpUuXbqI0Wi0m2fy5Mkybtw4i2VDhw6VGTNm2MwJQAwGg2pyvvjiiyJiu64ajUb69etnXr+xvCbz58+XUaNGmZ8vWbJE+vbta7HOlClTJDY21u42GjsnbtiwwSqnu7u76PV6VeXcunWr3bHq5+cnv//97+85691aMlcbMmSIJCQkmJ/X19dLp06dJC0tzSrrnj17BIBs2rTJvL5as5rq2qNHD/P8o7VZTfr06SOpqal2X1dirDo6p7PGqsnEiRNl+vTpDsuanp4uAJp9DXBmVltqa2slMDDQXNfmuu8+SW+ooqICANCxY0eFk6hTQkICxo0bhzFjxigdRXV2796NqKgoTJo0CUFBQRg4cCDeffddpWOpTnR0NHJycnDx4kUAd75i9Nlnn+GJJ55QOJl6FRYWoqSkxOK4a9++PYYOHYrjx48rmEzdKioqzF8dpgdHw+t4Xl4e6urqLI6fiIgIdOvWrUXHT01NDby8vCyWeXt74+rVqygqKrL7c8ePH7e6ZsbGxuLkyZM2c95NDTlN+25Y108++QQiggULFrQob0VFhcUcq6n92tLYOfHo0aMWOfPy8lBfXw+tVquqnMePH7c7VquqqjBz5sx7znovamtrkZeXZ5HVzc0NY8aMsZnVdA03fZtCzVlNdb1y5QpeeOEFh2U1Go2oqqpqdB0lxqqjczpzrBoMBhw7dgwjR450WNYrV64AQIuvAc7Iasvu3btRVlZmrmtz3ddNutFoRFJSEmJiYtCvXz+l46jOtm3bkJ+fj7S0NKWjqNLly5eRkZGBXr16Yf/+/Zg3bx4WLFiAzZs3Kx1NVV5++WVMnToVERER8PDwwMCBA5GUlIRp06YpHU21SkpKAADBwcEWy4ODg82vkaXbt29j6dKliIuLg5+fn9JxyElsXcdLSkrg6elp9WZNS4+f2NhY7Nq1Czk5OTAajbh48SLeeOMNAHf+rtaekpISq2M3MDAQhYWFNnM2pGRO075t1fWDDz4AADz//PPNznvs2DFs374dc+fObXK/lZWVuHXrlt2spvXuFhQUhAMHDljVVavVQqPRqCZncHAwiouLbY5VjUaDxx9/HF26dLnnrPfi+++/R319fbOzXr9+HcCdhqPh+mrLWlJSAjc3N/Tu3RvR0dEOy7pmzRrcuHEDkydPtruOEmPVkTmdNVa7dOkCnU6HqKgoJCQkYPbs2Q7LamraW3INcEZWe/v+y1/+gtjYWIu6Nsd93aQnJCTg/Pnz2LZtm9JRVOebb77BwoULkZ2dbfUOPd1hNBoxaNAgrFy5EgMHDsTcuXMxZ84cZGZmKh1NVXbs2IHs7Gxs2bIF+fn52Lx5M9asWcM3M8hh6urqMHnyZIgIMjIylI5DTtSW1/E5c+YgMTERTz31FDw9PTFs2DBMnToVwJ1P8f7zn/+gXbt25sfKlSvtbmv79u348ccfVZ/TxFZdDx8+DDc3t2bPCc6fP48JEyYgJSUFY8eObfa/Jzs72yLvkSNH7K6bn5+PioqKVtXVGTlNWRvWtKysDCJi/rS3rbIeOXLEImt2dnaj6xcVFbX6uFIya21tLYxGo0PrumXLFqSmpmLHjh0ICgoCoM6x2tqcpqxtPVaPHDmC06dPIzMzE2+//Ta2bt3qsKwt5aystly9ehX79+9vdl3vpm16FdeUmJiIvXv3Ijc3t8XvXDwI8vLycO3aNQwaNMi8rL6+Hrm5uVi3bh1qamrg7u6uYELlhYaGok+fPhbLevfujb///e8KJVKnxYsXmz9NB4D+/fujqKgIaWlpiI+PVzidOoWEhAAASktLERoaal5eWlpq9+7ODypTg15UVIRPP/2Un6I/QOxdx0NCQlBbW4vy8nKLT1JKS0vNx1ZzaDQarFq1CitXrkRJSQkCAwORk5MDAOjZsyc6dOiAM2fOmNc3fU0yJCTE4i7CiYmJOHv2LMLDw23mbEipnKZ919fXW9X1yJEj5k/lm1PXL7/8EqNHj8bcuXOxYsUKi9fs7dfPzw/e3t4YP348hg4dan6tc+fO5n3ffU5MTEzE1atXERcXZ1XXH3/80ep/EFEqJwAcPXoUlZWVOHv2rEXW/Px8AMDPf/7zVmVtSlRUlMUYCA4Ohk6ng7u7u9W/cffu3aisrMSJEycssprGTUVFRat+/87IarqD+oQJEyzWv9es27Ztw+zZs7Fz506LrzGrZaw6KifgvLEaFhYG4M6csLS0FL/73e8QFxfnkKymb3u09lzlyKz25m8bN25EQEDAvd30tkV/we4CjEajJCQkSKdOneTixYtKx1GtyspKOXfunMUjKipKpk+fLufOnVM6nirExcVZ3TguKSlJhg8frlAiderYsaOsX7/eYtnKlSulV69eCiVSH9i5cdyaNWvMyyoqKnjjuAY3jqutrZVnnnlG+vbtK9euXVMmGDldU9dx0w2OPvjgA/Oyr776qsU3ZLPl+eefb/IcP3nyZHnqqacsckZGRppvyNYwJ+66cZwSOU2MRqMEBweLXq+3qmt8fLxERkY2q67nz5+XoKAgWbx4sc39LlmyxOLmcyJ3rqfNuSHbmjVrzHUNDQ0VDw8Pq3OirZtxKZHT9HzOnDkCQN58802rdbt37y5ubm6trundWnoztsTERHOe+fPni5ubm/z617+2Wtd047jNmzerPmtMTIxoNBqH1HXLli3i5eUlH374YbNyKjFWW5vT9NzZY9UkNTVVunfv7pCsIv+7cZwasorYn78ZjUYJCwuzOYab475r0ufNmyft27eXQ4cOSXFxsflx8+ZNpaOpHu/ubunUqVOi1Wrl1VdflUuXLkl2drbo9Xp5//33lY6mKvHx8dK5c2fZu3evFBYWyq5du+Shhx6SJUuWKB1NUVVVVWIwGMRgMJgvNAaDwXxn8tdee038/f3lo48+krNnz8qECRMkLCxMbt26pXBy52msRrW1tTJ+/Hjp0qWLnDlzxuJ8XlNTo3R0akPNuY6/9NJL0q1bN/n000/l9OnTMnz4cKum9dKlS2IwGOTFF1+U8PBw81gzjZ/vvvtOMjIypKCgQAwGgyxYsEC8vLzk5MmTjeY7evSoaLVaGT58uPj6+kp8fLxotVo5ePCgOWdZWZkYDAZ58sknBYAkJydLdna2REVFOT3nmjVrpKCgQKKiogSAvPfeexZ1LSkpEb1eLxkZGU3W9dy5cxIYGCjTp0+32Mbdb6JdvnxZ9Hq9LF68WAoKCiQ9PV3c3d1l3759jeY1nROfeOIJ8fX1lZiYGOnatasUFhaa61pUVCQGg0GioqJEo9FIVlaWZGdny5AhQ5ye86OPPpLJkyeLVquVkJAQc05T1k8++UQAyJQpU1pdUxExj4vBgwfLc889JwaDQb744otGs27btk10Op1s2rRJpk6dKp6entKuXTs5e/aseT+XL18Wg8Eg7777rgCQ4OBgycrKkpycHNVlvXnzply6dEk0Go08+eSTra5rdna2aLVaSU9Pt1invLy80azOHqutzenMsbpu3TrZvXu3XLx4US5evCh//vOfxdfXV5YvX97qrBcuXBCDwSCpqanm17OysiQ3N1exrI3N30x1LSgoaHR79tx3TToAm4+NGzcqHU312KRb27Nnj/Tr1090Op1ERERIVlaW0pFUp7KyUhYuXCjdunUTLy8v6dmzpyxfvvyBb6QOHjxo81wUHx8vInfeYU1OTpbg4GDR6XQyevRouXDhgrKhnayxGhUWFto9nx88eFDp6NSGmnMdv3XrlsyfP186dOgger1eJk6cKMXFxRbbGTlypM3tFBYWisid5nfYsGHi4+Mjer1eRo8eLSdOnGhWxh07djSac+PGjTZfi4iIcHrO8PBw8fT0tJs3Pj5evL29pby8vMm6pqSk2NxGw0+eDh48KJGRkeLp6Sk9e/Zs1hzMdE5srK7x8fE2X3vkkUecnjM4OLjRrHFxcRIdHe2wmjZnHVveeecd6datm92sEyZMsLnc09NTdVk3btwoy5Ytk65du0p1dXWr62rv2DNdp+1x9lhtbU5njtW1a9dK3759Ra/Xi5+fnwwcOFDWr18v9fX1rc4aExNjc7mXl5diWRubv5nqeq80Ig3+UIKIiIiIiIiIFHFf392diIiIiIiIyJWwSSciIiIiIiJSCTbpRERERERERCrBJp2IiIiIiIhIJdikExEREREREakEm3QiIiIiIiIilWCTTkRERERERKQSbNKJiIiIiIiIVIJNOhEREREREZFKsEknIiIiIiIiUgk26UREREREREQqwSadiIiIiIiISCX+H4mfzWcufeweAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%explore_table" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "\n", "### Descriptive analysis \n", " \n", "What is the average age of users who made a purchase on the website? \n", "\n", "\n", "\n", "### Inferential Analysis \n", " \n", "Is there a significant difference in the average order value between male and female users? \n", "\n", "\n", "\n", "### Diagnostic Analysis \n", " \n", "What factors contribute to a higher return rate for certain products? \n", "\n", "\n", "\n", "### Predictive Analysis \n", " \n", "Which products are likely to be best-sellers in the next quarter based on historical sales data? \n", "\n", "\n", "\n", "### Prescriptive Analysis \n", " \n", "What actions can be taken to optimize the inventory levels at distribution centers to reduce shipping times? \n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%explore_questions" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'action': 'Task Resolution', 'reason': 'The task is well-defined and requires a data analysis plan to optimize inventory levels at distribution centers to reduce shipping times.', 'criticism': 'I should ensure that the data analysis plan is comprehensive and considers all relevant factors from the given data source.'}\n", "[{'name': 'Step 1: Calculate average shipping times', 'purpose': 'Determine the average shipping times for each distribution center to identify areas for improvement.', 'sql_query': \"SELECT oi.product_id, p.distribution_center_id, dc.name, AVG(TIMESTAMPDIFF(HOUR, oi.shipped_at, oi.delivered_at)) as avg_shipping_time FROM order_items oi JOIN products p ON oi.product_id = p.id JOIN distribution_centers dc ON p.distribution_center_id = dc.id WHERE oi.status = 'Complete' GROUP BY p.distribution_center_id, dc.name\", 'data_analysis_methods': [{'method_name': 'Descriptive statistics', 'explanation': 'Calculate the mean, median, and standard deviation of shipping times for each distribution center to understand the overall performance and variability.'}]}, {'name': 'Step 2: Analyze product demand', 'purpose': 'Identify the most popular products and their demand patterns to optimize inventory levels.', 'sql_query': 'SELECT p.id as product_id, p.name as product_name, p.distribution_center_id, dc.name as distribution_center_name, COUNT(oi.id) as total_orders FROM products p JOIN order_items oi ON p.id = oi.product_id JOIN distribution_centers dc ON p.distribution_center_id = dc.id GROUP BY p.id, p.name, p.distribution_center_id, dc.name ORDER BY total_orders DESC', 'data_analysis_methods': [{'method_name': 'Time series analysis', 'explanation': 'Analyze the demand patterns of popular products over time to identify trends, seasonality, and potential inventory optimization opportunities.'}]}, {'name': 'Step 3: Calculate inventory turnover', 'purpose': 'Determine the inventory turnover rate for each distribution center to assess the efficiency of inventory management.', 'sql_query': \"SELECT p.distribution_center_id, dc.name, SUM(oi.sale_price) as total_sales, AVG(ii.cost) as avg_inventory_cost, COUNT(ii.id) as total_inventory_items FROM order_items oi JOIN products p ON oi.product_id = p.id JOIN distribution_centers dc ON p.distribution_center_id = dc.id JOIN inventory_items ii ON oi.inventory_item_id = ii.id WHERE oi.status = 'Complete' GROUP BY p.distribution_center_id, dc.name\", 'data_analysis_methods': [{'method_name': 'Inventory turnover ratio', 'explanation': 'Calculate the inventory turnover ratio by dividing total sales by average inventory cost for each distribution center. A higher ratio indicates more efficient inventory management.'}]}, {'name': 'Step 4: Optimize inventory levels', 'purpose': 'Recommend optimal inventory levels for each distribution center based on demand patterns and inventory turnover.', 'sql_query': '', 'data_analysis_methods': [{'method_name': 'Safety stock calculation', 'explanation': 'Calculate the safety stock levels for each product at each distribution center based on demand variability and lead time to ensure adequate inventory levels while minimizing excess stock.'}, {'method_name': 'Reorder point calculation', 'explanation': 'Determine the reorder point for each product at each distribution center based on demand patterns, lead time, and safety stock levels to optimize inventory replenishment.'}]}]\n" ] }, { "data": { "text/markdown": [ "## What actions can be taken to optimize the inventory levels at distribution centers to reduce shipping times \n", " **The parent task belongs**: \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Step 1: Calculate average shipping times \n", " #### The Purpose \n", " Determine the average shipping times for each distribution center to identify areas for improvement. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### The relevant Data \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "73422fb2822245edbbd8c27b942b22eb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SQLDataViewerWidget(sql=\"SELECT p.distribution_center_id, dc.name, AVG(TIMESTAMPDIFF(HOUR, oi.shipped_at, oi.d…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ " \n", "#### Gain insights methods \n", "**Descriptive statistics** : Calculate the mean, median, and standard deviation of shipping times for each distribution center to understand the overall performance and variability. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Step 2: Analyze product demand \n", " #### The Purpose \n", " Identify the most popular products and their demand patterns to optimize inventory levels. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### The relevant Data \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b6219857bb7143aca8e012ade331b38c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SQLDataViewerWidget(sql='SELECT p.id as product_id, p.name as product_name, p.distribution_center_id, dc.name …" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ " \n", "#### Gain insights methods \n", "**Time series analysis** : Analyze the demand patterns of popular products over time to identify trends, seasonality, and potential inventory optimization opportunities. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Step 3: Calculate inventory turnover \n", " #### The Purpose \n", " Determine the inventory turnover rate for each distribution center to assess the efficiency of inventory management. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "#### The relevant Data \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cba45d403b5a4955ab911549e5fa1cd9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SQLDataViewerWidget(sql=\"SELECT p.distribution_center_id, dc.name, SUM(oi.sale_price) as total_sales, AVG(ii.c…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ " \n", "#### Gain insights methods \n", "**Inventory turnover ratio** : Calculate the inventory turnover ratio by dividing total sales by average inventory cost for each distribution center. A higher ratio indicates more efficient inventory management. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "### Step 4: Optimize inventory levels \n", " #### The Purpose \n", " Recommend optimal inventory levels for each distribution center based on demand patterns and inventory turnover. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ " \n", "#### Gain insights methods \n", "**Safety stock calculation** : Calculate the safety stock levels for each product at each distribution center based on demand variability and lead time to ensure adequate inventory levels while minimizing excess stock. \n", " \n", "**Reorder point calculation** : Determine the reorder point for each product at each distribution center based on demand patterns, lead time, and safety stock levels to optimize inventory replenishment. \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%auto_task What actions can be taken to optimize the inventory levels at distribution centers to reduce shipping times" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.10.11" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }