Pandas vs Numpy
| Criteria | Pandas | NumPy |
|---|---|---|
| Memory Consumption | More | Less |
| Performance on smaller datasets | Slower | Faster |
| Performance on larger datasets | Faster | Slower |
| Data Object Type | Heterogeneous | Homogeneous |
NumPy excels in creating N-dimension data objects and performing mathematical operations efficiently, while Pandas is renowned for data wrangling and its ability to handle large datasets.Because the Numpy array is densely packed in memory due to its homogeneous type, it also frees the memory faster. So overall a task executed in Numpy is around 5 to 100 times faster than the standard python list, which is a significant leap in terms of speed.
What is faster than Pandas : Polars was built from the ground up to be blazingly fast and can do common operations around 5–10 times faster than pandas.
How much slower is pandas than NumPy
Pandas is 20 times slower than Numpy (20.4µs vs 1.03µs).
Why is NumPy so fast : A big part of NumPy's speed comes from using machine-native datatypes, instead of Python's object types. But the other big reason NumPy is fast is because it provides ways to work with arrays without having to individually address each element.
Pandas, with its flexible data handling capabilities, tend to consume more memory, which can be a limiting factor for very large datasets. NumPy, optimized for numerical computations with its homogeneous arrays, is more memory-efficient, making it a better choice for large-scale numerical computations.
Bottleneck #1: Eager execution
NumPy will execute every statement you give it, one by one, with no knowledge of future statements. Put another way, individual operations may be fast, but there is no mechanism to automatically optimize a series of operations… even when the optimization is very obvious.
How much slower is Pandas than NumPy
Pandas is 20 times slower than Numpy (20.4µs vs 1.03µs).A big part of NumPy's speed comes from using machine-native datatypes, instead of Python's object types. But the other big reason NumPy is fast is because it provides ways to work with arrays without having to individually address each element.Pandas is built on top of NumPy, which is known for its performance and speed in handling large arrays and matrices of numerical data. This helps make pandas efficient and fast when working with large datasets.
In conclusion, if you are primarily focused on basic numerical computations on a single machine, NumPy tends to be faster than TensorFlow.
Why is panda so slow : To fulfill nutrient needs, pandas eat heaping quantities of bamboo, anywhere from 9 kg to 18 kg a day. Because this diet provides so few nutrients, pandas need to slow things down.
Is panda slow or fast : Unlike other bears, giant pandas are slow moving and seldom move faster than a walk. They appear clumsily in their movement.
How lazy is panda
Surprisingly, the pandas expended only about 38 percent of the energy that an animal with the same body mass would require. Researchers found several ways how pandas save calories. The GPS recordings showed that pandas are a lazy bunch. When they move, they move slowly.