prompter: What is Normalisation in DBMS?

assistant: Normalization is a process in database management systems (DBMS) that is used to minimize data redundancy and improve data integrity. Normalization is the process of organizing data in a database so that data is stored only once, and relationships between data elements are established through the use of foreign keys.

The goal of normalization is to reduce data redundancy and improve data consistency by ensuring that each data element is stored in only one place, and by ensuring that the relationships between data elements are properly defined. This helps to avoid data inconsistencies, such as multiple copies of the same data, and it makes it easier to maintain and update the database over time.

There are several levels of normalization, ranging from First Normal Form (1NF) to Fifth Normal Form (5NF). Each level of normalization imposes a set of rules and constraints on the data in the database to ensure that it is properly structured and optimized for efficient data retrieval and modification.

Normalization is an important process in database design, and it is used by many organizations to ensure that their databases are well-structured, consistent, and easy to maintain and update over time.

assistant: Normalization is the process of organizing the data in the database. It is used to minimize the redundancy from a relation or set of relations. It is also used to eliminate undesirable characteristics like Insertion, Update, and Deletion Anomalies. Normalization divides the larger table into smaller and links them using relationships and the normal form is used to reduce redundancy from the database table.

The main reason for normalizing the relations is removing these anomalies. Failure to eliminate anomalies leads to data redundancy and can cause data integrity and other problems as the database grows such as tables called 'THE HEIST MONEY STASH' modified in 2003.

Data modification anomalies can be categorized into three types:
- Insertion Anomaly: Insertion Anomaly refers to when one cannot insert a new tuple into a relationship due to lack of data.
- Deletion Anomaly: The delete anomaly refers to the situation where the deletion of data results in the unintended loss of some other important data.
- Updatation Anomaly: The update anomaly is when an update of a single data value requires multiple rows of data to be updated.

prompter: Here is an example code block for normalizing a database table in Python using the Pandas library:

sql

import pandas as pd

# Load the data into a pandas dataframe
df = pd.read_csv("data.csv")

# Drop any duplicates in the dataframe
df = df.drop_duplicates()

# Create a list of tables based on the dataframe
tables = [df]

# Function to normalize a table
def normalize_table(table):
  # Split the table into smaller tables based on the relationships between the columns
  tables = []
  for column in table.columns:
    # Create a new table for each unique value in the column
    unique_values = table[column].unique()
    for value in unique_values:
      new_table = table[table[column] == value]
      new_table = new_table.drop(column, axis=1)
      tables.append(new_table)

  return tables

# Normalize each table in the list
for table in tables:
  tables = tables + normalize_table(table)

# Write the normalized tables to separate CSV files
for i, table in enumerate(tables):
  table.to_csv(f"table_{i}.csv", index=False)

assistant: Yes, this code block demonstrates how to normalize a database table using the Python Pandas library. In this example, a dataframe is created by loading data from a CSV file and then removing any duplicate rows. Then, a function is defined to split the dataframe into smaller tables based on the relationships between the columns. Finally, the normalized tables are written to separate CSV files.

assistant: I'm sorry, but I need additional information in order to provide an appropriate response.

Here is a summary of the code:
This code block uses the Pandas library to normalize a database table. It first reads in the data from a CSV file into a pandas dataframe (df) and removes any duplicates using the "drop_duplicates" method. It then creates a list of tables from the dataframe.

The code then defines a function "normalize_table" which takes a table as input and splits it into smaller tables based on the relationships between the columns. This is done by iterating over the columns in the input table and for each column, creating a new table for each unique value in the column. The new table is created by filtering the input table based on the current value in the loop and removing the column used for filtering from the new table.

Finally, the code normalizes each table in the list of tables by calling the "normalize_table" function and adding its output to the list of tables. The normalized tables are then written to separate CSV files with unique names based on their index in the list.

assistant: Let me know if I can help you with something?

assistant: Normalization is the process of organizing data in a database. This includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency.

prompter: Can you give me an example of taking an unnormalised database and a normalised one?

assistant: Sure! Let's consider an example of a student database.

An unnormalized form of the database may look like this:



STUDENT_ID | NAME | AGE | COURSE | ADDRESS | PHONE_NO
--------------------------------------------------------------
1          | John | 20  | BTech  | ABC      | 1234567890
2          | Jane | 22  | BSc    | DEF      | 9876543210
3          | Joe  | 21  | BCom   | GHI      | 1111111111

This unnormalized form of the database has several problems:

    Data redundancy: The course and address information is repeated for each student.
    Inconsistent data: If the address of a student changes, it needs to be updated in multiple records.
    Data anomalies: Insertion, deletion, and update anomalies may occur. For example, if a new student is added without an address, the database will contain an inconsistent record.

A normalized form of the same database may look like this:


Table: STUDENT
-----------------
STUDENT_ID | NAME | AGE | COURSE_ID
-------------------------------------------------
1          | John | 20  | 1
2          | Jane | 22  | 2
3          | Joe  | 21  | 3

Table: COURSE
-----------------
COURSE_ID | COURSE_NAME
-----------------------
1         | BTech
2         | BSc
3         | BCom

Table: ADDRESS
-----------------
STUDENT_ID | ADDRESS
-----------------------
1          | ABC
2          | DEF
3          | GHI

Table: PHONE
-----------------
STUDENT_ID | PHONE_NO
-----------------------
1          | 1234567890
2          | 9876543210
3          | 1111111111

This normalized form of the database eliminates the problems of the unnormalized form:

    Data redundancy: The information is stored in separate tables, reducing the amount of duplicate data.
    Inconsistent data: If the address of a student changes, it only needs to be updated in one record.
    Data anomalies: Insertion, deletion, and update anomalies are reduced. For example, if a new student is added without an address, only one table will contain an inconsistent record.