# DataITO

<center><img src="https://camo.githubusercontent.com/8ea5ab2f59ce09a175cb2fd87d0a75b86bde024cbb8b96a596f9d698a89dea15/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f707972696768742d4d49542d677265656e"><img src="https://camo.githubusercontent.com/036c3fa7badfd718f1d5f594921b9eeb0f3122a0529d3f4113aeb584cae74f1b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f707974686f6e2d646174612d626c7565"></center>
## 安装(install)
- 安装开发版(Install development version)
```python
py -m pip install --index-url https://test.pypi.org/simple/ --no-deps dataito
```
- 安装稳定版( Install stable version )
```python
pip install dataito
```
## 使用手册 (中文版)
Python数据输入(Input)、转换(transform)、输出(output),一行代码读取/转换多种格式的数据文件
dataito仅有三个函数,分别是<kbd>read()</kbd>、<kbd>transform()</kbd>、<kbd>save()</kbd>,具体参数及调用方式如下:
### 格式
- 目前支持的读取格式
- txt
- xlsx
- csv
- json(仅支持结构化数据)
- 目前支持的转换格式
- dataframe (pandas)
- array (numpy)
- list
- 目前支持的保存格式
- xlsx(目前仅支持保存为xlsx,在考虑是否要增加自定义格式保存功能)
### 调用方式
- read( )
```python
read(filepath)
```
注:只能读取支持的文件格式(建议filepath之前加个`r`,具体看example)
- transform( )
```python
transform(data,'parameter')
```
parameter中填写为需要转换的目标数据类型,其与type(data)获取的数据类型的关系如下:
| type | type(data) |
| ---------------- | ------------------------------------- |
| dataframe/pandas | <class 'pandas.core.frame.DataFrame'> |
| array/numpy | <class 'numpy.ndarray'> |
| list | <class 'list'> |
```python
>>> data= dataito.transform(data,'dataframe')
>>> type(data)
<class 'pandas.core.frame.DataFrame'>
>>> data= dataito.transform(data,'array')
>>> type(data)
<class 'numpy.ndarray'>
>>> data= dataito.transform(data,'list')
>>> type(data)
<class 'list'>
```
- save( )
```
save(filepath)
```
(建议filepath之前加个`r`,具体看example)
- example
```python
import dataito
filepath = r'data/data.xlsx' #读取支持格式的数据文件
data = dataito.read(filepath) #调用函数读取(读取其他支持的格式也是这个函数)
data= dataito.transform(data,'dataframe') #数据格式转换为想要的格式(转换为其他支持的格式也是这个)
dataito.save(data,r'D:\data\data.xlsx') #保存在data文件夹(默认文件名为data)
```
## User manual (English version)
### format
Python data input (i), transform (t), output (o), a line of code to read / convert a variety of formats of data files
- Currently supported read formats
- txt
- xlsx
- csv
- json (only supports structured data)
- Currently supported conversion formats
- dataframe
- array (numpy)
- list
- Currently supported save formats
- xlsx ( it only supports saving as xlsx. We are considering whether to add the function of saving in custom format.)
### Call mode
- read( )
```python
read(filepath)
```
Note: only the supported file formats can be read (it is recommended to add `r` before filepath, see example for details)
- transform( )
```python
transform(data,'parameter')
```
parameter is the target data type to be converted, and its relationship with the data type obtained by type (data) is as follows:
| type | type(data) |
| ---------------- | ------------------------------------- |
| dataframe/pandas | <class 'pandas.core.frame.DataFrame'> |
| array/numpy | <class 'numpy.ndarray'> |
| list | <class 'list'> |
```python
>>> data= dataito.transform(data,'dataframe')
>>> type(data)
<class 'pandas.core.frame.DataFrame'>
>>> data= dataito.transform(data,'array')
>>> type(data)
<class 'numpy.ndarray'>
>>> data= dataito.transform(data,'list')
>>> type(data)
<class 'list'>
```
- save( )
```
save(filepath)
```
(it is recommended to add `r` before filepath, see example for details)
- example
```python
import dataito
filepath = r'data/data.xlsx' #Read data files in supported formats
data = dataito.read(filepath) #Call the function to read (read other supported formats as well as this function)
data= dataito.transform(data,'dataframe') #Convert the data format to the desired format (and other supported formats)
dataito.save(data,r'D:\data\data.xlsx') #Save in the data folder (the default file name is data). If the path is not written, the file is saved in the root directory
```