Data.Analysis.with.Python.and.PySpark:4 Analyzing tabular data with pyspark.sql_jonathan rioux, data analysis with python and pysp

创建SparkSession对象以开始使用PySpark

from pyspark.sql import SparkSession
import pyspark.sql.functions as F
spark = SparkSession.builder.getOrCreate()

PySpark如何表示表格数据？

my_grocery_list = [
["Banana", 2, 1.74],
["Apple", 4, 2.04],
["Carrot", 1, 1.09],
["Cake", 1, 10.99],
]
df_grocery_list = spark.createDataFrame(
my_grocery_list, ["Item", "Quantity", "Price"]
)
df_grocery_list.printSchema()

我们的第一个参数是数据本身。您可以提供项目列表（这里是列表列表）、data frame或弹性分布式数据集；第二个参数是data frame的模式。同时，传递列名列表会推断出我们的列的类型（分别是string、long和double）。

主节点知道数据帧的结构，但实际数据在工作节点上表示。每一列都映射到存储在PySpark管理的集群中某个位置的数据。我们在抽象结构上操作，让主节点高效地委派工作。

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PySpark用于分析和处理表格数据

名词解释：exploratory data analysis (or EDA)

PySpark不提供任何图表功能，也不使用其他图表库（如Matplotlib、seaborn、Altair或plot.ly）通常的解决方案是使用PySpark转换数据，使用toPandas（）方法将PySpark数据框转换为pandas数据框，然后使用您最喜欢的图表库。

数据准备：

download the file on the Canada Open Data portal (http://mng.bz/y4YJ);
select the BroadcastLogs_2018_Q3_M8 file.

You also need to download the Data Dictionary in .doc form, as well as the
Reference Tables zip file, unzipping them into a ReferenceTables directory in data/
broadcast_logs.

make sure you have the following：

在PySpark中读取和评估分隔的数据 

通过SparkReader专门处理CSV文件

数据操作的基础：选择、删除、重命名、排序、诊断

logs.select("BroadcastLogID", "LogServiceID", "LogDate").show(5, False)
# +--------------+------------+-------------------+
# |BroadcastLogID|LogServiceID|LogDate |
# +--------------+------------+-------------------+
# |1196192316 |3157 |2018-08-01 00:00:00|
# |1196192317 |3157 |2018-08-01 00:00:00|
# |1196192318 |3157 |2018-08-01 00:00:00|
# |1196192319 |3157 |2018-08-01 00:00:00|
# |1196192320 |3157 |2018-08-01 00:00:00|
# +--------------+------------+-------------------+
# only showing top 5 rows

Four ways to select columns in PySpark, all equivalent in terms of results

# Using the string to column conversion
logs.select("BroadCastLogID", "LogServiceID", "LogDate")
logs.select(*["BroadCastLogID", "LogServiceID", "LogDate"])
# Passing the column object explicitly
logs.select(
F.col("BroadCastLogID"), F.col("LogServiceID"), F.col("LogDate")
)
logs.select(
*[F.col("BroadCastLogID"), F.col("LogServiceID"), F.col("LogDate")]
)

当显式选择几列时，不必将它们包装到列表中。如果已经在处理列列表，可以使用*前缀将其解压缩。

data frame在columns属性中跟踪其列；logs.columns是一个Python列表，包含logs daya frame的所有列名。

使用drop（）方法除去列

logs = logs.drop("BroadcastLogID", "SequenceNO")

Getting rid of columns, select style

logs = logs.select(
*[x for x in logs.columns if x not in ["BroadcastLogID", "SequenceNO"]]
)

创建不存在的内容：使用withColumn（）创建新列

Extracting the hours, minutes, and seconds from the Duration column

 

WARNING If you create a column withColumn() and give it a name that
already exists in your data frame, PySpark will happily overwrite the column.

WARNING Creating many (100+) new columns using withColumns() will slow
Spark down to a grind. If you need to create a lot of columns at once, use the
select() approach. While it will generate the same work, it is less tasking on
the query planner.