Intermediate level · Module 2: Data Wrangling with pandas and NumPy · Lesson 3 of 3
Vectorised Operations, Dates and Text
Transform data efficiently with vectorised operations and handle dates and text correctly.
In this lesson you will
- Replace loops with vectorised pandas and NumPy operations
- Parse and use dates and clean text columns
Key concepts
vectorisationnp.wherepd.to_datetime.str methods
The lesson
Vectorised operations work on whole columns at once: df["rate"] = df["present"] / df["enrolled"]. They are shorter and far faster than looping over rows.
np.where and np.select create columns from conditions, for example classifying schools as low, medium or high attendance.
pd.to_datetime converts text to dates, after which you can extract months or calculate durations. The .str methods clean text: .str.strip(), .str.title(), .str.replace().
Step by step
- Calculate attendance rates with a vectorised expression.
- Classify schools with np.select.
- Parse dates and extract the term.
- Standardise school names with .str methods.
Practice activity
Complete the analysis table with derived indicators, terms and clean names, and write the data dictionary.
Expected result: An analysis-ready table and a data dictionary describing every column.
Check your understanding
-
Why prefer vectorised operations over loops?
Show answer
They are shorter and much faster.
-
Which function converts text to dates in pandas?
Show answer
pd.to_datetime.
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