——Exploring the “Ontario Climate Change Data Portal”: A Case Study in Data Exploration Prompts searchable table Examples Download csv file from internet ChatGPT prompt I want you to act as a Python developer tasked with downloading a CSV file from the internet using the provided URL: https://lamps.math.yorku.ca/OntarioClimate/data/content/grids/Historical/Monthly_2m_temperature_8964_ERA5_January1981toMarch2021.csv. To accomplish this, write a Python script that ..
Category : Data
Select a subset of xarray data for a specified year range with Python by using the sel() method. Here’s an example: import xarray as xr # Load your xarray dataset ds = xr.open_dataset(‘my_dataset.nc’) # Select a subset of data for a specified year range subset = ds.sel(time=slice(‘start_year’, ‘end_year’)) Replace my_dataset.nc with the name of your ..
There are a few Python packages that can be used to read RData files. Here are three popular ones: #install rpy2 !pip install rpy2 #An example of how to use rpy2 to load an RData file: #—————————————————- import rpy2.robjects as robjects # Load RData file robjects.r[‘load’](‘file.RData’) # Access R objects from Python r_var = robjects.globalenv[‘var_name’] ..
The following code is used to count the order of the date of February 29 in leap years for a period. import sys import warnings if not sys.warnoptions: warnings.simplefilter(‘ignore’) #Process data import numpy as np import matplotlib.pyplot as plt #Process data import numpy as np import xarray as xr #Writing data files import pandas as ..
The maps posted here are generated based on SPAM 2010 v2.0 data. These maps are for my own reference only when conducting climate change risk assessment studies. Please do not cite them in any academic..
Global maps of major crops for reference. These maps are plotted based on ISIMIP2b’s land use data. for the period of 2005-2014. Only rainfed land use for crops is mapped.These maps are for personal reference only, please do not ..
Group DataFrame using a mapper or by a Series of columns.Return a GroupBy object, grouped by values in column named “col”.Grouping and aggregation functions to help you to learn features of your dataset, like the sum, mean, or average value of a group of elements. The sample code below may help. Prepare data import pandas as ..
Sample code for reducing overfitting problems in deep learning.Answer the following questions: import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # create range of monthly dates download_dates = pd.date_range(start=’2019-01-01′, end=’2020-01-01′, freq=’MS’) # URL from Chrome DevTools Console base_url = (“https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&” “stationID=51442&Year={}&Month={}&Day=7&timeframe=1&submit=Download+Data”) # add format option to year ..
Example code for a regression model with multiple layers. In addition to the input of the first layer, it keeps adding new inputs to the later layers. Prepare data import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # create range of monthly dates download_dates = pd.date_range(start=’2019-01-01′, end=’2020-01-01′, ..
Example code for developing a regression model with keras. It can also answer following questions: Prepare data Read data import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split # create range of monthly dates download_dates = pd.date_range(start=’2019-01-01′, end=’2020-01-01′, freq=’MS’) # URL from Chrome DevTools Console base_url = (“https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&” ..