How to loop through each row of dataFrame in pyspark - apache-spark

Question

E.g
sqlContext = SQLContext(sc)
sample=sqlContext.sql("select Name ,age ,city from user")
sample.show()
The above statement prints theentire table on terminal. But I want to access each row in that table using for or while to perform further calculations.

Answer 1

You simply cannot. DataFrames, same as other distributed data structures, are not iterable and can be accessed using only dedicated higher order function and / or SQL methods.
You can of course collect
for row in df.rdd.collect():
do_something(row)
or convert toLocalIterator
for row in df.rdd.toLocalIterator():
do_something(row)
and iterate locally as shown above, but it beats all purpose of using Spark.

Answer 2

To "loop" and take advantage of Spark's parallel computation framework, you could define a custom function and use map.
def customFunction(row):
return (row.name, row.age, row.city)
sample2 = sample.rdd.map(customFunction)
or
sample2 = sample.rdd.map(lambda x: (x.name, x.age, x.city))
The custom function would then be applied to every row of the dataframe. Note that sample2 will be a RDD, not a dataframe.
Map may be needed if you are going to perform more complex computations. If you just need to add a simple derived column, you can use the withColumn, with returns a dataframe.
sample3 = sample.withColumn('age2', sample.age + 2)

Answer 3

Using list comprehensions in python, you can collect an entire column of values into a list using just two lines:
df = sqlContext.sql("show tables in default")
tableList = [x["tableName"] for x in df.rdd.collect()]
In the above example, we return a list of tables in database 'default', but the same can be adapted by replacing the query used in sql().
Or more abbreviated:
tableList = [x["tableName"] for x in sqlContext.sql("show tables in default").rdd.collect()]
And for your example of three columns, we can create a list of dictionaries, and then iterate through them in a for loop.
sql_text = "select name, age, city from user"
tupleList = [{name:x["name"], age:x["age"], city:x["city"]}
for x in sqlContext.sql(sql_text).rdd.collect()]
for row in tupleList:
print("{} is a {} year old from {}".format(
row["name"],
row["age"],
row["city"]))

Answer 4

It might not be the best practice, but you can simply target a specific column using collect(), export it as a list of Rows, and loop through the list.
Assume this is your df:
+----------+----------+-------------------+-----------+-----------+------------------+
| Date| New_Date| New_Timestamp|date_sub_10|date_add_10|time_diff_from_now|
+----------+----------+-------------------+-----------+-----------+------------------+
|2020-09-23|2020-09-23|2020-09-23 00:00:00| 2020-09-13| 2020-10-03| 51148 |
|2020-09-24|2020-09-24|2020-09-24 00:00:00| 2020-09-14| 2020-10-04| -35252 |
|2020-01-25|2020-01-25|2020-01-25 00:00:00| 2020-01-15| 2020-02-04| 20963548 |
|2020-01-11|2020-01-11|2020-01-11 00:00:00| 2020-01-01| 2020-01-21| 22173148 |
+----------+----------+-------------------+-----------+-----------+------------------+
to loop through rows in Date column:
rows = df3.select('Date').collect()
final_list = []
for i in rows:
final_list.append(i[0])
print(final_list)

Answer 5

Give A Try Like this
result = spark.createDataFrame([('SpeciesId','int'), ('SpeciesName','string')],["col_name", "data_type"]);
for f in result.collect():
print (f.col_name)

Answer 6

If you want to do something to each row in a DataFrame object, use map. This will allow you to perform further calculations on each row. It's the equivalent of looping across the entire dataset from 0 to len(dataset)-1.
Note that this will return a PipelinedRDD, not a DataFrame.

Answer 7

above
tupleList = [{name:x["name"], age:x["age"], city:x["city"]}
should be
tupleList = [{'name':x["name"], 'age':x["age"], 'city':x["city"]}
for name, age, and city are not variables but simply keys of the dictionary.