# To create DataFrame using SQLContext people = sqlContext.read.parquet("...") department = sqlContext.read.parquet("...") people.filter(people.age > 30).join(department, people.deptId == department.id) \ .groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"}) ...
data.orderBy('timestamp', ascending = False).coalesce(1).dropDuplicates(['method', 'orderid']) 现在的问题是,我还需要考虑friend值和friend = 1所在的一行,我不知道如何应用第二个条件。非常感谢您的帮助。所需输出为: +---+---+---+---+ |timestamp |method |orderid | friend| +---+--...
dataframe是一个time-series,在循环之外,我应用aux = df.filter("id='x'")转换,然后函数运行没有问题;问题出在循环本身。但是,当我做aux.show()时,它显示一个空的数据帧。dataframe是一个time-series,在循环之外,我应用aux = df.filter("id='x'")转换,然后函数运行没有问题;问题出在循环本身。 有人知道...
from pyspark.sql.functions import col df_that_one_customer = df_customer.filter(col("c_custkey") == 412449) To filter on multiple conditions, use logical operators. For example, & and | enable you to AND and OR conditions, respectively. The following example filters rows where the c_nati...
from pyspark.sql import SparkSession from pyspark.sql.functions import when # 创建SparkSession spark = SparkSession.builder.appName("Multiple WHEN Conditions").getOrCreate() # 创建示例数据 data = [("John", 25), ("Alice", 30), ("Mike", 35)] df = spark.createDataFrame(data, ["Name",...
collect تقوم عملية التحويل، مثل filter أو groupBy، بإرجاع DataFrame ولكنها لا تنفذ حتى يقوم إجراء بتشغيله. يعرف ...
# Easily reference these as F.my_function() and T.my_type() belowfrompyspark.sqlimportfunctionsasF,typesasT Filtering # Filter on equals conditiondf=df.filter(df.is_adult=='Y')# Filter on >, <, >=, <= conditiondf=df.filter(df.age>25)# Multiple conditions require parentheses around ...
sql.functions import when processed = add_nested_field( df, column_to_process="payload.array.booleanField", new_column_name="payload.array.booleanFieldAsString", f=lambda column: when(column, "Y").when(~column, "N").otherwise(""), ) Date Format Format a nested date field from current_...
input_path="input_path"input_path="wasbs://%s@%s.blob.core.windows.net/%s"%(blob_container_name,blob_account_name,rel_input_path)df=sqlContext.read.format("com.databricks.spark.csv").option("delimiter","~").option("header","false").load(input_path)rel_output_path="ou...
from pyspark.sql.functions import * spark = SparkSession.builder.getOrCreate() storage_account_name = "###" storage_account_access_key = "###3" spark.conf.set("fs.azure.account.key." + storage_account_name + ".blob.core.windows.net",storage_account_acc...