common returned json data from py req

## Dealing w JSON Data from API Requests
 
These code snippets provide a basic framework for handling various scenarios in data engineering tasks involving JSON data and PySpark. The actual implementation may require more specific details based on the context and data.
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1. **Nested JSON Objects**:
    ```python
    from pyspark.sql.functions import col
 
    # Assuming df is your DataFrame
    df.select(col("main_column.nested_field1"), col("main_column.nested_field2")).show()
    ```
 
2. **JSON Arrays**:
    ```python
    from pyspark.sql.functions import explode
 
    # Exploding the array into separate rows
    df.withColumn("exploded_column", explode(col("array_column"))).show()
    ```
 
3. **Paginated API Responses**:
    ```python
    # Assume getPage is a function to fetch a page of data
    for page_number in range(1, total_pages + 1):
        page_data = getPage(api_endpoint, page_number)
        # Process page_data with PySpark
    ```
 
4. **Inconsistent Data Formats**:
    ```python
    from pyspark.sql.functions import when
 
    # Handling different data formats
    df.withColumn("unified_column", 
                  when(col("column1").isNotNull(), col("column1"))
                  .otherwise(col("column2")))
    ```
 
5. **Error Handling and Retry Logic**:
    ```python
    import time
 
    retries = 3
    for _ in range(retries):
        try:
            response = api_call()
            # Process response
            break
        except Exception as e:
            time.sleep(5)  # Wait before retrying
    ```
 
6. **Dynamic Schema Detection**:
    ```python
    from pyspark.sql import SparkSession
 
    spark = SparkSession.builder.getOrCreate()
    df = spark.read.option("inferSchema", "true").json(json_file_path)
    ```
 
7. **Real-time Data Streaming**:
    ```python
    df = spark.readStream.format("kafka")
         .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
         .option("subscribe", "topic")
         .load()
    ```
 
8. **Data Transformation and Aggregation**:
    ```python
    from pyspark.sql.functions import avg
 
    df.groupBy("group_column").agg(avg("value_column")).show()
    ```
 
9. **Time Series Data**:
    ```python
    from pyspark.sql.functions import to_timestamp
 
    df.withColumn("timestamp", to_timestamp(col("date_string")))
      .groupBy("timestamp").mean("value").show()
    ```
 
10. **Integrating with Other Data Sources**:
    ```python
    df1 = spark.read.json(json_file_path1)
    df2 = spark.read.json(json_file_path2)
 
    df_joined = df1.join(df2, df1.id == df2.id)
    ```