PySpark DataFrame选择某几行 1、collect(): print(dataframe.collect()[index]) 2、dataframe.first() 3、dataframe.head(num_rows)、dataframe.tail(num_rows),head、tail配合使用可以取得中间指定位置的行 4、dataframe.select([columns]).collect()[index] 5、dataframe.take(num_rows),同head()方法 转自:h...
StringIndexer StringIndexer将一列字符串label编码为一列索引号(从0到label种类数-1),根据label出现的频率排序,最频繁出现的label的index为0。 在该例子中,label会被编码成从0到32的整数,最频繁的 label(LARCENY/THEFT) 会被编码成0。 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 from pyspark....
import pyspark from pyspark.sql import SparkSession spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate() data = [("James","Smith","USA","CA"), ("Michael","Rose","USA","NY"), ("Robert","Williams","USA","CA"), ("Maria","Jones","USA","FL") ] columns =...
复制 defcompute(inputIterator:Iterator[IN],partitionIndex:Int,context:TaskContext):Iterator[OUT]={// ...val worker:Socket=env.createPythonWorker(pythonExec,envVars.asScala.toMap)// Start a thread to feed the process input from our parent's iteratorval writerThread=newWriterThread(env,worker,input...
index=[1,2,3,4]) pd_df spark = SparkSession.builder.getOrCreate() sp_df=spark.createDataFrame(pd_df) sp_df.rdd.collect() sp_df.sort(sp_df.old.desc()).collect() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 总体而言操作Row一般都为全体操作,取得dataframe一般都是通过spark.sql(sql...
1#除去一些不要的列,并展示前五行2drop_list = ['Dates','DayOfWeek','PdDistrict','Resolution','Address','X','Y']3data = data.select([columnforcolumnindata.columnsifcolumnnotindrop_list])4data.show(5) 1.2 显示数据结构 1#利用printSchema()方法显示数据的结构2data.printSchema() ...
reset_index() # 切片 pandas_df['a':'c'] # a-c三行 pandas_df.iloc[1:3, 0:2] # 1-2行,0-1列。左闭右开 pandas_df.iloc[[0, 2], [1, 2]] #第0,2行第0,2列 pandas_df.loc['a':'c', ['A', 'B']] #第a-c行A,B列 # 选择列 spark_df.select('A', 'B') pandas_...
df.createOrReplaceTempView('df1') res_unpivot = spark.sql(""" SELECT class ,year ,stack(2,'tt_score',tt_sales,'avg_score',avg_score) as (index,values) FROM df1""") #class、year为要保留的列,stack中第一个参数为需要列转行的列数,紧跟着是列名及值,列名要用引号引起来!!! #方法二: ...
fromseaborn import load_dataset(load_dataset('penguins').drop(columns=['bill_length_mm','bill_depth_mm']).rename(columns={'flipper_length_mm':'flipper','body_mass_g':'mass'}).to_csv('penguins.csv',index=False)) 1. 2. 3.
def compute(inputIterator: Iterator[IN],partitionIndex: Int,context: TaskContext): Iterator[OUT] = {// ...val worker: Socket = env.createPythonWorker(pythonExec, envVars.asScala.toMap)// Start a thread to feed the process input from our parent's iteratorval writerThread = newWriterThread(...