我正在读取PySpark的文件并形成它的rdd。然后我将其转换为普通dataframe，然后转换为pandas dataframe.我遇到的问题是我的输入文件中有标题行，我也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。这是我当前的代码：
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，当我查看df时，文本文件的标题行成为dataframe的第一行，并且df中还有额外的标题，0,1,2...作为标题。如何将第一行作为标题？
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，我将尝试使用您可以下载的数据文件nyctaxicab.csv来显示它。
如果文件是csv格式，则应使用 Databricks 提供的相关spark-csv包。无需显式下载，只需按如下方式运行pyspark：
$ 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
该文件有 250，000 行，包括标题，因此 249，999 是正确的实际记录数。下面是由包自动推断的架构：
>>> 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。
如果出于某种原因无法使用 spark-csv 包，则必须从数据中减去第一行，然后使用它来构造架构。这是一般的想法，您可以在我的另一篇博客文章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，这里所有列最终都是类型 string ，但在博客文章中，我详细展示了并解释了如何进一步细化特定字段所需的数据类型（和名称）。
简单的答案是header='true'
例如：
df = spark.read.csv('housing.csv', header='true')
或
df = spark.read.option("header","true").format("csv").schema(myManualSchema).load("maestraDestacados.csv")
另一种方法如下：
log_txt = sc.textFile(file_path)
header = log_txt.first() #get the first row to a variable
fields = [StructField(field_name, StringType(), True) for field_name in header] #get the types of header variable fields
schema = StructType(fields)
filter_data = log_txt.filter(lambda row:row != header) #remove the first row from or else there will be duplicate rows
df = spark.createDataFrame(filter_data, schema=schema) #convert to pyspark DF
df.show()