pyspark dataframe Column alias 重命名列(name) df = spark.createDataFrame( [(2, "Alice"), (5, "Bob")], ["age", "name"])df.select(df.age.alias("age2")).show()+---+|age2|+---+| 2|| 5|+---+ astype alias cast 修改列类型 data.schemaStructType([StructField('name', String...
agg(self, *exprs)计算聚合并将结果返回为:`DataFrame` 可用的聚合函数有“avg”、“max”、“min”、“sum”、“count”。 :param exprs:从列名(字符串)到聚合函数(字符串)的dict映射, 或:类:`Column`的列表。# 官方接口示例>>>gdf = df.groupBy(df.name)>>>sorted(gdf.agg({"*":"count"}).colle...
--- 6、去重 --- 6.1 distinct:返回一个不包含重复记录的DataFrame 6.2 dropDuplicates:根据指定字段去重 --- 7、 格式转换 --- pandas-spark.dataframe互转 转化为RDD --- 8、SQL操作 --- --- 9、读写csv --- 延伸一:去除两个表重复的内容 参考文献 1、--- 查 --- — 1.1 行元素查询操作 —...
1、 agg(expers:column*) 返回dataframe类型 ,同数学计算求值 df.agg(max("age"), avg("salary")) df.groupBy().agg(max("age"), avg("salary")) 2、 agg(exprs: Map[String, String]) 返回dataframe类型 ,同数学计算求值 map类型的 df.agg(Map("age" -> "max", "salary" -> "avg")) df....
PySpark-引用DataFrame中名为“name”的列 我正在尝试使用PySpark解析json数据。下面是脚本。 arrayData = [ {"resource": { "id": "123456789", "name2": "test123" } } ] df = spark.createDataFrame(data=arrayData) df3 = df.select(df.resource.id, df.resource.name2)...
python list dataframe apache-spark pyspark 我有一个PySpark dataframe,如下所示。我需要将dataframe行折叠成包含column:value对的Python dictionary行。最后,将字典转换为Python list of tuples,如下所示。我使用的是Spark 2.4。DataFrame:>>> myDF.show() +---+---+---+---+ |fname |age|location | do...
计算给定列的协方差,有他们的names指定,作为一个double值。DataFrame.cov() 和 DataFrameStatFunctions.cov()是彼此的别名 Parameters: col1 - The name of the first column col2- The name of the second column New in version 1.4. createOrReplaceTempView(name) ...
To drop multiple columns from a PySpark DataFrame, we can pass a list of column names to the .drop() method. We can do this in two ways: # Option 1: Passing the names as a list df_dropped = df.drop(["team", "player_position"]) # Option 2: Passing the names as separate argume...
importpandasaspdfrompyspark.sqlimportSparkSessioncolors=['white','green','yellow','red','brown','pink']color_df=pd.DataFrame(colors,columns=['color'])color_df['length']=color_df['color'].apply(len)color_df=spark.createDataFrame(color_df)color_df.show() ...
Another way to traverse a PySpark DataFrame is to iterate over its columns. We can access the columns of a DataFrame using thecolumnsattribute, which returns a list of column names. We can then iterate over this list to access individual columns: ...