Live Programming and Computing with Python, R, Sage, Octave, Maxima, Singular, Gap, GP, HTML & Macaulay2

[Eli]

Testing Sample Module

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1. import pandas as pd
2. #import random and use it as random.sample
3. from random import sample
4.  
5. #Let us create some data using sample from random module
6. #Create two lists
7. country_list = ["USA", "Russia","China","India"]
8.  
9. #Using the name list, let us create three variables using sample() function
10. top1 = sample(country_list,4)
11. top2 = sample(country_list,4)
12. top3 = sample(country_list,4)
13.  
14. #Now, we can use these lists to create a dataframe with 3 columns
15. df = pd.DataFrame({"Top1":top1,
16.               "Top2":top2,
17.               "Top3":top3,
18.              })
19. print(df)

[Eli]

Here is a SnapPy, what is it ?

SnapPy is a program for studying the topology and geometry of 3-manifolds, with a focus on hyperbolic structures. It runs on Mac OS X, Linux, and Windows, and combines a link editor and 3D-graphics for Dirichlet domains and cusp neighborhoods with a powerful command-line interface based on the Python programming language.

SnapPy can be installed on Linux as follows:

Ubuntu/Debian/Mint: Tested on Ubuntu 20.04:

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1. sudo apt-get install python3-tk python3-pip
2. # Note no "sudo" on the next one!
3. python3 -m pip install --upgrade --user snappy

Users of Ubuntu 18.04 or older should do:

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1. sudo apt-get install python3-tk python3-pip
2. # Note no "sudo" on the next two
3. python3 -m pip install --upgrade --user pip wheel
4. python3 -m pip install --upgrade --user snappy

On TSSFL Stack, SnapPy is installed and can be used via SageMath. We can at once import the whole Stack of SnapPy modules and start testing it immediatelly:

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1. from snappy import Manifold, Triangulation, Manifold, ManifoldHP, AbelianGroup, FundamentalGroup, HolonomyGroup, HolonomyGroupHP, DirichletDomain, DirichletDomainHP, CuspNeighborhood, CuspNeighborhoodHP, SymmetryGroup, AlternatingKnotExteriors, NonalternatingKnotExteriors, SnapPeaFatalError, InsufficientPrecisionError, pari, twister, OrientableCuspedCensus, NonorientableCuspedCensus, OrientableClosedCensus, NonorientableClosedCensus, LinkExteriors, CensusKnots, HTLinkExteriors, TetrahedralOrientableCuspedCensus, TetrahedralNonorientableCuspedCensus, OctahedralOrientableCuspedCensus, OctahedralNonorientableCuspedCensus, CubicalOrientableCuspedCensus, CubicalNonorientableCuspedCensus, DodecahedralOrientableCuspedCensus, DodecahedralNonorientableCuspedCensus, IcosahedralNonorientableClosedCensus, IcosahedralOrientableClosedCensus, CubicalNonorientableClosedCensus, CubicalOrientableClosedCensus, DodecahedralNonorientableClosedCensus, DodecahedralOrientableClosedCensus, Crossing, Strand, Link, Tangle, RationalTangle, ZeroTangle, InfinityTangle, IdentityBraid, random_link, DTcodec
2.  
3. A = AbelianGroup(elementary_divisors=[5,15,0,0])
4. print(A)
5. print(A[0])
6.  
7. M = Manifold('m004')
8. print(M.symmetry_group())
9.  
10. M = Manifold('K7_1')
11. G = M.fundamental_group()
12. g = G.generators_in_originals()
13. print(g)
14. #M.inside_view()

To explore SnapPy Manifold class for example (unfortunately tinker is not installed on SageMath), open Ubuntu LInux Terminal and evoke Python/Ipython interpreter, import Manifold and execute the below simple code:

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1. from snappy import Manifold
2. M = Manifold('m004')
3. M.inside_view()
4. %gui tk
5.  
6. #Code 2
7. from snappy import ManifoldHP
8. M = ManifoldHP('14n12345') #Try M = ManifoldHP('15n4321')
9. M.volume()
10. M.plink()
11. M.browse()

[Eli]

https://bit.ly/3EhIheI

[Eli]

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1. import pandas as pd
2. import re
3.  
4. # Sample data
5. data = {'date_time': ['Random text 2023-09-03 00:20:00 more text', 'Some other text', '2023-09-03 00:20:00 additional text']}
6. df = pd.DataFrame(data)
7.  
8. # Extract the first datetime occurrence with the desired format
9. pattern = r'\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}'
10. first_datetime = re.search(pattern, ' '.join(df['date_time']))
11.  
12. if first_datetime:
13.     # Extract the time from the first datetime occurrence
14.     first_time = first_datetime.group().split()[1]
15.     print(first_time)
16. else:
17.     print("Datetime not found in DataFrame.")

[Eli]

Python Polars is an alternative to Pandas, written with performance in mind. Test it:

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1. import polars as pl
2.  
3. from datetime import datetime
4.  
5. df = pl.DataFrame(
6.     {
7.         "integer": [1, 2, 3, 4, 5],
8.         "date": [
9.             datetime(2023, 1, 1),
10.             datetime(2023, 1, 2),
11.             datetime(2023, 1, 3),
12.             datetime(2023, 1, 4),
13.             datetime(2023, 1, 5),
14.         ],
15.         "float": [4.0, 5.0, 6.0, 7.0, 8.0],
16.     }
17. )
18.  
19. print(df)
20.  
21. print(df.sample(2))
22. print(df.describe())

[Eli]

Test Vega Altair

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1. # import altair with an abbreviated alias
2. import altair as alt
3.  
4. # load a sample dataset as a pandas DataFrame
5. from vega_datasets import data
6. cars = data.cars()
7.  
8. # make the chart
9. chart = alt.Chart(cars).mark_point().encode(
10.     x='Horsepower',
11.     y='Miles_per_Gallon',
12.     color='Origin',
13. ).interactive()
14.  
15. chart.save('chart.html') # Save to file

[Eli]

These libraries are currently available, test:

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1. import langserve
2. import openai
3. import langchain
4. import langchain_openai
5. import sklearn
6. import fastapi
7. import flask