gspread-dataframe | Read/write Google spreadsheets using pandas DataFrames | GCP library
kandi X-RAY | gspread-dataframe Summary
kandi X-RAY | gspread-dataframe Summary
gspread-dataframe is a Python library typically used in Cloud, GCP, Numpy, Pandas applications. gspread-dataframe has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install gspread-dataframe' or download it from GitHub, PyPI.
Read/write Google spreadsheets using pandas DataFrames
Read/write Google spreadsheets using pandas DataFrames
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Quality
Security
License
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gspread-dataframe has a low active ecosystem.
It has 193 star(s) with 17 fork(s). There are 5 watchers for this library.
There were 1 major release(s) in the last 6 months.
There are 3 open issues and 32 have been closed. On average issues are closed in 21 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of gspread-dataframe is 4.0.0
Quality
gspread-dataframe has 0 bugs and 0 code smells.
Security
gspread-dataframe has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
gspread-dataframe code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
gspread-dataframe is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
gspread-dataframe releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
gspread-dataframe saves you 408 person hours of effort in developing the same functionality from scratch.
It has 983 lines of code, 60 functions and 7 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed gspread-dataframe and discovered the below as its top functions. This is intended to give you an instant insight into gspread-dataframe implemented functionality, and help decide if they suit your requirements.
- Return string representation of value
- Escape string
Get all kandi verified functions for this library.
gspread-dataframe Key Features
No Key Features are available at this moment for gspread-dataframe.
gspread-dataframe Examples and Code Snippets
No Code Snippets are available at this moment for gspread-dataframe.
Community Discussions
Trending Discussions on gspread-dataframe
QUESTION
Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas
Asked 2021-Oct-11 at 14:51
data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data
I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.
...ANSWER
Answered 2021-Oct-11 at 14:21geopandas 0.10.1
- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
shapely.geometry.MultiPoint()constructor does not work with a filtered series. Pass it a numpy array instead and it works. - full code below, have randomly selected a point to serve as
gpdPoint
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install gspread-dataframe
You can install using 'pip install gspread-dataframe' or download it from GitHub, PyPI.
You can use gspread-dataframe like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use gspread-dataframe like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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