meteostat-python | analyze historical weather and climate data | Dataset library

 by   meteostat Python Version: v1.6.5 License: MIT

kandi X-RAY | meteostat-python Summary

kandi X-RAY | meteostat-python Summary

meteostat-python is a Python library typically used in Institutions, Learning, Administration, Public Services, Artificial Intelligence, Dataset applications. meteostat-python has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However meteostat-python has 3 bugs. You can install using 'pip install meteostat-python' or download it from GitHub, PyPI.

The Meteostat Python library provides a simple API for accessing open weather and climate data. The historical observations and statistics are collected by Meteostat from different public interfaces, most of which are governmental. Among the data sources are national weather services like the National Oceanic and Atmospheric Administration (NOAA) and Germany's national meteorological service (DWD). Are you looking for a hosted solution? Try our JSON API.
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            kandi-support Support

              meteostat-python has a low active ecosystem.
              It has 273 star(s) with 36 fork(s). There are 6 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 11 open issues and 56 have been closed. On average issues are closed in 36 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of meteostat-python is v1.6.5

            kandi-Quality Quality

              OutlinedDot
              meteostat-python has 3 bugs (3 blocker, 0 critical, 0 major, 0 minor) and 10 code smells.

            kandi-Security Security

              meteostat-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              meteostat-python code analysis shows 0 unresolved vulnerabilities.
              There are 1 security hotspots that need review.

            kandi-License License

              meteostat-python is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              meteostat-python releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 1566 lines of code, 88 functions and 62 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed meteostat-python and discovered the below as its top functions. This is intended to give you an instant insight into meteostat-python implemented functionality, and help decide if they suit your requirements.
            • Load data for a given station and year
            • Check if a file is in the cache
            • Generate the endpoint path
            • Get local file path
            • Normalize the time series
            • Calculate coverage of rows
            • Warn a warning
            • Get flagings
            • Return a list of datasets
            • Processes a list of datasets
            • Return a copy of Stations with nearby stations
            • Calculate Earth distance between two points
            • Resolve a point to a dataframe
            • Adjust the temperature in a dataframe
            • Get the data for the simulation
            • Return a copy of the Stations object
            • Return a copy of the stations in the given bounding box
            • Normalize the dataframe
            • Fetch data from the cache
            • Interpolate the dataframe
            • Aggregate time series
            • Create a new temporal instance
            • Set start and end time
            • Load the flags for a given station
            • The number of elements in the Series
            Get all kandi verified functions for this library.

            meteostat-python Key Features

            No Key Features are available at this moment for meteostat-python.

            meteostat-python Examples and Code Snippets

            No Code Snippets are available at this moment for meteostat-python.

            Community Discussions

            QUESTION

            Replacing dataframe value given multiple condition from another dataframe with R
            Asked 2022-Apr-14 at 16:16

            I have two dataframes one with the dates (converted in months) of multiple survey replicates for a given grid cell and the other one with snow data for each month for the same grid cell, they have a matching ID column to identify the cells. What I would like to do is to replace in the first dataframe, the one with months of survey replicates, the month value with the snow value for that month considering the grid cell ID. Thank you

            ...

            ANSWER

            Answered 2022-Apr-14 at 14:50
            df3 <- df1
            df3[!is.na(df1)] <- df2[!is.na(df1)]
            #   CellID sampl1 sampl2 sampl3
            # 1      1    0.1    0.4    0.6
            # 2      2    0.1    0.5    0.7
            # 3      3    0.1    0.4    0.8
            # 4      4    0.1      
            # 5      5         
            # 6      6         
            

            Source https://stackoverflow.com/questions/71873315

            QUESTION

            Does Hub support integrations for MinIO, AWS, and GCP? If so, how does it work?
            Asked 2022-Mar-19 at 16:28

            I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.

            I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.

            I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).

            ...

            ANSWER

            Answered 2022-Mar-19 at 16:28

            Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.

            You can find more information about storage authentication in the Hub docs.

            Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.

            Source https://stackoverflow.com/questions/71539946

            QUESTION

            Custom Sampler correct use in Pytorch
            Asked 2022-Mar-17 at 19:22

            I have a map-stype dataset, which is used for instance segmentation tasks. The dataset is very imbalanced, in the sense that some images have only 10 objects while others have up to 1200.

            How can I limit the number of objects per batch?

            A minimal reproducible example is:

            ...

            ANSWER

            Answered 2022-Mar-17 at 19:22

            If what you are trying to solve really is:

            Source https://stackoverflow.com/questions/71500629

            QUESTION

            C++ what is the best sorting container and approach for large datasets (millions of lines)
            Asked 2022-Mar-08 at 11:24

            I'm tackling a exercise which is supposed to exactly benchmark the time complexity of such code.

            The data I'm handling is made up of pairs of strings like this hbFvMF,PZLmRb, each string is present two times in the dataset, once on position 1 and once on position 2 . so the first string would point to zvEcqe,hbFvMF for example and the list goes on....

            example dataset of 50k pairs

            I've been able to produce code which doesn't have much problem sorting these datasets up to 50k pairs, where it takes about 4-5 minutes. 10k gets sorted in a matter of seconds.

            The problem is that my code is supposed to handle datasets of up to 5 million pairs. So I'm trying to see what more I can do. I will post my two best attempts, initial one with vectors, which I thought I could upgrade by replacing vector with unsorted_map because of the better time complexity when searching, but to my surprise, there was almost no difference between the two containers when I tested it. I'm not sure if my approach to the problem or the containers I'm choosing are causing the steep sorting times...

            Attempt with vectors:

            ...

            ANSWER

            Answered 2022-Feb-22 at 07:13

            You can use a trie data structure, here's a paper that explains an algorithm to do that: https://people.eng.unimelb.edu.au/jzobel/fulltext/acsc03sz.pdf

            But you have to implement the trie from scratch because as far as I know there is no default trie implementation in c++.

            Source https://stackoverflow.com/questions/71215478

            QUESTION

            How to create a dataset for tensorflow from a txt file containing paths and labels?
            Asked 2022-Feb-09 at 08:09

            I'm trying to load the DomainNet dataset into a tensorflow dataset. Each of the domains contain two .txt files for the training and test data respectively, which is structured as follows:

            ...

            ANSWER

            Answered 2022-Feb-09 at 08:09

            You can use tf.data.TextLineDataset to load and process multiple txt files at a time:

            Source https://stackoverflow.com/questions/71045309

            QUESTION

            Converting 0-1 values in dataset with the name of the column if the value of the cell is 1
            Asked 2022-Feb-02 at 07:02

            I have a csv dataset with the values 0-1 for the features of the elements. I want to iterate each cell and replace the values 1 with the name of its column. There are more than 500 thousand rows and 200 columns and, because the table is exported from another annotation tool which I update often, I want to find a way in Python to do it automatically. This is not the table, but a sample test which I was using while trying to write a code I tried some, but without success. I would really appreciate it if you can share your knowledge with me. It will be a huge help. The final result I want to have is of the type: (abonojnë, token_pos_verb). If you know any method that I can do this in Excel without the help of Python, it would be even better. Thank you, Brikena

            ...

            ANSWER

            Answered 2022-Jan-31 at 10:08

            Using pandas, this is quite easy:

            Source https://stackoverflow.com/questions/70923533

            QUESTION

            How can i get person class and segmentation from MSCOCO dataset?
            Asked 2022-Jan-06 at 05:04

            I want to download only person class and binary segmentation from COCO dataset. How can I do it?

            ...

            ANSWER

            Answered 2022-Jan-06 at 05:04

            QUESTION

            R - If column contains a string from vector, append flag into another column
            Asked 2021-Dec-16 at 23:33
            My Data

            I have a vector of words, like the below. This is an oversimplification, my real vector is over 600 words:

            ...

            ANSWER

            Answered 2021-Dec-16 at 23:33

            Update: If a list is preferred: Using str_extract_all:

            Source https://stackoverflow.com/questions/70386370

            QUESTION

            How to divide a large image dataset into groups of pictures and save them inside subfolders using python?
            Asked 2021-Dec-08 at 15:13

            I have an image dataset that looks like this: Dataset

            The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).

            Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs). The result would ideally look like this: Sequences

            I have tried using os.walk and loop inside the folder where the image dataset is saved, then I created a dataframe using pandas because I thought I could handle the files more easily but I think I am totally off target here.

            ...

            ANSWER

            Answered 2021-Dec-08 at 15:10

            The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).

            Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs)

            I suggest exploiting datetime built-in libary to get desired result, for each file you have

            1. get substring which is holding timestamp
            2. parse it into datetime.datetime instance using datetime.datetime.strptime
            3. convert said instance into seconds since epoch using .timestamp method
            4. compute number of seconds integer division (//) 10800 (number of seconds inside 3hr)
            5. convert value you got into str and use it as target subfolder name

            Source https://stackoverflow.com/questions/70276989

            QUESTION

            Proper way of cleaning csv file
            Asked 2021-Nov-15 at 22:58

            I've got a huge CSV file, which looks like this:

            ...

            ANSWER

            Answered 2021-Nov-15 at 21:33

            You can use a regular expression for this:

            Source https://stackoverflow.com/questions/69981109

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install meteostat-python

            The Meteostat Python package is available through PyPI:. Meteostat requires Python 3.5 or higher. If you want to visualize data, please install Matplotlib, too.

            Support

            The Meteostat Python library is divided into multiple classes which provide access to the actual data. The documentation covers all aspects of the library:.
            Find more information at:

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            gh repo clone meteostat/meteostat-python

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            git@github.com:meteostat/meteostat-python.git

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