Pyspark

You are trying to apply the function to the column. The function is not in pyspark. You should go for it. Try this for a second.

import pyspark.sql.functions as F

df = df.withColumn(“AddCol”, F.when(F.col(“Pclass”).like(“3”), “three”).otherwise(“notthree”))

If you just want it to be the number 3, you should do that.

import pyspark.sql.functions as F

# If the column Pclass is numeric

df = df.withColumn(“AddCol”, F.when(F.col(“Pclass”) == F.lit(3), “three”).otherwise(“notthree”))

# If the column Pclass is string

df = df.withColumn(“AddCol”, F.when(F.col(“Pclass”) == F.lit(“3”), “three”).otherwise(“notthree”))

Exemple using java 8 and spark 2.1

df.show();

— — — — + — — — + — – + — — – + — — – + — — +

+ — — — — + — — — + — – + — — – + — — – + — — +

0 | 3 | 1 | 1 | 0 | 3 |

1 | 1 | 0 | 1 | 0 | 2 |

+ — — — — + — — — + — – + — — – + — — – + — — +

df = df.withColumn(“AddCol”, when(df.col(“Pclass”).contains(“3”), “three”).otherwise(“notthree”));

df.show();

— — — — + — — — + — – + — — – + — — – + — — + — — — — +

+ — — — — + — — — + — – + — — – + — — – + — — + — — — — +

0 | 3 | 1 | 1 | 0 | 3 | three |

1 | 1 | 0 | 1 | 0 | 2 | notthree |

+ — — — — + — — — + — – + — — – + — — – + — — + — — — — +

When we try to access the complete dataframe as a callable object we get a similar error. The basics are clear to us and we are getting this error.

We think it is a function here. That is the source of the error. Any type of function in Python is callable. NoneType, List, Tuple, int and str are not callable since they are not objects.

This is not a pyspark specific error The object in question is not callable. The above way is used since it is coming for pyspark dataframe.

The same error is also possible with pandas. We are going to uncover this mistake with one practical example. The best way to fix the error is with the rename column function.

import pyspark

from pyspark.sql

import SparkSession

spark = SparkSession.builder.appName(‘Data Science Learner’).getOrCreate()

data_df = [

[1, “Abhishek”, “A”],

[2, “Ankita”, “B”],

[3, “Sukesh”, “C”]

]

columns = [‘Seq’, ‘Name’, ‘Identifier’]

dataframe = spark.createDataFrame(data_df, columns)

dataframe.show()

We have come up with the same thing

Allow us to apply any condition over any column. This is where we will replicate the error.

dataframe.select(‘Identifier’).where(dataframe.Identifier() < B).show()

This is not a big deal as we have already explained. The same can be fixed by removing the parenthesis after the column name of the dataframe.

In the above example, we used dataframe.Identifier which is incorrect. If we remove the same and access the column in a different way, we will get rid of the error.

dataframe.select(‘Identifier’).where(dataframe.Identifier < ‘B’).show()

PySpark

It has been more difficult to translate this functionality to the data frame. The first thing you have to do is split the string element into floats.

The goal is to extract calculated features from each array and place them in a new column in the same data frame.

This can be accomplished with Pandas dataframes, if I stick with them and convert back to a SparkDF before saving to Hive table, would I be risking memory issues if the DF is too large?

from pyspark.sql

import HiveContext, Row #Import Spark Hive SQLhiveCtx = HiveContext(sc) #Cosntruct SQL contextrows = hiveCtx.sql(“SELECT collectiondate,serialno,system,accelerometerid,ispeakvue,wfdataseries,deltatimebetweenpoints,\

spectrumdataseries,maxfrequencyhz FROM test_vibration.vibrationblockdata”)

import pandas as pd

df = rows.toPandas()

df[‘wfdataseries’] = df.wfdataseries.apply(lambda x: x.encode(‘UTF8’).split(‘,’))

def str2flt(data): #Define

function

for converting list of strings to list of floats

return [float(value) for value in data]

df[‘wfdataseries’] = df.wfdataseries.apply(str2flt)

df[‘WF_Peak’] = df.wfdataseries.apply(lambda x: max(x)) #Calculate max value of nested array in each element of column ‘wfdataseries’

# Various vibration waveform statistics

import numpy as np

from scipy

import stats

df[‘WF_Var’] = df.wfdataseries.apply(lambda x: np.var(np.array(x)))

df[‘WF_Kurt’] = df.wfdataseries.apply(lambda x: stats.kurtosis(np.array(x), bias = True))

df[‘WF_Skew’] = df.wfdataseries.apply(lambda x: stats.skew(np.array(x), bias = True))

df = df.withColumn(‘WF_Peak’,max(‘wfdataseries’))

—————————————————————————

TypeError Traceback (most recent call last)

<ipython-input-97-be5735ba4c8a> in <module>()

—-> 1 df = df.withColumn(‘WF_Peak’,max(‘wfdataseries’))

TypeError: ‘Column’ object is not callable

df = df.withColumn(‘WF_Peak’, df.wfdataseries.max())

—————————————————————————

TypeError Traceback (most recent call last)

<ipython-input-99-a8b1116cac06> in <module>()

—-> 1 df = df.withColumn(‘WF_Peak’, df.wfdataseries.max())

TypeError: ‘Column’ object is not callable

from pyspark.sql.functions

import udf

def maxList(list):

max(list)

maxUdf == udf(scoreToCategory, FloatType())

df = df.withColumn(‘WF_Peak’, maxUdf(‘wfdataseries’))

from pyspark.sql.functions

import udf

def maxList(list):

max(list)

maxUdf == udf(scoreToCategory, FloatType())

df = df.withColumn(‘WF_Peak’, maxUdf(‘wfdataseries’))

#Waveform peak amplitude

udf_wf_peak = udf(lambda x: max(x), returnType = FloatType()) #Define UDF

function

df = df.withColumn(‘WF_Peak’, udf_wf_peak(‘wfdataseries’))

In example 2, I will show how to fix theTypeError: DataFrame object is not callable.

import pandas as pd # Load pandas

data = pd.DataFrame({

‘x1’: range(70, 64, -1),

# Create pandas DataFrame ‘x2’: [‘a’, ‘b’, ‘c’, ‘a’, ‘b’, ‘c’],

‘x3’: [1, 7, 5, 9, 1, 5]

})

print(data) # Print pandas DataFrame

# x1 x2 x3

# 0 70 a 1

# 1 69 b 7

# 2 68 c 5

# 3 67 a 9

# 4 66 b 1

# 5 65 c 5

data(‘x3’).var() # Code leads to error

# TypeError: ‘DataFrame’

object is not callable

data[‘x3’].var() # Code works fine

# Out[13]: 10.266666666666666

1.The problem is that isin was added to Spark in version

2.0 and therefore not yet avaiable in your version of Spark as shown in the documentation of isin here.

1.There is a function similar to the one that was introduced in

2.0 that accepts columns, but there are some differences in the input since it only accepts columns.

There is a function called inSetinstead in PySpark. Here are some usage examples from the documentation.

from pyspark.sql.functions

import udf, col variables = (‘852-PI-769’, ‘812-HC-037’, ‘852-PC-571-OUT’) df = sqlContext.read.option(“mergeSchema”, “true”).parquet(“parameters.parquet”) same_var = col(“Variable”).isin(variables) df2 = df.filter(same_var)

df[df.name.inSet(“Bob”, “Mike”)] df[df.age.inSet([1, 2, 3])]

callable(object)

callable (object)

>>> numbers = [1, 2, 3] >>> callable(numbers) False

>>>

numbers = [1, 2, 3] >>>

callable(numbers) False

>>> numbers = (1, 2, 3) >>> callable(numbers) False

>>>

callable(lambda x: x+1) True

>>> def calculate_sum(x, y): …

return x + y… >>> callable(calculate_sum) True

>>>

def calculate_sum(x, y): …

return x+y … >>>

callable(calculate_sum) True

>>> number = 10 >>> callable(number) False

>>>

number = 10 >>>

callable(number) False

class Person: def __init__(self, age): self.age = age

class Person:

def __init__(self, age):

self.age = age

john = Person(25)

print(john.__dict__) {

‘age’: 25

}

print(john.__dict__) {‘age’: 25}

>>>print(john.age()) Traceback (most recent call last): File “callable.py”, line 6, in <module>print(john.age()) TypeError: ‘int’object is not callable

>>>

The print(john.age()) Traceback (most recent call last): File “callable.py”, line 6, in <module>

print(john.age()) TypeError: ‘int’

object is not callable

import math number = float(input(“Please insert a number: “)) if number < math.pi(): print(“The number is smaller than Pi”)

else: print(“The number is bigger than Pi”)

import math number = float(input(“Please insert a number: “)) if number <

math.pi():

print(“The number is smaller than Pi”) else:

print(“The number is bigger than Pi”)

Please insert a number: 4 Traceback (most recent call last): File “callable.py”, line 12, in <module>if number <math.pi(): TypeError: ‘float’ object is not callable

>>> callable(4.0) False

>>>

callable(4.0) False

>>>import sys >>>print(sys.version()) Traceback (most recent call last): File “<stdin>”, line 1, in <module>TypeError: ‘str’object is not callable

>>>

callable(“Python”) False

>>> cities = [‘Paris’, ‘Rome’, ‘Warsaw’, ‘New York’]

>>>

cities = [‘Paris’, ‘Rome’, ‘Warsaw’, ‘New York’]

>>>print(cities(0)) Traceback (most recent call last): File “<stdin>”, line 1, in <module>TypeError: ‘list’object is not callable

>>>

print(cities(0)) Traceback (most recent call last): File “<stdin>”, line 1, in <module>

TypeError: ‘list’

object is not callable

>>> print(cities[0]) Paris

>>>

print(cities[0]) Paris

>>>matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] >>>[[2*row(index) for index in range(len(row))] for row in matrix] Traceback (most recent call last): File “<stdin>”, line 1, in <module> File “<stdin>”, line 1, in <listcomp> File “<stdin>”, line 1, in <listcomp>TypeError: ‘list’object is not callable

PySpark add_months() function takes the first argument as a column and the second argument is a literal value.

If you try to use Column type for the second argument you get “TypeError: Column is not iterable”. In order to fix this use expr() function as shown below.

Problem 1: When I try to add a month to the data column with a value from another column I am getting a PySpark error TypeError: Column is not iterable.

MoreKafkaApache Kafka Tutorials with ExamplesH2O.aiApache HadoopApache HBaseApache CassandraSnowflake DatabaseH2O Sparkling WaterScala Language,PySpark – Date and Timestamp Functions

Problem 1: when I try to add a month to the data column with a value from another column, I get a PySpark error: Column is not iterable.

from pyspark.sql.functions

import add_months

data = [(“2019-01-23”, 1), (“2019-06-24”, 2), (“2019-09-20”, 3)]

df = spark.createDataFrame(data).toDF(“date”, “increment”)

df.select(df.date, df.increment, add_months(df.date, df.increment)).show()

TypeError: Column is not iterable

PySpark add: The first argument is a column and the second is a value. Column type is not iterable if you use it for the second argument. The function expr is used to fix this.

df.select(df.date, df.increment,

expr(“add_months(date,increment)”)

.alias(“inc_date”)).show()

When you try to call a DataFrame by putting parentheses after it like a function, theTypeError object is not callable. The only functions that respond to function calls are the functions.

When you try to call a DataFrame as if it were a function, theTypeError object is not callable.

The type error occurs when you attempt to perform an illegal operation for a specific type of data. When parentheses are put after the DataFrame object, it is interpreted by Python as a function call.

The function is called if the Python interpreter executes the code inside it. Functions are the only ones that we can call in Python.

We can call functions by specifying the name of the function we want to use followed by a set of parentheses.

For Example: An example of a working function that returns a string is presented.

# Declare

function

def simple_function():

print(“Learning Python is fun!”)

# Call

function

simple_function()

# Declare function

def simple_function():

print(“Learning Python is fun!”)

# Call function

simple_function()

Learning Python is fun

If the method returns False, the object is not callable. The method will be tested with a DataFrame.

import pandas as pd

df = pd.DataFrame({

‘values’: [2, 4, 6, 8, 10, 12]

})

print(callable(df))

To calculate the mean monthly amount of vegetables sold by a farmer over the course of a year, we need to look at an example. The first thing we will do is look at the dataset.

Month, Amount

1, 200

2, 150

3, 300

4, 350

5, 234

6, 500

7, 900

8, 1000

9, 959

10, 888

11, 3000

12, 1500

We are going to load the dataset into a DataFrame.

import pandas as pd

df = pd.read_csv(‘veg_sold.csv’)

print(df)

import pandas as pd

df = pd.read_csv(‘veg_sold.csv’)

print(df)

Month Amount

0 1 200

1 2 150

2 3 300

3 4 350

4 5 234

5 6 500

6 7 900

7 8 1000

8 9 959

9 10 888

10 11 3000

11 12 1500

Next, To calculate the mean amount sold, we will call mean on the column and use the column name as an index in the DataFrame.

mean_sold = df(‘Amount’).mean()

print(f ‘Mean sold over the year: {mean_sold}kg’)

Let’s run the code to see what happens:

—————————————————————————

TypeError Traceback (most recent call last)

<ipython-input-8-5237331dba60> in <module>

—-> 1 mean_sold = df(‘Amount’).mean()

2 print(f’Mean sold over the year: {mean_sold}kg’)

TypeError: ‘DataFrame’ object is not callable

Square brackets can be used to access the column of the DataFrame. The mean method will be used to call the object a Series. The revised code needs to be looked at.

mean_sold = df[‘Amount’].mean()

print(f ‘Mean sold over the year: {mean_sold}kg’)