df = spark.createDataFrame(data, ["name", "json_string"]) # 定义目标数据结构的模式 schema = StructType([ StructField("age", StringType()), StructField("city", StringType()) ]) # 使用from_json函数转换JSON字符串列 df = df.withColumn("json_struct", from_json(df.json_string, ...
print(df1.toJSON().collect()) print(df1.toJSON().map(lambda str_json: json.loads(str_json)).collect()) ['{"objectid":5,"$geometry":{"x":106.36697069600007,"y":37.85252578200004}}'] [{'objectid': 5, '$geometry': {'x': 106.36697069600007, 'y': 37.85252578200004}}] +---+-...
Rows = Rows.withColumn(col, Rows[col].cast(StringType())) 我正在寻找一种方法,在将Column4的内容转换为字符串类型之前,将其更正为表示原始JSON对象的内容。以下是我到目前为止所写的内容(不包括DB插入操作) import pyspark.sql.types as T from pyspark.sql import functions as SF df = spark.read.optio...
步骤2:读取JSON文件并创建DataFrame 接下来,我们需要使用SparkSession对象来读取JSON文件并创建一个DataFrame。DataFrame是一个分布式的数据集,它以表格形式组织和表示数据。 # 读取JSON文件并创建DataFramedf=spark.read.json("path/to/json/file.json") 1. 2. 在上面的代码中,"path/to/json/file.json"是你要解析...
]'''# 将JSON字符串转换为RDDrdd=spark.sparkContext.parallelize([json_string])# 读取JSON数据df=spark.read.json(rdd)# 显示DataFrame内容df.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 输出结果
类似于 JSONdata=[{"first_name":"John","last_name":"Doe","年龄":25},{"first_name":"Jane","last_name":"Doe","年龄":22},{"first_name":"Bob","last_name":"B","年龄":90},]## 转换为 dfdf=spark.createDataFrame(data)## Showdf.show()...
不像pandas直接用df['cols']就可以了 # 需要在filter,select等操作符中才能使用 color_df.select('length').show...', lit(0)).show() # dataframe转json,转完是个rdd color_df.toJSON().first() 5、排序 # pandas的排序 df.sort_values...import math from pyspark.sql import functions as func ...
下面用pyspark实现读取几种格式json 1. Simple JSON: JSON文件 (Simple.json) 代码 frompyspark.sqlimportSparkSessionspark=SparkSession.builder.config("spark.sql.warehouse.dir","file:///C:/temp").appName("readJSON").getOrCreate()readJSONDF=spark.read.json('Simple.json')readJSONDF.show(truncate=...
df.write.text("data_txt") 3.写入json文件 df.write.json("data_json") # 或者 df.write.format("json").mode("overwrite").save("data_json") 结果如下: 4.写入parquet文件(二进制) df.write.parquet("data_parquet") # 或者 df.write.format("parquet").mode("overwrite").save("data_parquet"...
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