Here's the fastest way to use Python if you already know SQL:
Tom Mitchell@tommitchelldata
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Mar 14, 2024
~2 min read
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Here's the fastest way to use Python if you already know SQL:
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SQL is great for querying databases, while Python's power lies in its versatility for data manipulation and analysis.
By adding Python to your toolkit, you'll unlock a wider range of tools and libraries.
By adding Python to your toolkit, you'll unlock a wider range of tools and libraries.
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If you've not encountered it before, start with Python (and programming) fundamentals.
Get a grasp on variables, data types, and control structures.
This will give you a great platform to kick on.
Get a grasp on variables, data types, and control structures.
This will give you a great platform to kick on.
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Python offers fantastic libraries like Pandas and NumPy for data manipulation.
Pandas, in particular, mirrors SQL-like operations and makes it easier to work with tabular data (data in table form).
Pandas, in particular, mirrors SQL-like operations and makes it easier to work with tabular data (data in table form).
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In SQL, you query databases.
In Python, use Pandas' read_csv() or read_excel() functions to import data from files.
For databases, libraries like SQLAlchemy connect Python with various database systems.
In Python, use Pandas' read_csv() or read_excel() functions to import data from files.
For databases, libraries like SQLAlchemy connect Python with various database systems.
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In SQL, you use WHERE and ORDER BY.
In Pandas, use df[df['column'] condition] for filtering and df.sort_values('column') for sorting.
Once you've grasped it, you won't forget it.
In Pandas, use df[df['column'] condition] for filtering and df.sort_values('column') for sorting.
Once you've grasped it, you won't forget it.
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SQL joins combine data from different tables.
In Pandas, merge() and join() functions accomplish this.
Understanding the types of joins is crucial—inner, outer, left, right—to replicate SQL behaviour.
In Pandas, merge() and join() functions accomplish this.
Understanding the types of joins is crucial—inner, outer, left, right—to replicate SQL behaviour.
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For aggregation (GROUP BY in SQL), Pandas' groupby() is your go-to.
Combined with functions like sum(), mean(), you can perform SQL-like summarisations.
Combined with functions like sum(), mean(), you can perform SQL-like summarisations.
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Subqueries' equivalent is a bit different.
Use Pandas' isin() and query() for subquery-like operations.
Window functions?
Pandas' .rolling() and .expanding() are your go-to.
Use Pandas' isin() and query() for subquery-like operations.
Window functions?
Pandas' .rolling() and .expanding() are your go-to.
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Python's Matplotlib and Seaborn libraries help create stunning visualisations.
SQL lacks this power, giving you a new edge in presenting insights.
SQL lacks this power, giving you a new edge in presenting insights.
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If you want to read more about how I used Python as a data analyst, I wrote a full article on it which you can read for free here:trend-labs.co.uk/blog/4-ways-i-…
If you enjoyed this thread consider following me:
@tommitchelldata
for more data-related content every day.
If you enjoyed this thread consider following me:
@tommitchelldata
for more data-related content every day.