# Don't change this queryquery="FROM flights SELECT * LIMIT 10"# Get the first 10 rows of flightsflights10=spark.sql(query)# Show the resultsflights10.show() Pandafy a Spark DataFrame 使用pandas的形式可视化数据框 <script.py>output:origin ... N0SEA ...81SEA ...982SEA ...23SEA .....
# Don't change this query query = "FROM flights SELECT * LIMIT 10" # Get the first 10 rows of flights flights10 = spark.sql(query) # Show the results flights10.show() 1. 2. 3. 4. 5. 6. 7. 8. Pandafy a Spark DataFrame 使用pandas的形式可视化数据框 <script.py> output: origin...
first(t1.exchange_type_t01) AS exchange_type_t01, first(t1.user_id) AS user_id, first(t1.pay_id) AS pay_id, first(t1.charge_time) AS charge_time, first(t2.has_yxs_payment) AS has_yxs_payment, first(t2.has_sxy_payment) AS has_sxy_payment, first(t2.has_cxy_payment) AS has...
5.1、“Select”操作 可以通过属性(“author”)或索引(dataframe[‘author’])来获取列。 #Show all entries in title column dataframe.select("author").show(10) #Show all entries in title, author, rank, price columns dataframe.select("author", "title", "rank", "price").show(10) 第一个结果...
dataframe.select("title",when(dataframe.title != 'ODD HOURS', 1).otherwise(0)).show(10) 展示特定条件下的10行数据 在第二个例子中,应用“isin”操作而不是“when”,它也可用于定义一些针对行的条件。 # Show rows with specified authors if in the given options ...
df = ss.sql("""SELECT first(t1.sku_mode) AS sku_mode, first(t1.exchange_type_t01) AS exchange_type_t01, first(t1.user_id) AS user_id, first(t1.pay_id) AS pay_id, first(t1.charge_time) AS charge_time, first(t2.has_yxs_payment) AS has_yxs_payment, ...
first_rows=data.head(n=2)print(first_rows)# 返回全部列名 cols=data.columnsprint(cols)# 返回维度 dimensision=data.shapeprint(dimensision)print(data.info())returndata defmain():col_names=['1','2','3']file_test=u'''test.csv'''print(sum_analysis(file_test,col_names))if__name__==...
row = spark_df.select('col_1', 'col_2').first() col_1_value = row.col_1 col_2_value = row.col_2 # first() 返回的是 Row 类型,可以看做是dict类型,用 row.col_name 来获取值 (3)获取一列的所有值,或者多列的所有值 rows= df.select('col_1', 'col_2').collect() ...
row = df.select('col_1', 'col_2').first() col_1_value = row.col_1 col_2_value = row.col_2 # first() 返回的是 Row 类型,可以看做是dict类型,用 row.col_name 来获取值 (3)获取一列的所有值,或者多列的所有值 rows= df.select('col_1', 'col_2').collect() ...
一个样本数据会有所帮助。目前，我假设您的数据如下所示：