Please, anyone knows how to make this filtering in pyspark?
I want to convert this code pandas to pyspark.
Thank for your help
df.loc[df['A'] == 1, 'B'] = np.NaN|
The equivalent pyspark code using pyspark.sql.functions.when and pyspark.sql.Column.otherwise is:
from pyspark.sql import functions as F
import numpy as np
df = df.withColumn("B", F.when(F.col("A") == 1, np.nan).otherwise(F.col("B")))
Below is the pyspark code for matrix multiplication. I need same code logic in scala for matrix multiplication as this logic is good for large volume dataset.
from pyspark import SparkConf, SparkContext
from pyspark.sql import functions as F
from functools import reduce
df = spark.sql("select * from tablename")
colDFs = []
for c2 in df.columns:
colDFs.append( df.select( [ F.sum(df[c1]*df[c2]).alias("op_{0}".format(i)) for i,c1 in enumerate(df.columns) ] ) )
mtx = reduce(lambda a,b: a.select(a.columns).union(b.select(a.columns)), colDFs )
mtx.show()
for enumerate you can use zipWithIndex as in df.columns.zipWithIndex
I didn't test it but overall code should be something like
val colsDf=df.columns.flatMap{ case c =>
df.columns.zipWithIndex.map{ case (c2,i) =>
df.select(sum(col(c)*col(c2).alias(s"op_$i")))
}
}
colsDf.reduce((a,b)=>a.select(a.columns.map(col):_*).union(b.select(b.columns.map(col):_*)))
import pandas as pd
import numpy as np
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
from pyspark.sql.functions import PandasUDFType, pandas_udf
from pyspark.sql.types import *
import os
#pandas_udf(schema, functionType=PandasUDFType.GROUPED_MAP)
def split(df, validation_period):
""Logic""
return df
def train_test_split(spark, data_frame, request_json_data):
data_frame = spark.createDataFrame(data_frame)
print(data_frame.schema)
validation_period = request_json_data['validation_period']
groupby_key = request_json_data['groupby_key']
data_frame.groupby(groupby_key).apply(split, validation_period).show()
Can't call split function it gives error. apply() takes 2 positional arguments but 3 were given. I want to pass validation_period as argument to split function.
Short answer: you can't pass extra argument to pandas grouped map udf since it excepts only a single pandas df as an argument.
Long answer: There are other ways you can pass validation_period to the function
use some form of a closure
def split_fabric(validation_period):
#pandas_udf(schema, functionType=PandasUDFType.GROUPED_MAP)
def split(df):
""Logic""
return df
pass it as a column
data_frame \
.withColumn("validation_period", F.lit(validation_period)) \
.groupby(groupby_key).apply(split, validation_period).show()
I have a dataframe df with a VectorUDT column named features. How do I get an element of the column, say first element?
I've tried doing the following
from pyspark.sql.functions import udf
first_elem_udf = udf(lambda row: row.values[0])
df.select(first_elem_udf(df.features)).show()
but I get a net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict(for numpy.dtype) error. Same error if I do first_elem_udf = first_elem_udf(lambda row: row.toArray()[0]) instead.
I also tried explode() but I get an error because it requires an array or map type.
This should be a common operation, I think.
Convert output to float:
from pyspark.sql.types import DoubleType
from pyspark.sql.functions import lit, udf
def ith_(v, i):
try:
return float(v[i])
except ValueError:
return None
ith = udf(ith_, DoubleType())
Example usage:
from pyspark.ml.linalg import Vectors
df = sc.parallelize([
(1, Vectors.dense([1, 2, 3])),
(2, Vectors.sparse(3, [1], [9]))
]).toDF(["id", "features"])
df.select(ith("features", lit(1))).show()
## +-----------------+
## |ith_(features, 1)|
## +-----------------+
## | 2.0|
## | 9.0|
## +-----------------+
Explanation:
Output values have to be reserialized to equivalent Java objects. If you want to access values (beware of SparseVectors) you should use item method:
v.values.item(0)
which return standard Python scalars. Similarly if you want to access all values as a dense structure:
v.toArray().tolist()
If you prefer using spark.sql, you can use the follow custom function 'to_array' to convert the vector to array. Then you can manipulate it as an array.
from pyspark.sql.types import ArrayType, DoubleType
def to_array_(v):
return v.toArray().tolist()
from pyspark.sql import SQLContext
sqlContext=SQLContext(spark.sparkContext, sparkSession=spark, jsqlContext=None)
sqlContext.udf.register("to_array",to_array_, ArrayType(DoubleType()))
example
from pyspark.ml.linalg import Vectors
df = sc.parallelize([
(1, Vectors.dense([1, 2, 3])),
(2, Vectors.sparse(3, [1], [9]))
]).toDF(["id", "features"])
df.createOrReplaceTempView("tb")
spark.sql("""select * , to_array(features)[1] Second from tb """).toPandas()
output
id features Second
0 1 [1.0, 2.0, 3.0] 2.0
1 2 (0.0, 9.0, 0.0) 9.0
I ran into the same problem with not being able to use explode(). One thing you can do is use VectorSlice from the pyspark.ml.feature library. Like so:
from pyspark.ml.feature import VectorSlicer
from pyspark.ml.linalg import Vectors
from pyspark.sql.types import Row
slicer = VectorSlicer(inputCol="features", outputCol="features_one", indices=[0])
output = slicer.transform(df)
output.select("features", "features_one").show()
For anyone trying to split the probability columns generated after training a PySpark ML model into usable columns. This does not use UDF or numpy. And this will only work for binary classification. Here lr_pred is the dataframe which has the predictions from the Logistic Regression Model.
prob_df1=lr_pred.withColumn("probability",lr_pred["probability"].cast("String"))
prob_df =prob_df1.withColumn('probabilityre',split(regexp_replace("probability", "^\[|\]", ""), ",")[1].cast(DoubleType()))
Since Spark 3.0.0 this can be done without using UDF.
from pyspark.ml.functions import vector_to_array
https://discuss.dizzycoding.com/how-to-split-vector-into-columns-using-pyspark/
Why is Vector[Double] is used in the results? That's not a very nice data type.
I want to scale data with StandardScaler (from pyspark.mllib.feature import StandardScaler), by now I can do it by passing the values of RDD to transform function, but the problem is that I want to preserve the key. is there anyway that I scale my data by preserving its key?
Sample dataset
0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,9,9,1.00,0.00,0.11,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,235,1337,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,29,29,1.00,0.00,0.03,0.00,0.00,0.00,0.00,0.00,smurf.
Imports
import sys
import os
from collections import OrderedDict
from numpy import array
from math import sqrt
try:
from pyspark import SparkContext, SparkConf
from pyspark.mllib.clustering import KMeans
from pyspark.mllib.feature import StandardScaler
from pyspark.statcounter import StatCounter
print ("Successfully imported Spark Modules")
except ImportError as e:
print ("Can not import Spark Modules", e)
sys.exit(1)
Portion of code
sc = SparkContext(conf=conf)
raw_data = sc.textFile(data_file)
parsed_data = raw_data.map(Parseline)
Parseline function:
def Parseline(line):
line_split = line.split(",")
clean_line_split = [line_split[0]]+line_split[4:-1]
return (line_split[-1], array([float(x) for x in clean_line_split]))
Not exactly a pretty solution but you can adjust my answer to the similar Scala question. Lets start with an example data:
import numpy as np
np.random.seed(323)
keys = ["foo"] * 50 + ["bar"] * 50
values = (
np.vstack([np.repeat(-10, 500), np.repeat(10, 500)]).reshape(100, -1) +
np.random.rand(100, 10)
)
rdd = sc.parallelize(zip(keys, values))
Unfortunately MultivariateStatisticalSummary is just a wrapper around a JVM model and it is not really Python friendly. Luckily with NumPy array we can use standard StatCounter to compute statistics by key:
from pyspark.statcounter import StatCounter
def compute_stats(rdd):
return rdd.aggregateByKey(
StatCounter(), StatCounter.merge, StatCounter.mergeStats
).collectAsMap()
Finally we can map to normalize:
def scale(rdd, stats):
def scale_(kv):
k, v = kv
return (v - stats[k].mean()) / stats[k].stdev()
return rdd.map(scale_)
scaled = scale(rdd, compute_stats(rdd))
scaled.first()
## array([ 1.59879188, -1.66816084, 1.38546532, 1.76122047, 1.48132643,
## 0.01512487, 1.49336769, 0.47765982, -1.04271866, 1.55288814])