Question

我正在使用spark版本2.0.1＆amp; python 2.7。我正在运行以下代码

# This will return a new DF with all the columns + id
data1 = data.withColumn("id", monotonically_increasing_id()) # Create an integer index
data1.show()

def create_indexes(df,
                   fields=['country', 'state_id', 'airport', 'airport_id']):
    """ Create indexes for the different element ids
        for CMRs. This allows us to select CMRs that match
        a given element and element value very quickly.
    """
    if fields == None:
        print("No fields specified, returning")
        return
    for field in fields:
        if field not in df.columns:
            print('field: ', field, " is not in the data...")
            return
    indexes = {}
    for field in fields:
        print(field)
        res = df.groupby(field)
        index = {label: np.array(vals['id'], np.int32) for label, vals in res}
        indexes[field] = index
    return indexes

# Create indexes. Some of them take a lot of time!
#Changed dom_client_id by gbl_buy_grp_id as it was changed in Line Number 
indexes = create_indexes(data1, fields=['country', 'state_id', 'airport', 'airport_id'])
print type(indexes)

我在运行此代码时收到以下错误消息

TypeError: 'GroupedData' object is not iterable

你能帮我解决这个问题吗？

Answer 1

您必须对GroupedData执行汇总并收集结果，然后才能对其进行迭代，例如每组计数项目：res = df.groupby(field).count().collect()

TypeError：＆＃39; GroupedData＆＃39;对象在pyspark中不可迭代

1 个答案: