选择一列或多列:select 代码语言:javascript 代码运行次数:0 运行 AI代码解释 df["age"]df.age df.select(“name”)df.select(df[‘name’],df[‘age’]+1)df.select(df.a,df.b,df.c)# 选择a、b、c三列 df.select(df["a"],df["b"],df["c"])# 选择a、b、c三
参数可以是 column对象、str、list[str]、list[column对象] df.select('name').show() df.select(df['name']).show() # df['name'] 返回 Column对象 ''' +---+ |name| +---+ |张三| |李四| |王五| +---+ +---+ |name| +---+ |张三| |李四| |王五| +---+ ''' # 1.4 df.fi...
现在,我们可以通过以下方式计算包含单词Spark的行数: lines_with_spark = text_file.filter(text_file.value.contains("Spark")) 在这里,我们使用filter()函数过滤了行,并在filter()函数内部指定了text_file_value.contains包含单词"Spark",然后将这些结果放入了lines_with_spark变量中。 我们可以修改上述命令,简单...
data.select('columns').distinct().show() 随机抽样有两种方式,一种是在HIVE里面查数随机;另一种是在pyspark之中 1 2 3 4 5 #HIVE里面查数随机 sql="select * from data order by rand() limit 2000" #pyspark之中 sample=result.sample(False,0.5,0)# randomly select 50% of lines 1.2 列元素操作...
# inserts 100,000 rows presto-cli --execute """ INSERT INTO hive.default.customer SELECT * FROM tpcds.sf1.customer; """# inserts 50,000 rows across 52 partitions presto-cli --execute """ INSERT INTO hive.default.customer_address
# keep rows with certain length data.filter("length(col) > 20") # get distinct value of the column data.select("col").distinct() # remove row which has certain character data.filter(~F.col('col').contains('abc')) 列值处理 (1)列值分割 # split column based on space data = data...
You can use the row_number() function to add a new column with a row number as value to the PySpark DataFrame. Therow_number()function assigns a unique numerical rank to each row within a specified window or partition of a DataFrame. Rows are ordered based on the condition specified, and...
1.lit 给数据框增加一列常数 2.dayofmonth，dayofyear返回给定日期的当月/当年天数 3.dayofweek返回给定...
The following example adds an additional condition, filtering to just the rows that have o_totalprice greater than 500,000:Python Копирај df_customer = spark.table('samples.tpch.customer') df_order = spark.table('samples.tpch.orders') df_complex_joined = df_order.join( df_...
Yes, we can join on multiple columns. Joining on multiple columns involves more join conditions with multiple keys for matching the rows between the datasets.It can be achieved by passing a list of column names as the join condition when using the.join()method. ...