import random def generate_random_data(num_rows): data = [] for _ in range(num_rows): id = random.randint(1, 1000) name = f"Name_{random.randint(1, 1000)}" age = random.randint(18, 65) data.append((id, name, age)) return data 使用pyspark的DataFrame API来创建数据表: 将生成...
from lib.random import RandomRDDs numExamples = 10000 # number of examples to generate fraction = 0.1 # fraction of data to sample # Example: RandomRDDs.normalRDD normalRDD = RandomRDDs.normalRDD(sc, numExamples) print('Generated RDD of %d examples sampled from the standard normal distributi...
如果要访问MaxCompute表,则需要编译datasource包,详细步骤请参见搭建Linux开发环境。 SparkSQL应用示例(Spark1.6) 详细代码 frompysparkimportSparkContext, SparkConffrompyspark.sqlimportOdpsContextif__name__ =='__main__': conf = SparkConf().setAppName("odps_pyspark") sc = SparkContext(conf=conf) sql...
[1, 2, 3, 4, 5] # Define a function to generate random event data def generate_orders(): current_time = time.time() order_id = random.randint(100000, 999999) product_id = random.choice(product_ids) quantity = random.choice(quantities) timestamp = datetime.datetime.fromtimestamp(...
importnumpyasnpimportpandasaspd# Enable Arrow-based columnar data transfersspark.conf.set("spark.sql.execution.arrow.pyspark.enabled","true")# Generate a pandas DataFramepdf = pd.DataFrame(np.random.rand(100,3))# Create a Spark DataFrame from a pandas DataFrame using Arrowdf = spark.createDataF...
importnumpyasnpimportpandasaspd# Enable Arrow-based columnar data transfersspark.conf.set("spark.sql.execution.arrow.pyspark.enabled","true")# Generate a pandas DataFramepdf = pd.DataFrame(np.random.rand(100,3))# Create a Spark DataFrame from a pandas DataFrame using Arrowdf = spark.crea...
importnumpyasnpimportpandasaspd# Enable Arrow-based columnar data transfersspark.conf.set("spark.sql.execution.arrow.pyspark.enabled","true")# Generate a pandas DataFramepdf = pd.DataFrame(np.random.rand(100,3))# Create a Spark DataFrame from a pandas DataFrame using Arrowdf = spark.createDataF...
print(user_data.count()) # tip1:把map()理解为要对每一行做这个事情,对每个元素做动作 # tip2:lambda x:f(x) x就是那个object,f(x)是要对object做的事 # 各类算子 # 1、map():对每行,用map()中的函数作用 # 2、filter():对每一个元素,括号里给出筛选条件,进行过滤 ...
sampling.sample( df, random_seed, samples_per_wiki)[1]), lambda df: df.withColumn( 'page_id', F.explode('hit_page_ids')).drop('hit_page_ids') ]) Example #2Source File: swissModelDataset.py From mmtf-pyspark with Apache License 2.0 6 votes def _flatten_dataset(ds): '''...
aggregates numerical data, providing a concise way to compute the total sum of numeric values within a DataFrame. This function is often used in combination with other DataFrame transformations, such asgroupBy(),agg(), orwithColumn(), to perform complex data manipulations and generate summary ...