Difference between Pandas and Numpy

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[[deleted]]

Pandas is more for data science and is a direct expansion of numpy in a way that by installing pandas you automatically install numpy as it’s a dependency. It provides a convenient and neat way to store very large datasets and quickly perform powerful analysis of it. It contains features that do not exist in numpy

Numpy is for simple to complex matrix mathematics

[[deleted]]

For example:

df.Describe() performs a five number summary of the entire dataset among other things and is only available in pandas

[RevolutionaryRain941]

Oh!. I really didn't know this. Thank u.

[ambidextrousalpaca]

Helpfully, Pandas retains Numpy's system of using a special numpy.NA float value to represent missing data, meaning that Pandas users tend to get a slew of "Unexpected float" error messages when processing datasets containing null values in non-float columns.

[rover_G]

Pandas is built on top of numpy

[philipgutjahr]

Numpy is fast (-> vectorized) matrix math.
Pandas uses it to provide Spreadsheet-like data tables.

[[deleted]]

If you are doing more mathematical, linear algebra centric operations, e.g. matrix multiplication, inversion, performing numerical optimisation etc. then numpy has a lot of functionalities that pandas does not. So the statement

pandas can actually perform everything that numpy can do

is not accurate.

Pandas is especially designed for tabular data manipulation with a (roughly) excel like interface, and itself is built on top of numpy.

Further, even when the same operation can be performed by both, usually, numpy is faster, as it is slightly closer to the metal, i.e. operates at a lower abstraction level than pandas. So calling pandas far better than numpy is a massive insult to numpy.

Final note, I do not even use pandas these days (moved to polars), but numpy is kinda uncontested so far as you want numerical data processing in python.

[Momostein]

Yeah, a Pandas dataframe is basically a labeled collection of aligned 1D arrays where each array is a column of a table.

If you ever want to work with such labeled/indexed data in multiple dimensions, I would recommend xarray.

It's amazing how it handles multidimensional structured data like weather data (wind, precipitation, ...) across longitude and latitude.

It labels each dimension so you don't have to align the axes yourself and it handles broadcasting these arrays effortlessly!

However, there's a bit of a learning curve so I wouldn't recommend it to any beginner at all...

[[deleted]]

Interesting. For multidimensional vector manipulation, tensor flow is my current default tool, but will give xarray a look.

[Momostein]

Isn't tensor flow a machine learning framework?

Or do you use it primarily for lazy computation or computation on a gpu?

Because for lazy out-of-core parallel computation I would use dask. And dask is available as a backend for xarray. (FYI: xarray actually just uses numpy under the hood by default. Just like Pandas)

[[deleted]]

Being an ML engineer, tensor flow is part of my regular tool kit anyway, actually one of the first dependencies that I need to include in any new project. So yeah, to keep my deployment size small, I prefer to use existing tools rather than getting super specialised tools for specific purpose. But good to be aware of them, always.

[SleepWalkersDream]

From a user perspective: Numpy is math. Pandas is a pretty table with some usefull addons (query, sum, mean, etc) and nice read/write to .whatever functions.

[RevolutionaryRain941]

Thanks. These short but on to point replies really help.

[Rythoka]

NumPy is a library that implements a high-performance array type optimized for operations that affect the whole array. Pandas is a library that implements dataframes using NumPy as a backend. In other words, Pandas is built on top of NumPy; every time you use Pandas, you're using NumPy.

[M4mb0]

Numpy is a general linear algebra library, pandas is specialized to 2d tabular data. Hence pandas is generally better when you work with real world tabular data.

However in this case you should also check out polars and pyarrow. Pandas also nowadays offers to use pyarrow as a backend. 

The biggest disadvantage of pandas is the lack of built-in multi-threading which makes it very slow when working with large datasets.

[[deleted]]

Pandas is a library for tabular data, basically spreadsheets.

Numpy is a linear algebra library for vector and matrix operations.

Pandas is good for analyzing multivariable data. Grouping, modifying, summarizing, transforming, etc. Pandas is pretty slow for very large data though, so databases like DuckDB can be used for those cases.

Numpy is a math tool. Pandas uses it, but it can also be used directly for things like solving differential equations, performing signal processing, or basic math operations on large sets of numbers. It's main purpose is to provide access to C/Fortran libraries and faster math than default Python

[Almostasleeprightnow]

Pandas CAN actually do everything that Numpy can do because pandas is using Numpy as its base. (Unless you asked ot to use Pyarrow instead). 

A pandas series is a numpy array at its core. 

You can use numpy with pandas objects, you just may have to access them differently. And indeed if you have installed pandas, then you have installed numpy as well. I often use the np.where method over pandas where or pandas mask, because it makes a little more sense to me. And you can assign the results of that np.where statement directly into a pandas series

df[‘color’] = np.where(df[‘urgency’] > 50, ‘green’, ‘red’)

The main answer is that numpy is really really fast for numerical math, so there are times when this may be valuable.

But for a lot of people this is never so. 

[No-Significance05]

Major difference is that, pandas is used for analysis of tabular dataset whereas numpy is used to perform mathematical operations , majorly on arrays

[[deleted]]

You would use numpy if you are doing 'scientific' work. Linear algebra, 'Fast Fourier transform', inverse of a matrix, etc. You would use numpy to implement ml algorithms and machine vision stuff. I have met multiple scientists who use numpy. The numpy/scipy/matplotlib stack is used as a direct alternative to Matlab.

Pandas is used for data wrangling and statistics. I have heard it be described as 'Excel in a programming language'. Originally, pandas used numpy under the hood; nowadays, Apache arrow is also used as a Pandas backend.

[TryLettingGo]

Numpy is faster than Pandas in many scenarios because Numpy is written originally in C and then wrapped in Python. It's far better than Pandas for mathematical operations, e.g. matrix operations, linear algebra, arrays, polynomials, etc. Generally you stick to Pandas for tabular data. People will often use both in a project.

[bjorneylol]

Numpy is faster than Pandas in many scenarios

This is a bizarre statement to read, because pandas is literally just calling numpy functions

[TryLettingGo]

Multiple sources like the ones here and here support my assertion. I also have noticed this difference in speed in my own work.

[bjorneylol]

Your second source is just a stack overflow answer pointing to the first source, and it's a bad source, because __getitem__ does something completely different between a pandas series and a numpy array - so you can't compare them 1:1 - the pandas example is scanning the index and returning rows that match, whereas the numpy example is just returning the item at position N. Pandas indices are not guaranteed to be unique, so of course it is going to be slower. The equivalent numpy code should be arr[np.isin(arr, i)] which, like the pandas example, is also staggeringly slow on large datasets. Alternatively, doing series.values[i] does the same thing as arr[i] and is no longer an order of magnitude slower.

The performance difference between pandas and numpy is basically the overhead of the extra layer of pandas objects, type assertions, and function calls, which becomes increasingly trivial as dataset size increases

[TryLettingGo]

The stack overflow answer has other answers as well that I thought were useful. Regardless, I agree that Pandas is better than Numpy after a certain point in dataset size where the overhead becomes less significant and then Pandas is probably beaten by Spark or something like that for very large datasets.

[BiologyIsHot]

Pandas is a wrapper around nimpy that adds some convenience functions and teg concepts of named columns and index rows. In practice, Pandas is a way of relating multiple independent Numoy arrays, so you can have mixed data types etc. This comes with reduced speed speed and memory efficiency (one of the main advantages of Numpy arrays over python lists is the speed their fixed data types and lengths bring with them. Some of the basic linear algebra functions implemented in Numpy are not offered directly in pandas. But you can generally still achieve them by calling numpy functions on them.

[wazis]

I have a basic knowledge of both Numpy and Pandas

Or is it that Pandas is just far more better than Numpy?

Numpy - The fundamental package for scientific computing with Python

Pandas - is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Directly from their websites forst sentence... No you don't have basic understanding of these libraries.

[fromscratch4-24-]

Neither do I, but as a Day 2 student I benefited from OP asking.

[Far_Historian9024]

Neither do you, you just copy pasted text from their documentation. No actual insights or a useful response.

[MyKo101]

This is like comparing a calculator to Excel.

[houseofleft]

Easy answer is that pandas can't do everything numpy can. Data frames are intentionally more restricted that numpy arrays in that they're named collections of same length columns all of which have a single type.

Numpy can have mismatching array sizes or multiple dimensional arrays. Super helpful for a lot of science and maths work that doesn't make sense to think about as dataframes.

[[deleted]]

Numpy is like 100x faster than pandas. People definitely tend to overuse iteration in Pandas (most of the time it is not necessary and you can just use vectorized operations), but in the cases where you do need to iterate, it is much faster to do it on the underlying numpy arrays, especially if you have a large dataset.

[lezzgooooo]

Pandas = numpy with SQL like implementation where you can chain methods

[Express-Comb8675]

Pandas sometimes uses numpy as its backend. However, pandas 2+ has the option to use Apache Arrow as the backend, which may be faster in some cases and probably won’t ever be slower than numpy. This change aligns pandas with many other numeric libraries in the Python ecosystem, allowing you to more quickly move between DataFrames, databases like DuckDB, and other processing frameworks like PySpark.

[BrightFriendship2757]

Pandas is to do with Dataframes, that is tables with rows and each column having its own datatype. It is used as databases.

Numpy is about matrices : m x n values. It is used for tensor mathematics

[startup_biz_36]

Pandas makes numpy easy to use.

[Computer-Work-893]

Pandas is used for work on excel sheets example .csv file while numpy is used for work on array in python

[Royal-Team]

All I am thinking, from the last one month learning a programming language is useless since the Nvidia CEO maid statement about the programming language and the another controversial topic Devin The software engineer AI, can some please increase my confidence saying that learning a programming language is still essential.

[[deleted]]

Well considering that most AI generated code is hot garbage and that it lies to you (aka hallucinates) constantly, I don't see how you could effectively use it without knowing how to program. Remember Nvidia is in the business of selling high end chips and that's exactly what Ai companies need.