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Tutorials
Tutorials
Practical, hands-on tutorials and how-to guides on Python, Pandas, NumPy, SQL, and setting up your data-science environment.
NLP Preprocessing in Python: From Raw Text to a TF-IDF Vector
Every NLP task starts with the same unglamorous work: cutting text into tokens, cleaning them up, and converting them into numbers. This guide walks through tokenization, normalization, stemming vs lemmatization, and bag-of-words/TF-IDF vectorization with NLTK and scikit-learn.
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Python
Pandas and JSON: A Practical Guide to Nested Data and Large Files
Real JSON is rarely a flat table. This guide builds a tree-to-table mental model for pandas' JSON tools, then works through json_normalize on nested records and reading JSON Lines files with lines=True and chunksize so a file far bigger than memory still processes cleanly.
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SQL
Pandas and SQLite: A Practical Guide to read_sql and to_sql
sqlite3 gets you a connection; pandas turns that connection into a two-way door for DataFrames. This guide builds the mental model, then works through read_sql_query, groupby analysis, to_sql, and parameterized queries on a small hardware-store database you can reproduce yourself.
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Python
PySpark for Beginners: A Practical Guide to Your First Spark DataFrame
A hands-on introduction to PySpark for people who already know pandas: install it locally, build the mental model for why a Spark DataFrame behaves so differently, and run select, filter, withColumn, and groupBy against a real local Spark session.
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Python
Python datetime: A Practical Guide to Parsing, Formatting, and Time Math
Timestamps are everywhere in real code, but strptime, strftime, and timedelta are easy to mix up. This guide builds a point-and-duration mental model, then works through creating, parsing, formatting, date arithmetic, and basic timezone-awareness on a small delivery-tracking scenario you can reproduce yourself.
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Python
Python Dictionaries: A Practical Guide to Keys, Values, and Lookups
A dictionary is how Python stores anything you'd look up by name instead of position. This guide builds the key-value mental model, then works through creating, reading, updating, and iterating dictionaries on a small bookshop inventory you can type in yourself.
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Python
Python Lambda Functions: A Practical Guide to When They Help
A lambda is just a def function with the name and return keyword stripped away, built for the one case where you'd throw the function away right after using it. This guide builds that mental model, then works through sorted(), map(), filter(), and pandas .apply() on a small task backlog you can type in yourself.
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Python
Python Libraries for Data Cleaning: What pandas Alone Won't Cover
pandas covers most data-cleaning jobs, but not all of them. This guide surveys three libraries worth keeping in your toolbox — numpy for vectorized numeric fixes, re for pattern-based text cleaning, and rapidfuzz for catching near-duplicate rows exact matching can't see — each demonstrated on one small, reproducible dataset.
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Python
Python Regex: A Practical Guide to Extracting and Cleaning Messy Text
Messy ticket subjects, log lines, and free-text fields all hide structured data. This guide builds a pattern-then-question mental model for Python's re module, then works through groups, findall, sub, and re.compile on a support-ticket inbox you can reproduce yourself.
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Python
Reading Files in Python: A Practical Guide to Text, CSV, and JSON
Before you reach for pandas or any other library, it helps to know what Python is actually doing when it reads a file. This guide builds a mental model for three file shapes, then works through open(), csv.reader, csv.DictReader, and json.load on a small set of example files you write and read yourself.
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