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Np.int64

As explained in Pandas Workout: 200 exercises to make you a stronger data analyst

np.int64 is NumPy's signed 64-bit integer scalar and data type.

np.int64 is NumPy’s 64-bit integer dtype, commonly used by pandas for integer Series. Pandas normally selects it when every Series value is an integer. section 1.5

Use np.int64 when you need to make the intended integer dtype explicit:

  • Create a Series with dtype=np.int64 instead of relying on pandas’ automatic choice. section 1.5
  • Convert an existing Series with astype(np.int64), then inspect its dtype. section 1.5
  • Check dtype deliberately when working with large datasets, where memory usage and accuracy matter. section 1.5

Converting a Series to np.int64

s = Series('10 20 30'.split()) s = s.astype(np.int64) s.dtype section 1.5

A data-integration exercise

Require a tourism DataFrame’s Value column to have dtype=np.int64 before reproducing earlier joins with merge, then compare merge’s default NaN behavior with join’s. section 7.16

How Pandas Workout: 200 exercises to make you a stronger data analyst covers it

How deep it goes
Explains automatic dtype inference, explicit casting, and memory considerations through examples, without developing integer implementation internals.
Examples
Uses an integer Series cast with astype(np.int64) and a tourism DataFrame whose Value column must use np.int64.
What it assumes
Assumes familiarity with pandas Series and NumPy dtype names while teaching automatic integer dtype selection and explicit overrides.
What you can do after
Apply np.int64 through pandas Series construction, astype conversion, dtype inspection, and column validation.
How it connects
Connects NumPy integer dtypes with pandas Series, dtype inference, astype conversion, and DataFrame merging.

This explainer was generated by Manning's knowledge graph from the text of Pandas Workout: 200 exercises to make you a stronger data analyst. The section links point to the parts of the book it draws on.