├── piggy_bank.jpg
├── ReadME.md
├── .ipynb_checkpoints
    ├── notebook-checkpoint.ipynb
    └── Designing a Bank Marketing Database-checkpoint.ipynb
└── Designing a Bank Marketing Database.ipynb


/piggy_bank.jpg:
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https://raw.githubusercontent.com/Chisomnwa/Designing-a-Bank-Marketing-Database/main/piggy_bank.jpg


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/ReadME.md:
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 1 | # <p align="left" style="margin-top: 0px;"> Designing a Bank Marketing Database
 2 | 
 3 | ![piggy_bank](piggy_bank.jpg)
 4 | 
 5 | <br>
 6 | 
 7 | ---
 8 | *This **Data Engineering** project involves creating a comprehensive database to store and manage customer information for a bank's marketing campaigns.*
 9 | 
10 | <br>
11 | 
12 | ## Project Description
13 | 
14 | Personal loans are a lucrative revenue stream for banks. The typical interest rate of a two year loan in the United Kingdom is [around 10%](https://www.experian.com/blogs/ask-experian/whats-a-good-interest-rate-for-a-personal-loan/). This might not sound like a lot, but in September 2022 alone UK consumers borrowed [around £1.5 billion](https://www.ukfinance.org.uk/system/files/2022-12/Household%20Finance%20Review%202022%20Q3-%20Final.pdf), which would mean approximately £300 million in interest generated by banks over two years!
15 | 
16 | You have been asked to work with a bank to clean and store the data they collected as part of a recent marketing campaign, which aimed to get customers to take out a personal loan. They plan to conduct more marketing campaigns going forward so would like you to set up a PostgreSQL database to store this campaign's data, designing the schema in a way that would allow data from future campaigns to be easily imported. 
17 | 
18 | They have supplied you with a csv file called `"bank_marketing.csv"`, which you will need to clean, reformat, and split, in order to save separate files based on the tables you will create. It is recommended to use `pandas` for these tasks.
19 | 
20 | Lastly, you will write the SQL code that the bank can execute to create the tables and populate with the data from the csv files. As the bank are quite strict about their security, you'll save SQL files as multiline string variables that they can then use to create the database on their end. 
21 | 
22 | You have been asked to design a database that will have three tables:
23 | 
24 | ## client
25 | 
26 | | column | data type | description | original column in dataset |
27 | |--------|-----------|-------------|----------------------------|
28 | | `id` | `serial` | Client ID - primary key | `client_id` |
29 | | `age` | `integer` | Client's age in years | `age` |
30 | | `job` | `text` | Client's type of job | `job` |
31 | | `marital` | `text` | Client's marital status | `marital` | 
32 | | `education` | `text` | Client's level of education | `education` |
33 | | `credit_default` | `boolean` | Whether the client's credit is in default | `credit_default` |
34 | | `housing` | `boolean` | Whether the client has an existing housing loan (mortgage) | `housing` | 
35 | | `loan` | `boolean` | Whether the client has an existing personal loan | `loan` |
36 | 
37 | <br>
38 | 
39 | ## campaign
40 | 
41 | | column | data type | description | original column in dataset |
42 | |--------|-----------|-------------|----------------------------|
43 | | `campaign_id` | `serial` | Campaign ID - primary key | N/A - new column |
44 | | `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |
45 | | `number_contacts` | `integer` | Number of contact attempts to the client in the current campaign | `campaign` |
46 | | `contact_duration` | `integer` | Last contact duration in seconds | `duration` |
47 | | `pdays` | `integer` | Number of days since contact in previous campaign (`999` = not previously contacted) | `pdays` |
48 | | `previous_campaign_contacts` | `integer` | Number of contact attempts to the client in the previous campaign | `previous` |
49 | | `previous_outcome` | `boolean` | Outcome of the previous campaign | `poutcome` |
50 | | `campaign_outcome` | `boolean` | Outcome of the current campaign | `y` |
51 | | `last_contact_date` | `date` | Last date the client was contacted | A combination of `day`, `month`, and the newly created `year` |
52 | 
53 | <br>
54 | 
55 | ## economics
56 | 
57 | | column | data type | description | original column in dataset |
58 | |--------|-----------|-------------|----------------------------|
59 | | `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |
60 | | `emp_var_rate` | `float` | Employment variation rate (quarterly indicator) | `emp_var_rate` |
61 | | `cons_price_idx` | `float` | Consumer price index (monthly indicator) | `cons_price_idx` |
62 | | `euribor_three_months` | `float` | Euro Interbank Offered Rate (euribor) three month rate (daily indicator) | `euribor3m` |
63 | | `number_employed` | `float` | Number of employees (quarterly indicator)| `nr_employed` |
64 | 
65 | <br>
66 | 
67 | ## Project Tasks
68 | 
69 | View the [notebook](https://github.com/Chisomnwa/Designing-a-Bank-Marketing-Database/blob/main/Designing%20a%20Bank%20Marketing%20Database.ipynb) to see the project tasks and all expected output for each task and finally see how to successfully load the data into a PostgreSQL database after cleaning and getting it ready.
70 | 
71 | In case, GitHub doesn't render the notebook withing the 5 seconds timeframe of clicking the link, you can view the notebook [here](https://nbviewer.org/github/Chisomnwa/Designing-a-Bank-Marketing-Database/blob/main/Designing%20a%20Bank%20Marketing%20Database.ipynb).
72 | 
73 | <br>
74 | 
75 | ---
76 | 
77 | And you'd love to read my blog post on this project [here](https://medium.com/@chisompromise/designing-a-bank-marketing-database-a033f3eee479).
78 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/notebook-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "02077ee3-e1e4-4fc5-8de1-16e987afa5fb",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "![piggy_bank](piggy_bank.jpg)\n",
  9 |     "\n",
 10 |     "<br>\n",
 11 |     "\n",
 12 |     "Personal loans are a lucrative revenue stream for banks. The typical interest rate of a two year loan in the United Kingdom is [around 10%](https://www.experian.com/blogs/ask-experian/whats-a-good-interest-rate-for-a-personal-loan/). This might not sound like a lot, but in September 2022 alone UK consumers borrowed [around £1.5 billion](https://www.ukfinance.org.uk/system/files/2022-12/Household%20Finance%20Review%202022%20Q3-%20Final.pdf), which would mean approximately £300 million in interest generated by banks over two years!\n",
 13 |     "\n",
 14 |     "You have been asked to work with a bank to clean and store the data they collected as part of a recent marketing campaign, which aimed to get customers to take out a personal loan. They plan to conduct more marketing campaigns going forward so would like you to set up a PostgreSQL database to store this campaign's data, designing the schema in a way that would allow data from future campaigns to be easily imported. \n",
 15 |     "\n",
 16 |     "They have supplied you with a csv file called `\"bank_marketing.csv\"`, which you will need to clean, reformat, and split, in order to save separate files based on the tables you will create. It is recommended to use `pandas` for these tasks.\n",
 17 |     "\n",
 18 |     "Lastly, you will write the SQL code that the bank can execute to create the tables and populate with the data from the csv files. As the bank are quite strict about their security, you'll save SQL files as multiline string variables that they can then use to create the database on their end. \n",
 19 |     "\n",
 20 |     "You have been asked to design a database that will have three tables:\n",
 21 |     "\n",
 22 |     "## client\n",
 23 |     "\n",
 24 |     "| column | data type | description | original column in dataset |\n",
 25 |     "|--------|-----------|-------------|----------------------------|\n",
 26 |     "| `id` | `serial` | Client ID - primary key | `client_id` |\n",
 27 |     "| `age` | `integer` | Client's age in years | `age` |\n",
 28 |     "| `job` | `text` | Client's type of job | `job` |\n",
 29 |     "| `marital` | `text` | Client's marital status | `marital` | \n",
 30 |     "| `education` | `text` | Client's level of education | `education` |\n",
 31 |     "| `credit_default` | `boolean` | Whether the client's credit is in default | `credit_default` |\n",
 32 |     "| `housing` | `boolean` | Whether the client has an existing housing loan (mortgage) | `housing` | \n",
 33 |     "| `loan` | `boolean` | Whether the client has an existing personal loan | `loan` |\n",
 34 |     "\n",
 35 |     "<br>\n",
 36 |     "\n",
 37 |     "## campaign\n",
 38 |     "\n",
 39 |     "| column | data type | description | original column in dataset |\n",
 40 |     "|--------|-----------|-------------|----------------------------|\n",
 41 |     "| `campaign_id` | `serial` | Campaign ID - primary key | N/A - new column |\n",
 42 |     "| `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |\n",
 43 |     "| `number_contacts` | `integer` | Number of contact attempts to the client in the current campaign | `campaign` |\n",
 44 |     "| `contact_duration` | `integer` | Last contact duration in seconds | `duration` |\n",
 45 |     "| `pdays` | `integer` | Number of days since contact in previous campaign (`999` = not previously contacted) | `pdays` |\n",
 46 |     "| `previous_campaign_contacts` | `integer` | Number of contact attempts to the client in the previous campaign | `previous` |\n",
 47 |     "| `previous_outcome` | `boolean` | Outcome of the previous campaign | `poutcome` |\n",
 48 |     "| `campaign_outcome` | `boolean` | Outcome of the current campaign | `y` |\n",
 49 |     "| `last_contact_date` | `date` | Last date the client was contacted | A combination of `day`, `month`, and the newly created `year` |\n",
 50 |     "\n",
 51 |     "<br>\n",
 52 |     "\n",
 53 |     "## economics\n",
 54 |     "\n",
 55 |     "| column | data type | description | original column in dataset |\n",
 56 |     "|--------|-----------|-------------|----------------------------|\n",
 57 |     "| `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |\n",
 58 |     "| `emp_var_rate` | `float` | Employment variation rate (quarterly indicator) | `emp_var_rate` |\n",
 59 |     "| `cons_price_idx` | `float` | Consumer price index (monthly indicator) | `cons_price_idx` |\n",
 60 |     "| `euribor_three_months` | `float` | Euro Interbank Offered Rate (euribor) three month rate (daily indicator) | `euribor3m` |\n",
 61 |     "| `number_employed` | `float` | Number of employees (quarterly indicator)| `nr_employed` |"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 1,
 67 |    "id": "e2edad3c-8286-4983-b5b7-35d94fd78023",
 68 |    "metadata": {
 69 |     "executionCancelledAt": null,
 70 |     "executionTime": 1057,
 71 |     "lastExecutedAt": 1686069923599,
 72 |     "lastScheduledRunId": null,
 73 |     "lastSuccessfullyExecutedCode": "# Start coding...\nimport pandas as pd\nimport numpy as np\n\n# Read in csv\nmarketing = pd.read_csv(\"bank_marketing.csv\")\n\n# Split into the three tables\nclient = marketing[[\"client_id\", \"age\", \"job\", \"marital\", \"education\", \n             \"credit_default\", \"housing\", \"loan\"]]\ncampaign = marketing[[\"client_id\", \"campaign\", \"month\", \"day\", \n               \"duration\", \"pdays\", \"previous\", \"poutcome\", \"y\"]]\neconomics = marketing[[\"client_id\", \"emp_var_rate\", \"cons_price_idx\", \n                \"euribor3m\", \"nr_employed\"]]\n\n# Rename client_id in the client table\nclient.rename(columns={\"client_id\": \"id\"}, inplace=True)\n\n# Rename duration, y, and campaign columns\ncampaign.rename(columns={\"duration\": \"contact_duration\", \n                         \"y\": \"campaign_outcome\", \n                         \"campaign\": \"number_contacts\",\n                         \"previous\": \"previous_campaign_contacts\",\n                         \"poutcome\": \"previous_outcome\"}, \n                         inplace=True)\n\n# Rename euribor3m and nr_employed\neconomics.rename(columns={\"euribor3m\": \"euribor_three_months\", \n                          \"nr_employed\": \"number_employed\"}, \n                          inplace=True)\n\n# Clean education column\nclient[\"education\"] = client[\"education\"].str.replace(\".\", \"_\")\nclient[\"education\"] = client[\"education\"].replace(\"unknown\", np.NaN)\n\n# Clean job column\nclient[\"job\"] = client[\"job\"].str.replace(\".\", \"\")\n\n# Change campaign_outcome to binary values\ncampaign[\"campaign_outcome\"] = campaign[\"campaign_outcome\"].map({\"yes\": 1, \n                                                                 \"no\": 0})\n\n# Convert poutcome to binary values\ncampaign[\"previous_outcome\"] = campaign[\"previous_outcome\"].replace(\"nonexistent\", \n                                                                    np.NaN)\ncampaign[\"previous_outcome\"] = campaign[\"previous_outcome\"].map({\"success\": 1, \n                                                                 \"failure\": 0})\n\n# Add campaign_id column\ncampaign[\"campaign_id\"] = 1\n\n# Capitalize month and day columns\ncampaign[\"month\"] = campaign[\"month\"].str.capitalize()\n\n# Add year column\ncampaign[\"year\"] = \"2022\"\n\n# Convert day to string\ncampaign[\"day\"] = campaign[\"day\"].astype(str)\n\n# Add last_contact_date column\ncampaign[\"last_contact_date\"] = campaign[\"year\"] + \"-\" + campaign[\"month\"] + \"-\" + campaign[\"day\"]\n\n# Convert to datetime\ncampaign[\"last_contact_date\"] = pd.to_datetime(campaign[\"last_contact_date\"], \n                                               format=\"%Y-%b-%d\")\n\n# Drop unneccessary columns\ncampaign.drop(columns=[\"month\", \"day\", \"year\"], inplace=True)\n\n# Save tables to individual csv files\nclient.to_csv(\"client.csv\", index=False)\ncampaign.to_csv(\"campaign.csv\", index=False)\neconomics.to_csv(\"economics.csv\", index=False)\n\n# Store and print database_design\nclient_table = \"\"\"CREATE TABLE client\n(\n    id SERIAL PRIMARY KEY,\n    age INTEGER,\n    job TEXT,\n    marital TEXT,\n    education TEXT,\n    credit_default BOOLEAN,\n    housing BOOLEAN,\n    loan BOOLEAN\n);\n\\copy client from 'client.csv' DELIMITER ',' CSV HEADER\n\"\"\"\n\ncampaign_table = \"\"\"CREATE TABLE campaign\n(\n    campaign_id SERIAL PRIMARY KEY,\n    client_id SERIAL references client (id),\n    number_contacts INTEGER,\n    contact_duration INTEGER,\n    pdays INTEGER,\n    previous_campaign_contacts INTEGER,\n    previous_outcome BOOLEAN,\n    campaign_outcome BOOLEAN,\n    last_contact_date DATE    \n);\n\\copy campaign from 'campaign.csv' DELIMITER ',' CSV HEADER\n\"\"\"\n\neconomics_table = \"\"\"CREATE TABLE economics\n(\n    client_id SERIAL references client (id),\n    emp_var_rate FLOAT,\n    cons_price_idx FLOAT,\n    euribor_three_months FLOAT,\n    number_employed FLOAT\n);\n\\copy economics from 'economics.csv' DELIMITER ',' CSV HEADER\n\"\"\""
 74 |    },
 75 |    "outputs": [],
 76 |    "source": [
 77 |     "import pandas as pd\n",
 78 |     "import numpy as np\n",
 79 |     "\n",
 80 |     "# Start coding here..."
 81 |    ]
 82 |   }
 83 |  ],
 84 |  "metadata": {
 85 |   "editor": "DataCamp Workspace",
 86 |   "kernelspec": {
 87 |    "display_name": "Python 3 (ipykernel)",
 88 |    "language": "python",
 89 |    "name": "python3"
 90 |   },
 91 |   "language_info": {
 92 |    "codemirror_mode": {
 93 |     "name": "ipython",
 94 |     "version": 3
 95 |    },
 96 |    "file_extension": ".py",
 97 |    "mimetype": "text/x-python",
 98 |    "name": "python",
 99 |    "nbconvert_exporter": "python",
100 |    "pygments_lexer": "ipython3",
101 |    "version": "3.11.5"
102 |   }
103 |  },
104 |  "nbformat": 4,
105 |  "nbformat_minor": 5
106 | }
107 | 


--------------------------------------------------------------------------------
/Designing a Bank Marketing Database.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "02077ee3-e1e4-4fc5-8de1-16e987afa5fb",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "![piggy_bank](piggy_bank.jpg)\n",
   9 |     "\n",
  10 |     "<br>\n",
  11 |     "\n",
  12 |     "Personal loans are a lucrative revenue stream for banks. The typical interest rate of a two year loan in the United Kingdom is [around 10%](https://www.experian.com/blogs/ask-experian/whats-a-good-interest-rate-for-a-personal-loan/). This might not sound like a lot, but in September 2022 alone UK consumers borrowed [around £1.5 billion](https://www.ukfinance.org.uk/system/files/2022-12/Household%20Finance%20Review%202022%20Q3-%20Final.pdf), which would mean approximately £300 million in interest generated by banks over two years!\n",
  13 |     "\n",
  14 |     "You have been asked to work with a bank to clean and store the data they collected as part of a recent marketing campaign, which aimed to get customers to take out a personal loan. They plan to conduct more marketing campaigns going forward so would like you to set up a PostgreSQL database to store this campaign's data, designing the schema in a way that would allow data from future campaigns to be easily imported. \n",
  15 |     "\n",
  16 |     "They have supplied you with a csv file called `\"bank_marketing.csv\"`, which you will need to clean, reformat, and split, in order to save separate files based on the tables you will create. It is recommended to use `pandas` for these tasks.\n",
  17 |     "\n",
  18 |     "Lastly, you will write the SQL code that the bank can execute to create the tables and populate with the data from the csv files. As the bank are quite strict about their security, you'll save SQL files as multiline string variables that they can then use to create the database on their end. \n",
  19 |     "\n",
  20 |     "You have been asked to design a database that will have three tables:\n",
  21 |     "\n",
  22 |     "## client\n",
  23 |     "\n",
  24 |     "| column | data type | description | original column in dataset |\n",
  25 |     "|--------|-----------|-------------|----------------------------|\n",
  26 |     "| `id` | `serial` | Client ID - primary key | `client_id` |\n",
  27 |     "| `age` | `integer` | Client's age in years | `age` |\n",
  28 |     "| `job` | `text` | Client's type of job | `job` |\n",
  29 |     "| `marital` | `text` | Client's marital status | `marital` | \n",
  30 |     "| `education` | `text` | Client's level of education | `education` |\n",
  31 |     "| `credit_default` | `boolean` | Whether the client's credit is in default | `credit_default` |\n",
  32 |     "| `housing` | `boolean` | Whether the client has an existing housing loan (mortgage) | `housing` | \n",
  33 |     "| `loan` | `boolean` | Whether the client has an existing personal loan | `loan` |\n",
  34 |     "\n",
  35 |     "<br>\n",
  36 |     "\n",
  37 |     "## campaign\n",
  38 |     "\n",
  39 |     "| column | data type | description | original column in dataset |\n",
  40 |     "|--------|-----------|-------------|----------------------------|\n",
  41 |     "| `campaign_id` | `serial` | Campaign ID - primary key | N/A - new column |\n",
  42 |     "| `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |\n",
  43 |     "| `number_contacts` | `integer` | Number of contact attempts to the client in the current campaign | `campaign` |\n",
  44 |     "| `contact_duration` | `integer` | Last contact duration in seconds | `duration` |\n",
  45 |     "| `pdays` | `integer` | Number of days since contact in previous campaign (`999` = not previously contacted) | `pdays` |\n",
  46 |     "| `previous_campaign_contacts` | `integer` | Number of contact attempts to the client in the previous campaign | `previous` |\n",
  47 |     "| `previous_outcome` | `boolean` | Outcome of the previous campaign | `poutcome` |\n",
  48 |     "| `campaign_outcome` | `boolean` | Outcome of the current campaign | `y` |\n",
  49 |     "| `last_contact_date` | `date` | Last date the client was contacted | A combination of `day`, `month`, and the newly created `year` |\n",
  50 |     "\n",
  51 |     "<br>\n",
  52 |     "\n",
  53 |     "## economics\n",
  54 |     "\n",
  55 |     "| column | data type | description | original column in dataset |\n",
  56 |     "|--------|-----------|-------------|----------------------------|\n",
  57 |     "| `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |\n",
  58 |     "| `emp_var_rate` | `float` | Employment variation rate (quarterly indicator) | `emp_var_rate` |\n",
  59 |     "| `cons_price_idx` | `float` | Consumer price index (monthly indicator) | `cons_price_idx` |\n",
  60 |     "| `euribor_three_months` | `float` | Euro Interbank Offered Rate (euribor) three month rate (daily indicator) | `euribor3m` |\n",
  61 |     "| `number_employed` | `float` | Number of employees (quarterly indicator)| `nr_employed` |"
  62 |    ]
  63 |   },
  64 |   {
  65 |    "cell_type": "code",
  66 |    "execution_count": 1,
  67 |    "id": "e2edad3c-8286-4983-b5b7-35d94fd78023",
  68 |    "metadata": {
  69 |     "executionCancelledAt": null,
  70 |     "executionTime": 1057,
  71 |     "lastExecutedAt": 1686069923599,
  72 |     "lastScheduledRunId": null,
  73 |     "lastSuccessfullyExecutedCode": "# Start coding...\nimport pandas as pd\nimport numpy as np\n\n# Read in csv\nmarketing = pd.read_csv(\"bank_marketing.csv\")\n\n# Split into the three tables\nclient = marketing[[\"client_id\", \"age\", \"job\", \"marital\", \"education\", \n             \"credit_default\", \"housing\", \"loan\"]]\ncampaign = marketing[[\"client_id\", \"campaign\", \"month\", \"day\", \n               \"duration\", \"pdays\", \"previous\", \"poutcome\", \"y\"]]\neconomics = marketing[[\"client_id\", \"emp_var_rate\", \"cons_price_idx\", \n                \"euribor3m\", \"nr_employed\"]]\n\n# Rename client_id in the client table\nclient.rename(columns={\"client_id\": \"id\"}, inplace=True)\n\n# Rename duration, y, and campaign columns\ncampaign.rename(columns={\"duration\": \"contact_duration\", \n                         \"y\": \"campaign_outcome\", \n                         \"campaign\": \"number_contacts\",\n                         \"previous\": \"previous_campaign_contacts\",\n                         \"poutcome\": \"previous_outcome\"}, \n                         inplace=True)\n\n# Rename euribor3m and nr_employed\neconomics.rename(columns={\"euribor3m\": \"euribor_three_months\", \n                          \"nr_employed\": \"number_employed\"}, \n                          inplace=True)\n\n# Clean education column\nclient[\"education\"] = client[\"education\"].str.replace(\".\", \"_\")\nclient[\"education\"] = client[\"education\"].replace(\"unknown\", np.NaN)\n\n# Clean job column\nclient[\"job\"] = client[\"job\"].str.replace(\".\", \"\")\n\n# Change campaign_outcome to binary values\ncampaign[\"campaign_outcome\"] = campaign[\"campaign_outcome\"].map({\"yes\": 1, \n                                                                 \"no\": 0})\n\n# Convert poutcome to binary values\ncampaign[\"previous_outcome\"] = campaign[\"previous_outcome\"].replace(\"nonexistent\", \n                                                                    np.NaN)\ncampaign[\"previous_outcome\"] = campaign[\"previous_outcome\"].map({\"success\": 1, \n                                                                 \"failure\": 0})\n\n# Add campaign_id column\ncampaign[\"campaign_id\"] = 1\n\n# Capitalize month and day columns\ncampaign[\"month\"] = campaign[\"month\"].str.capitalize()\n\n# Add year column\ncampaign[\"year\"] = \"2022\"\n\n# Convert day to string\ncampaign[\"day\"] = campaign[\"day\"].astype(str)\n\n# Add last_contact_date column\ncampaign[\"last_contact_date\"] = campaign[\"year\"] + \"-\" + campaign[\"month\"] + \"-\" + campaign[\"day\"]\n\n# Convert to datetime\ncampaign[\"last_contact_date\"] = pd.to_datetime(campaign[\"last_contact_date\"], \n                                               format=\"%Y-%b-%d\")\n\n# Drop unneccessary columns\ncampaign.drop(columns=[\"month\", \"day\", \"year\"], inplace=True)\n\n# Save tables to individual csv files\nclient.to_csv(\"client.csv\", index=False)\ncampaign.to_csv(\"campaign.csv\", index=False)\neconomics.to_csv(\"economics.csv\", index=False)\n\n# Store and print database_design\nclient_table = \"\"\"CREATE TABLE client\n(\n    id SERIAL PRIMARY KEY,\n    age INTEGER,\n    job TEXT,\n    marital TEXT,\n    education TEXT,\n    credit_default BOOLEAN,\n    housing BOOLEAN,\n    loan BOOLEAN\n);\n\\copy client from 'client.csv' DELIMITER ',' CSV HEADER\n\"\"\"\n\ncampaign_table = \"\"\"CREATE TABLE campaign\n(\n    campaign_id SERIAL PRIMARY KEY,\n    client_id SERIAL references client (id),\n    number_contacts INTEGER,\n    contact_duration INTEGER,\n    pdays INTEGER,\n    previous_campaign_contacts INTEGER,\n    previous_outcome BOOLEAN,\n    campaign_outcome BOOLEAN,\n    last_contact_date DATE    \n);\n\\copy campaign from 'campaign.csv' DELIMITER ',' CSV HEADER\n\"\"\"\n\neconomics_table = \"\"\"CREATE TABLE economics\n(\n    client_id SERIAL references client (id),\n    emp_var_rate FLOAT,\n    cons_price_idx FLOAT,\n    euribor_three_months FLOAT,\n    number_employed FLOAT\n);\n\\copy economics from 'economics.csv' DELIMITER ',' CSV HEADER\n\"\"\""
  74 |    },
  75 |    "outputs": [],
  76 |    "source": [
  77 |     "# Import libraries\n",
  78 |     "import pandas as pd\n",
  79 |     "import numpy as np\n",
  80 |     "\n",
  81 |     "# suppress warnings from final output\n",
  82 |     "import warnings\n",
  83 |     "warnings.simplefilter(\"ignore\")\n",
  84 |     "\n",
  85 |     "# Start coding here..."
  86 |    ]
  87 |   },
  88 |   {
  89 |    "cell_type": "markdown",
  90 |    "id": "9d94086e",
  91 |    "metadata": {},
  92 |    "source": [
  93 |     "#### Instruction 1:  Read in bank_marketing.csv as a pandas DataFrame."
  94 |    ]
  95 |   },
  96 |   {
  97 |    "cell_type": "code",
  98 |    "execution_count": 2,
  99 |    "id": "ac57dc04",
 100 |    "metadata": {},
 101 |    "outputs": [
 102 |     {
 103 |      "data": {
 104 |       "text/html": [
 105 |        "<div>\n",
 106 |        "<style scoped>\n",
 107 |        "    .dataframe tbody tr th:only-of-type {\n",
 108 |        "        vertical-align: middle;\n",
 109 |        "    }\n",
 110 |        "\n",
 111 |        "    .dataframe tbody tr th {\n",
 112 |        "        vertical-align: top;\n",
 113 |        "    }\n",
 114 |        "\n",
 115 |        "    .dataframe thead th {\n",
 116 |        "        text-align: right;\n",
 117 |        "    }\n",
 118 |        "</style>\n",
 119 |        "<table border=\"1\" class=\"dataframe\">\n",
 120 |        "  <thead>\n",
 121 |        "    <tr style=\"text-align: right;\">\n",
 122 |        "      <th></th>\n",
 123 |        "      <th>client_id</th>\n",
 124 |        "      <th>age</th>\n",
 125 |        "      <th>job</th>\n",
 126 |        "      <th>marital</th>\n",
 127 |        "      <th>education</th>\n",
 128 |        "      <th>credit_default</th>\n",
 129 |        "      <th>housing</th>\n",
 130 |        "      <th>loan</th>\n",
 131 |        "      <th>contact</th>\n",
 132 |        "      <th>month</th>\n",
 133 |        "      <th>...</th>\n",
 134 |        "      <th>campaign</th>\n",
 135 |        "      <th>pdays</th>\n",
 136 |        "      <th>previous</th>\n",
 137 |        "      <th>poutcome</th>\n",
 138 |        "      <th>emp_var_rate</th>\n",
 139 |        "      <th>cons_price_idx</th>\n",
 140 |        "      <th>cons_conf_idx</th>\n",
 141 |        "      <th>euribor3m</th>\n",
 142 |        "      <th>nr_employed</th>\n",
 143 |        "      <th>y</th>\n",
 144 |        "    </tr>\n",
 145 |        "  </thead>\n",
 146 |        "  <tbody>\n",
 147 |        "    <tr>\n",
 148 |        "      <th>0</th>\n",
 149 |        "      <td>0</td>\n",
 150 |        "      <td>56</td>\n",
 151 |        "      <td>housemaid</td>\n",
 152 |        "      <td>married</td>\n",
 153 |        "      <td>basic.4y</td>\n",
 154 |        "      <td>no</td>\n",
 155 |        "      <td>no</td>\n",
 156 |        "      <td>no</td>\n",
 157 |        "      <td>telephone</td>\n",
 158 |        "      <td>may</td>\n",
 159 |        "      <td>...</td>\n",
 160 |        "      <td>1</td>\n",
 161 |        "      <td>999</td>\n",
 162 |        "      <td>0</td>\n",
 163 |        "      <td>nonexistent</td>\n",
 164 |        "      <td>1.1</td>\n",
 165 |        "      <td>93.994</td>\n",
 166 |        "      <td>-36.4</td>\n",
 167 |        "      <td>4.857</td>\n",
 168 |        "      <td>5191.0</td>\n",
 169 |        "      <td>no</td>\n",
 170 |        "    </tr>\n",
 171 |        "    <tr>\n",
 172 |        "      <th>1</th>\n",
 173 |        "      <td>1</td>\n",
 174 |        "      <td>57</td>\n",
 175 |        "      <td>services</td>\n",
 176 |        "      <td>married</td>\n",
 177 |        "      <td>high.school</td>\n",
 178 |        "      <td>unknown</td>\n",
 179 |        "      <td>no</td>\n",
 180 |        "      <td>no</td>\n",
 181 |        "      <td>telephone</td>\n",
 182 |        "      <td>may</td>\n",
 183 |        "      <td>...</td>\n",
 184 |        "      <td>1</td>\n",
 185 |        "      <td>999</td>\n",
 186 |        "      <td>0</td>\n",
 187 |        "      <td>nonexistent</td>\n",
 188 |        "      <td>1.1</td>\n",
 189 |        "      <td>93.994</td>\n",
 190 |        "      <td>-36.4</td>\n",
 191 |        "      <td>4.857</td>\n",
 192 |        "      <td>5191.0</td>\n",
 193 |        "      <td>no</td>\n",
 194 |        "    </tr>\n",
 195 |        "    <tr>\n",
 196 |        "      <th>2</th>\n",
 197 |        "      <td>2</td>\n",
 198 |        "      <td>37</td>\n",
 199 |        "      <td>services</td>\n",
 200 |        "      <td>married</td>\n",
 201 |        "      <td>high.school</td>\n",
 202 |        "      <td>no</td>\n",
 203 |        "      <td>yes</td>\n",
 204 |        "      <td>no</td>\n",
 205 |        "      <td>telephone</td>\n",
 206 |        "      <td>may</td>\n",
 207 |        "      <td>...</td>\n",
 208 |        "      <td>1</td>\n",
 209 |        "      <td>999</td>\n",
 210 |        "      <td>0</td>\n",
 211 |        "      <td>nonexistent</td>\n",
 212 |        "      <td>1.1</td>\n",
 213 |        "      <td>93.994</td>\n",
 214 |        "      <td>-36.4</td>\n",
 215 |        "      <td>4.857</td>\n",
 216 |        "      <td>5191.0</td>\n",
 217 |        "      <td>no</td>\n",
 218 |        "    </tr>\n",
 219 |        "    <tr>\n",
 220 |        "      <th>3</th>\n",
 221 |        "      <td>3</td>\n",
 222 |        "      <td>40</td>\n",
 223 |        "      <td>admin.</td>\n",
 224 |        "      <td>married</td>\n",
 225 |        "      <td>basic.6y</td>\n",
 226 |        "      <td>no</td>\n",
 227 |        "      <td>no</td>\n",
 228 |        "      <td>no</td>\n",
 229 |        "      <td>telephone</td>\n",
 230 |        "      <td>may</td>\n",
 231 |        "      <td>...</td>\n",
 232 |        "      <td>1</td>\n",
 233 |        "      <td>999</td>\n",
 234 |        "      <td>0</td>\n",
 235 |        "      <td>nonexistent</td>\n",
 236 |        "      <td>1.1</td>\n",
 237 |        "      <td>93.994</td>\n",
 238 |        "      <td>-36.4</td>\n",
 239 |        "      <td>4.857</td>\n",
 240 |        "      <td>5191.0</td>\n",
 241 |        "      <td>no</td>\n",
 242 |        "    </tr>\n",
 243 |        "    <tr>\n",
 244 |        "      <th>4</th>\n",
 245 |        "      <td>4</td>\n",
 246 |        "      <td>56</td>\n",
 247 |        "      <td>services</td>\n",
 248 |        "      <td>married</td>\n",
 249 |        "      <td>high.school</td>\n",
 250 |        "      <td>no</td>\n",
 251 |        "      <td>no</td>\n",
 252 |        "      <td>yes</td>\n",
 253 |        "      <td>telephone</td>\n",
 254 |        "      <td>may</td>\n",
 255 |        "      <td>...</td>\n",
 256 |        "      <td>1</td>\n",
 257 |        "      <td>999</td>\n",
 258 |        "      <td>0</td>\n",
 259 |        "      <td>nonexistent</td>\n",
 260 |        "      <td>1.1</td>\n",
 261 |        "      <td>93.994</td>\n",
 262 |        "      <td>-36.4</td>\n",
 263 |        "      <td>4.857</td>\n",
 264 |        "      <td>5191.0</td>\n",
 265 |        "      <td>no</td>\n",
 266 |        "    </tr>\n",
 267 |        "  </tbody>\n",
 268 |        "</table>\n",
 269 |        "<p>5 rows × 22 columns</p>\n",
 270 |        "</div>"
 271 |       ],
 272 |       "text/plain": [
 273 |        "   client_id  age        job  marital    education credit_default housing  \\\n",
 274 |        "0          0   56  housemaid  married     basic.4y             no      no   \n",
 275 |        "1          1   57   services  married  high.school        unknown      no   \n",
 276 |        "2          2   37   services  married  high.school             no     yes   \n",
 277 |        "3          3   40     admin.  married     basic.6y             no      no   \n",
 278 |        "4          4   56   services  married  high.school             no      no   \n",
 279 |        "\n",
 280 |        "  loan    contact month  ...  campaign  pdays  previous     poutcome  \\\n",
 281 |        "0   no  telephone   may  ...         1    999         0  nonexistent   \n",
 282 |        "1   no  telephone   may  ...         1    999         0  nonexistent   \n",
 283 |        "2   no  telephone   may  ...         1    999         0  nonexistent   \n",
 284 |        "3   no  telephone   may  ...         1    999         0  nonexistent   \n",
 285 |        "4  yes  telephone   may  ...         1    999         0  nonexistent   \n",
 286 |        "\n",
 287 |        "   emp_var_rate cons_price_idx  cons_conf_idx  euribor3m  nr_employed   y  \n",
 288 |        "0           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 289 |        "1           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 290 |        "2           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 291 |        "3           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 292 |        "4           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 293 |        "\n",
 294 |        "[5 rows x 22 columns]"
 295 |       ]
 296 |      },
 297 |      "execution_count": 2,
 298 |      "metadata": {},
 299 |      "output_type": "execute_result"
 300 |     }
 301 |    ],
 302 |    "source": [
 303 |     "# Read in bank_marketing.csv as a pandas DataFrame.\n",
 304 |     "bank_marketing_data = pd.read_csv(\"bank_marketing.csv\")\n",
 305 |     "bank_marketing_data.head()"
 306 |    ]
 307 |   },
 308 |   {
 309 |    "cell_type": "markdown",
 310 |    "id": "11195a84",
 311 |    "metadata": {},
 312 |    "source": [
 313 |     "#### Instruction 2:\n",
 314 |     "\n",
 315 |     "Split the data into three DataFrames using information provided about the desired tables as your guide: \n",
 316 |     "\n",
 317 |     "* one with information about the client, \n",
 318 |     "* another containing campaign data, and \n",
 319 |     "* a third to store information about economics at the time of the campaign."
 320 |    ]
 321 |   },
 322 |   {
 323 |    "cell_type": "code",
 324 |    "execution_count": 3,
 325 |    "id": "6707773f",
 326 |    "metadata": {},
 327 |    "outputs": [
 328 |     {
 329 |      "name": "stdout",
 330 |      "output_type": "stream",
 331 |      "text": [
 332 |       "Client Data:\n",
 333 |       "   client_id  age        job  marital    education credit_default housing loan\n",
 334 |       "0          0   56  housemaid  married     basic.4y             no      no   no\n",
 335 |       "1          1   57   services  married  high.school        unknown      no   no\n",
 336 |       "2          2   37   services  married  high.school             no     yes   no\n",
 337 |       "3          3   40     admin.  married     basic.6y             no      no   no\n",
 338 |       "4          4   56   services  married  high.school             no      no  yes\n",
 339 |       "\n",
 340 |       "Campaign Data:\n",
 341 |       "   client_id  campaign  duration  pdays  previous     poutcome   y month  day\n",
 342 |       "0          0         1       261    999         0  nonexistent  no   may   13\n",
 343 |       "1          1         1       149    999         0  nonexistent  no   may   19\n",
 344 |       "2          2         1       226    999         0  nonexistent  no   may   23\n",
 345 |       "3          3         1       151    999         0  nonexistent  no   may   27\n",
 346 |       "4          4         1       307    999         0  nonexistent  no   may    3\n",
 347 |       "\n",
 348 |       "Economic Data:\n",
 349 |       "   client_id  emp_var_rate  cons_price_idx  euribor3m  nr_employed\n",
 350 |       "0          0           1.1          93.994      4.857       5191.0\n",
 351 |       "1          1           1.1          93.994      4.857       5191.0\n",
 352 |       "2          2           1.1          93.994      4.857       5191.0\n",
 353 |       "3          3           1.1          93.994      4.857       5191.0\n",
 354 |       "4          4           1.1          93.994      4.857       5191.0\n"
 355 |      ]
 356 |     }
 357 |    ],
 358 |    "source": [
 359 |     "# Define the columns for each table\n",
 360 |     "client_columns = ['client_id', 'age', 'job', 'marital', 'education', 'credit_default', 'housing', 'loan']\n",
 361 |     "campaign_columns = ['client_id', 'campaign', 'duration', 'pdays', 'previous', 'poutcome', 'y', 'month', 'day']\n",
 362 |     "economic_columns = ['client_id', 'emp_var_rate', 'cons_price_idx', 'euribor3m', 'nr_employed']\n",
 363 |     "\n",
 364 |     "# Create Dataframes for each table\n",
 365 |     "client = bank_marketing_data[client_columns]\n",
 366 |     "campaign = bank_marketing_data[campaign_columns]\n",
 367 |     "economics = bank_marketing_data[economic_columns]\n",
 368 |     "\n",
 369 |     "# Print the first few rows of each DataFrame to verify the split\n",
 370 |     "print(\"Client Data:\")\n",
 371 |     "print(client.head())\n",
 372 |     "\n",
 373 |     "print(\"\\nCampaign Data:\")\n",
 374 |     "print(campaign.head())\n",
 375 |     "\n",
 376 |     "print(\"\\nEconomic Data:\")\n",
 377 |     "print(economics.head())"
 378 |    ]
 379 |   },
 380 |   {
 381 |    "cell_type": "markdown",
 382 |    "id": "a77f1836",
 383 |    "metadata": {},
 384 |    "source": [
 385 |     "#### Instruction 3:\n",
 386 |     "\n",
 387 |     "Rename the column \"client_id\" to \"id\" in client (leave as-is in the other subsets); \"duration\" to \"contact_duration\",\n",
 388 |     "\"previous\" to \"previous_campaign_contacts\", \"y\" to \"campaign_outcome\", \"poutcome\" to \"previous_outcome\", and \n",
 389 |     "\"campaign\" to \"number_contacts\" in campaign; and \"euribor3m\" to \"euribor_three_months\" and \"nr_employed\" to\n",
 390 |     "\"number_employed\" in economics."
 391 |    ]
 392 |   },
 393 |   {
 394 |    "cell_type": "code",
 395 |    "execution_count": 4,
 396 |    "id": "6f49b4d5",
 397 |    "metadata": {},
 398 |    "outputs": [
 399 |     {
 400 |      "name": "stdout",
 401 |      "output_type": "stream",
 402 |      "text": [
 403 |       "Client Data:\n",
 404 |       "   id  age        job  marital    education credit_default housing loan\n",
 405 |       "0   0   56  housemaid  married     basic.4y             no      no   no\n",
 406 |       "1   1   57   services  married  high.school        unknown      no   no\n",
 407 |       "2   2   37   services  married  high.school             no     yes   no\n",
 408 |       "3   3   40     admin.  married     basic.6y             no      no   no\n",
 409 |       "4   4   56   services  married  high.school             no      no  yes\n",
 410 |       "\n",
 411 |       "Campaign Data:\n",
 412 |       "   client_id  number_contacts  contact_duration  pdays  \\\n",
 413 |       "0          0                1               261    999   \n",
 414 |       "1          1                1               149    999   \n",
 415 |       "2          2                1               226    999   \n",
 416 |       "3          3                1               151    999   \n",
 417 |       "4          4                1               307    999   \n",
 418 |       "\n",
 419 |       "   previous_campaign_contacts previous_outcome campaign_outcome month  day  \n",
 420 |       "0                           0      nonexistent               no   may   13  \n",
 421 |       "1                           0      nonexistent               no   may   19  \n",
 422 |       "2                           0      nonexistent               no   may   23  \n",
 423 |       "3                           0      nonexistent               no   may   27  \n",
 424 |       "4                           0      nonexistent               no   may    3  \n",
 425 |       "\n",
 426 |       "Economic Data:\n",
 427 |       "   client_id  emp_var_rate  cons_price_idx  euribor_three_months  \\\n",
 428 |       "0          0           1.1          93.994                 4.857   \n",
 429 |       "1          1           1.1          93.994                 4.857   \n",
 430 |       "2          2           1.1          93.994                 4.857   \n",
 431 |       "3          3           1.1          93.994                 4.857   \n",
 432 |       "4          4           1.1          93.994                 4.857   \n",
 433 |       "\n",
 434 |       "   number_employed  \n",
 435 |       "0           5191.0  \n",
 436 |       "1           5191.0  \n",
 437 |       "2           5191.0  \n",
 438 |       "3           5191.0  \n",
 439 |       "4           5191.0  \n"
 440 |      ]
 441 |     }
 442 |    ],
 443 |    "source": [
 444 |     "# Rename columns in the client DataFrame\n",
 445 |     "client.rename(columns={'client_id': 'id'}, inplace=True)\n",
 446 |     "\n",
 447 |     "# Rename columns in the campaign DataFrame\n",
 448 |     "campaign.rename(columns={'duration': 'contact_duration',\n",
 449 |     "                             'previous': 'previous_campaign_contacts',\n",
 450 |     "                             'y': 'campaign_outcome',\n",
 451 |     "                             'poutcome': 'previous_outcome',\n",
 452 |     "                             'campaign': 'number_contacts'}, inplace=True)\n",
 453 |     "\n",
 454 |     "# Rename columns in the economic DataFrame\n",
 455 |     "economics.rename(columns={'euribor3m': 'euribor_three_months',\n",
 456 |     "                             'nr_employed': 'number_employed'}, inplace=True)\n",
 457 |     "\n",
 458 |     "# Print the first few rows of each DataFrame to verify the renaming of columns\n",
 459 |     "print(\"Client Data:\")\n",
 460 |     "print(client.head())\n",
 461 |     "\n",
 462 |     "print(\"\\nCampaign Data:\")\n",
 463 |     "print(campaign.head())\n",
 464 |     "\n",
 465 |     "print(\"\\nEconomic Data:\")\n",
 466 |     "print(economics.head())"
 467 |    ]
 468 |   },
 469 |   {
 470 |    "cell_type": "markdown",
 471 |    "id": "08f13310",
 472 |    "metadata": {},
 473 |    "source": [
 474 |     "#### Instruction 4:\n",
 475 |     "\n",
 476 |     "Clean the \"education\" column, changing \".\" to \"_\" and \"unknown\" to NumPy's null values."
 477 |    ]
 478 |   },
 479 |   {
 480 |    "cell_type": "code",
 481 |    "execution_count": 5,
 482 |    "id": "f9a9ddd1",
 483 |    "metadata": {},
 484 |    "outputs": [],
 485 |    "source": [
 486 |     "# Replace \".\" with \"_\"\n",
 487 |     "client['education'] = client['education'].str.replace('.', '_')\n",
 488 |     "\n",
 489 |     "# Replace \"unknown\" with Numpy's NaN\n",
 490 |     "client['education'].replace('unknown', np.nan, inplace=True)"
 491 |    ]
 492 |   },
 493 |   {
 494 |    "cell_type": "code",
 495 |    "execution_count": 6,
 496 |    "id": "61f72247",
 497 |    "metadata": {},
 498 |    "outputs": [
 499 |     {
 500 |      "name": "stdout",
 501 |      "output_type": "stream",
 502 |      "text": [
 503 |       "The 'education' column has been successfully cleaned.\n"
 504 |      ]
 505 |     }
 506 |    ],
 507 |    "source": [
 508 |     "# Check for \".\" in the \"education\" column\n",
 509 |     "dot_count = client['education'].str.contains('\\.').sum()\n",
 510 |     "\n",
 511 |     "# Check for \"unknown\" in the \"education\" column\n",
 512 |     "unknown_count = (client['education'] == 'unknown').sum()\n",
 513 |     "\n",
 514 |     "if dot_count == 0 and unknown_count == 0:\n",
 515 |     "    print(\"The 'education' column has been successfully cleaned.\")\n",
 516 |     "else:\n",
 517 |     "    print(f\"The 'education' column still contains {dot_count} '.' and {unknown_count} 'unknown' values.\")"
 518 |    ]
 519 |   },
 520 |   {
 521 |    "cell_type": "markdown",
 522 |    "id": "7606b988",
 523 |    "metadata": {},
 524 |    "source": [
 525 |     "#### Instrucion 5:\n",
 526 |     "\n",
 527 |     "Remove periods from the \"job\" column."
 528 |    ]
 529 |   },
 530 |   {
 531 |    "cell_type": "code",
 532 |    "execution_count": 7,
 533 |    "id": "196ea3e0",
 534 |    "metadata": {},
 535 |    "outputs": [
 536 |     {
 537 |      "name": "stdout",
 538 |      "output_type": "stream",
 539 |      "text": [
 540 |       "Periods have been successfully removed from the 'job' column.\n"
 541 |      ]
 542 |     }
 543 |    ],
 544 |    "source": [
 545 |     "# Remove \".\" from the \"job\" column\n",
 546 |     "client['job'] = client['job'].str.replace('.', '')\n",
 547 |     "\n",
 548 |     "# Check if periods are removed\n",
 549 |     "if '.' not in client['job'].values:\n",
 550 |     "    print(\"Periods have been successfully removed from the 'job' column.\")\n",
 551 |     "else:\n",
 552 |     "    print(\"Periods still exist in the job column.\")"
 553 |    ]
 554 |   },
 555 |   {
 556 |    "cell_type": "markdown",
 557 |    "id": "9a0eb339",
 558 |    "metadata": {},
 559 |    "source": [
 560 |     "#### Instruction 6:\n",
 561 |     "\n",
 562 |     "Convert \"success\" and \"failure\" in the \"previous_outcome\" and \"campaign_outcome\" columns to binary (1 or 0), \n",
 563 |     "along with the changing \"nonexistent\" to NumPy's null values in \"previous_outcome\"."
 564 |    ]
 565 |   },
 566 |   {
 567 |    "cell_type": "code",
 568 |    "execution_count": 8,
 569 |    "id": "fe0ace15",
 570 |    "metadata": {},
 571 |    "outputs": [
 572 |     {
 573 |      "name": "stdout",
 574 |      "output_type": "stream",
 575 |      "text": [
 576 |       "Conversions and changes in 'previous_outcome' and 'campaign_outcome' columns were successfully applied.\n"
 577 |      ]
 578 |     }
 579 |    ],
 580 |    "source": [
 581 |     "# Convert \"success\" and \"failure\" to binary (1 or 0) in \"previous_outcome\" and \"campaign_outcome\"\n",
 582 |     "campaign['previous_outcome'] = campaign['previous_outcome'].map({'success': 1, 'failure': 0})\n",
 583 |     "campaign['campaign_outcome'] = campaign['campaign_outcome'].map({'success': 1, 'failure': 0})\n",
 584 |     "\n",
 585 |     "# Change \"nonexistent\" to Numpy's NaN in \"previous_outcome\"\n",
 586 |     "campaign['previous_outcome'].replace('nonexistent', np.nan, inplace=True)\n",
 587 |     "\n",
 588 |     "# Check if the conversions and changes are applied\n",
 589 |     "if ('success' not in campaign['previous_outcome'].values and\n",
 590 |     "    'failure' not in campaign['previous_outcome'].values and\n",
 591 |     "    'nonexistent' not in campaign['previous_outcome'].values and\n",
 592 |     "    'success' not in campaign['campaign_outcome'].values and\n",
 593 |     "    'failure' not in campaign['campaign_outcome'].values):\n",
 594 |     "    print(\"Conversions and changes in 'previous_outcome' and 'campaign_outcome' columns were successfully applied.\")\n",
 595 |     "else:\n",
 596 |     "    print(\"Conversions and changes were not fully applied in 'previous_outcome' and 'campaign_outcome' columns.\")"
 597 |    ]
 598 |   },
 599 |   {
 600 |    "cell_type": "markdown",
 601 |    "id": "fa0e6239",
 602 |    "metadata": {},
 603 |    "source": [
 604 |     "#### Instruction 7:\n",
 605 |     "\n",
 606 |     "Add a column called campaign_id in campaign, where all rows have a value of 1."
 607 |    ]
 608 |   },
 609 |   {
 610 |    "cell_type": "code",
 611 |    "execution_count": 9,
 612 |    "id": "6b4ba272",
 613 |    "metadata": {
 614 |     "scrolled": true
 615 |    },
 616 |    "outputs": [
 617 |     {
 618 |      "data": {
 619 |       "text/html": [
 620 |        "<div>\n",
 621 |        "<style scoped>\n",
 622 |        "    .dataframe tbody tr th:only-of-type {\n",
 623 |        "        vertical-align: middle;\n",
 624 |        "    }\n",
 625 |        "\n",
 626 |        "    .dataframe tbody tr th {\n",
 627 |        "        vertical-align: top;\n",
 628 |        "    }\n",
 629 |        "\n",
 630 |        "    .dataframe thead th {\n",
 631 |        "        text-align: right;\n",
 632 |        "    }\n",
 633 |        "</style>\n",
 634 |        "<table border=\"1\" class=\"dataframe\">\n",
 635 |        "  <thead>\n",
 636 |        "    <tr style=\"text-align: right;\">\n",
 637 |        "      <th></th>\n",
 638 |        "      <th>client_id</th>\n",
 639 |        "      <th>number_contacts</th>\n",
 640 |        "      <th>contact_duration</th>\n",
 641 |        "      <th>pdays</th>\n",
 642 |        "      <th>previous_campaign_contacts</th>\n",
 643 |        "      <th>previous_outcome</th>\n",
 644 |        "      <th>campaign_outcome</th>\n",
 645 |        "      <th>month</th>\n",
 646 |        "      <th>day</th>\n",
 647 |        "      <th>campaign_id</th>\n",
 648 |        "    </tr>\n",
 649 |        "  </thead>\n",
 650 |        "  <tbody>\n",
 651 |        "    <tr>\n",
 652 |        "      <th>0</th>\n",
 653 |        "      <td>0</td>\n",
 654 |        "      <td>1</td>\n",
 655 |        "      <td>261</td>\n",
 656 |        "      <td>999</td>\n",
 657 |        "      <td>0</td>\n",
 658 |        "      <td>NaN</td>\n",
 659 |        "      <td>NaN</td>\n",
 660 |        "      <td>may</td>\n",
 661 |        "      <td>13</td>\n",
 662 |        "      <td>1</td>\n",
 663 |        "    </tr>\n",
 664 |        "    <tr>\n",
 665 |        "      <th>1</th>\n",
 666 |        "      <td>1</td>\n",
 667 |        "      <td>1</td>\n",
 668 |        "      <td>149</td>\n",
 669 |        "      <td>999</td>\n",
 670 |        "      <td>0</td>\n",
 671 |        "      <td>NaN</td>\n",
 672 |        "      <td>NaN</td>\n",
 673 |        "      <td>may</td>\n",
 674 |        "      <td>19</td>\n",
 675 |        "      <td>1</td>\n",
 676 |        "    </tr>\n",
 677 |        "    <tr>\n",
 678 |        "      <th>2</th>\n",
 679 |        "      <td>2</td>\n",
 680 |        "      <td>1</td>\n",
 681 |        "      <td>226</td>\n",
 682 |        "      <td>999</td>\n",
 683 |        "      <td>0</td>\n",
 684 |        "      <td>NaN</td>\n",
 685 |        "      <td>NaN</td>\n",
 686 |        "      <td>may</td>\n",
 687 |        "      <td>23</td>\n",
 688 |        "      <td>1</td>\n",
 689 |        "    </tr>\n",
 690 |        "    <tr>\n",
 691 |        "      <th>3</th>\n",
 692 |        "      <td>3</td>\n",
 693 |        "      <td>1</td>\n",
 694 |        "      <td>151</td>\n",
 695 |        "      <td>999</td>\n",
 696 |        "      <td>0</td>\n",
 697 |        "      <td>NaN</td>\n",
 698 |        "      <td>NaN</td>\n",
 699 |        "      <td>may</td>\n",
 700 |        "      <td>27</td>\n",
 701 |        "      <td>1</td>\n",
 702 |        "    </tr>\n",
 703 |        "    <tr>\n",
 704 |        "      <th>4</th>\n",
 705 |        "      <td>4</td>\n",
 706 |        "      <td>1</td>\n",
 707 |        "      <td>307</td>\n",
 708 |        "      <td>999</td>\n",
 709 |        "      <td>0</td>\n",
 710 |        "      <td>NaN</td>\n",
 711 |        "      <td>NaN</td>\n",
 712 |        "      <td>may</td>\n",
 713 |        "      <td>3</td>\n",
 714 |        "      <td>1</td>\n",
 715 |        "    </tr>\n",
 716 |        "  </tbody>\n",
 717 |        "</table>\n",
 718 |        "</div>"
 719 |       ],
 720 |       "text/plain": [
 721 |        "   client_id  number_contacts  contact_duration  pdays  \\\n",
 722 |        "0          0                1               261    999   \n",
 723 |        "1          1                1               149    999   \n",
 724 |        "2          2                1               226    999   \n",
 725 |        "3          3                1               151    999   \n",
 726 |        "4          4                1               307    999   \n",
 727 |        "\n",
 728 |        "   previous_campaign_contacts  previous_outcome  campaign_outcome month  day  \\\n",
 729 |        "0                           0               NaN               NaN   may   13   \n",
 730 |        "1                           0               NaN               NaN   may   19   \n",
 731 |        "2                           0               NaN               NaN   may   23   \n",
 732 |        "3                           0               NaN               NaN   may   27   \n",
 733 |        "4                           0               NaN               NaN   may    3   \n",
 734 |        "\n",
 735 |        "   campaign_id  \n",
 736 |        "0            1  \n",
 737 |        "1            1  \n",
 738 |        "2            1  \n",
 739 |        "3            1  \n",
 740 |        "4            1  "
 741 |       ]
 742 |      },
 743 |      "execution_count": 9,
 744 |      "metadata": {},
 745 |      "output_type": "execute_result"
 746 |     }
 747 |    ],
 748 |    "source": [
 749 |     "# Add a new column 'campaign_id' with all values set to 1\n",
 750 |     "campaign['campaign_id'] = 1\n",
 751 |     "\n",
 752 |     "# Check if column was succsssfully created\n",
 753 |     "campaign.head()"
 754 |    ]
 755 |   },
 756 |   {
 757 |    "cell_type": "code",
 758 |    "execution_count": null,
 759 |    "id": "a15240cc",
 760 |    "metadata": {},
 761 |    "outputs": [],
 762 |    "source": []
 763 |   },
 764 |   {
 765 |    "cell_type": "markdown",
 766 |    "id": "c91d964e",
 767 |    "metadata": {},
 768 |    "source": [
 769 |     "#### Instruction 8:\n",
 770 |     "\n",
 771 |     "Create a datetime column called last_contact_date, in the format of \"year-month-day\", where the year is 2022, and the month and day values are taken from the \"month\" and \"day\" columns."
 772 |    ]
 773 |   },
 774 |   {
 775 |    "cell_type": "code",
 776 |    "execution_count": 10,
 777 |    "id": "f473a21f",
 778 |    "metadata": {},
 779 |    "outputs": [
 780 |     {
 781 |      "name": "stdout",
 782 |      "output_type": "stream",
 783 |      "text": [
 784 |       "The 'last_contact_date' column was successfully created.\n"
 785 |      ]
 786 |     },
 787 |     {
 788 |      "data": {
 789 |       "text/html": [
 790 |        "<div>\n",
 791 |        "<style scoped>\n",
 792 |        "    .dataframe tbody tr th:only-of-type {\n",
 793 |        "        vertical-align: middle;\n",
 794 |        "    }\n",
 795 |        "\n",
 796 |        "    .dataframe tbody tr th {\n",
 797 |        "        vertical-align: top;\n",
 798 |        "    }\n",
 799 |        "\n",
 800 |        "    .dataframe thead th {\n",
 801 |        "        text-align: right;\n",
 802 |        "    }\n",
 803 |        "</style>\n",
 804 |        "<table border=\"1\" class=\"dataframe\">\n",
 805 |        "  <thead>\n",
 806 |        "    <tr style=\"text-align: right;\">\n",
 807 |        "      <th></th>\n",
 808 |        "      <th>client_id</th>\n",
 809 |        "      <th>number_contacts</th>\n",
 810 |        "      <th>contact_duration</th>\n",
 811 |        "      <th>pdays</th>\n",
 812 |        "      <th>previous_campaign_contacts</th>\n",
 813 |        "      <th>previous_outcome</th>\n",
 814 |        "      <th>campaign_outcome</th>\n",
 815 |        "      <th>month</th>\n",
 816 |        "      <th>day</th>\n",
 817 |        "      <th>campaign_id</th>\n",
 818 |        "      <th>year</th>\n",
 819 |        "      <th>last_contact_date</th>\n",
 820 |        "    </tr>\n",
 821 |        "  </thead>\n",
 822 |        "  <tbody>\n",
 823 |        "    <tr>\n",
 824 |        "      <th>0</th>\n",
 825 |        "      <td>0</td>\n",
 826 |        "      <td>1</td>\n",
 827 |        "      <td>261</td>\n",
 828 |        "      <td>999</td>\n",
 829 |        "      <td>0</td>\n",
 830 |        "      <td>NaN</td>\n",
 831 |        "      <td>NaN</td>\n",
 832 |        "      <td>may</td>\n",
 833 |        "      <td>13</td>\n",
 834 |        "      <td>1</td>\n",
 835 |        "      <td>2022</td>\n",
 836 |        "      <td>2022-05-13</td>\n",
 837 |        "    </tr>\n",
 838 |        "    <tr>\n",
 839 |        "      <th>1</th>\n",
 840 |        "      <td>1</td>\n",
 841 |        "      <td>1</td>\n",
 842 |        "      <td>149</td>\n",
 843 |        "      <td>999</td>\n",
 844 |        "      <td>0</td>\n",
 845 |        "      <td>NaN</td>\n",
 846 |        "      <td>NaN</td>\n",
 847 |        "      <td>may</td>\n",
 848 |        "      <td>19</td>\n",
 849 |        "      <td>1</td>\n",
 850 |        "      <td>2022</td>\n",
 851 |        "      <td>2022-05-19</td>\n",
 852 |        "    </tr>\n",
 853 |        "    <tr>\n",
 854 |        "      <th>2</th>\n",
 855 |        "      <td>2</td>\n",
 856 |        "      <td>1</td>\n",
 857 |        "      <td>226</td>\n",
 858 |        "      <td>999</td>\n",
 859 |        "      <td>0</td>\n",
 860 |        "      <td>NaN</td>\n",
 861 |        "      <td>NaN</td>\n",
 862 |        "      <td>may</td>\n",
 863 |        "      <td>23</td>\n",
 864 |        "      <td>1</td>\n",
 865 |        "      <td>2022</td>\n",
 866 |        "      <td>2022-05-23</td>\n",
 867 |        "    </tr>\n",
 868 |        "    <tr>\n",
 869 |        "      <th>3</th>\n",
 870 |        "      <td>3</td>\n",
 871 |        "      <td>1</td>\n",
 872 |        "      <td>151</td>\n",
 873 |        "      <td>999</td>\n",
 874 |        "      <td>0</td>\n",
 875 |        "      <td>NaN</td>\n",
 876 |        "      <td>NaN</td>\n",
 877 |        "      <td>may</td>\n",
 878 |        "      <td>27</td>\n",
 879 |        "      <td>1</td>\n",
 880 |        "      <td>2022</td>\n",
 881 |        "      <td>2022-05-27</td>\n",
 882 |        "    </tr>\n",
 883 |        "    <tr>\n",
 884 |        "      <th>4</th>\n",
 885 |        "      <td>4</td>\n",
 886 |        "      <td>1</td>\n",
 887 |        "      <td>307</td>\n",
 888 |        "      <td>999</td>\n",
 889 |        "      <td>0</td>\n",
 890 |        "      <td>NaN</td>\n",
 891 |        "      <td>NaN</td>\n",
 892 |        "      <td>may</td>\n",
 893 |        "      <td>3</td>\n",
 894 |        "      <td>1</td>\n",
 895 |        "      <td>2022</td>\n",
 896 |        "      <td>2022-05-03</td>\n",
 897 |        "    </tr>\n",
 898 |        "  </tbody>\n",
 899 |        "</table>\n",
 900 |        "</div>"
 901 |       ],
 902 |       "text/plain": [
 903 |        "   client_id  number_contacts  contact_duration  pdays  \\\n",
 904 |        "0          0                1               261    999   \n",
 905 |        "1          1                1               149    999   \n",
 906 |        "2          2                1               226    999   \n",
 907 |        "3          3                1               151    999   \n",
 908 |        "4          4                1               307    999   \n",
 909 |        "\n",
 910 |        "   previous_campaign_contacts  previous_outcome  campaign_outcome month  day  \\\n",
 911 |        "0                           0               NaN               NaN   may   13   \n",
 912 |        "1                           0               NaN               NaN   may   19   \n",
 913 |        "2                           0               NaN               NaN   may   23   \n",
 914 |        "3                           0               NaN               NaN   may   27   \n",
 915 |        "4                           0               NaN               NaN   may    3   \n",
 916 |        "\n",
 917 |        "   campaign_id  year last_contact_date  \n",
 918 |        "0            1  2022        2022-05-13  \n",
 919 |        "1            1  2022        2022-05-19  \n",
 920 |        "2            1  2022        2022-05-23  \n",
 921 |        "3            1  2022        2022-05-27  \n",
 922 |        "4            1  2022        2022-05-03  "
 923 |       ]
 924 |      },
 925 |      "execution_count": 10,
 926 |      "metadata": {},
 927 |      "output_type": "execute_result"
 928 |     }
 929 |    ],
 930 |    "source": [
 931 |     "# Add the \"year\" column with the value 2022 to the 'campaign_data' DataFrame\n",
 932 |     "campaign['year'] = 2022\n",
 933 |     "\n",
 934 |     "# Create a datetime column \"last_contact_date\"\n",
 935 |     "campaign['last_contact_date'] = pd.to_datetime(\n",
 936 |     "    campaign['year'].astype(str) + '-' +\n",
 937 |     "    campaign['month'].astype(str) + '-' +\n",
 938 |     "    campaign['day'].astype(str),\n",
 939 |     "    errors='coerce'\n",
 940 |     ")\n",
 941 |     "\n",
 942 |     "# Check if the \"last_contact_date\" column was successfully created.\n",
 943 |     "if 'last_contact_date' in campaign.columns:\n",
 944 |     "    print(\"The 'last_contact_date' column was successfully created.\")\n",
 945 |     "else:\n",
 946 |     "    print(\"The 'last_contact_date' column was not created.\")\n",
 947 |     "\n",
 948 |     "# Print the first few rows of 'campaign_data' DataFrame to verify the creation of the date column\n",
 949 |     "campaign.head()"
 950 |    ]
 951 |   },
 952 |   {
 953 |    "cell_type": "code",
 954 |    "execution_count": 11,
 955 |    "id": "d7ef5f26",
 956 |    "metadata": {},
 957 |    "outputs": [
 958 |     {
 959 |      "data": {
 960 |       "text/plain": [
 961 |        "dtype('<M8[ns]')"
 962 |       ]
 963 |      },
 964 |      "execution_count": 11,
 965 |      "metadata": {},
 966 |      "output_type": "execute_result"
 967 |     }
 968 |    ],
 969 |    "source": [
 970 |     "# Confirm the last_contact_date is in datetime format\n",
 971 |     "campaign['last_contact_date'].dtype"
 972 |    ]
 973 |   },
 974 |   {
 975 |    "cell_type": "markdown",
 976 |    "id": "bf870a11",
 977 |    "metadata": {},
 978 |    "source": [
 979 |     "#### Instruction 9:\n",
 980 |     "\n",
 981 |     "Remove any redundant data that might have been used to create new columns, ensuring the columns in each subset of the data match the table displayed in the notebook."
 982 |    ]
 983 |   },
 984 |   {
 985 |    "cell_type": "code",
 986 |    "execution_count": 12,
 987 |    "id": "82415610",
 988 |    "metadata": {},
 989 |    "outputs": [
 990 |     {
 991 |      "name": "stdout",
 992 |      "output_type": "stream",
 993 |      "text": [
 994 |       "Client Data:\n",
 995 |       "   id  age        job  marital    education credit_default housing loan\n",
 996 |       "0   0   56  housemaid  married     basic_4y             no      no   no\n",
 997 |       "1   1   57   services  married  high_school        unknown      no   no\n",
 998 |       "2   2   37   services  married  high_school             no     yes   no\n",
 999 |       "3   3   40      admin  married     basic_6y             no      no   no\n",
1000 |       "4   4   56   services  married  high_school             no      no  yes\n",
1001 |       "\n",
1002 |       "Campaign Data:\n",
1003 |       "   client_id  number_contacts  contact_duration  pdays  \\\n",
1004 |       "0          0                1               261    999   \n",
1005 |       "1          1                1               149    999   \n",
1006 |       "2          2                1               226    999   \n",
1007 |       "3          3                1               151    999   \n",
1008 |       "4          4                1               307    999   \n",
1009 |       "\n",
1010 |       "   previous_campaign_contacts  previous_outcome  campaign_outcome  \\\n",
1011 |       "0                           0               NaN               NaN   \n",
1012 |       "1                           0               NaN               NaN   \n",
1013 |       "2                           0               NaN               NaN   \n",
1014 |       "3                           0               NaN               NaN   \n",
1015 |       "4                           0               NaN               NaN   \n",
1016 |       "\n",
1017 |       "   campaign_id last_contact_date  \n",
1018 |       "0            1        2022-05-13  \n",
1019 |       "1            1        2022-05-19  \n",
1020 |       "2            1        2022-05-23  \n",
1021 |       "3            1        2022-05-27  \n",
1022 |       "4            1        2022-05-03  \n",
1023 |       "\n",
1024 |       "Economic Data:\n",
1025 |       "   client_id  emp_var_rate  cons_price_idx  euribor_three_months  \\\n",
1026 |       "0          0           1.1          93.994                 4.857   \n",
1027 |       "1          1           1.1          93.994                 4.857   \n",
1028 |       "2          2           1.1          93.994                 4.857   \n",
1029 |       "3          3           1.1          93.994                 4.857   \n",
1030 |       "4          4           1.1          93.994                 4.857   \n",
1031 |       "\n",
1032 |       "   number_employed  \n",
1033 |       "0           5191.0  \n",
1034 |       "1           5191.0  \n",
1035 |       "2           5191.0  \n",
1036 |       "3           5191.0  \n",
1037 |       "4           5191.0  \n"
1038 |      ]
1039 |     }
1040 |    ],
1041 |    "source": [
1042 |     "# Remove \"day\", \"month\", and \"year\" columns that were used to create the \"last_contact_date\" column\n",
1043 |     "campaign = campaign.drop(['day', 'month', 'year'], axis=1)\n",
1044 |     "\n",
1045 |     "# Print the first few rows of each of the DataFrame to verify they match tables expected to be created.\n",
1046 |     "print(\"Client Data:\")\n",
1047 |     "print(client.head())\n",
1048 |     "\n",
1049 |     "print(\"\\nCampaign Data:\")\n",
1050 |     "print(campaign.head())\n",
1051 |     "\n",
1052 |     "print(\"\\nEconomic Data:\")\n",
1053 |     "print(economics.head())"
1054 |    ]
1055 |   },
1056 |   {
1057 |    "cell_type": "markdown",
1058 |    "id": "e3f3db6f",
1059 |    "metadata": {},
1060 |    "source": [
1061 |     "#### Instruction 10:\n",
1062 |     "\n",
1063 |     "Save the three DataFrames to csv files without an index as client.csv, campaign.csv, and economics.csv respectively."
1064 |    ]
1065 |   },
1066 |   {
1067 |    "cell_type": "code",
1068 |    "execution_count": 13,
1069 |    "id": "4b7e681b",
1070 |    "metadata": {},
1071 |    "outputs": [],
1072 |    "source": [
1073 |     "# Save client data to 'client.csv'\n",
1074 |     "client.to_csv('client.csv', index=False)\n",
1075 |     "\n",
1076 |     "# Save campaign data to 'campaign.csv'\n",
1077 |     "campaign.to_csv('campaign.csv', index=False)\n",
1078 |     "\n",
1079 |     "# Save economics data to 'economics.csv'\n",
1080 |     "economics.to_csv('economics.csv', index=False)"
1081 |    ]
1082 |   },
1083 |   {
1084 |    "cell_type": "markdown",
1085 |    "id": "ff7acff3",
1086 |    "metadata": {},
1087 |    "source": [
1088 |     "#### Instruction 11:\n",
1089 |     "\n",
1090 |     "Create a Python variable called client_table, containing SQL code as a multi-line string to create a table called client using values from client.csv."
1091 |    ]
1092 |   },
1093 |   {
1094 |    "cell_type": "code",
1095 |    "execution_count": 14,
1096 |    "id": "c6075770",
1097 |    "metadata": {},
1098 |    "outputs": [],
1099 |    "source": [
1100 |     "# Specify the SQL code as a multi-line string\n",
1101 |     "client_table = \"\"\"\n",
1102 |     "CREATE TABLE client (\n",
1103 |     "    id SERIAL PRIMARY KEY,\n",
1104 |     "    age INT,\n",
1105 |     "    job TEXT,\n",
1106 |     "    marital TEXT,\n",
1107 |     "    education TEXT,\n",
1108 |     "    credit_default BOOLEAN,\n",
1109 |     "    housing BOOLEAN,\n",
1110 |     "    loan BOOLEAN\n",
1111 |     ");\n",
1112 |     "\n",
1113 |     "-- Copy data from client.csv into the client table\n",
1114 |     "\\copy client FROM 'client.csv' DELIMITER ',' CSV HEADER;\n",
1115 |     "\"\"\""
1116 |    ]
1117 |   },
1118 |   {
1119 |    "cell_type": "markdown",
1120 |    "id": "a2b2aea1",
1121 |    "metadata": {},
1122 |    "source": [
1123 |     "#### Instruction 12:\n",
1124 |     "\n",
1125 |     "Create a Python variable called campaign_table, containing SQL code as a multi-line string to create a table called campaign using values from campaign.csv."
1126 |    ]
1127 |   },
1128 |   {
1129 |    "cell_type": "code",
1130 |    "execution_count": 15,
1131 |    "id": "519c0758",
1132 |    "metadata": {},
1133 |    "outputs": [],
1134 |    "source": [
1135 |     "# Specify SQL code as a multi-line string\n",
1136 |     "campaign_table = \"\"\"\"\n",
1137 |     "CREATE TABLE campaign (\n",
1138 |     "    campaign_id SERIAL PRIMARY KEY,\n",
1139 |     "    client_id SERIAL REFERENCES client (id),\n",
1140 |     "    number_contacts INT,\n",
1141 |     "    contact_duration INT,\n",
1142 |     "    pdays INT,\n",
1143 |     "    previous_campaign_contacts INT,\n",
1144 |     "    previous_outcome BOOLEAN,\n",
1145 |     "    campaign_outcome BOOLEAN,\n",
1146 |     "    last_contact_date DATE\n",
1147 |     ");\n",
1148 |     "\n",
1149 |     "-- Copy data from campaign.csv into the campaign table\n",
1150 |     "\\copy campaign FROM 'campaign.csv' DELIMETER ',' CSV HEADER;\n",
1151 |     "\"\"\""
1152 |    ]
1153 |   },
1154 |   {
1155 |    "cell_type": "markdown",
1156 |    "id": "1595e7ae",
1157 |    "metadata": {},
1158 |    "source": [
1159 |     "#### Instruction 13:\n",
1160 |     "\n",
1161 |     "Create a Python variable called economics_table, containing SQL code as a multi-line string to create a table called economics using values from economics.csv."
1162 |    ]
1163 |   },
1164 |   {
1165 |    "cell_type": "code",
1166 |    "execution_count": 16,
1167 |    "id": "3d86fc77",
1168 |    "metadata": {},
1169 |    "outputs": [],
1170 |    "source": [
1171 |     "# Specify SQL code as a multi-line string\n",
1172 |     "economics_table = \"\"\"\n",
1173 |     "CREATE TABLE economics (\n",
1174 |     "    client_id SERIAL REFERENCES client (id),\n",
1175 |     "    emp_var_rate FLOAT,\n",
1176 |     "    cons_price_idx FLOAT,\n",
1177 |     "    euribor_three_months FLOAT,\n",
1178 |     "    number_employed FLOAT\n",
1179 |     ");\n",
1180 |     "\n",
1181 |     "-- Copy data from economics.csv into the economics table\n",
1182 |     "\\copy economics FROM 'economics.csv' DELIMETER ',' CSV HEADER;\n",
1183 |     "\"\"\""
1184 |    ]
1185 |   },
1186 |   {
1187 |    "cell_type": "code",
1188 |    "execution_count": null,
1189 |    "id": "b19ff9bb",
1190 |    "metadata": {},
1191 |    "outputs": [],
1192 |    "source": []
1193 |   },
1194 |   {
1195 |    "cell_type": "markdown",
1196 |    "id": "2d2629c2",
1197 |    "metadata": {},
1198 |    "source": [
1199 |     "### Completeing This Part Yourself. \n",
1200 |     "\n",
1201 |     "**Loading the three different dataframes into a PostgreSQL database you created.** \n",
1202 |     "\n",
1203 |     "Before Completing this part, you will download and PostgreSQL and create a after setting it up following the [instructions here](https://www.youtube.com/watch?v=0n41UTkOBb0&t=298s), you go ahead and create a database called marketing_campaign."
1204 |    ]
1205 |   },
1206 |   {
1207 |    "cell_type": "code",
1208 |    "execution_count": 17,
1209 |    "id": "72bc6161",
1210 |    "metadata": {},
1211 |    "outputs": [],
1212 |    "source": [
1213 |     "# import library\n",
1214 |     "import psycopg2\n",
1215 |     "from sqlalchemy import create_engine\n",
1216 |     "\n",
1217 |     "# Specify parameters\n",
1218 |     "# You get these parameters from your postgresSQL DBMS server properties\n",
1219 |     "hostname = 'localhost'\n",
1220 |     "database = 'marketing_campaign'\n",
1221 |     "username = 'postgres'\n",
1222 |     "password = '#####' #pass your password\n",
1223 |     "port_id = 5432\n",
1224 |     "\n",
1225 |     "# Provide connection parameters\n",
1226 |     "conn = psycopg2.connect(\n",
1227 |     "    host=hostname,\n",
1228 |     "    database=database,\n",
1229 |     "    user=username,\n",
1230 |     "    password=password,\n",
1231 |     "    port=port_id\n",
1232 |     ")\n"
1233 |    ]
1234 |   },
1235 |   {
1236 |    "cell_type": "code",
1237 |    "execution_count": 18,
1238 |    "id": "b654c953",
1239 |    "metadata": {},
1240 |    "outputs": [],
1241 |    "source": [
1242 |     "# Create engine\n",
1243 |     "# connection string: dialect+driver://user:password@server/database\n",
1244 |     "engine = create_engine('postgresql+psycopg2://postgres:promzy2099@localhost/marketing_campaign')"
1245 |    ]
1246 |   },
1247 |   {
1248 |    "cell_type": "code",
1249 |    "execution_count": 19,
1250 |    "id": "012765f4",
1251 |    "metadata": {},
1252 |    "outputs": [
1253 |     {
1254 |      "data": {
1255 |       "text/plain": [
1256 |        "Engine(postgresql+psycopg2://postgres:***@localhost/marketing_campaign)"
1257 |       ]
1258 |      },
1259 |      "execution_count": 19,
1260 |      "metadata": {},
1261 |      "output_type": "execute_result"
1262 |     }
1263 |    ],
1264 |    "source": [
1265 |     "# Display engine info\n",
1266 |     "engine"
1267 |    ]
1268 |   },
1269 |   {
1270 |    "cell_type": "code",
1271 |    "execution_count": 20,
1272 |    "id": "10423b7f",
1273 |    "metadata": {},
1274 |    "outputs": [
1275 |     {
1276 |      "data": {
1277 |       "text/plain": [
1278 |        "188"
1279 |       ]
1280 |      },
1281 |      "execution_count": 20,
1282 |      "metadata": {},
1283 |      "output_type": "execute_result"
1284 |     }
1285 |    ],
1286 |    "source": [
1287 |     "# Insert data from client DataFrame into the \"client_data\" table\n",
1288 |     "client.to_sql('client_data', engine, if_exists='replace', index=False)\n",
1289 |     "\n",
1290 |     "# Insert data from campaign DataFrame into the \"campaign_data\" table\n",
1291 |     "campaign.to_sql('campaign_data', engine, if_exists='replace', index=False)\n",
1292 |     "\n",
1293 |     "# Insert data from economics DataFrame into the \"economics_data\" table\n",
1294 |     "economics.to_sql('economics_data', engine, if_exists='replace', index=False)"
1295 |    ]
1296 |   },
1297 |   {
1298 |    "cell_type": "code",
1299 |    "execution_count": 21,
1300 |    "id": "2fc5b801",
1301 |    "metadata": {},
1302 |    "outputs": [
1303 |     {
1304 |      "data": {
1305 |       "text/html": [
1306 |        "<div>\n",
1307 |        "<style scoped>\n",
1308 |        "    .dataframe tbody tr th:only-of-type {\n",
1309 |        "        vertical-align: middle;\n",
1310 |        "    }\n",
1311 |        "\n",
1312 |        "    .dataframe tbody tr th {\n",
1313 |        "        vertical-align: top;\n",
1314 |        "    }\n",
1315 |        "\n",
1316 |        "    .dataframe thead th {\n",
1317 |        "        text-align: right;\n",
1318 |        "    }\n",
1319 |        "</style>\n",
1320 |        "<table border=\"1\" class=\"dataframe\">\n",
1321 |        "  <thead>\n",
1322 |        "    <tr style=\"text-align: right;\">\n",
1323 |        "      <th></th>\n",
1324 |        "      <th>id</th>\n",
1325 |        "      <th>age</th>\n",
1326 |        "      <th>job</th>\n",
1327 |        "      <th>marital</th>\n",
1328 |        "      <th>education</th>\n",
1329 |        "      <th>credit_default</th>\n",
1330 |        "      <th>housing</th>\n",
1331 |        "      <th>loan</th>\n",
1332 |        "    </tr>\n",
1333 |        "  </thead>\n",
1334 |        "  <tbody>\n",
1335 |        "    <tr>\n",
1336 |        "      <th>0</th>\n",
1337 |        "      <td>0</td>\n",
1338 |        "      <td>56</td>\n",
1339 |        "      <td>housemaid</td>\n",
1340 |        "      <td>married</td>\n",
1341 |        "      <td>basic_4y</td>\n",
1342 |        "      <td>no</td>\n",
1343 |        "      <td>no</td>\n",
1344 |        "      <td>no</td>\n",
1345 |        "    </tr>\n",
1346 |        "    <tr>\n",
1347 |        "      <th>1</th>\n",
1348 |        "      <td>1</td>\n",
1349 |        "      <td>57</td>\n",
1350 |        "      <td>services</td>\n",
1351 |        "      <td>married</td>\n",
1352 |        "      <td>high_school</td>\n",
1353 |        "      <td>unknown</td>\n",
1354 |        "      <td>no</td>\n",
1355 |        "      <td>no</td>\n",
1356 |        "    </tr>\n",
1357 |        "    <tr>\n",
1358 |        "      <th>2</th>\n",
1359 |        "      <td>2</td>\n",
1360 |        "      <td>37</td>\n",
1361 |        "      <td>services</td>\n",
1362 |        "      <td>married</td>\n",
1363 |        "      <td>high_school</td>\n",
1364 |        "      <td>no</td>\n",
1365 |        "      <td>yes</td>\n",
1366 |        "      <td>no</td>\n",
1367 |        "    </tr>\n",
1368 |        "    <tr>\n",
1369 |        "      <th>3</th>\n",
1370 |        "      <td>3</td>\n",
1371 |        "      <td>40</td>\n",
1372 |        "      <td>admin</td>\n",
1373 |        "      <td>married</td>\n",
1374 |        "      <td>basic_6y</td>\n",
1375 |        "      <td>no</td>\n",
1376 |        "      <td>no</td>\n",
1377 |        "      <td>no</td>\n",
1378 |        "    </tr>\n",
1379 |        "    <tr>\n",
1380 |        "      <th>4</th>\n",
1381 |        "      <td>4</td>\n",
1382 |        "      <td>56</td>\n",
1383 |        "      <td>services</td>\n",
1384 |        "      <td>married</td>\n",
1385 |        "      <td>high_school</td>\n",
1386 |        "      <td>no</td>\n",
1387 |        "      <td>no</td>\n",
1388 |        "      <td>yes</td>\n",
1389 |        "    </tr>\n",
1390 |        "    <tr>\n",
1391 |        "      <th>...</th>\n",
1392 |        "      <td>...</td>\n",
1393 |        "      <td>...</td>\n",
1394 |        "      <td>...</td>\n",
1395 |        "      <td>...</td>\n",
1396 |        "      <td>...</td>\n",
1397 |        "      <td>...</td>\n",
1398 |        "      <td>...</td>\n",
1399 |        "      <td>...</td>\n",
1400 |        "    </tr>\n",
1401 |        "    <tr>\n",
1402 |        "      <th>41183</th>\n",
1403 |        "      <td>41183</td>\n",
1404 |        "      <td>73</td>\n",
1405 |        "      <td>retired</td>\n",
1406 |        "      <td>married</td>\n",
1407 |        "      <td>professional_course</td>\n",
1408 |        "      <td>no</td>\n",
1409 |        "      <td>yes</td>\n",
1410 |        "      <td>no</td>\n",
1411 |        "    </tr>\n",
1412 |        "    <tr>\n",
1413 |        "      <th>41184</th>\n",
1414 |        "      <td>41184</td>\n",
1415 |        "      <td>46</td>\n",
1416 |        "      <td>blue-collar</td>\n",
1417 |        "      <td>married</td>\n",
1418 |        "      <td>professional_course</td>\n",
1419 |        "      <td>no</td>\n",
1420 |        "      <td>no</td>\n",
1421 |        "      <td>no</td>\n",
1422 |        "    </tr>\n",
1423 |        "    <tr>\n",
1424 |        "      <th>41185</th>\n",
1425 |        "      <td>41185</td>\n",
1426 |        "      <td>56</td>\n",
1427 |        "      <td>retired</td>\n",
1428 |        "      <td>married</td>\n",
1429 |        "      <td>university_degree</td>\n",
1430 |        "      <td>no</td>\n",
1431 |        "      <td>yes</td>\n",
1432 |        "      <td>no</td>\n",
1433 |        "    </tr>\n",
1434 |        "    <tr>\n",
1435 |        "      <th>41186</th>\n",
1436 |        "      <td>41186</td>\n",
1437 |        "      <td>44</td>\n",
1438 |        "      <td>technician</td>\n",
1439 |        "      <td>married</td>\n",
1440 |        "      <td>professional_course</td>\n",
1441 |        "      <td>no</td>\n",
1442 |        "      <td>no</td>\n",
1443 |        "      <td>no</td>\n",
1444 |        "    </tr>\n",
1445 |        "    <tr>\n",
1446 |        "      <th>41187</th>\n",
1447 |        "      <td>41187</td>\n",
1448 |        "      <td>74</td>\n",
1449 |        "      <td>retired</td>\n",
1450 |        "      <td>married</td>\n",
1451 |        "      <td>professional_course</td>\n",
1452 |        "      <td>no</td>\n",
1453 |        "      <td>yes</td>\n",
1454 |        "      <td>no</td>\n",
1455 |        "    </tr>\n",
1456 |        "  </tbody>\n",
1457 |        "</table>\n",
1458 |        "<p>41188 rows × 8 columns</p>\n",
1459 |        "</div>"
1460 |       ],
1461 |       "text/plain": [
1462 |        "          id  age          job  marital            education credit_default  \\\n",
1463 |        "0          0   56    housemaid  married             basic_4y             no   \n",
1464 |        "1          1   57     services  married          high_school        unknown   \n",
1465 |        "2          2   37     services  married          high_school             no   \n",
1466 |        "3          3   40        admin  married             basic_6y             no   \n",
1467 |        "4          4   56     services  married          high_school             no   \n",
1468 |        "...      ...  ...          ...      ...                  ...            ...   \n",
1469 |        "41183  41183   73      retired  married  professional_course             no   \n",
1470 |        "41184  41184   46  blue-collar  married  professional_course             no   \n",
1471 |        "41185  41185   56      retired  married    university_degree             no   \n",
1472 |        "41186  41186   44   technician  married  professional_course             no   \n",
1473 |        "41187  41187   74      retired  married  professional_course             no   \n",
1474 |        "\n",
1475 |        "      housing loan  \n",
1476 |        "0          no   no  \n",
1477 |        "1          no   no  \n",
1478 |        "2         yes   no  \n",
1479 |        "3          no   no  \n",
1480 |        "4          no  yes  \n",
1481 |        "...       ...  ...  \n",
1482 |        "41183     yes   no  \n",
1483 |        "41184      no   no  \n",
1484 |        "41185     yes   no  \n",
1485 |        "41186      no   no  \n",
1486 |        "41187     yes   no  \n",
1487 |        "\n",
1488 |        "[41188 rows x 8 columns]"
1489 |       ]
1490 |      },
1491 |      "execution_count": 21,
1492 |      "metadata": {},
1493 |      "output_type": "execute_result"
1494 |     }
1495 |    ],
1496 |    "source": [
1497 |     "# Confirm if all three tables were successfully created \n",
1498 |     "# and if all the data was successfully inserted into the three different tables\n",
1499 |     "\n",
1500 |     "# Read client_data from database to pandas dataframe\n",
1501 |     "client_df = pd.read_sql_query('SELECT * FROM client_data', engine)\n",
1502 |     "client_df"
1503 |    ]
1504 |   },
1505 |   {
1506 |    "cell_type": "code",
1507 |    "execution_count": 22,
1508 |    "id": "3f83cdb5",
1509 |    "metadata": {},
1510 |    "outputs": [
1511 |     {
1512 |      "data": {
1513 |       "text/html": [
1514 |        "<div>\n",
1515 |        "<style scoped>\n",
1516 |        "    .dataframe tbody tr th:only-of-type {\n",
1517 |        "        vertical-align: middle;\n",
1518 |        "    }\n",
1519 |        "\n",
1520 |        "    .dataframe tbody tr th {\n",
1521 |        "        vertical-align: top;\n",
1522 |        "    }\n",
1523 |        "\n",
1524 |        "    .dataframe thead th {\n",
1525 |        "        text-align: right;\n",
1526 |        "    }\n",
1527 |        "</style>\n",
1528 |        "<table border=\"1\" class=\"dataframe\">\n",
1529 |        "  <thead>\n",
1530 |        "    <tr style=\"text-align: right;\">\n",
1531 |        "      <th></th>\n",
1532 |        "      <th>client_id</th>\n",
1533 |        "      <th>number_contacts</th>\n",
1534 |        "      <th>contact_duration</th>\n",
1535 |        "      <th>pdays</th>\n",
1536 |        "      <th>previous_campaign_contacts</th>\n",
1537 |        "      <th>previous_outcome</th>\n",
1538 |        "      <th>campaign_outcome</th>\n",
1539 |        "      <th>campaign_id</th>\n",
1540 |        "      <th>last_contact_date</th>\n",
1541 |        "    </tr>\n",
1542 |        "  </thead>\n",
1543 |        "  <tbody>\n",
1544 |        "    <tr>\n",
1545 |        "      <th>0</th>\n",
1546 |        "      <td>0</td>\n",
1547 |        "      <td>1</td>\n",
1548 |        "      <td>261</td>\n",
1549 |        "      <td>999</td>\n",
1550 |        "      <td>0</td>\n",
1551 |        "      <td>NaN</td>\n",
1552 |        "      <td>None</td>\n",
1553 |        "      <td>1</td>\n",
1554 |        "      <td>2022-05-13</td>\n",
1555 |        "    </tr>\n",
1556 |        "    <tr>\n",
1557 |        "      <th>1</th>\n",
1558 |        "      <td>1</td>\n",
1559 |        "      <td>1</td>\n",
1560 |        "      <td>149</td>\n",
1561 |        "      <td>999</td>\n",
1562 |        "      <td>0</td>\n",
1563 |        "      <td>NaN</td>\n",
1564 |        "      <td>None</td>\n",
1565 |        "      <td>1</td>\n",
1566 |        "      <td>2022-05-19</td>\n",
1567 |        "    </tr>\n",
1568 |        "    <tr>\n",
1569 |        "      <th>2</th>\n",
1570 |        "      <td>2</td>\n",
1571 |        "      <td>1</td>\n",
1572 |        "      <td>226</td>\n",
1573 |        "      <td>999</td>\n",
1574 |        "      <td>0</td>\n",
1575 |        "      <td>NaN</td>\n",
1576 |        "      <td>None</td>\n",
1577 |        "      <td>1</td>\n",
1578 |        "      <td>2022-05-23</td>\n",
1579 |        "    </tr>\n",
1580 |        "    <tr>\n",
1581 |        "      <th>3</th>\n",
1582 |        "      <td>3</td>\n",
1583 |        "      <td>1</td>\n",
1584 |        "      <td>151</td>\n",
1585 |        "      <td>999</td>\n",
1586 |        "      <td>0</td>\n",
1587 |        "      <td>NaN</td>\n",
1588 |        "      <td>None</td>\n",
1589 |        "      <td>1</td>\n",
1590 |        "      <td>2022-05-27</td>\n",
1591 |        "    </tr>\n",
1592 |        "    <tr>\n",
1593 |        "      <th>4</th>\n",
1594 |        "      <td>4</td>\n",
1595 |        "      <td>1</td>\n",
1596 |        "      <td>307</td>\n",
1597 |        "      <td>999</td>\n",
1598 |        "      <td>0</td>\n",
1599 |        "      <td>NaN</td>\n",
1600 |        "      <td>None</td>\n",
1601 |        "      <td>1</td>\n",
1602 |        "      <td>2022-05-03</td>\n",
1603 |        "    </tr>\n",
1604 |        "    <tr>\n",
1605 |        "      <th>...</th>\n",
1606 |        "      <td>...</td>\n",
1607 |        "      <td>...</td>\n",
1608 |        "      <td>...</td>\n",
1609 |        "      <td>...</td>\n",
1610 |        "      <td>...</td>\n",
1611 |        "      <td>...</td>\n",
1612 |        "      <td>...</td>\n",
1613 |        "      <td>...</td>\n",
1614 |        "      <td>...</td>\n",
1615 |        "    </tr>\n",
1616 |        "    <tr>\n",
1617 |        "      <th>41183</th>\n",
1618 |        "      <td>41183</td>\n",
1619 |        "      <td>1</td>\n",
1620 |        "      <td>334</td>\n",
1621 |        "      <td>999</td>\n",
1622 |        "      <td>0</td>\n",
1623 |        "      <td>NaN</td>\n",
1624 |        "      <td>None</td>\n",
1625 |        "      <td>1</td>\n",
1626 |        "      <td>2022-11-30</td>\n",
1627 |        "    </tr>\n",
1628 |        "    <tr>\n",
1629 |        "      <th>41184</th>\n",
1630 |        "      <td>41184</td>\n",
1631 |        "      <td>1</td>\n",
1632 |        "      <td>383</td>\n",
1633 |        "      <td>999</td>\n",
1634 |        "      <td>0</td>\n",
1635 |        "      <td>NaN</td>\n",
1636 |        "      <td>None</td>\n",
1637 |        "      <td>1</td>\n",
1638 |        "      <td>2022-11-06</td>\n",
1639 |        "    </tr>\n",
1640 |        "    <tr>\n",
1641 |        "      <th>41185</th>\n",
1642 |        "      <td>41185</td>\n",
1643 |        "      <td>2</td>\n",
1644 |        "      <td>189</td>\n",
1645 |        "      <td>999</td>\n",
1646 |        "      <td>0</td>\n",
1647 |        "      <td>NaN</td>\n",
1648 |        "      <td>None</td>\n",
1649 |        "      <td>1</td>\n",
1650 |        "      <td>2022-11-24</td>\n",
1651 |        "    </tr>\n",
1652 |        "    <tr>\n",
1653 |        "      <th>41186</th>\n",
1654 |        "      <td>41186</td>\n",
1655 |        "      <td>1</td>\n",
1656 |        "      <td>442</td>\n",
1657 |        "      <td>999</td>\n",
1658 |        "      <td>0</td>\n",
1659 |        "      <td>NaN</td>\n",
1660 |        "      <td>None</td>\n",
1661 |        "      <td>1</td>\n",
1662 |        "      <td>2022-11-17</td>\n",
1663 |        "    </tr>\n",
1664 |        "    <tr>\n",
1665 |        "      <th>41187</th>\n",
1666 |        "      <td>41187</td>\n",
1667 |        "      <td>3</td>\n",
1668 |        "      <td>239</td>\n",
1669 |        "      <td>999</td>\n",
1670 |        "      <td>1</td>\n",
1671 |        "      <td>0.0</td>\n",
1672 |        "      <td>None</td>\n",
1673 |        "      <td>1</td>\n",
1674 |        "      <td>2022-11-23</td>\n",
1675 |        "    </tr>\n",
1676 |        "  </tbody>\n",
1677 |        "</table>\n",
1678 |        "<p>41188 rows × 9 columns</p>\n",
1679 |        "</div>"
1680 |       ],
1681 |       "text/plain": [
1682 |        "       client_id  number_contacts  contact_duration  pdays  \\\n",
1683 |        "0              0                1               261    999   \n",
1684 |        "1              1                1               149    999   \n",
1685 |        "2              2                1               226    999   \n",
1686 |        "3              3                1               151    999   \n",
1687 |        "4              4                1               307    999   \n",
1688 |        "...          ...              ...               ...    ...   \n",
1689 |        "41183      41183                1               334    999   \n",
1690 |        "41184      41184                1               383    999   \n",
1691 |        "41185      41185                2               189    999   \n",
1692 |        "41186      41186                1               442    999   \n",
1693 |        "41187      41187                3               239    999   \n",
1694 |        "\n",
1695 |        "       previous_campaign_contacts  previous_outcome campaign_outcome  \\\n",
1696 |        "0                               0               NaN             None   \n",
1697 |        "1                               0               NaN             None   \n",
1698 |        "2                               0               NaN             None   \n",
1699 |        "3                               0               NaN             None   \n",
1700 |        "4                               0               NaN             None   \n",
1701 |        "...                           ...               ...              ...   \n",
1702 |        "41183                           0               NaN             None   \n",
1703 |        "41184                           0               NaN             None   \n",
1704 |        "41185                           0               NaN             None   \n",
1705 |        "41186                           0               NaN             None   \n",
1706 |        "41187                           1               0.0             None   \n",
1707 |        "\n",
1708 |        "       campaign_id last_contact_date  \n",
1709 |        "0                1        2022-05-13  \n",
1710 |        "1                1        2022-05-19  \n",
1711 |        "2                1        2022-05-23  \n",
1712 |        "3                1        2022-05-27  \n",
1713 |        "4                1        2022-05-03  \n",
1714 |        "...            ...               ...  \n",
1715 |        "41183            1        2022-11-30  \n",
1716 |        "41184            1        2022-11-06  \n",
1717 |        "41185            1        2022-11-24  \n",
1718 |        "41186            1        2022-11-17  \n",
1719 |        "41187            1        2022-11-23  \n",
1720 |        "\n",
1721 |        "[41188 rows x 9 columns]"
1722 |       ]
1723 |      },
1724 |      "execution_count": 22,
1725 |      "metadata": {},
1726 |      "output_type": "execute_result"
1727 |     }
1728 |    ],
1729 |    "source": [
1730 |     "# Read campaign_data from database to pandas dataframe\n",
1731 |     "campaign_df = pd.read_sql_query('SELECT * FROM campaign_data', engine)\n",
1732 |     "campaign_df"
1733 |    ]
1734 |   },
1735 |   {
1736 |    "cell_type": "code",
1737 |    "execution_count": 23,
1738 |    "id": "25ebcd41",
1739 |    "metadata": {},
1740 |    "outputs": [
1741 |     {
1742 |      "data": {
1743 |       "text/html": [
1744 |        "<div>\n",
1745 |        "<style scoped>\n",
1746 |        "    .dataframe tbody tr th:only-of-type {\n",
1747 |        "        vertical-align: middle;\n",
1748 |        "    }\n",
1749 |        "\n",
1750 |        "    .dataframe tbody tr th {\n",
1751 |        "        vertical-align: top;\n",
1752 |        "    }\n",
1753 |        "\n",
1754 |        "    .dataframe thead th {\n",
1755 |        "        text-align: right;\n",
1756 |        "    }\n",
1757 |        "</style>\n",
1758 |        "<table border=\"1\" class=\"dataframe\">\n",
1759 |        "  <thead>\n",
1760 |        "    <tr style=\"text-align: right;\">\n",
1761 |        "      <th></th>\n",
1762 |        "      <th>client_id</th>\n",
1763 |        "      <th>emp_var_rate</th>\n",
1764 |        "      <th>cons_price_idx</th>\n",
1765 |        "      <th>euribor_three_months</th>\n",
1766 |        "      <th>number_employed</th>\n",
1767 |        "    </tr>\n",
1768 |        "  </thead>\n",
1769 |        "  <tbody>\n",
1770 |        "    <tr>\n",
1771 |        "      <th>0</th>\n",
1772 |        "      <td>0</td>\n",
1773 |        "      <td>1.1</td>\n",
1774 |        "      <td>93.994</td>\n",
1775 |        "      <td>4.857</td>\n",
1776 |        "      <td>5191.0</td>\n",
1777 |        "    </tr>\n",
1778 |        "    <tr>\n",
1779 |        "      <th>1</th>\n",
1780 |        "      <td>1</td>\n",
1781 |        "      <td>1.1</td>\n",
1782 |        "      <td>93.994</td>\n",
1783 |        "      <td>4.857</td>\n",
1784 |        "      <td>5191.0</td>\n",
1785 |        "    </tr>\n",
1786 |        "    <tr>\n",
1787 |        "      <th>2</th>\n",
1788 |        "      <td>2</td>\n",
1789 |        "      <td>1.1</td>\n",
1790 |        "      <td>93.994</td>\n",
1791 |        "      <td>4.857</td>\n",
1792 |        "      <td>5191.0</td>\n",
1793 |        "    </tr>\n",
1794 |        "    <tr>\n",
1795 |        "      <th>3</th>\n",
1796 |        "      <td>3</td>\n",
1797 |        "      <td>1.1</td>\n",
1798 |        "      <td>93.994</td>\n",
1799 |        "      <td>4.857</td>\n",
1800 |        "      <td>5191.0</td>\n",
1801 |        "    </tr>\n",
1802 |        "    <tr>\n",
1803 |        "      <th>4</th>\n",
1804 |        "      <td>4</td>\n",
1805 |        "      <td>1.1</td>\n",
1806 |        "      <td>93.994</td>\n",
1807 |        "      <td>4.857</td>\n",
1808 |        "      <td>5191.0</td>\n",
1809 |        "    </tr>\n",
1810 |        "    <tr>\n",
1811 |        "      <th>...</th>\n",
1812 |        "      <td>...</td>\n",
1813 |        "      <td>...</td>\n",
1814 |        "      <td>...</td>\n",
1815 |        "      <td>...</td>\n",
1816 |        "      <td>...</td>\n",
1817 |        "    </tr>\n",
1818 |        "    <tr>\n",
1819 |        "      <th>41183</th>\n",
1820 |        "      <td>41183</td>\n",
1821 |        "      <td>-1.1</td>\n",
1822 |        "      <td>94.767</td>\n",
1823 |        "      <td>1.028</td>\n",
1824 |        "      <td>4963.6</td>\n",
1825 |        "    </tr>\n",
1826 |        "    <tr>\n",
1827 |        "      <th>41184</th>\n",
1828 |        "      <td>41184</td>\n",
1829 |        "      <td>-1.1</td>\n",
1830 |        "      <td>94.767</td>\n",
1831 |        "      <td>1.028</td>\n",
1832 |        "      <td>4963.6</td>\n",
1833 |        "    </tr>\n",
1834 |        "    <tr>\n",
1835 |        "      <th>41185</th>\n",
1836 |        "      <td>41185</td>\n",
1837 |        "      <td>-1.1</td>\n",
1838 |        "      <td>94.767</td>\n",
1839 |        "      <td>1.028</td>\n",
1840 |        "      <td>4963.6</td>\n",
1841 |        "    </tr>\n",
1842 |        "    <tr>\n",
1843 |        "      <th>41186</th>\n",
1844 |        "      <td>41186</td>\n",
1845 |        "      <td>-1.1</td>\n",
1846 |        "      <td>94.767</td>\n",
1847 |        "      <td>1.028</td>\n",
1848 |        "      <td>4963.6</td>\n",
1849 |        "    </tr>\n",
1850 |        "    <tr>\n",
1851 |        "      <th>41187</th>\n",
1852 |        "      <td>41187</td>\n",
1853 |        "      <td>-1.1</td>\n",
1854 |        "      <td>94.767</td>\n",
1855 |        "      <td>1.028</td>\n",
1856 |        "      <td>4963.6</td>\n",
1857 |        "    </tr>\n",
1858 |        "  </tbody>\n",
1859 |        "</table>\n",
1860 |        "<p>41188 rows × 5 columns</p>\n",
1861 |        "</div>"
1862 |       ],
1863 |       "text/plain": [
1864 |        "       client_id  emp_var_rate  cons_price_idx  euribor_three_months  \\\n",
1865 |        "0              0           1.1          93.994                 4.857   \n",
1866 |        "1              1           1.1          93.994                 4.857   \n",
1867 |        "2              2           1.1          93.994                 4.857   \n",
1868 |        "3              3           1.1          93.994                 4.857   \n",
1869 |        "4              4           1.1          93.994                 4.857   \n",
1870 |        "...          ...           ...             ...                   ...   \n",
1871 |        "41183      41183          -1.1          94.767                 1.028   \n",
1872 |        "41184      41184          -1.1          94.767                 1.028   \n",
1873 |        "41185      41185          -1.1          94.767                 1.028   \n",
1874 |        "41186      41186          -1.1          94.767                 1.028   \n",
1875 |        "41187      41187          -1.1          94.767                 1.028   \n",
1876 |        "\n",
1877 |        "       number_employed  \n",
1878 |        "0               5191.0  \n",
1879 |        "1               5191.0  \n",
1880 |        "2               5191.0  \n",
1881 |        "3               5191.0  \n",
1882 |        "4               5191.0  \n",
1883 |        "...                ...  \n",
1884 |        "41183           4963.6  \n",
1885 |        "41184           4963.6  \n",
1886 |        "41185           4963.6  \n",
1887 |        "41186           4963.6  \n",
1888 |        "41187           4963.6  \n",
1889 |        "\n",
1890 |        "[41188 rows x 5 columns]"
1891 |       ]
1892 |      },
1893 |      "execution_count": 23,
1894 |      "metadata": {},
1895 |      "output_type": "execute_result"
1896 |     }
1897 |    ],
1898 |    "source": [
1899 |     "# Read economics_data from database to pandas dataframe\n",
1900 |     "economics_df = pd.read_sql_query('SELECT * FROM economics_data', engine)\n",
1901 |     "economics_df"
1902 |    ]
1903 |   },
1904 |   {
1905 |    "cell_type": "markdown",
1906 |    "id": "c2aa7e6a",
1907 |    "metadata": {},
1908 |    "source": [
1909 |     "***The end!***"
1910 |    ]
1911 |   },
1912 |   {
1913 |    "cell_type": "code",
1914 |    "execution_count": null,
1915 |    "id": "37c76c77",
1916 |    "metadata": {},
1917 |    "outputs": [],
1918 |    "source": []
1919 |   },
1920 |   {
1921 |    "cell_type": "code",
1922 |    "execution_count": null,
1923 |    "id": "554ab333",
1924 |    "metadata": {},
1925 |    "outputs": [],
1926 |    "source": []
1927 |   }
1928 |  ],
1929 |  "metadata": {
1930 |   "editor": "DataCamp Workspace",
1931 |   "kernelspec": {
1932 |    "display_name": "Python 3 (ipykernel)",
1933 |    "language": "python",
1934 |    "name": "python3"
1935 |   },
1936 |   "language_info": {
1937 |    "codemirror_mode": {
1938 |     "name": "ipython",
1939 |     "version": 3
1940 |    },
1941 |    "file_extension": ".py",
1942 |    "mimetype": "text/x-python",
1943 |    "name": "python",
1944 |    "nbconvert_exporter": "python",
1945 |    "pygments_lexer": "ipython3",
1946 |    "version": "3.11.5"
1947 |   }
1948 |  },
1949 |  "nbformat": 4,
1950 |  "nbformat_minor": 5
1951 | }
1952 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Designing a Bank Marketing Database-checkpoint.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "02077ee3-e1e4-4fc5-8de1-16e987afa5fb",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "![piggy_bank](piggy_bank.jpg)\n",
   9 |     "\n",
  10 |     "<br>\n",
  11 |     "\n",
  12 |     "Personal loans are a lucrative revenue stream for banks. The typical interest rate of a two year loan in the United Kingdom is [around 10%](https://www.experian.com/blogs/ask-experian/whats-a-good-interest-rate-for-a-personal-loan/). This might not sound like a lot, but in September 2022 alone UK consumers borrowed [around £1.5 billion](https://www.ukfinance.org.uk/system/files/2022-12/Household%20Finance%20Review%202022%20Q3-%20Final.pdf), which would mean approximately £300 million in interest generated by banks over two years!\n",
  13 |     "\n",
  14 |     "You have been asked to work with a bank to clean and store the data they collected as part of a recent marketing campaign, which aimed to get customers to take out a personal loan. They plan to conduct more marketing campaigns going forward so would like you to set up a PostgreSQL database to store this campaign's data, designing the schema in a way that would allow data from future campaigns to be easily imported. \n",
  15 |     "\n",
  16 |     "They have supplied you with a csv file called `\"bank_marketing.csv\"`, which you will need to clean, reformat, and split, in order to save separate files based on the tables you will create. It is recommended to use `pandas` for these tasks.\n",
  17 |     "\n",
  18 |     "Lastly, you will write the SQL code that the bank can execute to create the tables and populate with the data from the csv files. As the bank are quite strict about their security, you'll save SQL files as multiline string variables that they can then use to create the database on their end. \n",
  19 |     "\n",
  20 |     "You have been asked to design a database that will have three tables:\n",
  21 |     "\n",
  22 |     "## client\n",
  23 |     "\n",
  24 |     "| column | data type | description | original column in dataset |\n",
  25 |     "|--------|-----------|-------------|----------------------------|\n",
  26 |     "| `id` | `serial` | Client ID - primary key | `client_id` |\n",
  27 |     "| `age` | `integer` | Client's age in years | `age` |\n",
  28 |     "| `job` | `text` | Client's type of job | `job` |\n",
  29 |     "| `marital` | `text` | Client's marital status | `marital` | \n",
  30 |     "| `education` | `text` | Client's level of education | `education` |\n",
  31 |     "| `credit_default` | `boolean` | Whether the client's credit is in default | `credit_default` |\n",
  32 |     "| `housing` | `boolean` | Whether the client has an existing housing loan (mortgage) | `housing` | \n",
  33 |     "| `loan` | `boolean` | Whether the client has an existing personal loan | `loan` |\n",
  34 |     "\n",
  35 |     "<br>\n",
  36 |     "\n",
  37 |     "## campaign\n",
  38 |     "\n",
  39 |     "| column | data type | description | original column in dataset |\n",
  40 |     "|--------|-----------|-------------|----------------------------|\n",
  41 |     "| `campaign_id` | `serial` | Campaign ID - primary key | N/A - new column |\n",
  42 |     "| `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |\n",
  43 |     "| `number_contacts` | `integer` | Number of contact attempts to the client in the current campaign | `campaign` |\n",
  44 |     "| `contact_duration` | `integer` | Last contact duration in seconds | `duration` |\n",
  45 |     "| `pdays` | `integer` | Number of days since contact in previous campaign (`999` = not previously contacted) | `pdays` |\n",
  46 |     "| `previous_campaign_contacts` | `integer` | Number of contact attempts to the client in the previous campaign | `previous` |\n",
  47 |     "| `previous_outcome` | `boolean` | Outcome of the previous campaign | `poutcome` |\n",
  48 |     "| `campaign_outcome` | `boolean` | Outcome of the current campaign | `y` |\n",
  49 |     "| `last_contact_date` | `date` | Last date the client was contacted | A combination of `day`, `month`, and the newly created `year` |\n",
  50 |     "\n",
  51 |     "<br>\n",
  52 |     "\n",
  53 |     "## economics\n",
  54 |     "\n",
  55 |     "| column | data type | description | original column in dataset |\n",
  56 |     "|--------|-----------|-------------|----------------------------|\n",
  57 |     "| `client_id` | `serial` | Client ID - references `id` in the `client` table | `client_id` |\n",
  58 |     "| `emp_var_rate` | `float` | Employment variation rate (quarterly indicator) | `emp_var_rate` |\n",
  59 |     "| `cons_price_idx` | `float` | Consumer price index (monthly indicator) | `cons_price_idx` |\n",
  60 |     "| `euribor_three_months` | `float` | Euro Interbank Offered Rate (euribor) three month rate (daily indicator) | `euribor3m` |\n",
  61 |     "| `number_employed` | `float` | Number of employees (quarterly indicator)| `nr_employed` |"
  62 |    ]
  63 |   },
  64 |   {
  65 |    "cell_type": "code",
  66 |    "execution_count": 1,
  67 |    "id": "e2edad3c-8286-4983-b5b7-35d94fd78023",
  68 |    "metadata": {
  69 |     "executionCancelledAt": null,
  70 |     "executionTime": 1057,
  71 |     "lastExecutedAt": 1686069923599,
  72 |     "lastScheduledRunId": null,
  73 |     "lastSuccessfullyExecutedCode": "# Start coding...\nimport pandas as pd\nimport numpy as np\n\n# Read in csv\nmarketing = pd.read_csv(\"bank_marketing.csv\")\n\n# Split into the three tables\nclient = marketing[[\"client_id\", \"age\", \"job\", \"marital\", \"education\", \n             \"credit_default\", \"housing\", \"loan\"]]\ncampaign = marketing[[\"client_id\", \"campaign\", \"month\", \"day\", \n               \"duration\", \"pdays\", \"previous\", \"poutcome\", \"y\"]]\neconomics = marketing[[\"client_id\", \"emp_var_rate\", \"cons_price_idx\", \n                \"euribor3m\", \"nr_employed\"]]\n\n# Rename client_id in the client table\nclient.rename(columns={\"client_id\": \"id\"}, inplace=True)\n\n# Rename duration, y, and campaign columns\ncampaign.rename(columns={\"duration\": \"contact_duration\", \n                         \"y\": \"campaign_outcome\", \n                         \"campaign\": \"number_contacts\",\n                         \"previous\": \"previous_campaign_contacts\",\n                         \"poutcome\": \"previous_outcome\"}, \n                         inplace=True)\n\n# Rename euribor3m and nr_employed\neconomics.rename(columns={\"euribor3m\": \"euribor_three_months\", \n                          \"nr_employed\": \"number_employed\"}, \n                          inplace=True)\n\n# Clean education column\nclient[\"education\"] = client[\"education\"].str.replace(\".\", \"_\")\nclient[\"education\"] = client[\"education\"].replace(\"unknown\", np.NaN)\n\n# Clean job column\nclient[\"job\"] = client[\"job\"].str.replace(\".\", \"\")\n\n# Change campaign_outcome to binary values\ncampaign[\"campaign_outcome\"] = campaign[\"campaign_outcome\"].map({\"yes\": 1, \n                                                                 \"no\": 0})\n\n# Convert poutcome to binary values\ncampaign[\"previous_outcome\"] = campaign[\"previous_outcome\"].replace(\"nonexistent\", \n                                                                    np.NaN)\ncampaign[\"previous_outcome\"] = campaign[\"previous_outcome\"].map({\"success\": 1, \n                                                                 \"failure\": 0})\n\n# Add campaign_id column\ncampaign[\"campaign_id\"] = 1\n\n# Capitalize month and day columns\ncampaign[\"month\"] = campaign[\"month\"].str.capitalize()\n\n# Add year column\ncampaign[\"year\"] = \"2022\"\n\n# Convert day to string\ncampaign[\"day\"] = campaign[\"day\"].astype(str)\n\n# Add last_contact_date column\ncampaign[\"last_contact_date\"] = campaign[\"year\"] + \"-\" + campaign[\"month\"] + \"-\" + campaign[\"day\"]\n\n# Convert to datetime\ncampaign[\"last_contact_date\"] = pd.to_datetime(campaign[\"last_contact_date\"], \n                                               format=\"%Y-%b-%d\")\n\n# Drop unneccessary columns\ncampaign.drop(columns=[\"month\", \"day\", \"year\"], inplace=True)\n\n# Save tables to individual csv files\nclient.to_csv(\"client.csv\", index=False)\ncampaign.to_csv(\"campaign.csv\", index=False)\neconomics.to_csv(\"economics.csv\", index=False)\n\n# Store and print database_design\nclient_table = \"\"\"CREATE TABLE client\n(\n    id SERIAL PRIMARY KEY,\n    age INTEGER,\n    job TEXT,\n    marital TEXT,\n    education TEXT,\n    credit_default BOOLEAN,\n    housing BOOLEAN,\n    loan BOOLEAN\n);\n\\copy client from 'client.csv' DELIMITER ',' CSV HEADER\n\"\"\"\n\ncampaign_table = \"\"\"CREATE TABLE campaign\n(\n    campaign_id SERIAL PRIMARY KEY,\n    client_id SERIAL references client (id),\n    number_contacts INTEGER,\n    contact_duration INTEGER,\n    pdays INTEGER,\n    previous_campaign_contacts INTEGER,\n    previous_outcome BOOLEAN,\n    campaign_outcome BOOLEAN,\n    last_contact_date DATE    \n);\n\\copy campaign from 'campaign.csv' DELIMITER ',' CSV HEADER\n\"\"\"\n\neconomics_table = \"\"\"CREATE TABLE economics\n(\n    client_id SERIAL references client (id),\n    emp_var_rate FLOAT,\n    cons_price_idx FLOAT,\n    euribor_three_months FLOAT,\n    number_employed FLOAT\n);\n\\copy economics from 'economics.csv' DELIMITER ',' CSV HEADER\n\"\"\""
  74 |    },
  75 |    "outputs": [],
  76 |    "source": [
  77 |     "# Import libraries\n",
  78 |     "import pandas as pd\n",
  79 |     "import numpy as np\n",
  80 |     "\n",
  81 |     "# suppress warnings from final output\n",
  82 |     "import warnings\n",
  83 |     "warnings.simplefilter(\"ignore\")\n",
  84 |     "\n",
  85 |     "# Start coding here..."
  86 |    ]
  87 |   },
  88 |   {
  89 |    "cell_type": "markdown",
  90 |    "id": "9d94086e",
  91 |    "metadata": {},
  92 |    "source": [
  93 |     "#### Instruction 1:  Read in bank_marketing.csv as a pandas DataFrame."
  94 |    ]
  95 |   },
  96 |   {
  97 |    "cell_type": "code",
  98 |    "execution_count": 2,
  99 |    "id": "ac57dc04",
 100 |    "metadata": {},
 101 |    "outputs": [
 102 |     {
 103 |      "data": {
 104 |       "text/html": [
 105 |        "<div>\n",
 106 |        "<style scoped>\n",
 107 |        "    .dataframe tbody tr th:only-of-type {\n",
 108 |        "        vertical-align: middle;\n",
 109 |        "    }\n",
 110 |        "\n",
 111 |        "    .dataframe tbody tr th {\n",
 112 |        "        vertical-align: top;\n",
 113 |        "    }\n",
 114 |        "\n",
 115 |        "    .dataframe thead th {\n",
 116 |        "        text-align: right;\n",
 117 |        "    }\n",
 118 |        "</style>\n",
 119 |        "<table border=\"1\" class=\"dataframe\">\n",
 120 |        "  <thead>\n",
 121 |        "    <tr style=\"text-align: right;\">\n",
 122 |        "      <th></th>\n",
 123 |        "      <th>client_id</th>\n",
 124 |        "      <th>age</th>\n",
 125 |        "      <th>job</th>\n",
 126 |        "      <th>marital</th>\n",
 127 |        "      <th>education</th>\n",
 128 |        "      <th>credit_default</th>\n",
 129 |        "      <th>housing</th>\n",
 130 |        "      <th>loan</th>\n",
 131 |        "      <th>contact</th>\n",
 132 |        "      <th>month</th>\n",
 133 |        "      <th>...</th>\n",
 134 |        "      <th>campaign</th>\n",
 135 |        "      <th>pdays</th>\n",
 136 |        "      <th>previous</th>\n",
 137 |        "      <th>poutcome</th>\n",
 138 |        "      <th>emp_var_rate</th>\n",
 139 |        "      <th>cons_price_idx</th>\n",
 140 |        "      <th>cons_conf_idx</th>\n",
 141 |        "      <th>euribor3m</th>\n",
 142 |        "      <th>nr_employed</th>\n",
 143 |        "      <th>y</th>\n",
 144 |        "    </tr>\n",
 145 |        "  </thead>\n",
 146 |        "  <tbody>\n",
 147 |        "    <tr>\n",
 148 |        "      <th>0</th>\n",
 149 |        "      <td>0</td>\n",
 150 |        "      <td>56</td>\n",
 151 |        "      <td>housemaid</td>\n",
 152 |        "      <td>married</td>\n",
 153 |        "      <td>basic.4y</td>\n",
 154 |        "      <td>no</td>\n",
 155 |        "      <td>no</td>\n",
 156 |        "      <td>no</td>\n",
 157 |        "      <td>telephone</td>\n",
 158 |        "      <td>may</td>\n",
 159 |        "      <td>...</td>\n",
 160 |        "      <td>1</td>\n",
 161 |        "      <td>999</td>\n",
 162 |        "      <td>0</td>\n",
 163 |        "      <td>nonexistent</td>\n",
 164 |        "      <td>1.1</td>\n",
 165 |        "      <td>93.994</td>\n",
 166 |        "      <td>-36.4</td>\n",
 167 |        "      <td>4.857</td>\n",
 168 |        "      <td>5191.0</td>\n",
 169 |        "      <td>no</td>\n",
 170 |        "    </tr>\n",
 171 |        "    <tr>\n",
 172 |        "      <th>1</th>\n",
 173 |        "      <td>1</td>\n",
 174 |        "      <td>57</td>\n",
 175 |        "      <td>services</td>\n",
 176 |        "      <td>married</td>\n",
 177 |        "      <td>high.school</td>\n",
 178 |        "      <td>unknown</td>\n",
 179 |        "      <td>no</td>\n",
 180 |        "      <td>no</td>\n",
 181 |        "      <td>telephone</td>\n",
 182 |        "      <td>may</td>\n",
 183 |        "      <td>...</td>\n",
 184 |        "      <td>1</td>\n",
 185 |        "      <td>999</td>\n",
 186 |        "      <td>0</td>\n",
 187 |        "      <td>nonexistent</td>\n",
 188 |        "      <td>1.1</td>\n",
 189 |        "      <td>93.994</td>\n",
 190 |        "      <td>-36.4</td>\n",
 191 |        "      <td>4.857</td>\n",
 192 |        "      <td>5191.0</td>\n",
 193 |        "      <td>no</td>\n",
 194 |        "    </tr>\n",
 195 |        "    <tr>\n",
 196 |        "      <th>2</th>\n",
 197 |        "      <td>2</td>\n",
 198 |        "      <td>37</td>\n",
 199 |        "      <td>services</td>\n",
 200 |        "      <td>married</td>\n",
 201 |        "      <td>high.school</td>\n",
 202 |        "      <td>no</td>\n",
 203 |        "      <td>yes</td>\n",
 204 |        "      <td>no</td>\n",
 205 |        "      <td>telephone</td>\n",
 206 |        "      <td>may</td>\n",
 207 |        "      <td>...</td>\n",
 208 |        "      <td>1</td>\n",
 209 |        "      <td>999</td>\n",
 210 |        "      <td>0</td>\n",
 211 |        "      <td>nonexistent</td>\n",
 212 |        "      <td>1.1</td>\n",
 213 |        "      <td>93.994</td>\n",
 214 |        "      <td>-36.4</td>\n",
 215 |        "      <td>4.857</td>\n",
 216 |        "      <td>5191.0</td>\n",
 217 |        "      <td>no</td>\n",
 218 |        "    </tr>\n",
 219 |        "    <tr>\n",
 220 |        "      <th>3</th>\n",
 221 |        "      <td>3</td>\n",
 222 |        "      <td>40</td>\n",
 223 |        "      <td>admin.</td>\n",
 224 |        "      <td>married</td>\n",
 225 |        "      <td>basic.6y</td>\n",
 226 |        "      <td>no</td>\n",
 227 |        "      <td>no</td>\n",
 228 |        "      <td>no</td>\n",
 229 |        "      <td>telephone</td>\n",
 230 |        "      <td>may</td>\n",
 231 |        "      <td>...</td>\n",
 232 |        "      <td>1</td>\n",
 233 |        "      <td>999</td>\n",
 234 |        "      <td>0</td>\n",
 235 |        "      <td>nonexistent</td>\n",
 236 |        "      <td>1.1</td>\n",
 237 |        "      <td>93.994</td>\n",
 238 |        "      <td>-36.4</td>\n",
 239 |        "      <td>4.857</td>\n",
 240 |        "      <td>5191.0</td>\n",
 241 |        "      <td>no</td>\n",
 242 |        "    </tr>\n",
 243 |        "    <tr>\n",
 244 |        "      <th>4</th>\n",
 245 |        "      <td>4</td>\n",
 246 |        "      <td>56</td>\n",
 247 |        "      <td>services</td>\n",
 248 |        "      <td>married</td>\n",
 249 |        "      <td>high.school</td>\n",
 250 |        "      <td>no</td>\n",
 251 |        "      <td>no</td>\n",
 252 |        "      <td>yes</td>\n",
 253 |        "      <td>telephone</td>\n",
 254 |        "      <td>may</td>\n",
 255 |        "      <td>...</td>\n",
 256 |        "      <td>1</td>\n",
 257 |        "      <td>999</td>\n",
 258 |        "      <td>0</td>\n",
 259 |        "      <td>nonexistent</td>\n",
 260 |        "      <td>1.1</td>\n",
 261 |        "      <td>93.994</td>\n",
 262 |        "      <td>-36.4</td>\n",
 263 |        "      <td>4.857</td>\n",
 264 |        "      <td>5191.0</td>\n",
 265 |        "      <td>no</td>\n",
 266 |        "    </tr>\n",
 267 |        "  </tbody>\n",
 268 |        "</table>\n",
 269 |        "<p>5 rows × 22 columns</p>\n",
 270 |        "</div>"
 271 |       ],
 272 |       "text/plain": [
 273 |        "   client_id  age        job  marital    education credit_default housing  \\\n",
 274 |        "0          0   56  housemaid  married     basic.4y             no      no   \n",
 275 |        "1          1   57   services  married  high.school        unknown      no   \n",
 276 |        "2          2   37   services  married  high.school             no     yes   \n",
 277 |        "3          3   40     admin.  married     basic.6y             no      no   \n",
 278 |        "4          4   56   services  married  high.school             no      no   \n",
 279 |        "\n",
 280 |        "  loan    contact month  ...  campaign  pdays  previous     poutcome  \\\n",
 281 |        "0   no  telephone   may  ...         1    999         0  nonexistent   \n",
 282 |        "1   no  telephone   may  ...         1    999         0  nonexistent   \n",
 283 |        "2   no  telephone   may  ...         1    999         0  nonexistent   \n",
 284 |        "3   no  telephone   may  ...         1    999         0  nonexistent   \n",
 285 |        "4  yes  telephone   may  ...         1    999         0  nonexistent   \n",
 286 |        "\n",
 287 |        "   emp_var_rate cons_price_idx  cons_conf_idx  euribor3m  nr_employed   y  \n",
 288 |        "0           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 289 |        "1           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 290 |        "2           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 291 |        "3           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 292 |        "4           1.1         93.994          -36.4      4.857       5191.0  no  \n",
 293 |        "\n",
 294 |        "[5 rows x 22 columns]"
 295 |       ]
 296 |      },
 297 |      "execution_count": 2,
 298 |      "metadata": {},
 299 |      "output_type": "execute_result"
 300 |     }
 301 |    ],
 302 |    "source": [
 303 |     "# Read in bank_marketing.csv as a pandas DataFrame.\n",
 304 |     "bank_marketing_data = pd.read_csv(\"bank_marketing.csv\")\n",
 305 |     "bank_marketing_data.head()"
 306 |    ]
 307 |   },
 308 |   {
 309 |    "cell_type": "markdown",
 310 |    "id": "11195a84",
 311 |    "metadata": {},
 312 |    "source": [
 313 |     "#### Instruction 2:\n",
 314 |     "\n",
 315 |     "Split the data into three DataFrames using information provided about the desired tables as your guide: \n",
 316 |     "\n",
 317 |     "* one with information about the client, \n",
 318 |     "* another containing campaign data, and \n",
 319 |     "* a third to store information about economics at the time of the campaign."
 320 |    ]
 321 |   },
 322 |   {
 323 |    "cell_type": "code",
 324 |    "execution_count": 3,
 325 |    "id": "6707773f",
 326 |    "metadata": {},
 327 |    "outputs": [
 328 |     {
 329 |      "name": "stdout",
 330 |      "output_type": "stream",
 331 |      "text": [
 332 |       "Client Data:\n",
 333 |       "   client_id  age        job  marital    education credit_default housing loan\n",
 334 |       "0          0   56  housemaid  married     basic.4y             no      no   no\n",
 335 |       "1          1   57   services  married  high.school        unknown      no   no\n",
 336 |       "2          2   37   services  married  high.school             no     yes   no\n",
 337 |       "3          3   40     admin.  married     basic.6y             no      no   no\n",
 338 |       "4          4   56   services  married  high.school             no      no  yes\n",
 339 |       "\n",
 340 |       "Campaign Data:\n",
 341 |       "   client_id  campaign  duration  pdays  previous     poutcome   y month  day\n",
 342 |       "0          0         1       261    999         0  nonexistent  no   may   13\n",
 343 |       "1          1         1       149    999         0  nonexistent  no   may   19\n",
 344 |       "2          2         1       226    999         0  nonexistent  no   may   23\n",
 345 |       "3          3         1       151    999         0  nonexistent  no   may   27\n",
 346 |       "4          4         1       307    999         0  nonexistent  no   may    3\n",
 347 |       "\n",
 348 |       "Economic Data:\n",
 349 |       "   client_id  emp_var_rate  cons_price_idx  euribor3m  nr_employed\n",
 350 |       "0          0           1.1          93.994      4.857       5191.0\n",
 351 |       "1          1           1.1          93.994      4.857       5191.0\n",
 352 |       "2          2           1.1          93.994      4.857       5191.0\n",
 353 |       "3          3           1.1          93.994      4.857       5191.0\n",
 354 |       "4          4           1.1          93.994      4.857       5191.0\n"
 355 |      ]
 356 |     }
 357 |    ],
 358 |    "source": [
 359 |     "# Define the columns for each table\n",
 360 |     "client_columns = ['client_id', 'age', 'job', 'marital', 'education', 'credit_default', 'housing', 'loan']\n",
 361 |     "campaign_columns = ['client_id', 'campaign', 'duration', 'pdays', 'previous', 'poutcome', 'y', 'month', 'day']\n",
 362 |     "economic_columns = ['client_id', 'emp_var_rate', 'cons_price_idx', 'euribor3m', 'nr_employed']\n",
 363 |     "\n",
 364 |     "# Create Dataframes for each table\n",
 365 |     "client = bank_marketing_data[client_columns]\n",
 366 |     "campaign = bank_marketing_data[campaign_columns]\n",
 367 |     "economics = bank_marketing_data[economic_columns]\n",
 368 |     "\n",
 369 |     "# Print the first few rows of each DataFrame to verify the split\n",
 370 |     "print(\"Client Data:\")\n",
 371 |     "print(client.head())\n",
 372 |     "\n",
 373 |     "print(\"\\nCampaign Data:\")\n",
 374 |     "print(campaign.head())\n",
 375 |     "\n",
 376 |     "print(\"\\nEconomic Data:\")\n",
 377 |     "print(economics.head())"
 378 |    ]
 379 |   },
 380 |   {
 381 |    "cell_type": "markdown",
 382 |    "id": "a77f1836",
 383 |    "metadata": {},
 384 |    "source": [
 385 |     "#### Instruction 3:\n",
 386 |     "\n",
 387 |     "Rename the column \"client_id\" to \"id\" in client (leave as-is in the other subsets); \"duration\" to \"contact_duration\",\n",
 388 |     "\"previous\" to \"previous_campaign_contacts\", \"y\" to \"campaign_outcome\", \"poutcome\" to \"previous_outcome\", and \n",
 389 |     "\"campaign\" to \"number_contacts\" in campaign; and \"euribor3m\" to \"euribor_three_months\" and \"nr_employed\" to\n",
 390 |     "\"number_employed\" in economics."
 391 |    ]
 392 |   },
 393 |   {
 394 |    "cell_type": "code",
 395 |    "execution_count": 4,
 396 |    "id": "6f49b4d5",
 397 |    "metadata": {},
 398 |    "outputs": [
 399 |     {
 400 |      "name": "stdout",
 401 |      "output_type": "stream",
 402 |      "text": [
 403 |       "Client Data:\n",
 404 |       "   id  age        job  marital    education credit_default housing loan\n",
 405 |       "0   0   56  housemaid  married     basic.4y             no      no   no\n",
 406 |       "1   1   57   services  married  high.school        unknown      no   no\n",
 407 |       "2   2   37   services  married  high.school             no     yes   no\n",
 408 |       "3   3   40     admin.  married     basic.6y             no      no   no\n",
 409 |       "4   4   56   services  married  high.school             no      no  yes\n",
 410 |       "\n",
 411 |       "Campaign Data:\n",
 412 |       "   client_id  number_contacts  contact_duration  pdays  \\\n",
 413 |       "0          0                1               261    999   \n",
 414 |       "1          1                1               149    999   \n",
 415 |       "2          2                1               226    999   \n",
 416 |       "3          3                1               151    999   \n",
 417 |       "4          4                1               307    999   \n",
 418 |       "\n",
 419 |       "   previous_campaign_contacts previous_outcome campaign_outcome month  day  \n",
 420 |       "0                           0      nonexistent               no   may   13  \n",
 421 |       "1                           0      nonexistent               no   may   19  \n",
 422 |       "2                           0      nonexistent               no   may   23  \n",
 423 |       "3                           0      nonexistent               no   may   27  \n",
 424 |       "4                           0      nonexistent               no   may    3  \n",
 425 |       "\n",
 426 |       "Economic Data:\n",
 427 |       "   client_id  emp_var_rate  cons_price_idx  euribor_three_months  \\\n",
 428 |       "0          0           1.1          93.994                 4.857   \n",
 429 |       "1          1           1.1          93.994                 4.857   \n",
 430 |       "2          2           1.1          93.994                 4.857   \n",
 431 |       "3          3           1.1          93.994                 4.857   \n",
 432 |       "4          4           1.1          93.994                 4.857   \n",
 433 |       "\n",
 434 |       "   number_employed  \n",
 435 |       "0           5191.0  \n",
 436 |       "1           5191.0  \n",
 437 |       "2           5191.0  \n",
 438 |       "3           5191.0  \n",
 439 |       "4           5191.0  \n"
 440 |      ]
 441 |     }
 442 |    ],
 443 |    "source": [
 444 |     "# Rename columns in the client DataFrame\n",
 445 |     "client.rename(columns={'client_id': 'id'}, inplace=True)\n",
 446 |     "\n",
 447 |     "# Rename columns in the campaign DataFrame\n",
 448 |     "campaign.rename(columns={'duration': 'contact_duration',\n",
 449 |     "                             'previous': 'previous_campaign_contacts',\n",
 450 |     "                             'y': 'campaign_outcome',\n",
 451 |     "                             'poutcome': 'previous_outcome',\n",
 452 |     "                             'campaign': 'number_contacts'}, inplace=True)\n",
 453 |     "\n",
 454 |     "# Rename columns in the economic DataFrame\n",
 455 |     "economics.rename(columns={'euribor3m': 'euribor_three_months',\n",
 456 |     "                             'nr_employed': 'number_employed'}, inplace=True)\n",
 457 |     "\n",
 458 |     "# Print the first few rows of each DataFrame to verify the renaming of columns\n",
 459 |     "print(\"Client Data:\")\n",
 460 |     "print(client.head())\n",
 461 |     "\n",
 462 |     "print(\"\\nCampaign Data:\")\n",
 463 |     "print(campaign.head())\n",
 464 |     "\n",
 465 |     "print(\"\\nEconomic Data:\")\n",
 466 |     "print(economics.head())"
 467 |    ]
 468 |   },
 469 |   {
 470 |    "cell_type": "markdown",
 471 |    "id": "08f13310",
 472 |    "metadata": {},
 473 |    "source": [
 474 |     "#### Instruction 4:\n",
 475 |     "\n",
 476 |     "Clean the \"education\" column, changing \".\" to \"_\" and \"unknown\" to NumPy's null values."
 477 |    ]
 478 |   },
 479 |   {
 480 |    "cell_type": "code",
 481 |    "execution_count": 5,
 482 |    "id": "f9a9ddd1",
 483 |    "metadata": {},
 484 |    "outputs": [],
 485 |    "source": [
 486 |     "# Replace \".\" with \"_\"\n",
 487 |     "client['education'] = client['education'].str.replace('.', '_')\n",
 488 |     "\n",
 489 |     "# Replace \"unknown\" with Numpy's NaN\n",
 490 |     "client['education'].replace('unknown', np.nan, inplace=True)"
 491 |    ]
 492 |   },
 493 |   {
 494 |    "cell_type": "code",
 495 |    "execution_count": 6,
 496 |    "id": "61f72247",
 497 |    "metadata": {},
 498 |    "outputs": [
 499 |     {
 500 |      "name": "stdout",
 501 |      "output_type": "stream",
 502 |      "text": [
 503 |       "The 'education' column has been successfully cleaned.\n"
 504 |      ]
 505 |     }
 506 |    ],
 507 |    "source": [
 508 |     "# Check for \".\" in the \"education\" column\n",
 509 |     "dot_count = client['education'].str.contains('\\.').sum()\n",
 510 |     "\n",
 511 |     "# Check for \"unknown\" in the \"education\" column\n",
 512 |     "unknown_count = (client['education'] == 'unknown').sum()\n",
 513 |     "\n",
 514 |     "if dot_count == 0 and unknown_count == 0:\n",
 515 |     "    print(\"The 'education' column has been successfully cleaned.\")\n",
 516 |     "else:\n",
 517 |     "    print(f\"The 'education' column still contains {dot_count} '.' and {unknown_count} 'unknown' values.\")"
 518 |    ]
 519 |   },
 520 |   {
 521 |    "cell_type": "markdown",
 522 |    "id": "7606b988",
 523 |    "metadata": {},
 524 |    "source": [
 525 |     "#### Instrucion 5:\n",
 526 |     "\n",
 527 |     "Remove periods from the \"job\" column."
 528 |    ]
 529 |   },
 530 |   {
 531 |    "cell_type": "code",
 532 |    "execution_count": 7,
 533 |    "id": "196ea3e0",
 534 |    "metadata": {},
 535 |    "outputs": [
 536 |     {
 537 |      "name": "stdout",
 538 |      "output_type": "stream",
 539 |      "text": [
 540 |       "Periods have been successfully removed from the 'job' column.\n"
 541 |      ]
 542 |     }
 543 |    ],
 544 |    "source": [
 545 |     "# Remove \".\" from the \"job\" column\n",
 546 |     "client['job'] = client['job'].str.replace('.', '')\n",
 547 |     "\n",
 548 |     "# Check if periods are removed\n",
 549 |     "if '.' not in client['job'].values:\n",
 550 |     "    print(\"Periods have been successfully removed from the 'job' column.\")\n",
 551 |     "else:\n",
 552 |     "    print(\"Periods still exist in the job column.\")"
 553 |    ]
 554 |   },
 555 |   {
 556 |    "cell_type": "markdown",
 557 |    "id": "9a0eb339",
 558 |    "metadata": {},
 559 |    "source": [
 560 |     "#### Instruction 6:\n",
 561 |     "\n",
 562 |     "Convert \"success\" and \"failure\" in the \"previous_outcome\" and \"campaign_outcome\" columns to binary (1 or 0), \n",
 563 |     "along with the changing \"nonexistent\" to NumPy's null values in \"previous_outcome\"."
 564 |    ]
 565 |   },
 566 |   {
 567 |    "cell_type": "code",
 568 |    "execution_count": 8,
 569 |    "id": "fe0ace15",
 570 |    "metadata": {},
 571 |    "outputs": [
 572 |     {
 573 |      "name": "stdout",
 574 |      "output_type": "stream",
 575 |      "text": [
 576 |       "Conversions and changes in 'previous_outcome' and 'campaign_outcome' columns were successfully applied.\n"
 577 |      ]
 578 |     }
 579 |    ],
 580 |    "source": [
 581 |     "# Convert \"success\" and \"failure\" to binary (1 or 0) in \"previous_outcome\" and \"campaign_outcome\"\n",
 582 |     "campaign['previous_outcome'] = campaign['previous_outcome'].map({'success': 1, 'failure': 0})\n",
 583 |     "campaign['campaign_outcome'] = campaign['campaign_outcome'].map({'success': 1, 'failure': 0})\n",
 584 |     "\n",
 585 |     "# Change \"nonexistent\" to Numpy's NaN in \"previous_outcome\"\n",
 586 |     "campaign['previous_outcome'].replace('nonexistent', np.nan, inplace=True)\n",
 587 |     "\n",
 588 |     "# Check if the conversions and changes are applied\n",
 589 |     "if ('success' not in campaign['previous_outcome'].values and\n",
 590 |     "    'failure' not in campaign['previous_outcome'].values and\n",
 591 |     "    'nonexistent' not in campaign['previous_outcome'].values and\n",
 592 |     "    'success' not in campaign['campaign_outcome'].values and\n",
 593 |     "    'failure' not in campaign['campaign_outcome'].values):\n",
 594 |     "    print(\"Conversions and changes in 'previous_outcome' and 'campaign_outcome' columns were successfully applied.\")\n",
 595 |     "else:\n",
 596 |     "    print(\"Conversions and changes were not fully applied in 'previous_outcome' and 'campaign_outcome' columns.\")"
 597 |    ]
 598 |   },
 599 |   {
 600 |    "cell_type": "markdown",
 601 |    "id": "fa0e6239",
 602 |    "metadata": {},
 603 |    "source": [
 604 |     "#### Instruction 7:\n",
 605 |     "\n",
 606 |     "Add a column called campaign_id in campaign, where all rows have a value of 1."
 607 |    ]
 608 |   },
 609 |   {
 610 |    "cell_type": "code",
 611 |    "execution_count": 9,
 612 |    "id": "6b4ba272",
 613 |    "metadata": {
 614 |     "scrolled": true
 615 |    },
 616 |    "outputs": [
 617 |     {
 618 |      "data": {
 619 |       "text/html": [
 620 |        "<div>\n",
 621 |        "<style scoped>\n",
 622 |        "    .dataframe tbody tr th:only-of-type {\n",
 623 |        "        vertical-align: middle;\n",
 624 |        "    }\n",
 625 |        "\n",
 626 |        "    .dataframe tbody tr th {\n",
 627 |        "        vertical-align: top;\n",
 628 |        "    }\n",
 629 |        "\n",
 630 |        "    .dataframe thead th {\n",
 631 |        "        text-align: right;\n",
 632 |        "    }\n",
 633 |        "</style>\n",
 634 |        "<table border=\"1\" class=\"dataframe\">\n",
 635 |        "  <thead>\n",
 636 |        "    <tr style=\"text-align: right;\">\n",
 637 |        "      <th></th>\n",
 638 |        "      <th>client_id</th>\n",
 639 |        "      <th>number_contacts</th>\n",
 640 |        "      <th>contact_duration</th>\n",
 641 |        "      <th>pdays</th>\n",
 642 |        "      <th>previous_campaign_contacts</th>\n",
 643 |        "      <th>previous_outcome</th>\n",
 644 |        "      <th>campaign_outcome</th>\n",
 645 |        "      <th>month</th>\n",
 646 |        "      <th>day</th>\n",
 647 |        "      <th>campaign_id</th>\n",
 648 |        "    </tr>\n",
 649 |        "  </thead>\n",
 650 |        "  <tbody>\n",
 651 |        "    <tr>\n",
 652 |        "      <th>0</th>\n",
 653 |        "      <td>0</td>\n",
 654 |        "      <td>1</td>\n",
 655 |        "      <td>261</td>\n",
 656 |        "      <td>999</td>\n",
 657 |        "      <td>0</td>\n",
 658 |        "      <td>NaN</td>\n",
 659 |        "      <td>NaN</td>\n",
 660 |        "      <td>may</td>\n",
 661 |        "      <td>13</td>\n",
 662 |        "      <td>1</td>\n",
 663 |        "    </tr>\n",
 664 |        "    <tr>\n",
 665 |        "      <th>1</th>\n",
 666 |        "      <td>1</td>\n",
 667 |        "      <td>1</td>\n",
 668 |        "      <td>149</td>\n",
 669 |        "      <td>999</td>\n",
 670 |        "      <td>0</td>\n",
 671 |        "      <td>NaN</td>\n",
 672 |        "      <td>NaN</td>\n",
 673 |        "      <td>may</td>\n",
 674 |        "      <td>19</td>\n",
 675 |        "      <td>1</td>\n",
 676 |        "    </tr>\n",
 677 |        "    <tr>\n",
 678 |        "      <th>2</th>\n",
 679 |        "      <td>2</td>\n",
 680 |        "      <td>1</td>\n",
 681 |        "      <td>226</td>\n",
 682 |        "      <td>999</td>\n",
 683 |        "      <td>0</td>\n",
 684 |        "      <td>NaN</td>\n",
 685 |        "      <td>NaN</td>\n",
 686 |        "      <td>may</td>\n",
 687 |        "      <td>23</td>\n",
 688 |        "      <td>1</td>\n",
 689 |        "    </tr>\n",
 690 |        "    <tr>\n",
 691 |        "      <th>3</th>\n",
 692 |        "      <td>3</td>\n",
 693 |        "      <td>1</td>\n",
 694 |        "      <td>151</td>\n",
 695 |        "      <td>999</td>\n",
 696 |        "      <td>0</td>\n",
 697 |        "      <td>NaN</td>\n",
 698 |        "      <td>NaN</td>\n",
 699 |        "      <td>may</td>\n",
 700 |        "      <td>27</td>\n",
 701 |        "      <td>1</td>\n",
 702 |        "    </tr>\n",
 703 |        "    <tr>\n",
 704 |        "      <th>4</th>\n",
 705 |        "      <td>4</td>\n",
 706 |        "      <td>1</td>\n",
 707 |        "      <td>307</td>\n",
 708 |        "      <td>999</td>\n",
 709 |        "      <td>0</td>\n",
 710 |        "      <td>NaN</td>\n",
 711 |        "      <td>NaN</td>\n",
 712 |        "      <td>may</td>\n",
 713 |        "      <td>3</td>\n",
 714 |        "      <td>1</td>\n",
 715 |        "    </tr>\n",
 716 |        "  </tbody>\n",
 717 |        "</table>\n",
 718 |        "</div>"
 719 |       ],
 720 |       "text/plain": [
 721 |        "   client_id  number_contacts  contact_duration  pdays  \\\n",
 722 |        "0          0                1               261    999   \n",
 723 |        "1          1                1               149    999   \n",
 724 |        "2          2                1               226    999   \n",
 725 |        "3          3                1               151    999   \n",
 726 |        "4          4                1               307    999   \n",
 727 |        "\n",
 728 |        "   previous_campaign_contacts  previous_outcome  campaign_outcome month  day  \\\n",
 729 |        "0                           0               NaN               NaN   may   13   \n",
 730 |        "1                           0               NaN               NaN   may   19   \n",
 731 |        "2                           0               NaN               NaN   may   23   \n",
 732 |        "3                           0               NaN               NaN   may   27   \n",
 733 |        "4                           0               NaN               NaN   may    3   \n",
 734 |        "\n",
 735 |        "   campaign_id  \n",
 736 |        "0            1  \n",
 737 |        "1            1  \n",
 738 |        "2            1  \n",
 739 |        "3            1  \n",
 740 |        "4            1  "
 741 |       ]
 742 |      },
 743 |      "execution_count": 9,
 744 |      "metadata": {},
 745 |      "output_type": "execute_result"
 746 |     }
 747 |    ],
 748 |    "source": [
 749 |     "# Add a new column 'campaign_id' with all values set to 1\n",
 750 |     "campaign['campaign_id'] = 1\n",
 751 |     "\n",
 752 |     "# Check if column was succsssfully created\n",
 753 |     "campaign.head()"
 754 |    ]
 755 |   },
 756 |   {
 757 |    "cell_type": "code",
 758 |    "execution_count": null,
 759 |    "id": "a15240cc",
 760 |    "metadata": {},
 761 |    "outputs": [],
 762 |    "source": []
 763 |   },
 764 |   {
 765 |    "cell_type": "markdown",
 766 |    "id": "c91d964e",
 767 |    "metadata": {},
 768 |    "source": [
 769 |     "#### Instruction 8:\n",
 770 |     "\n",
 771 |     "Create a datetime column called last_contact_date, in the format of \"year-month-day\", where the year is 2022, and the month and day values are taken from the \"month\" and \"day\" columns."
 772 |    ]
 773 |   },
 774 |   {
 775 |    "cell_type": "code",
 776 |    "execution_count": 10,
 777 |    "id": "f473a21f",
 778 |    "metadata": {},
 779 |    "outputs": [
 780 |     {
 781 |      "name": "stdout",
 782 |      "output_type": "stream",
 783 |      "text": [
 784 |       "The 'last_contact_date' column was successfully created.\n"
 785 |      ]
 786 |     },
 787 |     {
 788 |      "data": {
 789 |       "text/html": [
 790 |        "<div>\n",
 791 |        "<style scoped>\n",
 792 |        "    .dataframe tbody tr th:only-of-type {\n",
 793 |        "        vertical-align: middle;\n",
 794 |        "    }\n",
 795 |        "\n",
 796 |        "    .dataframe tbody tr th {\n",
 797 |        "        vertical-align: top;\n",
 798 |        "    }\n",
 799 |        "\n",
 800 |        "    .dataframe thead th {\n",
 801 |        "        text-align: right;\n",
 802 |        "    }\n",
 803 |        "</style>\n",
 804 |        "<table border=\"1\" class=\"dataframe\">\n",
 805 |        "  <thead>\n",
 806 |        "    <tr style=\"text-align: right;\">\n",
 807 |        "      <th></th>\n",
 808 |        "      <th>client_id</th>\n",
 809 |        "      <th>number_contacts</th>\n",
 810 |        "      <th>contact_duration</th>\n",
 811 |        "      <th>pdays</th>\n",
 812 |        "      <th>previous_campaign_contacts</th>\n",
 813 |        "      <th>previous_outcome</th>\n",
 814 |        "      <th>campaign_outcome</th>\n",
 815 |        "      <th>month</th>\n",
 816 |        "      <th>day</th>\n",
 817 |        "      <th>campaign_id</th>\n",
 818 |        "      <th>year</th>\n",
 819 |        "      <th>last_contact_date</th>\n",
 820 |        "    </tr>\n",
 821 |        "  </thead>\n",
 822 |        "  <tbody>\n",
 823 |        "    <tr>\n",
 824 |        "      <th>0</th>\n",
 825 |        "      <td>0</td>\n",
 826 |        "      <td>1</td>\n",
 827 |        "      <td>261</td>\n",
 828 |        "      <td>999</td>\n",
 829 |        "      <td>0</td>\n",
 830 |        "      <td>NaN</td>\n",
 831 |        "      <td>NaN</td>\n",
 832 |        "      <td>may</td>\n",
 833 |        "      <td>13</td>\n",
 834 |        "      <td>1</td>\n",
 835 |        "      <td>2022</td>\n",
 836 |        "      <td>2022-05-13</td>\n",
 837 |        "    </tr>\n",
 838 |        "    <tr>\n",
 839 |        "      <th>1</th>\n",
 840 |        "      <td>1</td>\n",
 841 |        "      <td>1</td>\n",
 842 |        "      <td>149</td>\n",
 843 |        "      <td>999</td>\n",
 844 |        "      <td>0</td>\n",
 845 |        "      <td>NaN</td>\n",
 846 |        "      <td>NaN</td>\n",
 847 |        "      <td>may</td>\n",
 848 |        "      <td>19</td>\n",
 849 |        "      <td>1</td>\n",
 850 |        "      <td>2022</td>\n",
 851 |        "      <td>2022-05-19</td>\n",
 852 |        "    </tr>\n",
 853 |        "    <tr>\n",
 854 |        "      <th>2</th>\n",
 855 |        "      <td>2</td>\n",
 856 |        "      <td>1</td>\n",
 857 |        "      <td>226</td>\n",
 858 |        "      <td>999</td>\n",
 859 |        "      <td>0</td>\n",
 860 |        "      <td>NaN</td>\n",
 861 |        "      <td>NaN</td>\n",
 862 |        "      <td>may</td>\n",
 863 |        "      <td>23</td>\n",
 864 |        "      <td>1</td>\n",
 865 |        "      <td>2022</td>\n",
 866 |        "      <td>2022-05-23</td>\n",
 867 |        "    </tr>\n",
 868 |        "    <tr>\n",
 869 |        "      <th>3</th>\n",
 870 |        "      <td>3</td>\n",
 871 |        "      <td>1</td>\n",
 872 |        "      <td>151</td>\n",
 873 |        "      <td>999</td>\n",
 874 |        "      <td>0</td>\n",
 875 |        "      <td>NaN</td>\n",
 876 |        "      <td>NaN</td>\n",
 877 |        "      <td>may</td>\n",
 878 |        "      <td>27</td>\n",
 879 |        "      <td>1</td>\n",
 880 |        "      <td>2022</td>\n",
 881 |        "      <td>2022-05-27</td>\n",
 882 |        "    </tr>\n",
 883 |        "    <tr>\n",
 884 |        "      <th>4</th>\n",
 885 |        "      <td>4</td>\n",
 886 |        "      <td>1</td>\n",
 887 |        "      <td>307</td>\n",
 888 |        "      <td>999</td>\n",
 889 |        "      <td>0</td>\n",
 890 |        "      <td>NaN</td>\n",
 891 |        "      <td>NaN</td>\n",
 892 |        "      <td>may</td>\n",
 893 |        "      <td>3</td>\n",
 894 |        "      <td>1</td>\n",
 895 |        "      <td>2022</td>\n",
 896 |        "      <td>2022-05-03</td>\n",
 897 |        "    </tr>\n",
 898 |        "  </tbody>\n",
 899 |        "</table>\n",
 900 |        "</div>"
 901 |       ],
 902 |       "text/plain": [
 903 |        "   client_id  number_contacts  contact_duration  pdays  \\\n",
 904 |        "0          0                1               261    999   \n",
 905 |        "1          1                1               149    999   \n",
 906 |        "2          2                1               226    999   \n",
 907 |        "3          3                1               151    999   \n",
 908 |        "4          4                1               307    999   \n",
 909 |        "\n",
 910 |        "   previous_campaign_contacts  previous_outcome  campaign_outcome month  day  \\\n",
 911 |        "0                           0               NaN               NaN   may   13   \n",
 912 |        "1                           0               NaN               NaN   may   19   \n",
 913 |        "2                           0               NaN               NaN   may   23   \n",
 914 |        "3                           0               NaN               NaN   may   27   \n",
 915 |        "4                           0               NaN               NaN   may    3   \n",
 916 |        "\n",
 917 |        "   campaign_id  year last_contact_date  \n",
 918 |        "0            1  2022        2022-05-13  \n",
 919 |        "1            1  2022        2022-05-19  \n",
 920 |        "2            1  2022        2022-05-23  \n",
 921 |        "3            1  2022        2022-05-27  \n",
 922 |        "4            1  2022        2022-05-03  "
 923 |       ]
 924 |      },
 925 |      "execution_count": 10,
 926 |      "metadata": {},
 927 |      "output_type": "execute_result"
 928 |     }
 929 |    ],
 930 |    "source": [
 931 |     "# Add the \"year\" column with the value 2022 to the 'campaign_data' DataFrame\n",
 932 |     "campaign['year'] = 2022\n",
 933 |     "\n",
 934 |     "# Create a datetime column \"last_contact_date\"\n",
 935 |     "campaign['last_contact_date'] = pd.to_datetime(\n",
 936 |     "    campaign['year'].astype(str) + '-' +\n",
 937 |     "    campaign['month'].astype(str) + '-' +\n",
 938 |     "    campaign['day'].astype(str),\n",
 939 |     "    errors='coerce'\n",
 940 |     ")\n",
 941 |     "\n",
 942 |     "# Check if the \"last_contact_date\" column was successfully created.\n",
 943 |     "if 'last_contact_date' in campaign.columns:\n",
 944 |     "    print(\"The 'last_contact_date' column was successfully created.\")\n",
 945 |     "else:\n",
 946 |     "    print(\"The 'last_contact_date' column was not created.\")\n",
 947 |     "\n",
 948 |     "# Print the first few rows of 'campaign_data' DataFrame to verify the creation of the date column\n",
 949 |     "campaign.head()"
 950 |    ]
 951 |   },
 952 |   {
 953 |    "cell_type": "code",
 954 |    "execution_count": 11,
 955 |    "id": "d7ef5f26",
 956 |    "metadata": {},
 957 |    "outputs": [
 958 |     {
 959 |      "data": {
 960 |       "text/plain": [
 961 |        "dtype('<M8[ns]')"
 962 |       ]
 963 |      },
 964 |      "execution_count": 11,
 965 |      "metadata": {},
 966 |      "output_type": "execute_result"
 967 |     }
 968 |    ],
 969 |    "source": [
 970 |     "# Confirm the last_contact_date is in datetime format\n",
 971 |     "campaign['last_contact_date'].dtype"
 972 |    ]
 973 |   },
 974 |   {
 975 |    "cell_type": "markdown",
 976 |    "id": "bf870a11",
 977 |    "metadata": {},
 978 |    "source": [
 979 |     "#### Instruction 9:\n",
 980 |     "\n",
 981 |     "Remove any redundant data that might have been used to create new columns, ensuring the columns in each subset of the data match the table displayed in the notebook."
 982 |    ]
 983 |   },
 984 |   {
 985 |    "cell_type": "code",
 986 |    "execution_count": 12,
 987 |    "id": "82415610",
 988 |    "metadata": {},
 989 |    "outputs": [
 990 |     {
 991 |      "name": "stdout",
 992 |      "output_type": "stream",
 993 |      "text": [
 994 |       "Client Data:\n",
 995 |       "   id  age        job  marital    education credit_default housing loan\n",
 996 |       "0   0   56  housemaid  married     basic_4y             no      no   no\n",
 997 |       "1   1   57   services  married  high_school        unknown      no   no\n",
 998 |       "2   2   37   services  married  high_school             no     yes   no\n",
 999 |       "3   3   40      admin  married     basic_6y             no      no   no\n",
1000 |       "4   4   56   services  married  high_school             no      no  yes\n",
1001 |       "\n",
1002 |       "Campaign Data:\n",
1003 |       "   client_id  number_contacts  contact_duration  pdays  \\\n",
1004 |       "0          0                1               261    999   \n",
1005 |       "1          1                1               149    999   \n",
1006 |       "2          2                1               226    999   \n",
1007 |       "3          3                1               151    999   \n",
1008 |       "4          4                1               307    999   \n",
1009 |       "\n",
1010 |       "   previous_campaign_contacts  previous_outcome  campaign_outcome  \\\n",
1011 |       "0                           0               NaN               NaN   \n",
1012 |       "1                           0               NaN               NaN   \n",
1013 |       "2                           0               NaN               NaN   \n",
1014 |       "3                           0               NaN               NaN   \n",
1015 |       "4                           0               NaN               NaN   \n",
1016 |       "\n",
1017 |       "   campaign_id last_contact_date  \n",
1018 |       "0            1        2022-05-13  \n",
1019 |       "1            1        2022-05-19  \n",
1020 |       "2            1        2022-05-23  \n",
1021 |       "3            1        2022-05-27  \n",
1022 |       "4            1        2022-05-03  \n",
1023 |       "\n",
1024 |       "Economic Data:\n",
1025 |       "   client_id  emp_var_rate  cons_price_idx  euribor_three_months  \\\n",
1026 |       "0          0           1.1          93.994                 4.857   \n",
1027 |       "1          1           1.1          93.994                 4.857   \n",
1028 |       "2          2           1.1          93.994                 4.857   \n",
1029 |       "3          3           1.1          93.994                 4.857   \n",
1030 |       "4          4           1.1          93.994                 4.857   \n",
1031 |       "\n",
1032 |       "   number_employed  \n",
1033 |       "0           5191.0  \n",
1034 |       "1           5191.0  \n",
1035 |       "2           5191.0  \n",
1036 |       "3           5191.0  \n",
1037 |       "4           5191.0  \n"
1038 |      ]
1039 |     }
1040 |    ],
1041 |    "source": [
1042 |     "# Remove \"day\", \"month\", and \"year\" columns that were used to create the \"last_contact_date\" column\n",
1043 |     "campaign = campaign.drop(['day', 'month', 'year'], axis=1)\n",
1044 |     "\n",
1045 |     "# Print the first few rows of each of the DataFrame to verify they match tables expected to be created.\n",
1046 |     "print(\"Client Data:\")\n",
1047 |     "print(client.head())\n",
1048 |     "\n",
1049 |     "print(\"\\nCampaign Data:\")\n",
1050 |     "print(campaign.head())\n",
1051 |     "\n",
1052 |     "print(\"\\nEconomic Data:\")\n",
1053 |     "print(economics.head())"
1054 |    ]
1055 |   },
1056 |   {
1057 |    "cell_type": "markdown",
1058 |    "id": "e3f3db6f",
1059 |    "metadata": {},
1060 |    "source": [
1061 |     "#### Instruction 10:\n",
1062 |     "\n",
1063 |     "Save the three DataFrames to csv files without an index as client.csv, campaign.csv, and economics.csv respectively."
1064 |    ]
1065 |   },
1066 |   {
1067 |    "cell_type": "code",
1068 |    "execution_count": 13,
1069 |    "id": "4b7e681b",
1070 |    "metadata": {},
1071 |    "outputs": [],
1072 |    "source": [
1073 |     "# Save client data to 'client.csv'\n",
1074 |     "client.to_csv('client.csv', index=False)\n",
1075 |     "\n",
1076 |     "# Save campaign data to 'campaign.csv'\n",
1077 |     "campaign.to_csv('campaign.csv', index=False)\n",
1078 |     "\n",
1079 |     "# Save economics data to 'economics.csv'\n",
1080 |     "economics.to_csv('economics.csv', index=False)"
1081 |    ]
1082 |   },
1083 |   {
1084 |    "cell_type": "markdown",
1085 |    "id": "ff7acff3",
1086 |    "metadata": {},
1087 |    "source": [
1088 |     "#### Instruction 11:\n",
1089 |     "\n",
1090 |     "Create a Python variable called client_table, containing SQL code as a multi-line string to create a table called client using values from client.csv."
1091 |    ]
1092 |   },
1093 |   {
1094 |    "cell_type": "code",
1095 |    "execution_count": 14,
1096 |    "id": "c6075770",
1097 |    "metadata": {},
1098 |    "outputs": [],
1099 |    "source": [
1100 |     "# Specify the SQL code as a multi-line string\n",
1101 |     "client_table = \"\"\"\n",
1102 |     "CREATE TABLE client (\n",
1103 |     "    id SERIAL PRIMARY KEY,\n",
1104 |     "    age INT,\n",
1105 |     "    job TEXT,\n",
1106 |     "    marital TEXT,\n",
1107 |     "    education TEXT,\n",
1108 |     "    credit_default BOOLEAN,\n",
1109 |     "    housing BOOLEAN,\n",
1110 |     "    loan BOOLEAN\n",
1111 |     ");\n",
1112 |     "\n",
1113 |     "-- Copy data from client.csv into the client table\n",
1114 |     "\\copy client FROM 'client.csv' DELIMITER ',' CSV HEADER;\n",
1115 |     "\"\"\""
1116 |    ]
1117 |   },
1118 |   {
1119 |    "cell_type": "markdown",
1120 |    "id": "a2b2aea1",
1121 |    "metadata": {},
1122 |    "source": [
1123 |     "#### Instruction 12:\n",
1124 |     "\n",
1125 |     "Create a Python variable called campaign_table, containing SQL code as a multi-line string to create a table called campaign using values from campaign.csv."
1126 |    ]
1127 |   },
1128 |   {
1129 |    "cell_type": "code",
1130 |    "execution_count": 15,
1131 |    "id": "519c0758",
1132 |    "metadata": {},
1133 |    "outputs": [],
1134 |    "source": [
1135 |     "# Specify SQL code as a multi-line string\n",
1136 |     "campaign_table = \"\"\"\"\n",
1137 |     "CREATE TABLE campaign (\n",
1138 |     "    campaign_id SERIAL PRIMARY KEY,\n",
1139 |     "    client_id SERIAL REFERENCES client (id),\n",
1140 |     "    number_contacts INT,\n",
1141 |     "    contact_duration INT,\n",
1142 |     "    pdays INT,\n",
1143 |     "    previous_campaign_contacts INT,\n",
1144 |     "    previous_outcome BOOLEAN,\n",
1145 |     "    campaign_outcome BOOLEAN,\n",
1146 |     "    last_contact_date DATE\n",
1147 |     ");\n",
1148 |     "\n",
1149 |     "-- Copy data from campaign.csv into the campaign table\n",
1150 |     "\\copy campaign FROM 'campaign.csv' DELIMETER ',' CSV HEADER;\n",
1151 |     "\"\"\""
1152 |    ]
1153 |   },
1154 |   {
1155 |    "cell_type": "markdown",
1156 |    "id": "1595e7ae",
1157 |    "metadata": {},
1158 |    "source": [
1159 |     "#### Instruction 13:\n",
1160 |     "\n",
1161 |     "Create a Python variable called economics_table, containing SQL code as a multi-line string to create a table called economics using values from economics.csv."
1162 |    ]
1163 |   },
1164 |   {
1165 |    "cell_type": "code",
1166 |    "execution_count": 16,
1167 |    "id": "3d86fc77",
1168 |    "metadata": {},
1169 |    "outputs": [],
1170 |    "source": [
1171 |     "# Specify SQL code as a multi-line string\n",
1172 |     "economics_table = \"\"\"\n",
1173 |     "CREATE TABLE economics (\n",
1174 |     "    client_id SERIAL REFERENCES client (id),\n",
1175 |     "    emp_var_rate FLOAT,\n",
1176 |     "    cons_price_idx FLOAT,\n",
1177 |     "    euribor_three_months FLOAT,\n",
1178 |     "    number_employed FLOAT\n",
1179 |     ");\n",
1180 |     "\n",
1181 |     "-- Copy data from economics.csv into the economics table\n",
1182 |     "\\copy economics FROM 'economics.csv' DELIMETER ',' CSV HEADER;\n",
1183 |     "\"\"\""
1184 |    ]
1185 |   },
1186 |   {
1187 |    "cell_type": "code",
1188 |    "execution_count": null,
1189 |    "id": "b19ff9bb",
1190 |    "metadata": {},
1191 |    "outputs": [],
1192 |    "source": []
1193 |   },
1194 |   {
1195 |    "cell_type": "markdown",
1196 |    "id": "2d2629c2",
1197 |    "metadata": {},
1198 |    "source": [
1199 |     "### Completeing This Part Yourself. \n",
1200 |     "\n",
1201 |     "**Loading the three different dataframes into a PostgreSQL database you created.** \n",
1202 |     "\n",
1203 |     "Before Completing this part, you will download and PostgreSQL and create a after setting it up following the [instructions here](https://www.youtube.com/watch?v=0n41UTkOBb0&t=298s), you go ahead and create a database called marketing_campaign."
1204 |    ]
1205 |   },
1206 |   {
1207 |    "cell_type": "code",
1208 |    "execution_count": 17,
1209 |    "id": "72bc6161",
1210 |    "metadata": {},
1211 |    "outputs": [],
1212 |    "source": [
1213 |     "# import library\n",
1214 |     "import psycopg2\n",
1215 |     "from sqlalchemy import create_engine\n",
1216 |     "\n",
1217 |     "# Specify parameters\n",
1218 |     "# You get these parameters from your postgresSQL DBMS server properties\n",
1219 |     "hostname = 'localhost'\n",
1220 |     "database = 'marketing_campaign'\n",
1221 |     "username = 'postgres'\n",
1222 |     "password = '#####' #pass your password\n",
1223 |     "port_id = 5432\n",
1224 |     "\n",
1225 |     "# Provide connection parameters\n",
1226 |     "conn = psycopg2.connect(\n",
1227 |     "    host=hostname,\n",
1228 |     "    database=database,\n",
1229 |     "    user=username,\n",
1230 |     "    password=password,\n",
1231 |     "    port=port_id\n",
1232 |     ")\n"
1233 |    ]
1234 |   },
1235 |   {
1236 |    "cell_type": "code",
1237 |    "execution_count": 18,
1238 |    "id": "b654c953",
1239 |    "metadata": {},
1240 |    "outputs": [],
1241 |    "source": [
1242 |     "# Create engine\n",
1243 |     "# connection string: dialect+driver://user:password@server/database\n",
1244 |     "engine = create_engine('postgresql+psycopg2://postgres:promzy2099@localhost/marketing_campaign')"
1245 |    ]
1246 |   },
1247 |   {
1248 |    "cell_type": "code",
1249 |    "execution_count": 19,
1250 |    "id": "012765f4",
1251 |    "metadata": {},
1252 |    "outputs": [
1253 |     {
1254 |      "data": {
1255 |       "text/plain": [
1256 |        "Engine(postgresql+psycopg2://postgres:***@localhost/marketing_campaign)"
1257 |       ]
1258 |      },
1259 |      "execution_count": 19,
1260 |      "metadata": {},
1261 |      "output_type": "execute_result"
1262 |     }
1263 |    ],
1264 |    "source": [
1265 |     "# Display engine info\n",
1266 |     "engine"
1267 |    ]
1268 |   },
1269 |   {
1270 |    "cell_type": "code",
1271 |    "execution_count": 20,
1272 |    "id": "10423b7f",
1273 |    "metadata": {},
1274 |    "outputs": [
1275 |     {
1276 |      "data": {
1277 |       "text/plain": [
1278 |        "188"
1279 |       ]
1280 |      },
1281 |      "execution_count": 20,
1282 |      "metadata": {},
1283 |      "output_type": "execute_result"
1284 |     }
1285 |    ],
1286 |    "source": [
1287 |     "# Insert data from client DataFrame into the \"client_data\" table\n",
1288 |     "client.to_sql('client_data', engine, if_exists='replace', index=False)\n",
1289 |     "\n",
1290 |     "# Insert data from campaign DataFrame into the \"campaign_data\" table\n",
1291 |     "campaign.to_sql('campaign_data', engine, if_exists='replace', index=False)\n",
1292 |     "\n",
1293 |     "# Insert data from economics DataFrame into the \"economics_data\" table\n",
1294 |     "economics.to_sql('economics_data', engine, if_exists='replace', index=False)"
1295 |    ]
1296 |   },
1297 |   {
1298 |    "cell_type": "code",
1299 |    "execution_count": 21,
1300 |    "id": "2fc5b801",
1301 |    "metadata": {},
1302 |    "outputs": [
1303 |     {
1304 |      "data": {
1305 |       "text/html": [
1306 |        "<div>\n",
1307 |        "<style scoped>\n",
1308 |        "    .dataframe tbody tr th:only-of-type {\n",
1309 |        "        vertical-align: middle;\n",
1310 |        "    }\n",
1311 |        "\n",
1312 |        "    .dataframe tbody tr th {\n",
1313 |        "        vertical-align: top;\n",
1314 |        "    }\n",
1315 |        "\n",
1316 |        "    .dataframe thead th {\n",
1317 |        "        text-align: right;\n",
1318 |        "    }\n",
1319 |        "</style>\n",
1320 |        "<table border=\"1\" class=\"dataframe\">\n",
1321 |        "  <thead>\n",
1322 |        "    <tr style=\"text-align: right;\">\n",
1323 |        "      <th></th>\n",
1324 |        "      <th>id</th>\n",
1325 |        "      <th>age</th>\n",
1326 |        "      <th>job</th>\n",
1327 |        "      <th>marital</th>\n",
1328 |        "      <th>education</th>\n",
1329 |        "      <th>credit_default</th>\n",
1330 |        "      <th>housing</th>\n",
1331 |        "      <th>loan</th>\n",
1332 |        "    </tr>\n",
1333 |        "  </thead>\n",
1334 |        "  <tbody>\n",
1335 |        "    <tr>\n",
1336 |        "      <th>0</th>\n",
1337 |        "      <td>0</td>\n",
1338 |        "      <td>56</td>\n",
1339 |        "      <td>housemaid</td>\n",
1340 |        "      <td>married</td>\n",
1341 |        "      <td>basic_4y</td>\n",
1342 |        "      <td>no</td>\n",
1343 |        "      <td>no</td>\n",
1344 |        "      <td>no</td>\n",
1345 |        "    </tr>\n",
1346 |        "    <tr>\n",
1347 |        "      <th>1</th>\n",
1348 |        "      <td>1</td>\n",
1349 |        "      <td>57</td>\n",
1350 |        "      <td>services</td>\n",
1351 |        "      <td>married</td>\n",
1352 |        "      <td>high_school</td>\n",
1353 |        "      <td>unknown</td>\n",
1354 |        "      <td>no</td>\n",
1355 |        "      <td>no</td>\n",
1356 |        "    </tr>\n",
1357 |        "    <tr>\n",
1358 |        "      <th>2</th>\n",
1359 |        "      <td>2</td>\n",
1360 |        "      <td>37</td>\n",
1361 |        "      <td>services</td>\n",
1362 |        "      <td>married</td>\n",
1363 |        "      <td>high_school</td>\n",
1364 |        "      <td>no</td>\n",
1365 |        "      <td>yes</td>\n",
1366 |        "      <td>no</td>\n",
1367 |        "    </tr>\n",
1368 |        "    <tr>\n",
1369 |        "      <th>3</th>\n",
1370 |        "      <td>3</td>\n",
1371 |        "      <td>40</td>\n",
1372 |        "      <td>admin</td>\n",
1373 |        "      <td>married</td>\n",
1374 |        "      <td>basic_6y</td>\n",
1375 |        "      <td>no</td>\n",
1376 |        "      <td>no</td>\n",
1377 |        "      <td>no</td>\n",
1378 |        "    </tr>\n",
1379 |        "    <tr>\n",
1380 |        "      <th>4</th>\n",
1381 |        "      <td>4</td>\n",
1382 |        "      <td>56</td>\n",
1383 |        "      <td>services</td>\n",
1384 |        "      <td>married</td>\n",
1385 |        "      <td>high_school</td>\n",
1386 |        "      <td>no</td>\n",
1387 |        "      <td>no</td>\n",
1388 |        "      <td>yes</td>\n",
1389 |        "    </tr>\n",
1390 |        "    <tr>\n",
1391 |        "      <th>...</th>\n",
1392 |        "      <td>...</td>\n",
1393 |        "      <td>...</td>\n",
1394 |        "      <td>...</td>\n",
1395 |        "      <td>...</td>\n",
1396 |        "      <td>...</td>\n",
1397 |        "      <td>...</td>\n",
1398 |        "      <td>...</td>\n",
1399 |        "      <td>...</td>\n",
1400 |        "    </tr>\n",
1401 |        "    <tr>\n",
1402 |        "      <th>41183</th>\n",
1403 |        "      <td>41183</td>\n",
1404 |        "      <td>73</td>\n",
1405 |        "      <td>retired</td>\n",
1406 |        "      <td>married</td>\n",
1407 |        "      <td>professional_course</td>\n",
1408 |        "      <td>no</td>\n",
1409 |        "      <td>yes</td>\n",
1410 |        "      <td>no</td>\n",
1411 |        "    </tr>\n",
1412 |        "    <tr>\n",
1413 |        "      <th>41184</th>\n",
1414 |        "      <td>41184</td>\n",
1415 |        "      <td>46</td>\n",
1416 |        "      <td>blue-collar</td>\n",
1417 |        "      <td>married</td>\n",
1418 |        "      <td>professional_course</td>\n",
1419 |        "      <td>no</td>\n",
1420 |        "      <td>no</td>\n",
1421 |        "      <td>no</td>\n",
1422 |        "    </tr>\n",
1423 |        "    <tr>\n",
1424 |        "      <th>41185</th>\n",
1425 |        "      <td>41185</td>\n",
1426 |        "      <td>56</td>\n",
1427 |        "      <td>retired</td>\n",
1428 |        "      <td>married</td>\n",
1429 |        "      <td>university_degree</td>\n",
1430 |        "      <td>no</td>\n",
1431 |        "      <td>yes</td>\n",
1432 |        "      <td>no</td>\n",
1433 |        "    </tr>\n",
1434 |        "    <tr>\n",
1435 |        "      <th>41186</th>\n",
1436 |        "      <td>41186</td>\n",
1437 |        "      <td>44</td>\n",
1438 |        "      <td>technician</td>\n",
1439 |        "      <td>married</td>\n",
1440 |        "      <td>professional_course</td>\n",
1441 |        "      <td>no</td>\n",
1442 |        "      <td>no</td>\n",
1443 |        "      <td>no</td>\n",
1444 |        "    </tr>\n",
1445 |        "    <tr>\n",
1446 |        "      <th>41187</th>\n",
1447 |        "      <td>41187</td>\n",
1448 |        "      <td>74</td>\n",
1449 |        "      <td>retired</td>\n",
1450 |        "      <td>married</td>\n",
1451 |        "      <td>professional_course</td>\n",
1452 |        "      <td>no</td>\n",
1453 |        "      <td>yes</td>\n",
1454 |        "      <td>no</td>\n",
1455 |        "    </tr>\n",
1456 |        "  </tbody>\n",
1457 |        "</table>\n",
1458 |        "<p>41188 rows × 8 columns</p>\n",
1459 |        "</div>"
1460 |       ],
1461 |       "text/plain": [
1462 |        "          id  age          job  marital            education credit_default  \\\n",
1463 |        "0          0   56    housemaid  married             basic_4y             no   \n",
1464 |        "1          1   57     services  married          high_school        unknown   \n",
1465 |        "2          2   37     services  married          high_school             no   \n",
1466 |        "3          3   40        admin  married             basic_6y             no   \n",
1467 |        "4          4   56     services  married          high_school             no   \n",
1468 |        "...      ...  ...          ...      ...                  ...            ...   \n",
1469 |        "41183  41183   73      retired  married  professional_course             no   \n",
1470 |        "41184  41184   46  blue-collar  married  professional_course             no   \n",
1471 |        "41185  41185   56      retired  married    university_degree             no   \n",
1472 |        "41186  41186   44   technician  married  professional_course             no   \n",
1473 |        "41187  41187   74      retired  married  professional_course             no   \n",
1474 |        "\n",
1475 |        "      housing loan  \n",
1476 |        "0          no   no  \n",
1477 |        "1          no   no  \n",
1478 |        "2         yes   no  \n",
1479 |        "3          no   no  \n",
1480 |        "4          no  yes  \n",
1481 |        "...       ...  ...  \n",
1482 |        "41183     yes   no  \n",
1483 |        "41184      no   no  \n",
1484 |        "41185     yes   no  \n",
1485 |        "41186      no   no  \n",
1486 |        "41187     yes   no  \n",
1487 |        "\n",
1488 |        "[41188 rows x 8 columns]"
1489 |       ]
1490 |      },
1491 |      "execution_count": 21,
1492 |      "metadata": {},
1493 |      "output_type": "execute_result"
1494 |     }
1495 |    ],
1496 |    "source": [
1497 |     "# Confirm if all three tables were successfully created \n",
1498 |     "# and if all the data was successfully inserted into the three different tables\n",
1499 |     "\n",
1500 |     "# Read client_data from database to pandas dataframe\n",
1501 |     "client_df = pd.read_sql_query('SELECT * FROM client_data', engine)\n",
1502 |     "client_df"
1503 |    ]
1504 |   },
1505 |   {
1506 |    "cell_type": "code",
1507 |    "execution_count": 22,
1508 |    "id": "3f83cdb5",
1509 |    "metadata": {},
1510 |    "outputs": [
1511 |     {
1512 |      "data": {
1513 |       "text/html": [
1514 |        "<div>\n",
1515 |        "<style scoped>\n",
1516 |        "    .dataframe tbody tr th:only-of-type {\n",
1517 |        "        vertical-align: middle;\n",
1518 |        "    }\n",
1519 |        "\n",
1520 |        "    .dataframe tbody tr th {\n",
1521 |        "        vertical-align: top;\n",
1522 |        "    }\n",
1523 |        "\n",
1524 |        "    .dataframe thead th {\n",
1525 |        "        text-align: right;\n",
1526 |        "    }\n",
1527 |        "</style>\n",
1528 |        "<table border=\"1\" class=\"dataframe\">\n",
1529 |        "  <thead>\n",
1530 |        "    <tr style=\"text-align: right;\">\n",
1531 |        "      <th></th>\n",
1532 |        "      <th>client_id</th>\n",
1533 |        "      <th>number_contacts</th>\n",
1534 |        "      <th>contact_duration</th>\n",
1535 |        "      <th>pdays</th>\n",
1536 |        "      <th>previous_campaign_contacts</th>\n",
1537 |        "      <th>previous_outcome</th>\n",
1538 |        "      <th>campaign_outcome</th>\n",
1539 |        "      <th>campaign_id</th>\n",
1540 |        "      <th>last_contact_date</th>\n",
1541 |        "    </tr>\n",
1542 |        "  </thead>\n",
1543 |        "  <tbody>\n",
1544 |        "    <tr>\n",
1545 |        "      <th>0</th>\n",
1546 |        "      <td>0</td>\n",
1547 |        "      <td>1</td>\n",
1548 |        "      <td>261</td>\n",
1549 |        "      <td>999</td>\n",
1550 |        "      <td>0</td>\n",
1551 |        "      <td>NaN</td>\n",
1552 |        "      <td>None</td>\n",
1553 |        "      <td>1</td>\n",
1554 |        "      <td>2022-05-13</td>\n",
1555 |        "    </tr>\n",
1556 |        "    <tr>\n",
1557 |        "      <th>1</th>\n",
1558 |        "      <td>1</td>\n",
1559 |        "      <td>1</td>\n",
1560 |        "      <td>149</td>\n",
1561 |        "      <td>999</td>\n",
1562 |        "      <td>0</td>\n",
1563 |        "      <td>NaN</td>\n",
1564 |        "      <td>None</td>\n",
1565 |        "      <td>1</td>\n",
1566 |        "      <td>2022-05-19</td>\n",
1567 |        "    </tr>\n",
1568 |        "    <tr>\n",
1569 |        "      <th>2</th>\n",
1570 |        "      <td>2</td>\n",
1571 |        "      <td>1</td>\n",
1572 |        "      <td>226</td>\n",
1573 |        "      <td>999</td>\n",
1574 |        "      <td>0</td>\n",
1575 |        "      <td>NaN</td>\n",
1576 |        "      <td>None</td>\n",
1577 |        "      <td>1</td>\n",
1578 |        "      <td>2022-05-23</td>\n",
1579 |        "    </tr>\n",
1580 |        "    <tr>\n",
1581 |        "      <th>3</th>\n",
1582 |        "      <td>3</td>\n",
1583 |        "      <td>1</td>\n",
1584 |        "      <td>151</td>\n",
1585 |        "      <td>999</td>\n",
1586 |        "      <td>0</td>\n",
1587 |        "      <td>NaN</td>\n",
1588 |        "      <td>None</td>\n",
1589 |        "      <td>1</td>\n",
1590 |        "      <td>2022-05-27</td>\n",
1591 |        "    </tr>\n",
1592 |        "    <tr>\n",
1593 |        "      <th>4</th>\n",
1594 |        "      <td>4</td>\n",
1595 |        "      <td>1</td>\n",
1596 |        "      <td>307</td>\n",
1597 |        "      <td>999</td>\n",
1598 |        "      <td>0</td>\n",
1599 |        "      <td>NaN</td>\n",
1600 |        "      <td>None</td>\n",
1601 |        "      <td>1</td>\n",
1602 |        "      <td>2022-05-03</td>\n",
1603 |        "    </tr>\n",
1604 |        "    <tr>\n",
1605 |        "      <th>...</th>\n",
1606 |        "      <td>...</td>\n",
1607 |        "      <td>...</td>\n",
1608 |        "      <td>...</td>\n",
1609 |        "      <td>...</td>\n",
1610 |        "      <td>...</td>\n",
1611 |        "      <td>...</td>\n",
1612 |        "      <td>...</td>\n",
1613 |        "      <td>...</td>\n",
1614 |        "      <td>...</td>\n",
1615 |        "    </tr>\n",
1616 |        "    <tr>\n",
1617 |        "      <th>41183</th>\n",
1618 |        "      <td>41183</td>\n",
1619 |        "      <td>1</td>\n",
1620 |        "      <td>334</td>\n",
1621 |        "      <td>999</td>\n",
1622 |        "      <td>0</td>\n",
1623 |        "      <td>NaN</td>\n",
1624 |        "      <td>None</td>\n",
1625 |        "      <td>1</td>\n",
1626 |        "      <td>2022-11-30</td>\n",
1627 |        "    </tr>\n",
1628 |        "    <tr>\n",
1629 |        "      <th>41184</th>\n",
1630 |        "      <td>41184</td>\n",
1631 |        "      <td>1</td>\n",
1632 |        "      <td>383</td>\n",
1633 |        "      <td>999</td>\n",
1634 |        "      <td>0</td>\n",
1635 |        "      <td>NaN</td>\n",
1636 |        "      <td>None</td>\n",
1637 |        "      <td>1</td>\n",
1638 |        "      <td>2022-11-06</td>\n",
1639 |        "    </tr>\n",
1640 |        "    <tr>\n",
1641 |        "      <th>41185</th>\n",
1642 |        "      <td>41185</td>\n",
1643 |        "      <td>2</td>\n",
1644 |        "      <td>189</td>\n",
1645 |        "      <td>999</td>\n",
1646 |        "      <td>0</td>\n",
1647 |        "      <td>NaN</td>\n",
1648 |        "      <td>None</td>\n",
1649 |        "      <td>1</td>\n",
1650 |        "      <td>2022-11-24</td>\n",
1651 |        "    </tr>\n",
1652 |        "    <tr>\n",
1653 |        "      <th>41186</th>\n",
1654 |        "      <td>41186</td>\n",
1655 |        "      <td>1</td>\n",
1656 |        "      <td>442</td>\n",
1657 |        "      <td>999</td>\n",
1658 |        "      <td>0</td>\n",
1659 |        "      <td>NaN</td>\n",
1660 |        "      <td>None</td>\n",
1661 |        "      <td>1</td>\n",
1662 |        "      <td>2022-11-17</td>\n",
1663 |        "    </tr>\n",
1664 |        "    <tr>\n",
1665 |        "      <th>41187</th>\n",
1666 |        "      <td>41187</td>\n",
1667 |        "      <td>3</td>\n",
1668 |        "      <td>239</td>\n",
1669 |        "      <td>999</td>\n",
1670 |        "      <td>1</td>\n",
1671 |        "      <td>0.0</td>\n",
1672 |        "      <td>None</td>\n",
1673 |        "      <td>1</td>\n",
1674 |        "      <td>2022-11-23</td>\n",
1675 |        "    </tr>\n",
1676 |        "  </tbody>\n",
1677 |        "</table>\n",
1678 |        "<p>41188 rows × 9 columns</p>\n",
1679 |        "</div>"
1680 |       ],
1681 |       "text/plain": [
1682 |        "       client_id  number_contacts  contact_duration  pdays  \\\n",
1683 |        "0              0                1               261    999   \n",
1684 |        "1              1                1               149    999   \n",
1685 |        "2              2                1               226    999   \n",
1686 |        "3              3                1               151    999   \n",
1687 |        "4              4                1               307    999   \n",
1688 |        "...          ...              ...               ...    ...   \n",
1689 |        "41183      41183                1               334    999   \n",
1690 |        "41184      41184                1               383    999   \n",
1691 |        "41185      41185                2               189    999   \n",
1692 |        "41186      41186                1               442    999   \n",
1693 |        "41187      41187                3               239    999   \n",
1694 |        "\n",
1695 |        "       previous_campaign_contacts  previous_outcome campaign_outcome  \\\n",
1696 |        "0                               0               NaN             None   \n",
1697 |        "1                               0               NaN             None   \n",
1698 |        "2                               0               NaN             None   \n",
1699 |        "3                               0               NaN             None   \n",
1700 |        "4                               0               NaN             None   \n",
1701 |        "...                           ...               ...              ...   \n",
1702 |        "41183                           0               NaN             None   \n",
1703 |        "41184                           0               NaN             None   \n",
1704 |        "41185                           0               NaN             None   \n",
1705 |        "41186                           0               NaN             None   \n",
1706 |        "41187                           1               0.0             None   \n",
1707 |        "\n",
1708 |        "       campaign_id last_contact_date  \n",
1709 |        "0                1        2022-05-13  \n",
1710 |        "1                1        2022-05-19  \n",
1711 |        "2                1        2022-05-23  \n",
1712 |        "3                1        2022-05-27  \n",
1713 |        "4                1        2022-05-03  \n",
1714 |        "...            ...               ...  \n",
1715 |        "41183            1        2022-11-30  \n",
1716 |        "41184            1        2022-11-06  \n",
1717 |        "41185            1        2022-11-24  \n",
1718 |        "41186            1        2022-11-17  \n",
1719 |        "41187            1        2022-11-23  \n",
1720 |        "\n",
1721 |        "[41188 rows x 9 columns]"
1722 |       ]
1723 |      },
1724 |      "execution_count": 22,
1725 |      "metadata": {},
1726 |      "output_type": "execute_result"
1727 |     }
1728 |    ],
1729 |    "source": [
1730 |     "# Read campaign_data from database to pandas dataframe\n",
1731 |     "campaign_df = pd.read_sql_query('SELECT * FROM campaign_data', engine)\n",
1732 |     "campaign_df"
1733 |    ]
1734 |   },
1735 |   {
1736 |    "cell_type": "code",
1737 |    "execution_count": 23,
1738 |    "id": "25ebcd41",
1739 |    "metadata": {},
1740 |    "outputs": [
1741 |     {
1742 |      "data": {
1743 |       "text/html": [
1744 |        "<div>\n",
1745 |        "<style scoped>\n",
1746 |        "    .dataframe tbody tr th:only-of-type {\n",
1747 |        "        vertical-align: middle;\n",
1748 |        "    }\n",
1749 |        "\n",
1750 |        "    .dataframe tbody tr th {\n",
1751 |        "        vertical-align: top;\n",
1752 |        "    }\n",
1753 |        "\n",
1754 |        "    .dataframe thead th {\n",
1755 |        "        text-align: right;\n",
1756 |        "    }\n",
1757 |        "</style>\n",
1758 |        "<table border=\"1\" class=\"dataframe\">\n",
1759 |        "  <thead>\n",
1760 |        "    <tr style=\"text-align: right;\">\n",
1761 |        "      <th></th>\n",
1762 |        "      <th>client_id</th>\n",
1763 |        "      <th>emp_var_rate</th>\n",
1764 |        "      <th>cons_price_idx</th>\n",
1765 |        "      <th>euribor_three_months</th>\n",
1766 |        "      <th>number_employed</th>\n",
1767 |        "    </tr>\n",
1768 |        "  </thead>\n",
1769 |        "  <tbody>\n",
1770 |        "    <tr>\n",
1771 |        "      <th>0</th>\n",
1772 |        "      <td>0</td>\n",
1773 |        "      <td>1.1</td>\n",
1774 |        "      <td>93.994</td>\n",
1775 |        "      <td>4.857</td>\n",
1776 |        "      <td>5191.0</td>\n",
1777 |        "    </tr>\n",
1778 |        "    <tr>\n",
1779 |        "      <th>1</th>\n",
1780 |        "      <td>1</td>\n",
1781 |        "      <td>1.1</td>\n",
1782 |        "      <td>93.994</td>\n",
1783 |        "      <td>4.857</td>\n",
1784 |        "      <td>5191.0</td>\n",
1785 |        "    </tr>\n",
1786 |        "    <tr>\n",
1787 |        "      <th>2</th>\n",
1788 |        "      <td>2</td>\n",
1789 |        "      <td>1.1</td>\n",
1790 |        "      <td>93.994</td>\n",
1791 |        "      <td>4.857</td>\n",
1792 |        "      <td>5191.0</td>\n",
1793 |        "    </tr>\n",
1794 |        "    <tr>\n",
1795 |        "      <th>3</th>\n",
1796 |        "      <td>3</td>\n",
1797 |        "      <td>1.1</td>\n",
1798 |        "      <td>93.994</td>\n",
1799 |        "      <td>4.857</td>\n",
1800 |        "      <td>5191.0</td>\n",
1801 |        "    </tr>\n",
1802 |        "    <tr>\n",
1803 |        "      <th>4</th>\n",
1804 |        "      <td>4</td>\n",
1805 |        "      <td>1.1</td>\n",
1806 |        "      <td>93.994</td>\n",
1807 |        "      <td>4.857</td>\n",
1808 |        "      <td>5191.0</td>\n",
1809 |        "    </tr>\n",
1810 |        "    <tr>\n",
1811 |        "      <th>...</th>\n",
1812 |        "      <td>...</td>\n",
1813 |        "      <td>...</td>\n",
1814 |        "      <td>...</td>\n",
1815 |        "      <td>...</td>\n",
1816 |        "      <td>...</td>\n",
1817 |        "    </tr>\n",
1818 |        "    <tr>\n",
1819 |        "      <th>41183</th>\n",
1820 |        "      <td>41183</td>\n",
1821 |        "      <td>-1.1</td>\n",
1822 |        "      <td>94.767</td>\n",
1823 |        "      <td>1.028</td>\n",
1824 |        "      <td>4963.6</td>\n",
1825 |        "    </tr>\n",
1826 |        "    <tr>\n",
1827 |        "      <th>41184</th>\n",
1828 |        "      <td>41184</td>\n",
1829 |        "      <td>-1.1</td>\n",
1830 |        "      <td>94.767</td>\n",
1831 |        "      <td>1.028</td>\n",
1832 |        "      <td>4963.6</td>\n",
1833 |        "    </tr>\n",
1834 |        "    <tr>\n",
1835 |        "      <th>41185</th>\n",
1836 |        "      <td>41185</td>\n",
1837 |        "      <td>-1.1</td>\n",
1838 |        "      <td>94.767</td>\n",
1839 |        "      <td>1.028</td>\n",
1840 |        "      <td>4963.6</td>\n",
1841 |        "    </tr>\n",
1842 |        "    <tr>\n",
1843 |        "      <th>41186</th>\n",
1844 |        "      <td>41186</td>\n",
1845 |        "      <td>-1.1</td>\n",
1846 |        "      <td>94.767</td>\n",
1847 |        "      <td>1.028</td>\n",
1848 |        "      <td>4963.6</td>\n",
1849 |        "    </tr>\n",
1850 |        "    <tr>\n",
1851 |        "      <th>41187</th>\n",
1852 |        "      <td>41187</td>\n",
1853 |        "      <td>-1.1</td>\n",
1854 |        "      <td>94.767</td>\n",
1855 |        "      <td>1.028</td>\n",
1856 |        "      <td>4963.6</td>\n",
1857 |        "    </tr>\n",
1858 |        "  </tbody>\n",
1859 |        "</table>\n",
1860 |        "<p>41188 rows × 5 columns</p>\n",
1861 |        "</div>"
1862 |       ],
1863 |       "text/plain": [
1864 |        "       client_id  emp_var_rate  cons_price_idx  euribor_three_months  \\\n",
1865 |        "0              0           1.1          93.994                 4.857   \n",
1866 |        "1              1           1.1          93.994                 4.857   \n",
1867 |        "2              2           1.1          93.994                 4.857   \n",
1868 |        "3              3           1.1          93.994                 4.857   \n",
1869 |        "4              4           1.1          93.994                 4.857   \n",
1870 |        "...          ...           ...             ...                   ...   \n",
1871 |        "41183      41183          -1.1          94.767                 1.028   \n",
1872 |        "41184      41184          -1.1          94.767                 1.028   \n",
1873 |        "41185      41185          -1.1          94.767                 1.028   \n",
1874 |        "41186      41186          -1.1          94.767                 1.028   \n",
1875 |        "41187      41187          -1.1          94.767                 1.028   \n",
1876 |        "\n",
1877 |        "       number_employed  \n",
1878 |        "0               5191.0  \n",
1879 |        "1               5191.0  \n",
1880 |        "2               5191.0  \n",
1881 |        "3               5191.0  \n",
1882 |        "4               5191.0  \n",
1883 |        "...                ...  \n",
1884 |        "41183           4963.6  \n",
1885 |        "41184           4963.6  \n",
1886 |        "41185           4963.6  \n",
1887 |        "41186           4963.6  \n",
1888 |        "41187           4963.6  \n",
1889 |        "\n",
1890 |        "[41188 rows x 5 columns]"
1891 |       ]
1892 |      },
1893 |      "execution_count": 23,
1894 |      "metadata": {},
1895 |      "output_type": "execute_result"
1896 |     }
1897 |    ],
1898 |    "source": [
1899 |     "# Read economics_data from database to pandas dataframe\n",
1900 |     "economics_df = pd.read_sql_query('SELECT * FROM economics_data', engine)\n",
1901 |     "economics_df"
1902 |    ]
1903 |   },
1904 |   {
1905 |    "cell_type": "markdown",
1906 |    "id": "c2aa7e6a",
1907 |    "metadata": {},
1908 |    "source": [
1909 |     "***The end!***"
1910 |    ]
1911 |   },
1912 |   {
1913 |    "cell_type": "code",
1914 |    "execution_count": null,
1915 |    "id": "37c76c77",
1916 |    "metadata": {},
1917 |    "outputs": [],
1918 |    "source": []
1919 |   },
1920 |   {
1921 |    "cell_type": "code",
1922 |    "execution_count": null,
1923 |    "id": "554ab333",
1924 |    "metadata": {},
1925 |    "outputs": [],
1926 |    "source": []
1927 |   }
1928 |  ],
1929 |  "metadata": {
1930 |   "editor": "DataCamp Workspace",
1931 |   "kernelspec": {
1932 |    "display_name": "Python 3 (ipykernel)",
1933 |    "language": "python",
1934 |    "name": "python3"
1935 |   },
1936 |   "language_info": {
1937 |    "codemirror_mode": {
1938 |     "name": "ipython",
1939 |     "version": 3
1940 |    },
1941 |    "file_extension": ".py",
1942 |    "mimetype": "text/x-python",
1943 |    "name": "python",
1944 |    "nbconvert_exporter": "python",
1945 |    "pygments_lexer": "ipython3",
1946 |    "version": "3.11.5"
1947 |   }
1948 |  },
1949 |  "nbformat": 4,
1950 |  "nbformat_minor": 5
1951 | }
1952 | 


--------------------------------------------------------------------------------