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minimizes errors

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Antonin Python Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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Author: Andrew J. FeltonDate: 5/5/2024

This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis and figure production for the study entitled:

"Global estimates of the storage and transit time of water through vegetation"

Please note that 'turnover' and 'transit' are used interchangeably in this project.

Data information:

The data folder contains key data sets used for analysis. In particular:

"data/turnover_from_python/updated/annual/multi_year_average/average_annual_turnover.nc" contains a global array summarizing five year (2016-2020) averages of annual transit, storage, canopy transpiration, and number of months of data. This is the core dataset for the analysis; however, each folder has much more data, including a dataset for each year of the analysis. Data are also available is separate .csv files for each land cover type. Oterh data can be found for the minimum, monthly, and seasonal transit time found in their respective folders. These data were produced using the python code found in the "supporting_code" folder given the ease of working with .nc and EASE grid in the xarray python module. R was used primarily for data visualization purposes. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here.

Code information

Python scripts can be found in the "supporting_code" folder.

Each R script in this project has a particular function:

01_start.R: This script loads the R packages used in the analysis, sets thedirectory, and imports custom functions for the project. You can also load in the main transit time (turnover) datasets here using the source() function.

02_functions.R: This script contains the custom function for this analysis, primarily to work with importing the seasonal transit data. Load this using the source() function in the 01_start.R script.

03_generate_data.R: This script is not necessary to run and is primarilyfor documentation. The main role of this code was to import and wranglethe data needed to calculate ground-based estimates of aboveground water storage.

04_annual_turnover_storage_import.R: This script imports the annual turnover andstorage data for each landcover type. You load in these data from the 01_start.R scriptusing the source() function.

05_minimum_turnover_storage_import.R: This script imports the minimum turnover andstorage data for each landcover type. Minimum is defined as the lowest monthlyestimate.You load in these data from the 01_start.R scriptusing the source() function.

06_figures_tables.R: This is the main workhouse for figure/table production and supporting analyses. This script generates the key figures and summary statistics used in the study that then get saved in the manuscript_figures folder. Note that allmaps were produced using Python code found in the "supporting_code"" folder.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Traffy Fondue Data

Data pulled from Traffy Fondue, by accessing the Traffy Fondue Open API. Date January 2022 until January 2025

The following code pulled the data:

import os
import json
import requests
from datetime import datetime, timedelta
import time

class TraffyDataFetcher:
  def _init_(self, start_date, subfolder='traffyfonduedata'):
    self.url = "https://publicapi.traffy.in.th/share/teamchadchart/search"
    self.query = {'offset': '0'}
    self.payload = {}
    self.headers = {}
    self.start_date = datetime.strptime(start_date, '%Y-%m-%d')
    self.end_date = datetime.now()
    self.subfolder = subfolder
    self.max_requests_per_minute = 99

    if not os.path.exists(self.subfolder):
      os.makedirs(self.subfolder)

  def add_days_to_date(self, start_date_str, days_to_add):
    start_date = datetime.strptime(start_date_str, '%Y-%m-%d')
    new_date = start_date + timedelta(days=days_to_add)
    return new_date.strftime('%Y-%m-%d')

  def fetch_data(self):
    current_date = self.start_date
    index = 0

    while current_date <= self.end_date:
      start_time = datetime.now()

      self.query['start'] = current_date.strftime('%Y-%m-%d')
      new_date = self.add_days_to_date(self.query['start'], 10)
      self.query['end'] = new_date
      response = requests.request("GET", self.url, headers=self.headers, data=self.payload, params=self.query)
      print(f"offset: {index} response: {response.status_code}")

      filename = f"traffy_{current_date.strftime('%Y-%m-%d')}.json"
      file_path = os.path.join(self.subfolder, filename)

      with open(file_path, "w") as outfile:
        json_object = json.dumps(response.json(), indent=4)
        outfile.write(json_object)

      end_time = datetime.now()
      elapsed_time = (end_time - start_time).total_seconds()
      print(f"Elapsed time: {elapsed_time} s")

      index += 950
      current_date = datetime.strptime(new_date, '%Y-%m-%d') + timedelta(days=1)

      if index % self.max_requests_per_minute == 0:
        time.sleep(60 - elapsed_time)

if _name_ == "_main_":
  fetcher = TraffyDataFetcher(start_date='2022-01-01')
  fetcher.fetch_data()

--

And the following code converted the json to CSV files

import os
import glob
import json
import pandas as pd
#import numpy as np

class TraffyJSONFixer:
  def _init_(self, path_to_json='*.json', subfolder='traffyfonduedata'):
    self.path_to_json = path_to_json
    self.subfolder = subfolder
    self.outputfolder = 'fixedjson'
    self.excelfolder = 'exceloutput'
    self.file_path = os.path.join(self.subfolder, self.path_to_json)
    self.json_files = glob.glob(self.file_path)
    
    # Ensure the subfolder exists
    if not os.path.exists(self.subfolder):
      os.makedirs(self.subfolder)
    # Ensure the outputfolder exists
    if not os.path.exists(self.outputfolder):
      os.makedirs(self.outputfolder)
    # Ensure the excelfolder exists
    if not os.path.exists(self.excelfolder):
      os.makedirs(self.excelfolder)
    
    # Debugging: Print the current working directory and the list of JSON files
    print(f"Current working directory: {os.getcwd()}")
    print(f"Found JSON files: {self.json_files}")
    
  def fix_json_files(self):
    for count, ele in enumerate(self.json_files):
      new_file_name = os.path.join(self.outputfolder, f"data_{os.path.basename(ele)}")
      
      try:
        with open(ele, 'r', encoding='utf-8') as f:
          data = json.load(f)

        # Debugging: Print the type of data
        print(f"Processing file: {ele}")
        print(f"Type of data: {type(data)}")
        
        # Handle different JSON structures
        if isinstance(data, dict) and "results" in data:
          results = data["results"]
        elif isinstance(data, list):
          results = data
        else:
          print(f"Unexpected JSON structure in file: {ele}")
          continue

        # Ensure results is a list or dict before writing
        if isinstance(results, (list, dict)):
          with open(new_file_name, 'w', encoding='utf-8') as f:
            f.write(json.dumps(results, indent=4))
        else:
          print(f"Unexpected type for results in file: {ele}")
      except (json.JSONDecodeError, KeyError) as e:
        print(f"Error processing file {ele}: {e}")

  def jsontoexcel(self):
    jsonfile_path = os.path.join(self.out...

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Disclaimer: This is an artificially generated data using a python script based on arbitrary assumptions listed down.

The data consists of 100,000 examples of training data and 10,000 examples of test data, each representing a user who may or may not buy a smart watch.

----- Version 1 -------

trainingDataV1.csv, testDataV1.csv or trainingData.csv, testData.csv The data includes the following features for each user: 1. age: The age of the user (integer, 18-70) 1. income: The income of the user (integer, 25,000-200,000) 1. gender: The gender of the user (string, "male" or "female") 1. maritalStatus: The marital status of the user (string, "single", "married", or "divorced") 1. hour: The hour of the day (integer, 0-23) 1. weekend: A boolean indicating whether it is the weekend (True or False) 1. The data also includes a label for each user indicating whether they are likely to buy a smart watch or not (string, "yes" or "no"). The label is determined based on the following arbitrary conditions: - If the user is divorced and a random number generated by the script is less than 0.4, the label is "no" (i.e., assuming 40% of divorcees are not likely to buy a smart watch) - If it is the weekend and a random number generated by the script is less than 1.3, the label is "yes". (i.e., assuming sales are 30% more likely to occur on weekends) - If the user is male and under 30 with an income over 75,000, the label is "yes". - If the user is female and 30 or over with an income over 100,000, the label is "yes". Otherwise, the label is "no".

The training data is intended to be used to build and train a classification model, and the test data is intended to be used to evaluate the performance of the trained model.

Following Python script was used to generate this dataset

import random
import csv

# Set the number of examples to generate
numExamples = 100000

# Generate the training data
with open("trainingData.csv", "w", newline="") as csvfile:
  fieldnames = ["age", "income", "gender", "maritalStatus", "hour", "weekend", "buySmartWatch"]
  writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

  writer.writeheader()

  for i in range(numExamples):
    age = random.randint(18, 70)
    income = random.randint(25000, 200000)
    gender = random.choice(["male", "female"])
    maritalStatus = random.choice(["single", "married", "divorced"])
    hour = random.randint(0, 23)
    weekend = random.choice([True, False])

    # Randomly assign the label based on some arbitrary conditions
    # assuming 40% of divorcees won't buy a smart watch
    if maritalStatus == "divorced" and random.random() < 0.4:
      buySmartWatch = "no"
    # assuming sales are 30% more likely to occur on weekends.
    elif weekend == True and random.random() < 1.3:
      buySmartWatch = "yes"
    elif gender == "male" and age < 30 and income > 75000:
      buySmartWatch = "yes"
    elif gender == "female" and age >= 30 and income > 100000:
      buySmartWatch = "yes"
    else:
      buySmartWatch = "no"

    writer.writerow({
      "age": age,
      "income": income,
      "gender": gender,
      "maritalStatus": maritalStatus,
      "hour": hour,
      "weekend": weekend,
      "buySmartWatch": buySmartWatch
    })

----- Version 2 -------

trainingDataV2.csv, testDataV2.csv The data includes the following features for each user: 1. age: The age of the user (integer, 18-70) 1. income: The income of the user (integer, 25,000-200,000) 1. gender: The gender of the user (string, "male" or "female") 1. maritalStatus: The marital status of the user (string, "single", "married", or "divorced") 1. educationLevel: The education level of the user (string, "high school", "associate's degree", "bachelor's degree", "master's degree", or "doctorate") 1. occupation: The occupation of the user (string, "tech worker", "manager", "executive", "sales", "customer service", "creative", "manual labor", "healthcare", "education", "government", "unemployed", or "student") 1. familySize: The number of people in the user's family (integer, 1-5) 1. fitnessInterest: A boolean indicating whether the user is interested in fitness (True or False) 1. priorSmartwatchOwnership: A boolean indicating whether the user has owned a smartwatch in the past (True or False) 1. hour: The hour of the day when the user was surveyed (integer, 0-23) 1. weekend: A boolean indicating whether the user was surveyed on a weekend (True or False) 1. buySmartWatch: A boolean indicating whether the user purchased a smartwatch (True or False)

Python script used to generate the data:

import random
import csv

# Set the number of examples to generate
numExamples = 100000

with open("t...

Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py

The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).

This database dump includes all published structured data organized used by Open Context (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.

These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included.

IMPORTANT

This database dump contains data from roughly 190+ different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.

About the datasetThis record provides all data to reproduce the hyperfine coupling analysis of a T center defect in silicon. The results are reported in "Long-lived entanglement of a spin-qubit register in silicon photonics". DOI: https://doi.org/10.48550/arXiv.2504.15467 ## Files included- hyperfine_terms.npz: NumPy archive containing three float arrays (Fermi_contact, Dipolar, Hyperfine) saved via numpy.savez.- POSCAR: VASP format structure file specifying a 1002‑atom silicon supercell with a T center defect. ## Computational methods- Electronic structure calculations were performed with VASP using the HSE06 screened hybrid functional. ## Usage & reuse1. Load hyperfine data in Python python import numpy as np data = np.load('hyperfine_terms.npz') Fc = data['Fermi_contact'] Dip = data['Dipolar'] Hf = data['Hyperfine']2. **Load defect structures using pymatgen**python from pymatgen.io.vasp import Poscar poscar = Poscar.from_file('POSCAR') structure = poscar.structure

These data belongs to an actual printing company . Each record in Excel file Raw Data/Big_Data present an order from customers. In column "ColorMode" ; 4+0 means the order is one sided and 4+4 means it is two-sided. Files in Instances folder correspond to the instances used for computational tests in the article. Each of these instances has two related file with the same characteristics. One with gdx suffix and one with out any file extension.

Files with gdx suffix can be read by GAMS

Files without suffix are imported by pickle package in Python as objects of class Input (defined in "Input.py" ). You can read the files using the pickle package and Input.py. More information on pickle package at docs.python.org/3/library/pickle

These files are used to import data to the python implementation. The code and relevant description can be found in Read_input.py file.

Dataset Card for Python-DPO

This dataset is the larger version of Python-DPO dataset and has been created using Argilla.

  Load with datasets

To load this dataset with datasets, you'll just need to install datasets as pip install datasets --upgrade and then use the following code: from datasets import load_dataset

ds = load_dataset("NextWealth/Python-DPO")

  Data Fields

Each data instance contains:

instruction: The problem description/requirements chosen_code:… See the full description on the dataset page: https://huggingface.co/datasets/NextWealth/Python-DPO-Large.

This resource contains Jupyter Notebooks with examples for accessing USGS NWIS data via web services and performing subsequent analysis related to drought with particular focus on sites in Utah and the southwestern United States (could be modified to any USGS sites). The code uses the Python DataRetrieval package. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 6 example notebooks: 1. Example 1: Import and plot daily flow data 2. Example 2: Import and plot instantaneous flow data for multiple sites 3. Example 3: Perform analyses with USGS annual statistics data 4. Example 4: Retrieve data and find daily flow percentiles 3. Example 5: Further examination of drought year flows 6. Coding challenge: Assess drought severity

Introduction

This study has exploited the daily weather records of Seungjeongwon Ilgi from the NIKH database. Seungjeongwon Ilgi (http://sjw.history.go.kr/main.do) is a daily record of the Seungjeongwon, the Royal Secretariat of the Joseon Dynasty of Korea. These diaries span from 1623 to 1910 and generally involve daily weather records in the entry header. Their observational site would be located in Seoul (N37°35′, E126°59′). We have exploited the weather records from the NIKH database and classified the daily weather using text mining method. We have also converted the report dates from the traditional lunisolar calendar to the Gregorian calendar, to better contextualise our data into the contemporary daily measurements.

Data

We provide different formats (csv, xlsx, json) to facilitate the usage of data. The main contents of data are listed as below.

• ID: The unique identifier of a specific record in the metadata, which can also serve as the identifier to merge with external data in the NIKH digital database.
• Traditional calendar: The original lunar dates in the NIKH digital database, which are listed in data format "YYYY-MM-DD". More specifically, "L0" implies the leap year and "L1" implies the common year.
• Leap: The identifier of a leap year.
• Gregorian calendar: The Gregorian calendar date that converted by the traditional calendar date.
• Weather Text: The text that describe the weather conditions. Specifically, multiple weather descriptions of the same day have been put together.
• Flag: The computed value that indicates different combinations of weather conditions.
• Volume: The volume of text in the original record.
• Herbal Volume: The volume of text in the herbal record.
• Sunny: A dummy variable that represents whether the weather description contains the expression of sunny.
• Cloudy: A dummy variable that represents whether the weather description contains the expression of cloudy.
• Rainy: A dummy variable that represents whether the weather description contains the expression of rainy.
• Snow: A dummy variable that represents whether the weather description contains the expression of snow.
• Wind: A dummy variable that represents whether the weather description contains the expression of wind.

Import Data

# Python
# CSV file
import pandas as pd
data=pd.read_csv('~/SJWilgi_Seoul_Weather_YR1623_1910.csv',encoding="utf-8") 
# JSON file
data=pd.read_json('~/SJWilgi_Seoul_Weather_YR1623_1910.json',encoding="utf-8")
# Excel file
data=pd.read_excel('~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx') # Excel file

# R
# CSV file
library(readr)
data<- read_csv("~/SJWilgi_Seoul_Weather_YR1623_1910.csv")
# Excel file
library(readxl)
data <- read_excel("~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx")

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