- Hands-On Artificial Intelligence for IoT
- Amita Kapoor
- 203字
- 2021-07-02 14:02:00
JSON files with the pandas module
JSON strings or files can be read with the pandas.read_json() function, which returns a DataFrame or series object. For example, the following code reads the zips.json file:
df = pd.read_json(os.path.join(data_folder,data_file), lines=True)
print(df)
We set lines=True because each line contains a separate object in JSON format. Without this argument being set to True, pandas will raise ValueError. The DataFrame is printed as follows:
_id city loc pop state 0 1001 AGAWAM [-72.622739, 42.070206] 15338 MA 1 1002 CUSHMAN [-72.51565, 42.377017] 36963 MA ... ... ... ... ... ... 29351 99929 WRANGELL [-132.352918, 56.433524] 2573 AK 29352 99950 KETCHIKAN [-133.18479, 55.942471] 422 AK [29353 rows x 5 columns]
To save the pandas DataFrame or series object to a JSON file or string, use the Dataframe.to_json() function.
More information for both of these functions can be found at these links: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_json.html and https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_json.html.
While CSV and JSON remain the most popular data formats for IoT data, due to its large size, it is often necessary to distribute data. There are two popular distributed mechanisms for data storage and access: HDF5 and HDFS. Let's first learn about the HDF5 format.
推薦閱讀
- 基于C語言的程序設計
- R Data Mining
- Linux Mint System Administrator’s Beginner's Guide
- 嵌入式Linux上的C語言編程實踐
- 最后一個人類
- 大型數據庫管理系統技術、應用與實例分析:SQL Server 2005
- CentOS 8 Essentials
- 大數據時代
- SAP Business Intelligence Quick Start Guide
- Dreamweaver CS6精彩網頁制作與網站建設
- 奇點將至
- Creating ELearning Games with Unity
- PostgreSQL 10 High Performance
- 計算機硬件技術基礎學習指導與練習
- 巧學活用Linux