选项2。使用withColumnRenamed,请注意此方法允许您“覆盖”同一列。对于 Python3,将xrange替换为range from functools import reduce oldColumns = data.schema.names newColumns = ["name", "age"] df = reduce(lambda data, idx: data.withColumnRenamed(oldColumns[idx], newColumns[idx]), xrange(len(oldCol...
如(一)(二)中示例所示,printSchema用于打印DataSet/DataFrame数据集的树形结构定义。 3.4. withColumnRenamed withColumnRenamed用于对列重命名,类似于sql语句“select a as aa, b as bb from table”中的as。 3.5. join join用于按指定的join表达式与join类型(默认为inner join)将另一个DataSet/DataFrame与当前DataS...
对于新版DataFrame API,withColumnRenamed()函数通过两个参数使用。 # Update column 'amazon_product_url' with 'URL' dataframe = dataframe.withColumnRenamed('amazon_product_url', 'URL') dataframe.show(5) “Amazon_Product_URL”列名修改为“URL” 6.3、删除列 列的删除可通过两种方式实现:在drop()函数中...
() # Perform some analytics # Calculate total sales total_sales = orders_df.groupBy("order_date").sum("amount").withColumnRenamed("sum(amount)", "total_sales") total_sales.show() # Calculate sales by user sales_by_user = orders_df.groupBy("user_id").sum("amount").withColumnRenamed(...
.withColumnRenamed("mergedIds", "ids")) cached_new_vertices = AM.getCachedDataFrame(new_vertices) g2 = GraphFrame(cached_new_vertices, g2.edges) (g2.vertices .withColumn("closeness", closeness_udf("ids")) .sort("closeness", ascending=False) ...
data_coulumns=list(df.columns)#将pandas.DataFrame转为spark.dataFrame,需要转数据和列名df = spark.createDataFrame(data_values,data_coulumns)# 字段重命名# df = df.withColumnRenamed('name', '影片名称')forkeyincolumns_dict.keys() : df = df.withColumnRenamed(key , columns_dict[key]);print(df...
"state", col("order_datetime").cast("int").cast("timestamp").cast("date").alias("order_date"), ) ) @dlt.table() def daily_orders_by_state(): return (spark.read.table("customer_orders") .groupBy("state", "order_date") .count().withColumnRenamed("count", "order_count") ) ...
dataframe = dataframe.withColumnRenamed('amazon_product_url', 'URL') dataframe.show(5) “Amazon_Product_URL”列名修改为“URL” 6.3、删除列 列的删除可通过两种方式实现:在drop()函数中添加一个组列名,或在drop函数中指出具体的列。两个例子展示如下。
()# Perform some analytics# Calculate total salestotal_sales=orders_df.groupBy("order_date").sum("amount").withColumnRenamed("sum(amount)","total_sales")total_sales.show()# Calculate sales by usersales_by_user=orders_df.groupBy("user_id").sum("amount").withColumnRenamed("sum(amount)","...
(lambda x, idx: x.withColumnRenamed( group_col_lst[idx] ,imm_column_map[idx] ) , range( len(imm_column_map) ) , immDF ) res += [ immDF.groupBy(imm_column_map)\ .agg(*agg_func)\ .withColumn('group-key',F.lit(key)) .withColumn('group-value',F.lit('-'.join(group_col_...