知识问答

用Spark加载CSV文件

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问题：用Spark加载CSV文件
回答 0
回答 1
回答 2
回答 3
回答 4
回答 5
回答 6
回答 7
回答 8
回答 9
回答 10
回答 11

问题：用Spark加载CSV文件

我是Spark的新手，正在尝试使用Spark从文件读取CSV数据。这是我在做什么：

sc.textFile('file.csv')
    .map(lambda line: (line.split(',')[0], line.split(',')[1]))
    .collect()

我希望此调用可以给我列出文件的前两列，但出现此错误：

File "<ipython-input-60-73ea98550983>", line 1, in <lambda>
IndexError: list index out of range

尽管我的CSV文件不止一列。

点击查看英文原文

I’m new to Spark and I’m trying to read CSV data from a file with Spark. Here’s what I am doing :

sc.textFile('file.csv')
    .map(lambda line: (line.split(',')[0], line.split(',')[1]))
    .collect()

I would expect this call to give me a list of the two first columns of my file but I’m getting this error :

File "<ipython-input-60-73ea98550983>", line 1, in <lambda>
IndexError: list index out of range

although my CSV file as more than one column.

回答 0

您确定所有行都至少有2列？您可以尝试类似的方法吗？

sc.textFile("file.csv") \
    .map(lambda line: line.split(",")) \
    .filter(lambda line: len(line)>1) \
    .map(lambda line: (line[0],line[1])) \
    .collect()

或者，您可以打印罪魁祸首（如果有）：

sc.textFile("file.csv") \
    .map(lambda line: line.split(",")) \
    .filter(lambda line: len(line)<=1) \
    .collect()
点击查看英文原文

Are you sure that all the lines have at least 2 columns? Can you try something like, just to check?:

sc.textFile("file.csv") \
    .map(lambda line: line.split(",")) \
    .filter(lambda line: len(line)>1) \
    .map(lambda line: (line[0],line[1])) \
    .collect()

Alternatively, you could print the culprit (if any):

sc.textFile("file.csv") \
    .map(lambda line: line.split(",")) \
    .filter(lambda line: len(line)<=1) \
    .collect()

回答 1

Spark 2.0.0+

您可以直接使用内置的csv数据源：

spark.read.csv(
    "some_input_file.csv", header=True, mode="DROPMALFORMED", schema=schema
)

要么 

(spark.read
    .schema(schema)
    .option("header", "true")
    .option("mode", "DROPMALFORMED")
    .csv("some_input_file.csv"))

不包括任何外部依赖项。

火花<2.0.0：

我建议不要手动解析，这在一般情况下是不容易的，我建议spark-csv：

确保星火CSV包含在路径（--packages，--jars，--driver-class-path）

并按以下方式加载数据：

(df = sqlContext
    .read.format("com.databricks.spark.csv")
    .option("header", "true")
    .option("inferschema", "true")
    .option("mode", "DROPMALFORMED")
    .load("some_input_file.csv"))

它可以处理加载，模式推断，删除格式错误的行，并且不需要将数据从Python传递到JVM。

注意事项：

如果您知道架构，则最好避免架构推断并将其传递给DataFrameReader。假设您有三列-整数，双精度和字符串：

from pyspark.sql.types import StructType, StructField
from pyspark.sql.types import DoubleType, IntegerType, StringType

schema = StructType([
    StructField("A", IntegerType()),
    StructField("B", DoubleType()),
    StructField("C", StringType())
])

(sqlContext
    .read
    .format("com.databricks.spark.csv")
    .schema(schema)
    .option("header", "true")
    .option("mode", "DROPMALFORMED")
    .load("some_input_file.csv"))
点击查看英文原文

Spark 2.0.0+

You can use built-in csv data source directly:

spark.read.csv(
    "some_input_file.csv", header=True, mode="DROPMALFORMED", schema=schema
)

or 

(spark.read
    .schema(schema)
    .option("header", "true")
    .option("mode", "DROPMALFORMED")
    .csv("some_input_file.csv"))

without including any external dependencies.

Spark < 2.0.0:

Instead of manual parsing, which is far from trivial in a general case, I would recommend spark-csv:

Make sure that Spark CSV is included in the path (--packages, --jars, --driver-class-path)

And load your data as follows:

(df = sqlContext
    .read.format("com.databricks.spark.csv")
    .option("header", "true")
    .option("inferschema", "true")
    .option("mode", "DROPMALFORMED")
    .load("some_input_file.csv"))

It can handle loading, schema inference, dropping malformed lines and doesn’t require passing data from Python to the JVM.

Note:

If you know the schema, it is better to avoid schema inference and pass it to DataFrameReader. Assuming you have three columns – integer, double and string:

from pyspark.sql.types import StructType, StructField
from pyspark.sql.types import DoubleType, IntegerType, StringType

schema = StructType([
    StructField("A", IntegerType()),
    StructField("B", DoubleType()),
    StructField("C", StringType())
])

(sqlContext
    .read
    .format("com.databricks.spark.csv")
    .schema(schema)
    .option("header", "true")
    .option("mode", "DROPMALFORMED")
    .load("some_input_file.csv"))

回答 2

from pyspark.sql import SparkSession

spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

df = spark.read.csv("/home/stp/test1.csv",header=True,sep="|");

print(df.collect())
点击查看英文原文 
from pyspark.sql import SparkSession

spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

df = spark.read.csv("/home/stp/test1.csv",header=True,sep="|")

print(df.collect())

回答 3

还有另一个选择，包括使用Pandas读取CSV文件，然后将Pandas DataFrame导入Spark。

例如：

from pyspark import SparkContext
from pyspark.sql import SQLContext
import pandas as pd

sc = SparkContext('local','example')  # if using locally
sql_sc = SQLContext(sc)

pandas_df = pd.read_csv('file.csv')  # assuming the file contains a header
# pandas_df = pd.read_csv('file.csv', names = ['column 1','column 2']) # if no header
s_df = sql_sc.createDataFrame(pandas_df)
点击查看英文原文

And yet another option which consist in reading the CSV file using Pandas and then importing the Pandas DataFrame into Spark.

For example:

from pyspark import SparkContext
from pyspark.sql import SQLContext
import pandas as pd

sc = SparkContext('local','example')  # if using locally
sql_sc = SQLContext(sc)

pandas_df = pd.read_csv('file.csv')  # assuming the file contains a header
# pandas_df = pd.read_csv('file.csv', names = ['column 1','column 2']) # if no header
s_df = sql_sc.createDataFrame(pandas_df)

回答 4

只需按逗号分割也会将字段内的逗号分割（例如a,b,"1,2,3",c），因此不建议使用。如果要使用DataFrames API，zero323的答案很好，但是如果要坚持使用基本Spark，则可以使用csv模块在基本Python中解析csvs ：

# works for both python 2 and 3
import csv
rdd = sc.textFile("file.csv")
rdd = rdd.mapPartitions(lambda x: csv.reader(x))

编辑：正如@muon在评论中提到的那样，它将像其他任何行一样对待标头，因此您需要手动提取它。例如，header = rdd.first(); rdd = rdd.filter(lambda x: x != header)（确保header在评估过滤器之前不要进行修改）。但是在这一点上，最好使用内置的csv解析器。

点击查看英文原文

Simply splitting by comma will also split commas that are within fields (e.g. a,b,"1,2,3",c), so it’s not recommended. zero323’s answer is good if you want to use the DataFrames API, but if you want to stick to base Spark, you can parse csvs in base Python with the csv module:

# works for both python 2 and 3
import csv
rdd = sc.textFile("file.csv")
rdd = rdd.mapPartitions(lambda x: csv.reader(x))

EDIT: As @muon mentioned in the comments, this will treat the header like any other row so you’ll need to extract it manually. For example, header = rdd.first(); rdd = rdd.filter(lambda x: x != header) (make sure not to modify header before the filter evaluates). But at this point, you’re probably better off using a built-in csv parser.

回答 5

这是在PYSPARK中

path="Your file path with file name"

df=spark.read.format("csv").option("header","true").option("inferSchema","true").load(path)

那你可以检查

df.show(5)
df.count()
点击查看英文原文

This is in PYSPARK

path="Your file path with file name"

df=spark.read.format("csv").option("header","true").option("inferSchema","true").load(path)

Then you can check

df.show(5)
df.count()

回答 6

如果要将csv加载为数据帧，则可以执行以下操作：

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

df = sqlContext.read.format('com.databricks.spark.csv') \
    .options(header='true', inferschema='true') \
    .load('sampleFile.csv') # this is your csv file

对我来说很好。

点击查看英文原文

If you want to load csv as a dataframe then you can do the following:

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

df = sqlContext.read.format('com.databricks.spark.csv') \
    .options(header='true', inferschema='true') \
    .load('sampleFile.csv') # this is your csv file

It worked fine for me.

回答 7

这与JP Mercier最初提出的有关使用Pandas的建议是一致的，但进行了重大修改：如果将数据分块读取到Pandas中，应该更具延展性。这意味着，您可以解析比Pandas实际可处理的文件大得多的文件，并将其以较小的尺寸传递给Spark。（这也回答了有关为什么如果他们仍然可以将所有内容加载到Pandas中的人为什么要使用Spark的评论。）

from pyspark import SparkContext
from pyspark.sql import SQLContext
import pandas as pd

sc = SparkContext('local','example')  # if using locally
sql_sc = SQLContext(sc)

Spark_Full = sc.emptyRDD()
chunk_100k = pd.read_csv("Your_Data_File.csv", chunksize=100000)
# if you have headers in your csv file:
headers = list(pd.read_csv("Your_Data_File.csv", nrows=0).columns)

for chunky in chunk_100k:
    Spark_Full +=  sc.parallelize(chunky.values.tolist())

YourSparkDataFrame = Spark_Full.toDF(headers)
# if you do not have headers, leave empty instead:
# YourSparkDataFrame = Spark_Full.toDF()
YourSparkDataFrame.show()
点击查看英文原文

This is in-line with what JP Mercier initially suggested about using Pandas, but with a major modification: If you read data into Pandas in chunks, it should be more malleable. Meaning, that you can parse a much larger file than Pandas can actually handle as a single piece and pass it to Spark in smaller sizes. (This also answers the comment about why one would want to use Spark if they can load everything into Pandas anyways.)

from pyspark import SparkContext
from pyspark.sql import SQLContext
import pandas as pd

sc = SparkContext('local','example')  # if using locally
sql_sc = SQLContext(sc)

Spark_Full = sc.emptyRDD()
chunk_100k = pd.read_csv("Your_Data_File.csv", chunksize=100000)
# if you have headers in your csv file:
headers = list(pd.read_csv("Your_Data_File.csv", nrows=0).columns)

for chunky in chunk_100k:
    Spark_Full +=  sc.parallelize(chunky.values.tolist())

YourSparkDataFrame = Spark_Full.toDF(headers)
# if you do not have headers, leave empty instead:
# YourSparkDataFrame = Spark_Full.toDF()
YourSparkDataFrame.show()

回答 8

现在，对于任何常规的csv文件，还有另一个选项：https : //github.com/seahboonsiew/pyspark-csv，如下所示：

假设我们具有以下上下文 

sc = SparkContext
sqlCtx = SQLContext or HiveContext

首先，使用SparkContext将pyspark-csv.py分发给执行者

import pyspark_csv as pycsv
sc.addPyFile('pyspark_csv.py')

通过SparkContext读取CSV数据并将其转换为DataFrame

plaintext_rdd = sc.textFile('hdfs://x.x.x.x/blah.csv')
dataframe = pycsv.csvToDataFrame(sqlCtx, plaintext_rdd)
点击查看英文原文

Now, there’s also another option for any general csv file: https://github.com/seahboonsiew/pyspark-csv as follows:

Assume we have the following context 

sc = SparkContext
sqlCtx = SQLContext or HiveContext

First, distribute pyspark-csv.py to executors using SparkContext

import pyspark_csv as pycsv
sc.addPyFile('pyspark_csv.py')

Read csv data via SparkContext and convert it to DataFrame

plaintext_rdd = sc.textFile('hdfs://x.x.x.x/blah.csv')
dataframe = pycsv.csvToDataFrame(sqlCtx, plaintext_rdd)

回答 9

如果您的csv数据恰好在任何字段中都不包含换行符，则可以使用加载textFile()并解析数据

import csv
import StringIO

def loadRecord(line):
    input = StringIO.StringIO(line)
    reader = csv.DictReader(input, fieldnames=["name1", "name2"])
    return reader.next()

input = sc.textFile(inputFile).map(loadRecord)
点击查看英文原文

If your csv data happens to not contain newlines in any of the fields, you can load your data with textFile() and parse it

import csv
import StringIO

def loadRecord(line):
    input = StringIO.StringIO(line)
    reader = csv.DictReader(input, fieldnames=["name1", "name2"])
    return reader.next()

input = sc.textFile(inputFile).map(loadRecord)

回答 10

如果数据集中的任何一个或多个行的列数少于或多于2，则可能会出现此错误。

我也是Pyspark的新手，正在尝试读取CSV文件。以下代码为我工作：

在这段代码中，我使用来自kaggle的数据集，链接为：https ://www.kaggle.com/carrie1/ecommerce-data

1.不提架构：

from pyspark.sql import SparkSession  
scSpark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example: Reading CSV file without mentioning schema") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

sdfData = scSpark.read.csv("data.csv", header=True, sep=",")
sdfData.show()

现在检查列：sdfData.columns

输出将是：

['InvoiceNo', 'StockCode','Description','Quantity', 'InvoiceDate', 'CustomerID', 'Country']

检查每一列的数据类型：

sdfData.schema
StructType(List(StructField(InvoiceNo,StringType,true),StructField(StockCode,StringType,true),StructField(Description,StringType,true),StructField(Quantity,StringType,true),StructField(InvoiceDate,StringType,true),StructField(UnitPrice,StringType,true),StructField(CustomerID,StringType,true),StructField(Country,StringType,true)))

这将为数据框提供所有列，其数据类型为StringType

2.使用架构： 如果您知道架构或想要更改上表中任何列的数据类型，请使用此格式（假设我正在关注以下列，并希望它们具有特定的数据类型）

from pyspark.sql import SparkSession  
from pyspark.sql.types import StructType, StructField
from pyspark.sql.types import DoubleType, IntegerType, StringType
    schema = StructType([\
        StructField("InvoiceNo", IntegerType()),\
        StructField("StockCode", StringType()), \
        StructField("Description", StringType()),\
        StructField("Quantity", IntegerType()),\
        StructField("InvoiceDate", StringType()),\
        StructField("CustomerID", DoubleType()),\
        StructField("Country", StringType())\
    ])

scSpark = SparkSession \
    .builder \
    .appName("Python Spark SQL example: Reading CSV file with schema") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

sdfData = scSpark.read.csv("data.csv", header=True, sep=",", schema=schema)

现在检查每个列的数据类型的架构：

sdfData.schema

StructType(List(StructField(InvoiceNo,IntegerType,true),StructField(StockCode,StringType,true),StructField(Description,StringType,true),StructField(Quantity,IntegerType,true),StructField(InvoiceDate,StringType,true),StructField(CustomerID,DoubleType,true),StructField(Country,StringType,true)))

编辑：我们也可以使用以下代码行，而无需明确提及架构：

sdfData = scSpark.read.csv("data.csv", header=True, inferSchema = True)
sdfData.schema

输出为：

StructType(List(StructField(InvoiceNo,StringType,true),StructField(StockCode,StringType,true),StructField(Description,StringType,true),StructField(Quantity,IntegerType,true),StructField(InvoiceDate,StringType,true),StructField(UnitPrice,DoubleType,true),StructField(CustomerID,IntegerType,true),StructField(Country,StringType,true)))

输出将如下所示：

sdfData.show()

+---------+---------+--------------------+--------+--------------+----------+-------+
|InvoiceNo|StockCode|         Description|Quantity|   InvoiceDate|CustomerID|Country|
+---------+---------+--------------------+--------+--------------+----------+-------+
|   536365|   85123A|WHITE HANGING HEA...|       6|12/1/2010 8:26|      2.55|  17850|
|   536365|    71053| WHITE METAL LANTERN|       6|12/1/2010 8:26|      3.39|  17850|
|   536365|   84406B|CREAM CUPID HEART...|       8|12/1/2010 8:26|      2.75|  17850|
|   536365|   84029G|KNITTED UNION FLA...|       6|12/1/2010 8:26|      3.39|  17850|
|   536365|   84029E|RED WOOLLY HOTTIE...|       6|12/1/2010 8:26|      3.39|  17850|
|   536365|    22752|SET 7 BABUSHKA NE...|       2|12/1/2010 8:26|      7.65|  17850|
|   536365|    21730|GLASS STAR FROSTE...|       6|12/1/2010 8:26|      4.25|  17850|
|   536366|    22633|HAND WARMER UNION...|       6|12/1/2010 8:28|      1.85|  17850|
|   536366|    22632|HAND WARMER RED P...|       6|12/1/2010 8:28|      1.85|  17850|
|   536367|    84879|ASSORTED COLOUR B...|      32|12/1/2010 8:34|      1.69|  13047|
|   536367|    22745|POPPY'S PLAYHOUSE...|       6|12/1/2010 8:34|       2.1|  13047|
|   536367|    22748|POPPY'S PLAYHOUSE...|       6|12/1/2010 8:34|       2.1|  13047|
|   536367|    22749|FELTCRAFT PRINCES...|       8|12/1/2010 8:34|      3.75|  13047|
|   536367|    22310|IVORY KNITTED MUG...|       6|12/1/2010 8:34|      1.65|  13047|
|   536367|    84969|BOX OF 6 ASSORTED...|       6|12/1/2010 8:34|      4.25|  13047|
|   536367|    22623|BOX OF VINTAGE JI...|       3|12/1/2010 8:34|      4.95|  13047|
|   536367|    22622|BOX OF VINTAGE AL...|       2|12/1/2010 8:34|      9.95|  13047|
|   536367|    21754|HOME BUILDING BLO...|       3|12/1/2010 8:34|      5.95|  13047|
|   536367|    21755|LOVE BUILDING BLO...|       3|12/1/2010 8:34|      5.95|  13047|
|   536367|    21777|RECIPE BOX WITH M...|       4|12/1/2010 8:34|      7.95|  13047|
+---------+---------+--------------------+--------+--------------+----------+-------+
only showing top 20 rows
点击查看英文原文

If you are having any one or more row(s) with less or more number of columns than 2 in the dataset then this error may arise.

I am also new to Pyspark and trying to read CSV file. Following code worked for me:

In this code I am using dataset from kaggle the link is: https://www.kaggle.com/carrie1/ecommerce-data

1. Without mentioning the schema:

from pyspark.sql import SparkSession  
scSpark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example: Reading CSV file without mentioning schema") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

sdfData = scSpark.read.csv("data.csv", header=True, sep=",")
sdfData.show()

Now check the columns: sdfData.columns

Output will be:

['InvoiceNo', 'StockCode','Description','Quantity', 'InvoiceDate', 'CustomerID', 'Country']

Check the datatype for each column:

sdfData.schema
StructType(List(StructField(InvoiceNo,StringType,true),StructField(StockCode,StringType,true),StructField(Description,StringType,true),StructField(Quantity,StringType,true),StructField(InvoiceDate,StringType,true),StructField(UnitPrice,StringType,true),StructField(CustomerID,StringType,true),StructField(Country,StringType,true)))

This will give the data frame with all the columns with datatype as StringType

2. With schema: If you know the schema or want to change the datatype of any column in the above table then use this (let’s say I am having following columns and want them in a particular data type for each of them)

from pyspark.sql import SparkSession  
from pyspark.sql.types import StructType, StructField
from pyspark.sql.types import DoubleType, IntegerType, StringType
    schema = StructType([\
        StructField("InvoiceNo", IntegerType()),\
        StructField("StockCode", StringType()), \
        StructField("Description", StringType()),\
        StructField("Quantity", IntegerType()),\
        StructField("InvoiceDate", StringType()),\
        StructField("CustomerID", DoubleType()),\
        StructField("Country", StringType())\
    ])

scSpark = SparkSession \
    .builder \
    .appName("Python Spark SQL example: Reading CSV file with schema") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

sdfData = scSpark.read.csv("data.csv", header=True, sep=",", schema=schema)

Now check the schema for datatype of each column:

sdfData.schema

StructType(List(StructField(InvoiceNo,IntegerType,true),StructField(StockCode,StringType,true),StructField(Description,StringType,true),StructField(Quantity,IntegerType,true),StructField(InvoiceDate,StringType,true),StructField(CustomerID,DoubleType,true),StructField(Country,StringType,true)))

Edited: We can use the following line of code as well without mentioning schema explicitly:

sdfData = scSpark.read.csv("data.csv", header=True, inferSchema = True)
sdfData.schema

The output is:

StructType(List(StructField(InvoiceNo,StringType,true),StructField(StockCode,StringType,true),StructField(Description,StringType,true),StructField(Quantity,IntegerType,true),StructField(InvoiceDate,StringType,true),StructField(UnitPrice,DoubleType,true),StructField(CustomerID,IntegerType,true),StructField(Country,StringType,true)))

The output will look like this:

sdfData.show()

+---------+---------+--------------------+--------+--------------+----------+-------+
|InvoiceNo|StockCode|         Description|Quantity|   InvoiceDate|CustomerID|Country|
+---------+---------+--------------------+--------+--------------+----------+-------+
|   536365|   85123A|WHITE HANGING HEA...|       6|12/1/2010 8:26|      2.55|  17850|
|   536365|    71053| WHITE METAL LANTERN|       6|12/1/2010 8:26|      3.39|  17850|
|   536365|   84406B|CREAM CUPID HEART...|       8|12/1/2010 8:26|      2.75|  17850|
|   536365|   84029G|KNITTED UNION FLA...|       6|12/1/2010 8:26|      3.39|  17850|
|   536365|   84029E|RED WOOLLY HOTTIE...|       6|12/1/2010 8:26|      3.39|  17850|
|   536365|    22752|SET 7 BABUSHKA NE...|       2|12/1/2010 8:26|      7.65|  17850|
|   536365|    21730|GLASS STAR FROSTE...|       6|12/1/2010 8:26|      4.25|  17850|
|   536366|    22633|HAND WARMER UNION...|       6|12/1/2010 8:28|      1.85|  17850|
|   536366|    22632|HAND WARMER RED P...|       6|12/1/2010 8:28|      1.85|  17850|
|   536367|    84879|ASSORTED COLOUR B...|      32|12/1/2010 8:34|      1.69|  13047|
|   536367|    22745|POPPY'S PLAYHOUSE...|       6|12/1/2010 8:34|       2.1|  13047|
|   536367|    22748|POPPY'S PLAYHOUSE...|       6|12/1/2010 8:34|       2.1|  13047|
|   536367|    22749|FELTCRAFT PRINCES...|       8|12/1/2010 8:34|      3.75|  13047|
|   536367|    22310|IVORY KNITTED MUG...|       6|12/1/2010 8:34|      1.65|  13047|
|   536367|    84969|BOX OF 6 ASSORTED...|       6|12/1/2010 8:34|      4.25|  13047|
|   536367|    22623|BOX OF VINTAGE JI...|       3|12/1/2010 8:34|      4.95|  13047|
|   536367|    22622|BOX OF VINTAGE AL...|       2|12/1/2010 8:34|      9.95|  13047|
|   536367|    21754|HOME BUILDING BLO...|       3|12/1/2010 8:34|      5.95|  13047|
|   536367|    21755|LOVE BUILDING BLO...|       3|12/1/2010 8:34|      5.95|  13047|
|   536367|    21777|RECIPE BOX WITH M...|       4|12/1/2010 8:34|      7.95|  13047|
+---------+---------+--------------------+--------+--------------+----------+-------+
only showing top 20 rows

回答 11

使用时spark.read.csv，我发现使用这些选项escape='"'并multiLine=True为CSV标准提供最一致的解决方案，以我的经验，从Google表格中导出的CSV文件效果最好。

那是，

#set inferSchema=False to read everything as string
df = spark.read.csv("myData.csv", escape='"', multiLine=True,
     inferSchema=False, header=True)
点击查看英文原文

When using spark.read.csv, I find that using the options escape='"' and multiLine=True provide the most consistent solution to the CSV standard, and in my experience works the best with CSV files exported from Google Sheets.

That is,

#set inferSchema=False to read everything as string
df = spark.read.csv("myData.csv", escape='"', multiLine=True,
     inferSchema=False, header=True)