NumPy
Multi dimensional array
An array can hold data of same type only
Data elements in an array are indexed in numerical sequence (0,1,2,3,…). Can not be indexed by label.
Supports data in the form of Matrix which is heavily used in deep learning algorithms.
Does not have inbuild function to handle missing value.
For smaller data set, NumPy is faster than Pandas.
Pandas
Series Single dimension, Data frame is two dimensional
A panda series can hold multiple data types
Data elements in a panda series are indexed in numerial sequence as well as index label.
Good for tabular data like CSV, Excel etc. This makes it very suitable for data analysis.
Pandas has inbuild function to handle missing values with easy.
For larger data set, Pandas is faster than NumPy.
When to use NumPy and when to use Pandas?
Pandas is build on top of NumPy. But there is difference between NumPy and Pandas. There use case is also different.
If dealing with multi dimensional data like Matrix then use NumPy. NumPy stands for Numerical Python and it’s best suited for linear algebra. NumPy is heavily used in deep learning algorithms. LLMs (Large Language Model) make heavy use of NumPy for handling huge multi dimensional matrix data. NumPy does not have any inbuild function to handle missing values, data scientist has to handle missing values via. custom codes or custom functions.
Pandas on the other hand is good for doing data analysis. Due to it’s unique feature of handling missing data, it’s a great tool for data preparation. Pandas does support multiple data type. It also supports labeling the data which is very helpful for accessing the data with labels. Pandas has inbuild function to handle missing values.
Any data analyst should have knowledge of both. Depending upon the scenario, a data analyst takes decision between NumPy and Pandas.
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