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https://github.com/wdataorg/wdata
A database with multiple data sets that support drawing, These data sets are: World population data set, World Carbon dioxide Concentration data set, World Number of Cities data set, China number of population data set, China number of space vehicles data set......
https://github.com/wdataorg/wdata
chinese data database pip pypi python3
Last synced: 16 days ago
JSON representation
A database with multiple data sets that support drawing, These data sets are: World population data set, World Carbon dioxide Concentration data set, World Number of Cities data set, China number of population data set, China number of space vehicles data set......
- Host: GitHub
- URL: https://github.com/wdataorg/wdata
- Owner: Wdataorg
- License: apache-2.0
- Created: 2022-06-16T01:01:30.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-12-28T09:40:40.000Z (almost 2 years ago)
- Last Synced: 2024-10-29T08:27:55.223Z (16 days ago)
- Topics: chinese, data, database, pip, pypi, python3
- Language: Python
- Homepage:
- Size: 329 KB
- Stars: 8
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Codeowners: CodeOwners
Awesome Lists containing this project
README
[](https://github.com/Wdataorg/Wdata/issues)
[](https://github.com/Wdataorg/Wdata/network/members)
[](https://shiro.apache.org/license.html)
[](https://github.com/Wdataorg/Wdata/commits "Commit History")
[](#release-files "Release")
[简体中文](https://github.com/Wdataorg/Wdata/tree/main/README_SimpleChinese.md)
- [Function introduction](#Features)
- [download](#Download)
- [use](#Use)
- [Get data](#Get-data)
- [import data](#Import-data)
- [drawing](#Drawing)
- [Data save](#Data-save)
- [Additional Features](#Additional-Features)
- [Cosine similarity function](#Cosine-similarity-function)
- [Distance formula](#Distance-formula)
- [What data do we have](#What-data-do-we-have)
- [Donation](#Donate)
- [About Pypi](#About-Pypi)
- [license](#License)
# Features
This project is a dataset with multiple functions, there are many datasets in it, and it has been uploaded to Pypi.
# Download
This project uses Pypi, so it is recommended to use Pypi to download
There are some dependent libraries, please paste the following code into the terminal
```
pip3 install simplejson
pip3 install openpyxl
pip3 install matplotlib
pip3 install setuptools
```
Code: `pip3 install Wdatabase`
# Use
The package name when we upload is not the same as the package name used in actual use
When importing, use the following code
````python
from Wdata import WdataMain as main
````
The main class has the following functions:
| Functions | Introduction | Syntax | Return Type |
|:---------:|:------------:|:------------------:|:-----------:|
| draw | Draw | Func() | None |
| Save_file | Save file | Func(filename:str, type='json', Sheet='Data', RowOrColumn=True) | bool |
## Import Data
Wdata has a lot of data sets, here we use 200 years of population growth data as an example
The syntax of Wdata_class is as follows:
`WdataMain(json_fname: str)`
`json_fname` is the name of the dataset
````python
from Wdata import WdataMain as main
test = main('Population_growth') # import population growth over 200 years
````
## Get data
We can use the `dict()` function to fetch the data
such as these codes
````python
from Wdata import WdataMain as main
test = main('Population_growth') # import population growth over 200 years
print(dict(test))
````
after running
```shell
~/python test.py
{
'1800': 900000000,
'1820': 1100000000,
'1840': 1200000000,
'1860': 1300000000,
'1880': 1400000000,
'1900': 1650000000,
'1920': 1800000000,
'1940': 2200000000,
'1960': 3000000000,
'1980': 4400000000,
'2000': 5900000000,
'2022': 7400000000
}
````
## Drawing
Drawing functions use the `draw()` function
as the following code
````python
from Wdata import WdataMain as main
test = main('Population_growth') # import population growth over 200 years
test.draw()
````
The result is this
## Data save
You can use the `Save_file()` function to save data
The syntax of `Save_file` is `Save_file(filename:str, type=JSON, Sheet='Data',RowOrColumn=True) -> None`
Parameter description:
The `filename` 'parameter is used to describe saving a file
The `type` 'parameter is used to describe the file type
`Sheet` only takes effect when saving a `.xlsx` file, representing a saved worksheet
`RowOrColumn` only takes effect when saving a `.xlsx` file, indicating the saved format
The file types are as follows:
|File Type | Usage | Description|
|:---:|:---:|:---:|
|Csv | Wdata.CSV | Save File ` file.csv`|
|Json | Wdata.JSON | Save the file `file. json` as the default option|
|XLSX|Wdata.XLSX|Save File `file.xlsx`|
Such as the following code
```python
from Wdata import WdataMain as main
test = main('Population_growth')
test. Save_file('Package_test') # Default option
```
Save the code for the `CSV` file
```python
from Wdata import WdataMain as main
from Wdata import XLSX
test = main('Population_growth') # Population growth over the past 200 years
test. Save_file('Package_test', CSV) # The function automatically adds .csv suffix
```
Saving `.xlsx` files uses the `Sheet` and `RowOrColumn` parameters
`Sheet` means save cell, which defaults to `Data`
`RowOrColumn` means saved form, defaulting to `True`
```python
from Wdata import WdataMain as main
from Wdata import XLSX
test = main('Population_growth') # Population growth over the past 200 years
test. Save_file('Package_test', XLSX) # This function automatically adds .xlsx suffix
# test. Save_file('Package_test', XLSX, RowOrColumn=False) This code is saved as a column
```
When `RowOrColumn` is `True`, the saved form looks like this
| 1820 |1840 | 1860 | 1880 | 1900 | 1920 | 1940 |1960|1980 | 2000 |2022|
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|1100000000|1200000000|1300000000|1400000000|1650000000|1800000000|2200000000|3000000000|4400000000|5900000000|7400000000|
On the contrary, it is like this
| 1820 | 1100000000 |
|:---:|:---:|
|1840 |1200000000|
|1860|1300000000|
|1880|1400000000|
|1900|1650000000|
|1920|1800000000|
|1940|2200000000|
|1960|3000000000|
|1980|4400000000|
|2000|5900000000|
|2022|7400000000|
# Additional Features
## Cosine similarity function
The cosine similarity function can calculate the cosine similarity of two coordinates in two-dimensional space according to the cosine similarity formula
usage method:
```python
from Wdata import mathfunc
Xy1=(2, 3) # First coordinate
Xy2=(3, 5) # Second coordinate
Result=mathfunc.similarity (xy1, xy2) # Cosine similarity
print(result)
```
## Distance formula
Distance formula Use Euclid distance formula to calculate the distance between two coordinates in two-dimensional space
usage method:
```python
from Wdata import mathfunc
xy1 = (2, 3)
xy2 = (3, 5)
Result=mathfunc.distance (xy1, xy2) # Distance formula
print(result)
```
# What data do we have
Currently we have the following data
| name | description | unit of measure |
|:--------------------------------:|:---------------------:|:---------:|
| Population_growth | Population Growth 1800-2022 | People |
| Chinese_spacecraft | 2017-2020.06 Chinese spacecraft launches | Spacecraft |
| World_spacecraft | 2017-2020.06 World Spacecraft Launches | Spacecraft |
> The above data comes from Bing and Baidu. The author cannot guarantee the accuracy of the data and should not be used for professional purposes
# Donate
Due to special reasons, the author was unable to register a `Paypal` account and was forced to use Alipay
For details, please see [Donation Instructions](https://wdataorg.github.io/Sponsor/)
# About Pypi
The `Wdataorg` team has used `twine` to upload this library to `Pypi`
[Wdataorg Pypi account](https://pypi.org/user/Lucky_Pupil/)
[Wdatabase Pypi warehouse address](https://pypi.org/project/Wdatabase/)
# License
This open source project uses `Apache License 2.0`
In the process of using this open source project, please use it strictly in accordance with the license
The final interpretation right belongs to the development team `Wdataorg`
[Project License Link](https://github.com/Wdataorg/Wdata/blob/main/LICENSE)