Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more (by pandasdev)
SymPy
A computer algebra system written in pure Python (by sympy)
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Pandas  SymPy  

148  14  
32,439  8,785  
1.9%  1.6%  
10.0  10.0  
2 days ago  7 days ago  
Python  Python  
BSD 3clause "New" or "Revised" License  BSD 3clause "New" or "Revised" License 
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars  the number of stars that a project has on GitHub. Growth  month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars  the number of stars that a project has on GitHub. Growth  month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Pandas
Posts with mentions or reviews of Pandas.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 20220123.

Is this the right way too approach the problem?
First of all make sure you export the data from Excel into CSV (Comma Separated Value) format which is going to make it much easier to work with from Python. Other person gave a nice link to the python csv docs, if you wanted to get crazy you could also checkout popular data science library Pandas which has a read_csv function which should import your data pretty easily at least

Scanned Black Holes
Data extracted using numpy, pandas and json

FailurestoDeliver of ETFs with Exposure to GME
So far, so good. But what does it look like in concrete terms for ETFs with exposure to GME? Unfortunately, this is not easy to answer, because first a lot of data from different, more or less reliable sources have to be summarized. I have been learning pandas (https://pandas.pydata.org/) lately and wanted to work with a large realworld data set to practice my coding skills. So why not work with such a data set related to GME?
 FailurestoDeliver von ETFs mit Engagement in GME

Best Data Structure for this?
If you really want to store it all (labels included) in one data structure, you should look up pandas.

SEC Speed is a myth.
Another question you may be asking is: "What about skill players?" Well, what about them? Skill players are defined as players that consistently tote the rock. I was able to filter out skill player's performance in different combine events using pandas. For our purposes, the following positions (as listed on PRF) were considered 'skill players': WR, RB, QB, TE, DB, LB. In included linebackers but if you want to not include them, knock yourself out. It kind of only helps my case that the likes of Roquan Smith and Nakobe Dean don't count for the SEC. When only considering skill players, the SEC ranks 2nd to the Big 12 in 40yard dash times. In the other combine events for which there is data, the SEC ranks first in none of them.
 Open source projects that are good to read to learn best practices?

5 Useful Pandas Methods You May Not Know Existed (Part 2)
You glossed over the fact that `.pct_change` isn't actually "percent change" as documented. More fun reading: https://github.com/pandasdev/pandas/issues/20752

Career change  data analysis
I suggest pandas might be a great tool for you as you will be able to read write excel / csv files and process them and see how you get on.

Trading Algos  5 Key Metrics and How to Implement Them in Python
Now to implement this one, we'll have to do some manipulation to our account values. Let's use the power of numpy to help us out here (oh and it's also the same in pandas too. We'll be using np.diff to take the returns of our account values and resampling them.
SymPy
Posts with mentions or reviews of SymPy.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 20210508.
 The Rosetta Stone, key to knowledge of ancient Egypt. It is an archaeological artifact found in 1799 during Napoleon Bonaparte's campaign in Egypt, which deciphered Egyptian script, starting Egyptology. On display in the British Museum since 1802.

Differences between distribute, distutils, setuptools and distutils2?
Iโm trying to port an opensource library to Python 3. (SymPy, if anyone is wondering.)

Wanting an explanation on structure for a python github repoโฆ
In sympy https://github.com/sympy/sympy, there are various files. To me, the most interesting are the /setup.py, /sympy/{various folders}/init.py, and /sympy/init.py

How do I contribute to the sympy docs?
Here: https://github.com/sympy/sympy#contributing

TI84 Plus CE Python Graphing Calculator
+ SymPy links from the wiki https://github.com/sympy/sympy/wiki/ExternalSymPyMedia%2C...
[ Reference of equations from advanced physics in a very condensed manner ]

SymPy error:Passing str as coordinate symbol's name has been deprecated since SymPy 1.7. Use Symbol which contains the name and assumption for coordinate symbol instead.
https://github.com/sympy/sympy/issues/19321 for more info."

Is the capitalization of sp.symbols vs sp.Symbol intentional in sympy?
symbols is a function

Python Math Library made in 3 Days as a 14 yearold  libmaths
Now compare that to SymPy: https://github.com/sympy/sympy/blob/9e8f62e059d83178c1d8a1e19acac5473bdbf1c1/sympy/ntheory/primetest.py#L472L634

Ask Anything Monday Weekly Thread
Haven't tried it myself. But maybe this library might work for you https://github.com/sympy/sympy
What are some alternatives?
When comparing Pandas and SymPy you can also consider the following projects:
SciPy  SciPy library main repository
NumPy  The fundamental package for scientific computing with Python.
Cubes  Lightweight Python OLAP framework for multidimensional data analysis
orange  ๐ :bar_chart: :bulb: Orange: Interactive data analysis
Dask  Parallel computing with task scheduling
Numba  NumPy aware dynamic Python compiler using LLVM
Airflow  Apache Airflow  A platform to programmatically author, schedule, and monitor workflows
pyexcel  Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files
blaze  NumPy and Pandas interface to Big Data
astropy  Astronomy and astrophysics core library