I have a file in spark with following data
Property ID|Location|Price|Bedrooms|Bathrooms|Size|Price SQ Ft|Status
i have read this file as rdd using
a=sc.textFile("/FileStore/tables/realestate.txt")
Now I need to convert this rdd into dataframe. I am using the below mentioned command
d=spark.createDataFrame(a).toDF("Property ID","Location","Price","Bedrooms","Bathrooms","Size","Price SQ Ft","Status")
But i am getting an error as
TypeError: Can not infer schema for type: <class 'str'>
You can split the column first:
d = spark.createDataFrame(a.map(lambda x: x.split('|'))).toDF("Property ID","Location","Price","Bedrooms","Bathrooms","Size","Price SQ Ft","Status")
Or equivalently, calling toDF on the RDD directly
d = a.map(lambda x: x.split('|')).toDF(["Property ID","Location","Price","Bedrooms","Bathrooms","Size","Price SQ Ft","Status"])
In fact, I'd recommend using the Spark CSV reader for this purpose, which could handle the header appropriately too:
df = spark.read.csv('/FileStore/tables/realestate.txt', header=True, inferSchema=True, sep='|')
Related
I have a dataframe df that contains Three column of type array, i am trying to save output
to csv, so converted data type to string.
import org.apache.spark.sql.functions._
val df2 = df.withColumn("Total", col("total").cast("string")),
("BOOKID", col("BOOKID").cast("string"),
"PublisherID", col("PublisherID").cast("string")
.write
.csv(path="D:/pennymac/SOLUTION1/OUTPUT")
But getting error.
error as "Cannot Resolve symbol write"
Spark 2.2
Scala
Try below code.
Its not possible to add multiple columns inside withColumn function.
val df2 = df
.withColumn("Total", col("total").cast("string"))
.withColumn("BOOKID", col("BOOKID").cast("string"))
.withColumn("PublisherID", col("PublisherID").cast("string"))
.write
.csv(path="D:/pennymac/SOLUTION1/OUTPUT")
I am trying to capture a very large structured table from a postregres table. It has approximately: 200,000,000 records. I am using dask instead of pandas, because it is faster. When I am loading the data into df it is significantly faster than pandas.
I am trying to convert dask DataFrame into Pandas dataframe using compute, it keeps giving me ValueError NA/inf.
I have passed dtype='object', but it is not working. Any way to fix it?
df = dd.read_sql_table('mytable1',
index_col='mytable_id', schema='books',
uri='postgresql://myusername:mypassword#my-address-here12345678.us-west-1.rds.amazonaws.com:12345/BigDatabaseName')
pandas_df = df.compute(dtype='object')
Gives error:
ValueError: Cannot convert non-finite values (NA or inf) to integer
I would guess that one of your columns has nulls but dask inferred it as an integer. Dask looks at a sample of the data to infer dtypes so may not pick up sporadic nulls. Before you call compute you can inspect the dtypes and convert the column type using astype to object for the column you think may be the issue.
Here is the code that works for unknown column types!
my_cols = ['a', 'b',...]
meta_dict = dict(zip(my_cols, [object]*len(my_cols)))
ddf = dd.read_sql_table(..., meta=meta_dict, ....)
df = ddf.compute()
df['a_int'] = df['a'].astype('int64', errors='ignore')
I have a local text file kv_pair.log formatted such as that key value pairs are comma delimited and records begin and terminate with a new line:
"A"="foo","B"="bar","C"="baz"
"A"="oof","B"="rab","C"="zab"
"A"="aaa","B"="bbb","C"="zzz"
I am trying to read this to a Pair RDD using pySpark as follows:
from pyspark import SparkContext
sc=sparkContext()
# Read raw text to RDD
lines=sc.textFile('kv_pair.log')
# How to turn this into a Pair RDD?
pairs=lines.map(lambda x: (x.replace('"', '').split(",")))
print type(pairs)
print pairs.take(2)
I feel I am close! The output of above is:
[[u'A=foo', u'B=bar', u'C=baz'], [u'A=oof', u'B=rab', u'C=zab']]
So it looks like pairs is a list of records, which contains a list of the kv pairs as strings.
How can I use pySpark to transform this into a Pair RDD such as that the keys and values are properly separated?
Ultimate goal is to transform this Pair RDD into a DataFrame to perform SQL operations - but one step at a time, please help transforming this into a Pair RDD.
You can use flatMap with a custom function as lambda can't be used for multiple statements
def tranfrm(x):
lst = x.replace('"', '').split(",")
return [(x.split("=")[0], x.split("=")[1]) for x in lst]
pairs = lines.map(tranfrm)
This is really bad practice for a parser, but I believe your example could be done with something like this:
from pyspark import SparkContext
from pyspark.sql import Row
sc=sparkContext()
# Read raw text to RDD
lines=sc.textFile('kv_pair.log')
# How to turn this into a Pair RDD?
pairs=lines.map(lambda x: (x.replace('"', '').split(",")))\
.map(lambda r: Row(A=r[0].split('=')[1], B=r[1].split('=')[1], C=r[2].split('=')[1] )
print type(pairs)
print pairs.take(2)
I have a data frame in PySpark called df. I have registered this df as a temptable like below.
df.registerTempTable('mytempTable')
date=datetime.now().strftime('%Y-%m-%d %H:%M:%S')
Now from this temp table I will get certain values, like max_id of a column id
min_id = sqlContext.sql("select nvl(min(id),0) as minval from mytempTable").collect()[0].asDict()['minval']
max_id = sqlContext.sql("select nvl(max(id),0) as maxval from mytempTable").collect()[0].asDict()['maxval']
Now I will collect all these values like below.
test = ("{},{},{}".format(date,min_id,max_id))
I found that test is not a data frame but it is a str string
>>> type(test)
<type 'str'>
Now I want save this test as a file in HDFS. I would also like to append data to the same file in hdfs.
How can I do that using PySpark?
FYI I am using Spark 1.6 and don't have access to Databricks spark-csv package.
Here you go, you'll just need to concat your data with concat_ws and right it as a text:
query = """select concat_ws(',', date, nvl(min(id), 0), nvl(max(id), 0))
from mytempTable"""
sqlContext.sql(query).write("text").mode("append").save("/tmp/fooo")
Or even a better alternative :
from pyspark.sql import functions as f
(sqlContext
.table("myTempTable")
.select(f.concat_ws(",", f.first(f.lit(date)), f.min("id"), f.max("id")))
.coalesce(1)
.write.format("text").mode("append").save("/tmp/fooo"))
I have an rdd (we can call it myrdd) where each record in the rdd is of the form:
[('column 1',value), ('column 2',value), ('column 3',value), ... , ('column 100',value)]
I would like to convert this into a DataFrame in pyspark - what is the easiest way to do this?
How about use the toDF method? You only need add the field names.
df = rdd.toDF(['column', 'value'])
The answer by #dapangmao got me to this solution:
my_df = my_rdd.map(lambda l: Row(**dict(l))).toDF()
Take a look at the DataFrame documentation to make this example work for you, but this should work. I'm assuming your RDD is called my_rdd
from pyspark.sql import SQLContext, Row
sqlContext = SQLContext(sc)
# You have a ton of columns and each one should be an argument to Row
# Use a dictionary comprehension to make this easier
def record_to_row(record):
schema = {'column{i:d}'.format(i = col_idx):record[col_idx] for col_idx in range(1,100+1)}
return Row(**schema)
row_rdd = my_rdd.map(lambda x: record_to_row(x))
# Now infer the schema and you have a DataFrame
schema_my_rdd = sqlContext.inferSchema(row_rdd)
# Now you have a DataFrame you can register as a table
schema_my_rdd.registerTempTable("my_table")
I haven't worked much with DataFrames in Spark but this should do the trick
In pyspark, let's say you have a dataframe named as userDF.
>>> type(userDF)
<class 'pyspark.sql.dataframe.DataFrame'>
Lets just convert it to RDD (
userRDD = userDF.rdd
>>> type(userRDD)
<class 'pyspark.rdd.RDD'>
and now you can do some manipulations and call for example map function :
newRDD = userRDD.map(lambda x:{"food":x['favorite_food'], "name":x['name']})
Finally, lets create a DataFrame from resilient distributed dataset (RDD).
newDF = sqlContext.createDataFrame(newRDD, ["food", "name"])
>>> type(ffDF)
<class 'pyspark.sql.dataframe.DataFrame'>
That's all.
I was hitting this warning message before when I tried to call :
newDF = sc.parallelize(newRDD, ["food","name"] :
.../spark-2.0.0-bin-hadoop2.7/python/pyspark/sql/session.py:336: UserWarning: Using RDD of dict to inferSchema is deprecated. Use pyspark.sql.Row inst warnings.warn("Using RDD of dict to inferSchema is deprecated. "
So no need to do this anymore...