在PySpark中读取文件并将其转换为Pandas Dataframe时,如何将第一行作为标题

Question

我正在阅读一个文件PySpark并形成rdd它.然后我将它转换为正常然后转换dataframe为pandas dataframe.我遇到的问题是我的输入文件中有标题行,我想将其作为数据帧列的标题,但它们作为附加行而不是标题读入.这是我目前的代码:

def extract(line):
    return line


input_file = sc.textFile('file1.txt').zipWithIndex().filter(lambda (line,rownum): rownum>=0).map(lambda (line, rownum): line)

input_data = (input_file
    .map(lambda line: line.split(";"))
    .filter(lambda line: len(line) >=0 )
    .map(extract)) # Map to tuples

df_normal = input_data.toDF()
df= df_normal.toPandas()

现在,当我看df,然后文本文件的标题行成为第一行dataframe并没有额外的报头df与0,1,2...报头.如何将第一行作为标题？

Answer 1

有几种方法可以做到这一点,具体取决于数据的确切结构.由于您没有提供任何详细信息,我将尝试使用nyctaxicab.csv您可以下载的数据文件来显示它.

如果您的文件采用csv格式,则应使用spark-csvDatabricks提供的相关软件包.无需明确下载,只需运行pyspark如下:

$ pyspark --packages com.databricks:spark-csv_2.10:1.3.0

然后

>>> from pyspark.sql import SQLContext
>>> from pyspark.sql.types import *
>>> sqlContext = SQLContext(sc)

>>> df = sqlContext.read.load('file:///home/vagrant/data/nyctaxisub.csv', 
                      format='com.databricks.spark.csv', 
                      header='true', 
                      inferSchema='true')

>>> df.count()
249999

该文件包含250,000行,包括标题,因此249,999是正确的实际记录数.这是模式,由包自动推断:

>>> df.dtypes
[('_id', 'string'),
 ('_rev', 'string'),
 ('dropoff_datetime', 'string'),
 ('dropoff_latitude', 'double'),
 ('dropoff_longitude', 'double'),
 ('hack_license', 'string'),
 ('medallion', 'string'),
 ('passenger_count', 'int'),
 ('pickup_datetime', 'string'),
 ('pickup_latitude', 'double'),
 ('pickup_longitude', 'double'),
 ('rate_code', 'int'),
 ('store_and_fwd_flag', 'string'),
 ('trip_distance', 'double'),
 ('trip_time_in_secs', 'int'),
 ('vendor_id', 'string')]

您可以在我的相关博客文章中查看更多详细信息.

如果由于某种原因,您无法使用该spark-csv软件包,则必须从数据中减去第一行,然后使用它来构建模式.这是一般性的想法,您可以在我的另一篇博文中再次找到包含代码详细信息的完整示例:

>>> taxiFile = sc.textFile("file:///home/ctsats/datasets/BDU_Spark/nyctaxisub.csv")
>>> taxiFile.count()
250000
>>> taxiFile.take(5)
[u'"_id","_rev","dropoff_datetime","dropoff_latitude","dropoff_longitude","hack_license","medallion","passenger_count","pickup_datetime","pickup_latitude","pickup_longitude","rate_code","store_and_fwd_flag","trip_distance","trip_time_in_secs","vendor_id"',
 u'"29b3f4a30dea6688d4c289c9672cb996","1-ddfdec8050c7ef4dc694eeeda6c4625e","2013-01-11 22:03:00",+4.07033460000000E+001,-7.40144200000000E+001,"A93D1F7F8998FFB75EEF477EB6077516","68BC16A99E915E44ADA7E639B4DD5F59",2,"2013-01-11 21:48:00",+4.06760670000000E+001,-7.39810790000000E+001,1,,+4.08000000000000E+000,900,"VTS"',
 u'"2a80cfaa425dcec0861e02ae44354500","1-b72234b58a7b0018a1ec5d2ea0797e32","2013-01-11 04:28:00",+4.08190960000000E+001,-7.39467470000000E+001,"64CE1B03FDE343BB8DFB512123A525A4","60150AA39B2F654ED6F0C3AF8174A48A",1,"2013-01-11 04:07:00",+4.07280540000000E+001,-7.40020370000000E+001,1,,+8.53000000000000E+000,1260,"VTS"',
 u'"29b3f4a30dea6688d4c289c96758d87e","1-387ec30eac5abda89d2abefdf947b2c1","2013-01-11 22:02:00",+4.07277180000000E+001,-7.39942860000000E+001,"2D73B0C44F1699C67AB8AE322433BDB7","6F907BC9A85B7034C8418A24A0A75489",5,"2013-01-11 21:46:00",+4.07577480000000E+001,-7.39649810000000E+001,1,,+3.01000000000000E+000,960,"VTS"',
 u'"2a80cfaa425dcec0861e02ae446226e4","1-aa8b16d6ae44ad906a46cc6581ffea50","2013-01-11 10:03:00",+4.07643050000000E+001,-7.39544600000000E+001,"E90018250F0A009433F03BD1E4A4CE53","1AFFD48CC07161DA651625B562FE4D06",5,"2013-01-11 09:44:00",+4.07308080000000E+001,-7.39928280000000E+001,1,,+3.64000000000000E+000,1140,"VTS"']

# Construct the schema from the header 
>>> header = taxiFile.first()
>>> header
u'"_id","_rev","dropoff_datetime","dropoff_latitude","dropoff_longitude","hack_license","medallion","passenger_count","pickup_datetime","pickup_latitude","pickup_longitude","rate_code","store_and_fwd_flag","trip_distance","trip_time_in_secs","vendor_id"'
>>> schemaString = header.replace('"','')  # get rid of the double-quotes
>>> schemaString
u'_id,_rev,dropoff_datetime,dropoff_latitude,dropoff_longitude,hack_license,medallion,passenger_count,pickup_datetime,pickup_latitude,pickup_longitude,rate_code,store_and_fwd_flag,trip_distance,trip_time_in_secs,vendor_id'
>>> fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split(',')]
>>> schema = StructType(fields)

# Subtract header and use the above-constructed schema:
>>> taxiHeader = taxiFile.filter(lambda l: "_id" in l) # taxiHeader needs to be an RDD - the string we constructed above will not do the job
>>> taxiHeader.collect() # for inspection purposes only
[u'"_id","_rev","dropoff_datetime","dropoff_latitude","dropoff_longitude","hack_license","medallion","passenger_count","pickup_datetime","pickup_latitude","pickup_longitude","rate_code","store_and_fwd_flag","trip_distance","trip_time_in_secs","vendor_id"']
>>> taxiNoHeader = taxiFile.subtract(taxiHeader)
>>> taxi_df = taxiNoHeader.toDF(schema)  # Spark dataframe
>>> import pandas as pd
>>> taxi_DF = taxi_df.toPandas()  # pandas dataframe 

为简洁起见,此处所有列最终都是类型string,但在博客文章中我详细说明并解释如何进一步细化特定字段的所需数据类型(和名称).

Answer 2

简单的答案将被设置 header='true'

例如：

df = spark.read.csv('housing.csv', header='true')

或者

df = spark.read.option("header","true").format("csv").schema(myManualSchema).load("maestraDestacados.csv")