It is a dataset with notebook kind of learning. Download the whole package and you will find everything to learn basics to advanced pandas which is exactly what you will need in machine learning and in data science. 😄

This will gives you the overview and data analysis tools in pandas that is mostly required in the data manipulation and extraction important data.

Use this notebook as notes for pandas. whenever you forget the code or syntax open it and scroll through it and you will find the solution. 🥳

What is Pandas?

Pandas is a Python library used for working with data sets.

It has functions for analyzing, cleaning, exploring, and manipulating data.

The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.

Why Use Pandas?

Pandas allows us to analyze big data and make conclusions based on statistical theories.

Pandas can clean messy data sets, and make them readable and relevant.

Relevant data is very important in data science.

What Can Pandas Do?

Pandas gives you answers about the data. Like:

Is there a correlation between two or more columns?

What is average value?

Max value?

Min value?

Dataset

This dataset was created by Amir Raja

Contents

This dataset presents detailed building operation data from the three blocks (A, B and C) of the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX. The aim of PHOENIX is to improve buildings efficiency, and therefore we included information of:
(i) consumption data, aggregated by block in kWh; (ii) HVAC (Heating, Ventilation and Air Conditioning) data with several features, such as state (ON=1, OFF=0), operation mode (None=0, Heating=1, Cooling=2), setpoint and device type; (iii) indoor temperature per room; (iv) weather data, including temperature, humidity, radiation, dew point, wind direction and precipitation; (v) carbon dioxide and presence data for few rooms; (vi) relationships between HVAC, temperature, carbon dioxide and presence sensors identifiers with their respective rooms and blocks. Weather data was acquired from the IMIDA (Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario).

import pandas as pd

  Example dataset with new columns

data = [ { "title": "Pandas Library", "about": "Pandas is a Python library for data manipulation and analysis.", "procedure": "Install Pandas via pip, load data into DataFrames, clean and analyze data using built-in functions.", "content": """ Pandas provides data structures like Series and DataFrame for handling structured data. It supports indexing, slicing, aggregation, joining, and filtering… See the full description on the dataset page: https://huggingface.co/datasets/vicky3241/rag.

This dataset consists of three one-dimensional datasets, each containing a series of values. One-dimensional data is characterized by its simplicity, making it an ideal starting point for those new to data analysis and manipulation. With the power of Pandas Series, you can perform a wide range of operations and functions to gain insights and derive valuable information from these datasets.

This dataset includes data that is provided in the Udemy course "Data Analysis with Pandas and Python" by Boris Paskhaver.

The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from their ever-expanding datasets. The market, currently estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This growth is fueled by several factors, including the rising adoption of big data analytics, the proliferation of cloud-based solutions offering enhanced accessibility and scalability, and the growing demand for data-driven decision-making across diverse industries like finance, healthcare, and retail. The market is segmented by application (large enterprises and SMEs) and type (graphical and non-graphical tools), with graphical tools currently holding a larger market share due to their user-friendly interfaces and ability to effectively communicate complex data patterns. Large enterprises are currently the dominant segment, but the SME segment is anticipated to experience faster growth due to increasing affordability and accessibility of EDA solutions. Geographic expansion is another key driver, with North America currently holding the largest market share due to early adoption and a strong technological ecosystem. However, regions like Asia-Pacific are exhibiting high growth potential, fueled by rapid digitalization and a burgeoning data science talent pool. Despite these opportunities, the market faces certain restraints, including the complexity of some EDA tools requiring specialized skills and the challenge of integrating EDA tools with existing business intelligence platforms. Nonetheless, the overall market outlook for EDA tools remains highly positive, driven by ongoing technological advancements and the increasing importance of data analytics across all sectors. The competition among established players like IBM Cognos Analytics and Altair RapidMiner, and emerging innovative companies like Polymer Search and KNIME, further fuels market dynamism and innovation.

Discover the booming Exploratory Data Analysis (EDA) tools market! Our in-depth analysis reveals key trends, growth drivers, and top players shaping this $3 billion industry, projected for 15% CAGR through 2033. Learn about market segmentation, regional insights, and future opportunities.

This resource serves as a template for creating a curve number grid raster file which could be used to create corresponding maps or for further utilization, soil data and reclassified land-use raster files are created along the process, user has to provided or connect to a set of shape-files including boundary of watershed, soil data and land-use containing this watershed, land-use reclassification and curve number look up table. Script contained in this resource mainly uses PyQGIS through Jupyter Notebook for majority of the processing with a touch of Pandas for data manipulation. Detailed description of procedure are commented in the script.

Part of the dissertation Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods.© 2020, Bastian Bechtold. All rights reserved. Estimating the fundamental frequency of speech remains an active area of research, with varied applications in speech recognition, speaker identification, and speech compression. A vast number of algorithms for estimatimating this quantity have been proposed over the years, and a number of speech and noise corpora have been developed for evaluating their performance. The present dataset contains estimated fundamental frequency tracks of 25 algorithms, six speech corpora, two noise corpora, at nine signal-to-noise ratios between -20 and 20 dB SNR, as well as an additional evaluation of synthetic harmonic tone complexes in white noise.The dataset also contains pre-calculated performance measures both novel and traditional, in reference to each speech corpus’ ground truth, the algorithms’ own clean-speech estimate, and our own consensus truth. It can thus serve as the basis for a comparison study, or to replicate existing studies from a larger dataset, or as a reference for developing new fundamental frequency estimation algorithms. All source code and data is available to download, and entirely reproducible, albeit requiring about one year of processor-time.Included Code and Data

ground truth data.zip is a JBOF dataset of fundamental frequency estimates and ground truths of all speech files in the following corpora:

CMU-ARCTIC (consensus truth) [1]FDA (corpus truth and consensus truth) [2]KEELE (corpus truth and consensus truth) [3]MOCHA-TIMIT (consensus truth) [4]PTDB-TUG (corpus truth and consensus truth) [5]TIMIT (consensus truth) [6]

noisy speech data.zip is a JBOF datasets of fundamental frequency estimates of speech files mixed with noise from the following corpora:NOISEX [7]QUT-NOISE [8]

synthetic speech data.zip is a JBOF dataset of fundamental frequency estimates of synthetic harmonic tone complexes in white noise.noisy_speech.pkl and synthetic_speech.pkl are pickled Pandas dataframes of performance metrics derived from the above data for the following list of fundamental frequency estimation algorithms:AUTOC [9]AMDF [10]BANA [11]CEP [12]CREPE [13]DIO [14]DNN [15]KALDI [16]MAPSMBSC [17]NLS [18]PEFAC [19]PRAAT [20]RAPT [21]SACC [22]SAFE [23]SHR [24]SIFT [25]SRH [26]STRAIGHT [27]SWIPE [28]YAAPT [29]YIN [30]

noisy speech evaluation.py and synthetic speech evaluation.py are Python programs to calculate the above Pandas dataframes from the above JBOF datasets. They calculate the following performance measures:Gross Pitch Error (GPE), the percentage of pitches where the estimated pitch deviates from the true pitch by more than 20%.Fine Pitch Error (FPE), the mean error of grossly correct estimates.High/Low Octave Pitch Error (OPE), the percentage pitches that are GPEs and happens to be at an integer multiple of the true pitch.Gross Remaining Error (GRE), the percentage of pitches that are GPEs but not OPEs.Fine Remaining Bias (FRB), the median error of GREs.True Positive Rate (TPR), the percentage of true positive voicing estimates.False Positive Rate (FPR), the percentage of false positive voicing estimates.False Negative Rate (FNR), the percentage of false negative voicing estimates.F₁, the harmonic mean of precision and recall of the voicing decision.

Pipfile is a pipenv-compatible pipfile for installing all prerequisites necessary for running the above Python programs.

The Python programs take about an hour to compute on a fast 2019 computer, and require at least 32 Gb of memory.References:

John Kominek and Alan W Black. CMU ARCTIC database for speech synthesis, 2003.Paul C Bagshaw, Steven Hiller, and Mervyn A Jack. Enhanced Pitch Tracking and the Processing of F0 Contours for Computer Aided Intonation Teaching. In EUROSPEECH, 1993.F Plante, Georg F Meyer, and William A Ainsworth. A Pitch Extraction Reference Database. In Fourth European Conference on Speech Communication and Technology, pages 837–840, Madrid, Spain, 1995.Alan Wrench. MOCHA MultiCHannel Articulatory database: English, November 1999.Gregor Pirker, Michael Wohlmayr, Stefan Petrik, and Franz Pernkopf. A Pitch Tracking Corpus with Evaluation on Multipitch Tracking Scenario. page 4, 2011.John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, and Victor Zue. TIMIT Acoustic-Phonetic Continuous Speech Corpus, 1993.Andrew Varga and Herman J.M. Steeneken. Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recog- nition systems. Speech Communication, 12(3):247–251, July 1993.David B. Dean, Sridha Sridharan, Robert J. Vogt, and Michael W. Mason. The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms. Proceedings of Interspeech 2010, 2010.Man Mohan Sondhi. New methods of pitch extraction. Audio and Electroacoustics, IEEE Transactions on, 16(2):262—266, 1968.Myron J. Ross, Harry L. Shaffer, Asaf Cohen, Richard Freudberg, and Harold J. Manley. Average magnitude difference function pitch extractor. Acoustics, Speech and Signal Processing, IEEE Transactions on, 22(5):353—362, 1974.Na Yang, He Ba, Weiyang Cai, Ilker Demirkol, and Wendi Heinzelman. BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):1833–1848, December 2014.Michael Noll. Cepstrum Pitch Determination. The Journal of the Acoustical Society of America, 41(2):293–309, 1967.Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. CREPE: A Convolutional Representation for Pitch Estimation. arXiv:1802.06182 [cs, eess, stat], February 2018. arXiv: 1802.06182.Masanori Morise, Fumiya Yokomori, and Kenji Ozawa. WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications. IEICE Transactions on Information and Systems, E99.D(7):1877–1884, 2016.Kun Han and DeLiang Wang. Neural Network Based Pitch Tracking in Very Noisy Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):2158–2168, Decem- ber 2014.Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur. A pitch extraction algorithm tuned for automatic speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 2494–2498. IEEE, 2014.Lee Ngee Tan and Abeer Alwan. Multi-band summary correlogram-based pitch detection for noisy speech. Speech Communication, 55(7-8):841–856, September 2013.Jesper Kjær Nielsen, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, and Søren Holdt Jensen. Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient. Signal Processing, 135:188–197, June 2017.Sira Gonzalez and Mike Brookes. PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(2):518—530, February 2014.Paul Boersma. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In Proceedings of the institute of phonetic sciences, volume 17, page 97—110. Amsterdam, 1993.David Talkin. A robust algorithm for pitch tracking (RAPT). Speech coding and synthesis, 495:518, 1995.Byung Suk Lee and Daniel PW Ellis. Noise robust pitch tracking by subband autocorrelation classification. In Interspeech, pages 707–710, 2012.Wei Chu and Abeer Alwan. SAFE: a statistical algorithm for F0 estimation for both clean and noisy speech. In INTERSPEECH, pages 2590–2593, 2010.Xuejing Sun. Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, volume 1, page I—333. IEEE, 2002.Markel. The SIFT algorithm for fundamental frequency estimation. IEEE Transactions on Audio and Electroacoustics, 20(5):367—377, December 1972.Thomas Drugman and Abeer Alwan. Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics. In Interspeech, page 1973—1976, 2011.Hideki Kawahara, Masanori Morise, Toru Takahashi, Ryuichi Nisimura, Toshio Irino, and Hideki Banno. TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation. In Acous- tics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, pages 3933–3936. IEEE, 2008.Arturo Camacho. SWIPE: A sawtooth waveform inspired pitch estimator for speech and music. PhD thesis, University of Florida, 2007.Kavita Kasi and Stephen A. Zahorian. Yet Another Algorithm for Pitch Tracking. In IEEE International Conference on Acoustics Speech and Signal Processing, pages I–361–I–364, Orlando, FL, USA, May 2002. IEEE.Alain de Cheveigné and Hideki Kawahara. YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4):1917, 2002.

ObjectiveThe primary objective of this study was to analyze CpG dinucleotide dynamics in coronaviruses by comparing Wuhan-Hu-1 with its closest and most distant relatives. Heatmaps were generated to visualize CpG counts and O/E ratios across intergenic regions, providing a clear depiction of conserved and divergent CpG patterns.Methods1. Data CollectionSource : The dataset includes CpG counts and O/E ratios for various coronaviruses, extracted from publicly available genomic sequences.Format : Data was compiled into a CSV file containing columns for intergenic regions, CpG counts, and O/E ratios for each virus.2. PreprocessingData Cleaning :Missing values (NaN), infinite values (inf, -inf), and blank entries were handled using Python's pandas library.Missing values were replaced with column means, and infinite values were capped at a large finite value (1e9).Reshaping :The data was reshaped into matrices for CpG counts and O/E ratios using meltpandas[] and pivot[] functions.3. Distance CalculationEuclidean Distance :Pairwise Euclidean distances were calculated between Wuhan-Hu-1 and other viruses using the scipy.spatial.distance.euclidean function.Distances were computed separately for CpG counts and O/E ratios, and the total distance was derived as the sum of both metrics.4. Identification of Closest and Distant RelativesThe virus with the smallest total distance was identified as the closest relative .The virus with the largest total distance was identified as the most distant relative .5. Heatmap GenerationTools :Heatmaps were generated using Python's seaborn library (sns.heatmap) and matplotlib for visualization.Parameters :Heatmaps were annotated with numerical values for clarity.A color gradient (coolwarm) was used to represent varying CpG counts and O/E ratios.Titles and axis labels were added to describe the comparison between Wuhan-Hu-1 and its relatives.ResultsClosest Relative :The closest relative to Wuhan-Hu-1 was identified based on the smallest Euclidean distance.Heatmaps for CpG counts and O/E ratios show high similarity in specific intergenic regions.Most Distant Relative :The most distant relative was identified based on the largest Euclidean distance.Heatmaps reveal significant differences in CpG dynamics compared to Wuhan-Hu-1 .Tools and LibrariesThe following tools and libraries were used in this analysis:Programming Language :Python 3.13Libraries :pandas: For data manipulation and cleaning.numpy: For numerical operations and handling missing/infinite values.scipy.spatial.distance: For calculating Euclidean distances.seaborn: For generating heatmaps.matplotlib: For additional visualization enhancements.File Formats :Input: CSV files containing CpG counts and O/E ratios.Output: PNG images of heatmaps.Files IncludedCSV File :Contains the raw data of CpG counts and O/E ratios for all viruses.Heatmap Images :Heatmaps for CpG counts and O/E ratios comparing Wuhan-Hu-1 with its closest and most distant relatives.Python Script :Full Python code used for data processing, distance calculation, and heatmap generation.Usage NotesResearchers can use this dataset to further explore the evolutionary dynamics of CpG dinucleotides in coronaviruses.The Python script can be adapted to analyze other viral genomes or datasets.Heatmaps provide a visual summary of CpG dynamics, aiding in hypothesis generation and experimental design.AcknowledgmentsSpecial thanks to the open-source community for developing tools like pandas, numpy, seaborn, and matplotlib.This work was conducted as part of an independent research project in molecular biology and bioinformatics.LicenseThis dataset is shared under the CC BY 4.0 License , allowing others to share and adapt the material as long as proper attribution is given.DOI: 10.6084/m9.figshare.28736501

This database contains reactor physic data gathered from high-fidelity sodium fast reactor MCNP models. Each reactor design contains values such as k-eff, beta-eff, sodium void coefficient, Doppler coefficient, etc. The data is stored as an h5 database and can easily be converted to a Pandas dataframe for manipulation.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Pandu Pandas on 2025-12-10. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Timeseries Data Processing

This repository contains a script for loading and processing time series data using the datasets library and converting it to a pandas DataFrame for further analysis.

  Dataset

The dataset used contains time series data with the following features:

id: Identifier for the dataset, formatted as Country_Number of Household (e.g., GE_1 for Germany, household 1).
datetime: Timestamp indicating the date and time of the observation.
target: Energy… See the full description on the dataset page: https://huggingface.co/datasets/OpenSynth/TUDelft-Electricity-Consumption-1.0.

An in-depth analysis of millions of data entries from Chicago’s Field Museum underwent implementation, furnishing insights related to 25 Gorilla specimens and spanning the realms of biogeography, zoology, primatology, and biological anthropology. Taxonomically, and at first glance, all specimens examined belong to the kingdom Animalia, phylum Chordata, class Mammalia, order Primates, and family Hominidae. Furthermore, these specimens can be further categorized under the genus Gorilla and species gorilla, with most belonging to the subspecies Gorilla gorilla gorilla and some specimens being categorized as Gorilla gorilla. Biologically, specimens’ sex distribution entails 16 specimens (or 64% of the total) being identified as male and 5 (or 20%) identified as female, with 4 (or 16%) specimens having their sex unassigned. Furthermore, collectors, none of whom are unidentified by name, culled most of these specimens from unidentified zoos, with a few specimens having been sourced from Ward’s Natural Science Establishment, a well-known natural science materials supplier to North American museums. In terms of historicity, the specimens underwent collection between 1975 and 1993, with some entries lacking this information. Additionally, multiple organ preparations have been performed on the specimens, encompassing skulls, skeletons, skins, and endocrine organs being mounted and alcohol-preserved. Disappointingly, despite the existence of these preparations, tissue samples and coordinates are largely unavailable for the 25 specimens on record, limiting further research or analysis. In fact, tissue sampling is available for a sole specimen identified by IRN 2661980. Only one specimen, identifiable as IRN 2514759, has a specified geographical location indicated as “Africa, West Africa, West Indies,” while the rest have either “Unknown/None, Zoo” locations, signaling that no entry is available. Python code to extract data from the Field Museum’s zoological collections records and online database include the contents of the .py file herewith attached. This code constitutes a web scraping algorithm, retrieving data from the above-mentioned website, processing it, and storing it in a structured format. To achieve these tasks, it first imports necessary libraries by drawing on requests for making HTTP requests, Pandas for handling data, time for introducing delays, lxml for parsing HTML, and BeautifulSoup for web scraping. Furthermore, this algorithm defines the main URL for searching for Gorilla gorilla specimens before setting up headers for making HTTP requests, e.g., User-Agent and other headers to mimic a browser request. Next, an HTTP GET request to the main URL is made, and the response text is obtained. The next step consists of parsing the response text using BeautifulSoup and lxml. Extracting information from the search results page (e.g., Internal Record Number, Catalog Subset, Higher Classification, Catalog Number, Taxonomic Name, DwC Locality, Collector/field, Collection No., Coordinates Available, Tissue Available, and Sex) comes next. This information is then stored in a list called basic_data. The algorithm subsequently iterates through each record in basic_data, and accesses its detailed information page by making another HTTP GET request with the extracted URL. For each detailed information page, the code thereafter extracts additional data (e.g., FM Catalog, Scientific Name, Phylum, Class, Order, Family, Genus, Species, Field Number, Collector, Collection No., Geography, Date Collected, Preparations, Tissue Available, Co-ordinates Available, and Sex). Correspondingly, this information is stored in a list called main_data. The above algorithm processes the final main_data list and converts it into a structured format, i.e., a CSV file.

Dive into the world of data manipulation with pandas, the powerful Python library for data analysis. This series of exercises is designed for beginners who are eager to learn how to use pandas for data wrangling tasks. Each exercise will cover a different aspect of pandas, from loading and exploring datasets to manipulating data and performing basic analysis. Whether you're new to programming or just getting started with pandas, these exercises will help you build a solid foundation in data wrangling skills. Join us on this exciting journey and unleash the power of pandas!

Estimating the distributional impacts of energy subsidy removal and compensation schemes in Ecuador based on input-output and household data.

Import files: Dictionary Categories.csv, Dictionary ENI-IOT.csv, and Dictionary Subcategories.csv based on [1] Dictionary IOT.csv and IOT_2012.csv (cannot be redistruted) based on [2] Dictionary Taxes.csv and Dictionary Transfers.csv based on [3] ENIGHUR11_GASTOS_V.csv, ENIGHUR11_HOGARES_AGREGADOS.csv, and ENIGHUR11_PERSONAS_INGRESOS.csv based on [4] Price increase scenarios.csv based on [5]

Further basic files and documents: [1] 4_M&D_Mapping ENIGHUR expenditures to IOT_180605.xlsm [2] Input-output table 2012 (https://contenido.bce.fin.ec/documentos/PublicacionesNotas/Catalogo/CuentasNacionales/Anuales/Dolares/MIP2012Ampliada.xls). Save the sheet with the IOT 2012 (Matriz simétrica) as IOT_2012.csv and edit the format: first column and row: IOT labels [3] 4_M&D_ENIGHUR income_180606.xlsx [4] ENIGHUR data can be retrieved from http://www.ecuadorencifras.gob.ec/encuesta-nacional-de-ingresos-y-gastos-de-los-hogares-urbanos-y-rurales/ Household datasets are only available in SPSS file format and the free software PSPP is used to convert .sav- to .csv-files, as this format can be read directly and efficiently into a Python Pandas DataFrame. See PSPP syntax below: save translate /outfile = filename /type = CSV /textoptions decimal = DOT /textoptions delimiter = ';' /fieldnames /cells=values /replace. [5] 3_Ecuador_Energy subsidies and 4_M&D_Price scenarios_180610.xlsx

The giant panda is an example of a species that has faced extensive historical habitat fragmentation and anthropogenic disturbance, and is assumed to be isolated in numerous subpopulations with limited gene flow between them. To investigate the population size, health and connectivity of pandas in a key habitat area, we noninvasively collected a total of 539 fresh wild giant panda fecal samples for DNA extraction within Wolong Nature Reserve, Sichuan, China. Seven validated tetra-microsatellite markers were used to analyze each sample, and a total of 142 unique genotypes were identified. Non-spatial and spatial capture-recapture models estimated the population size of the reserve at 164 and 137 individuals (95% confidence intervals 153-175 and 115-163), respectively. Relatively high levels of genetic variation and low levels of inbreeding were estimated, indicating adequate genetic diversity. Surprisingly, no significant genetic boundaries were found within the population despite the national road G350 that bisects the reserve, which is also bordered with patches of development and agricultural land. We attribute this to high rates of migration, with 4 giant panda road-crossing events confirmed within a year based on repeated captures of individuals. This likely means that giant panda populations within mountain ranges are better connected than previously thought. Increased development and tourism traffic in the area and throughout the current panda distribution poses a threat of increasing population isolation, however. Maintaining and restoring adequate habitat corridors for dispersal is thus a vital step for preserving the levels of gene flow seen in our analysis and the continued conservation of the giant panda meta-population in both Wolong and throughout their current range.

A list of different projects selected to analyze class comments (available in the source code) of various languages such as Java, Python, and Pharo. The projects vary in terms of size, contributors, and domain.

Structure

Projects/
  Java_projects/
    eclipse.zip
    guava.zip
    guice.zip
    hadoop.zip
    spark.zip
    vaadin.zip

  Pharo_projects/
    images/
      GToolkit.zip
      Moose.zip
      PetitParser.zip
      Pillar.zip
      PolyMath.zip
      Roassal2.zip
      Seaside.zip

    vm/
      70-x64/Pharo

    Scripts/
      ClassCommentExtraction.st
      SampleSelectionScript.st    

  Python_projects/
    django.zip
    ipython.zip
    Mailpile.zip
    pandas.zip
    pipenv.zip
    pytorch.zip   
    requests.zip 
  

Contents of the Replication Package

Projects/ contains the raw projects of each language that are used to analyze class comments. - Java_projects/ - eclipse.zip - Eclipse project downloaded from the GitHub. More detail about the project is available on GitHub Eclipse. - guava.zip - Guava project downloaded from the GitHub. More detail about the project is available on GitHub Guava. - guice.zip - Guice project downloaded from the GitHub. More detail about the project is available on GitHub Guice - hadoop.zip - Apache Hadoop project downloaded from the GitHub. More detail about the project is available on GitHub Apache Hadoop - spark.zip - Apache Spark project downloaded from the GitHub. More detail about the project is available on GitHub Apache Spark - vaadin.zip - Vaadin project downloaded from the GitHub. More detail about the project is available on GitHub Vaadin

• Pharo_projects/

  • images/ -

    • GToolkit.zip - Gtoolkit project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Moose.zip - Moose project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • PetitParser.zip - Petit Parser project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Pillar.zip - Pillar project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • PolyMath.zip - PolyMath project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Roassal2.zip - Roassal2 project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.
    • Seaside.zip - Seaside project is imported into the Pharo image. We can run this image with the virtual machine given in the vm/ folder. The script to extract the comments is already provided in the image.

  • vm/ -

  • 70-x64/Pharo - Pharo7 (version 7 of Pharo) virtual machine to instantiate the Pharo images given in the images/ folder. The user can run the vm on macOS and select any of the Pharo image.

  • Scripts/ - It contains the sample Smalltalk scripts to extract class comments from various projects.

  • ClassCommentExtraction.st - A Smalltalk script to show how class comments are extracted from various Pharo projects. This script is already provided in the respective project image.

  • SampleSelectionScript.st - A Smalltalk script to show sample class comments of Pharo projects are selected. This script can be run in any of the Pharo images given in the images/ folder.

• Python_projects/

  • django.zip - Django project downloaded from the GitHub. More detail about the project is available on GitHub Django
  • ipython.zip - IPython project downloaded from the GitHub. More detail about the project is available on GitHub on IPython
  • Mailpile.zip - Mailpile project downloaded from the GitHub. More detail about the project is available on GitHub on Mailpile
  • pandas.zip - pandas project downloaded from the GitHub. More detail about the project is available on GitHub on pandas
  • pipenv.zip - Pipenv project downloaded from the GitHub. More detail about the project is available on GitHub on Pipenv
  • pytorch.zip - PyTorch project downloaded from the GitHub. More detail about the project is available on GitHub on PyTorch
  • requests.zip - Requests project downloaded from the GitHub. More detail about the project is available on GitHub on Requests