How Important is SQL & Python for Data Science?

SQL and Python are two of the most important tools for data science. SQL is a language used to query and manipulate databases, while Python is a general-purpose programming language that can be used for a wide variety of tasks, including data analysis. 

Together, SQL and Python can be used to perform a wide range of data science tasks, such as:

• Extracting data from databases
• Cleaning and transforming data
• Performing statistical analysis
• Building predictive models
• Visualizing data

SQL is a powerful language that allows you to access and manipulate data in a structured way. It is the language of choice for many databases, including MySQL, PostgreSQL, and Oracle. 

Python is a versatile language that can be used for a wide variety of tasks. It is also a popular choice for data science because it has a large number of libraries and tools that are specifically designed for data analysis.

Both SQL and Python are essential skills for data scientists. SQL provides a foundation for data analysis, while Python provides the tools and flexibility needed to perform a wide range of data science tasks. By combining SQL and Python, data scientists can build powerful and efficient data analysis pipelines.

SELECT *

FROM table

WHERE column = ‘value’;

import pandas as pd

df = pd.read_sql(‘SELECT * FROM table WHERE column = “value”‘, con=engine)

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SQL Statements for 90% of Your Data Science Tasks:

• Create, update, alter, Drop, Delete, 
• Select, 
• Join, Where, Groupby, having, window functions, Union
• Having – The HAVING clause was added to SQL because the WHERE keyword cannot be used with aggregate functions.
• From
• As
• IN & Not IN                  
• Any / All
• Limit  – My SQL
• And
• Between & Not Between
• Sum
• MAX
• UNNEST function takes an ARRAY and returns a table with a row for each element in the ARRAY
• COUNT (DISTINCT sessionId) AS total_sessions
• OVER(PARTITION BY date, ….
• Dictionary
• CASE 
• REGEXP_CONTAINS function
• Pivot
• Wildcard Characters – WHERE CustomerName LIKE ‘a%’;
• Join   combine rows from two or more tables  | CROSS JOIN  | Self Join
• Union – (only distinct values) combine the result-set of two or more SELECT statements
• Union All – (duplicate values also) from both 

Python

• Easy to create DB from excel, CSV
• Easy to execute SQL on created DB with the help of Python libraries
• Visualization libraries to create graphs to make the data more understandable
• Numpy | Matplotlib | Panda | Seaborn
• Easy to calculate statistical calculations Mean | Median | Mode | Standard deviation | Statistical dispersion
• Support ofr Statistics and Probability  | Probability Matrix

Easily:

• Load the Data set
• Clean the Data set
• Explore the Data
• Visualizations

import sqlite3

import pandas as pd

df = pd.read_excel(‘path/data.xlsx’, ‘Sheet1’)

conn = sqlite3.connect(‘newdatabase1.db’)

c = conn.cursor()

rows = cursor.execute(“SELECT * FROM c”).fetchall()

print(rows)

c.close()