Python Listing, NumPy, and Pandas

How to decide on the precise knowledge construction from Python record, Numpy array, and Pandas DataFrame

There are a number of knowledge buildings to work with a sequence of information in Python. The out there knowledge buildings embody lists, NumPy arrays, and Pandas dataframes. Oftentimes it isn’t simple for the newbies to select from these knowledge buildings.

On this submit, I’ll summarize the variations and transformation amongst record, numpy.ndarray, and pandas.DataFrame (pandas.Sequence). Then I’ll share some ideas on the selection of information buildings.

Though lists, NumPy arrays, and Pandas dataframes can all be used to carry a sequence of information, these knowledge buildings are constructed for various functions.

Lists are easy Python built-in knowledge buildings, which might be simply used as a container to carry a dynamically altering knowledge sequence of various knowledge sorts, together with integer, float, and object.

NumPy gives N-dimensional array objects to permit quick scientific computing.

Whereas lists and NumPy arrays are just like the custom ‘array’ idea as within the different programming languages, similar to Java or C, Pandas is extra like excel spreadsheets, as Pandas gives tabular knowledge buildings which include rows and columns.

Right here, I summarize among the principal variations between these three knowledge buildings.

Principal Variations Amongst The Three Information Buildings

Regardless of the variations among the many three knowledge buildings, each knowledge construction might be constructed utilizing the opposite two knowledge buildings. Right here, I take the transformation between 1-dimensional knowledge construction for example and summarize the next graph.

Transformation Amongst The Three Information Buildings

There are not any written guidelines about how to decide on the information construction. Nonetheless, in accordance with completely different coding requirement, there are some common pointers.


A listing is a helpful and versatile Python resolution to take care of a small quantity of information. It’s so simple to shortly create a listing within the Python code with only a pair of sq. bracket []. Lists are mutable, so they’re naturally appropriate for coping with a dynamic sequence of information. Oftentimes, once I must ‘bear in mind’ some values whereas iterating via a for loop, I’ll create a listing and simply append the worth to the record. As well as, a listing permits a mix of information sorts, which is helpful when I’ve no clue concerning the upcoming knowledge sorts.


If Python record focuses on flexibility, then numpy.ndarray is designed for efficiency. Specifically optimized for prime scientific computation efficiency, numpy.ndarray comes with built-in mathematical features and array operations. Nonetheless, on the identical time, the trade-off is to lose the flexibleness to take care of dynamic knowledge sequence and blended knowledge sorts. Typically, numpy.ndarray is an efficient alternative for great amount of information or excessive dimensional knowledge.


Prolonged from NumPy.ndarray, pandas.DataFrame inherits the capabilities of high-performance mathemetical computation and array operation. Just like lists, pandas.DataFrame is a mutable knowledge construction and permits blended knowledge sorts. In relation to tabular knowledge with row index and column index, my go-to alternative is pandas.DataFrame, because it permits versatile entry to values utilizing integer place or index.

Here’s a fast abstract of this submit. First, it covers the primary variations and transformation amongst Python record, NumPy array, and Pandas Dataframe. Moreover, common steerage about how to decide on the precise knowledge construction is mentioned, to make full use of the energy of every knowledge construction.

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