PandasPlotBench

PandasPlotBench is a benchmark to assess the capability of models in writing the code for visualizations given the description of the Pandas DataFrame. 🛠️ Task. Given the plotting task and the description of a Pandas DataFrame, write the code to build a plot. The dataset is based on the MatPlotLib gallery. The paper can be found in arXiv: https://arxiv.org/abs/2412.02764v1. To score your model on this dataset, you can use the our GitHub repository. 📩 If you have… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/PandasPlotBench.

polyOne Data Set

The data set contains 100 million hypothetical polymers each with 29 predicted properties using machine learning models. We use PSMILES strings to represent polymer structures, see here and here. The polymers are generated by decomposing previously synthesized polymers into unique chemical fragments. Random and enumerative compositions of these fragments yield 100 million hypothetical PSMILES strings. All PSMILES strings are chemically valid polymers but, mostly, have never been synthesized before. More information can be found in the paper. Please note the license agreement in the LICENSE file.

Full data set including the properties

The data files are in Apache Parquet format. The files start with polyOne_*.parquet.

I recommend using dask (pip install dask) to load and process the data set. Pandas also works but is slower.

Load sharded data set with dask python import dask.dataframe as dd ddf = dd.read_parquet("*.parquet", engine="pyarrow")

For example, compute the description of data set ```python df_describe = ddf.describe().compute() df_describe

PSMILES strings only



  
generated_polymer_smiles_train.txt - 80 million PSMILES strings for training polyBERT. One string per line.
  
generated_polymer_smiles_dev.txt - 20 million PSMILES strings for testing polyBERT. One string per line.

Analysis of ‘Datasets for Pandas’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rajacsp/datasets-for-pandas on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

This dataset provides an overview of companies listed in the Y Combinator directory, scraped on 13 July 2023. It offers a valuable resource for analysing the startup ecosystem, allowing users to explore companies by industry, geographic location, company size, and more. Y Combinator is a prominent startup accelerator that has funded over 4,000 companies, collectively valued at over $600 billion, with the primary aim of supporting new ventures in their growth.

Columns

• company_id: Unique identifier for each company, provided by Y Combinator.
• company_name: The name of the company.
• short_description: A concise, one-line summary of the company.
• long_description: A more detailed description of the company.
• batch: The specific Y Combinator batch the company belongs to.
• status: The current operational status of the company.
• tags: Industry-specific tags associated with the company.
• location: The physical location of the company.
• country: The country where the company is located.
• year_founded: The year the company was established.
• num_founders: The number of founders associated with the company.
• founders_names: The full names of the company's founders.
• team_size: The number of employees in the company.
• website: The official website URL for the company.
• cb_url: The Crunchbase URL for the company.
• linkedin_url: The LinkedIn profile URL for the company.

Distribution

The dataset is supplied as a CSV file, based on data scraped on 27 February 2023. While specific total row or record counts are not available, various distributions of column values have been noted.

Usage

This dataset is ideal for market research, competitive intelligence, and startup ecosystem analysis. It can be used to identify industry trends, study company demographics, or explore investment opportunities within the Y Combinator portfolio.

Coverage

The dataset covers companies globally, with locations and countries explicitly noted for each entry. The time range for company founding years spans from 2005 to 2023. The data was collected as of 13 July 2023.

License

CCO

Who Can Use It

• Researchers: For academic studies on startup accelerators, entrepreneurship, and tech industry trends.
• Business Analysts: To gain insights into market segments, competitor landscapes, and potential partnership opportunities.
• Investors: For identifying promising startups and understanding investment patterns.
• Aspiring Entrepreneurs: To learn about successful startup profiles and their development paths.

Dataset Name Suggestions

• Y Combinator Company Directory
• YC Startup Data
• Global Startup Accelerator Dataset
• Y Combinator Investment Portfolio

Attributes

Original Data Source: Y Combinator Directory

klib library enables us to quickly visualize missing data, perform data cleaning, visualize data distribution plot, visualize correlation plot and visualize categorical column values. klib is a Python library for importing, cleaning, analyzing and preprocessing data. Explanations on key functionalities can be found on Medium / TowardsDataScience in the examples section or on YouTube (Data Professor).

Original Github repo

https://raw.githubusercontent.com/akanz1/klib/main/examples/images/header.png" alt="klib Header">

Usage

!pip install klib

import klib
import pandas as pd

df = pd.DataFrame(data)

# klib.describe functions for visualizing datasets
- klib.cat_plot(df) # returns a visualization of the number and frequency of categorical features
- klib.corr_mat(df) # returns a color-encoded correlation matrix
- klib.corr_plot(df) # returns a color-encoded heatmap, ideal for correlations
- klib.dist_plot(df) # returns a distribution plot for every numeric feature
- klib.missingval_plot(df) # returns a figure containing information about missing values

Examples

Take a look at this starter notebook.

Further examples, as well as applications of the functions can be found here.

Contributing

Pull requests and ideas, especially for further functions are welcome. For major changes or feedback, please open an issue first to discuss what you would like to change. Take a look at this Github repo.

License

MIT

Download the dataset

At the moment to download the dataset you should use Pandas DataFrame: import pandas as pd df = pd.read_csv("https://huggingface.co/datasets/cybernetic-m/oldIT2modIT/resolve/main/oldIT2modIT_dataset.csv")

You can visualize the dataset with: df.head()

To convert into Huggingface dataset: from datasets import Dataset dataset = Dataset.from_pandas(df)

  Dataset Description

This is an italian dataset formed by 200 old (ancient) italian sentence and… See the full description on the dataset page: https://huggingface.co/datasets/cybernetic-m/oldIT2modIT.

A Benchmark Dataset for Deep Learning for 3D Topology Optimization

This dataset represents voxelized 3D topology optimization problems and solutions. The solutions have been generated in cooperation with the Ariane Group and Synera using the Altair OptiStruct implementation of SIMP within the Synera software. The SELTO dataset consists of four different 3D datasets for topology optimization, called disc simple, disc complex, sphere simple and sphere complex. Each of these datasets is further split into a training and a validation subset.

The following paper provides full documentation and examples:

Dittmer, S., Erzmann, D., Harms, H., Maass, P., SELTO: Sample-Efficient Learned Topology Optimization (2022) https://arxiv.org/abs/2209.05098.

The Python library DL4TO (https://github.com/dl4to/dl4to) can be used to download and access all SELTO dataset subsets.
Each TAR.GZ file container consists of multiple enumerated pairs of CSV files. Each pair describes a unique topology optimization problem and contains an associated ground truth solution. Each problem-solution pair consists of two files, where one contains voxel-wise information and the other file contains scalar information. For example, the i-th sample is stored in the files i.csv and i_info.csv, where i.csv contains all voxel-wise information and i_info.csv contains all scalar information. We define all spatially varying quantities at the center of the voxels, rather than on the vertices or surfaces. This allows for a shape-consistent tensor representation.

For the i-th sample, the columns of i_info.csv correspond to the following scalar information:

• E - Young's modulus [Pa]
• ν - Poisson's ratio [-]
• σ_ys - a yield stress [Pa]
• h - discretization size of the voxel grid [m]

The columns of i.csv correspond to the following voxel-wise information:

• x, y, z - the indices that state the location of the voxel within the voxel mesh
• Ω_design - design space information for each voxel. This is a ternary variable that indicates the type of density constraint on the voxel. 0 and 1 indicate that the density is fixed at 0 or 1, respectively. -1 indicates the absence of constraints, i.e., the density in that voxel can be freely optimized
• Ω_dirichlet_x, Ω_dirichlet_y, Ω_dirichlet_z - homogeneous Dirichlet boundary conditions for each voxel. These are binary variables that define whether the voxel is subject to homogeneous Dirichlet boundary constraints in the respective dimension
• F_x, F_y, F_z - floating point variables that define the three spacial components of external forces applied to each voxel. All forces are body forces given in [N/m^3]
• density - defines the binary voxel-wise density of the ground truth solution to the topology optimization problem

How to Import the Dataset

with DL4TO: With the Python library DL4TO (https://github.com/dl4to/dl4to) it is straightforward to download and access the dataset as a customized PyTorch torch.utils.data.Dataset object. As shown in the tutorial this can be done via:

from dl4to.datasets import SELTODataset

dataset = SELTODataset(root=root, name=name, train=train)

Here, root is the path where the dataset should be saved. name is the name of the SELTO subset and can be one of "disc_simple", "disc_complex", "sphere_simple" and "sphere_complex". train is a boolean that indicates whether the corresponding training or validation subset should be loaded. See here for further documentation on the SELTODataset class.

without DL4TO: After downloading and unzipping, any of the i.csv files can be manually imported into Python as a Pandas dataframe object:

import pandas as pd

root = ...
file_path = f'{root}/{i}.csv'
columns = ['x', 'y', 'z', 'Ω_design','Ω_dirichlet_x', 'Ω_dirichlet_y', 'Ω_dirichlet_z', 'F_x', 'F_y', 'F_z', 'density']
df = pd.read_csv(file_path, names=columns)

Similarly, we can import a i_info.csv file via:

file_path = f'{root}/{i}_info.csv'
info_column_names = ['E', 'ν', 'σ_ys', 'h']
df_info = pd.read_csv(file_path, names=info_columns)

We can extract PyTorch tensors from the Pandas dataframe df using the following function:

import torch

def get_torch_tensors_from_dataframe(df, dtype=torch.float32):
  shape = df[['x', 'y', 'z']].iloc[-1].values.astype(int) + 1
  voxels = [df['x'].values, df['y'].values, df['z'].values]

  Ω_design = torch.zeros(1, *shape, dtype=int)
  Ω_design[:, voxels[0], voxels[1], voxels[2]] = torch.from_numpy(data['Ω_design'].values.astype(int))

  Ω_Dirichlet = torch.zeros(3, *shape, dtype=dtype)
  Ω_Dirichlet[0, voxels[0], voxels[1], voxels[2]] = torch.tensor(df['Ω_dirichlet_x'].values, dtype=dtype)
  Ω_Dirichlet[1, voxels[0], voxels[1], voxels[2]] = torch.tensor(df['Ω_dirichlet_y'].values, dtype=dtype)
  Ω_Dirichlet[2, voxels[0], voxels[1], voxels[2]] = torch.tensor(df['Ω_dirichlet_z'].values, dtype=dtype)

  F = torch.zeros(3, *shape, dtype=dtype)
  F[0, voxels[0], voxels[1], voxels[2]] = torch.tensor(df['F_x'].values, dtype=dtype)
  F[1, voxels[0], voxels[1], voxels[2]] = torch.tensor(df['F_y'].values, dtype=dtype)
  F[2, voxels[0], voxels[1], voxels[2]] = torch.tensor(df['F_z'].values, dtype=dtype)

  density = torch.zeros(1, *shape, dtype=dtype)
  density[:, voxels[0], voxels[1], voxels[2]] = torch.tensor(df['density'].values, dtype=dtype)

  return Ω_design, Ω_Dirichlet, F, density

Date: 2022-07-10 Files: ner_dataset.csv Source: Kaggle entity annotated corpus notes: The dataset only contains the tokens and ner tag labels. Labels are uppercase.

  About Dataset

from Kaggle Datasets

  Context

Annotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set. Tip: Use Pandas Dataframe to load dataset if using Python for… See the full description on the dataset page: https://huggingface.co/datasets/rjac/kaggle-entity-annotated-corpus-ner-dataset.

About Data

I found two datasets about converting text with context to pandas code on Hugging Face, but the challenge is in the context. The context in both datasets is different which reduces the results of the model. First let's mention the data I found and then show examples, solution and some other problems. Rahima411/text-to-pandas : The data is divided into Train with 57.5k and Test with 19.2k.

The data has two columns as you can see in the example:… See the full description on the dataset page: https://huggingface.co/datasets/zeyadusf/text2pandas.