Pyspark with column example

Pyspark with column example DEFAULT

PySpark is a transformation function of DataFrame which is used to change the value, convert the datatype of an existing column, create a new column, and many more. In this post, I will walk you through commonly used PySpark DataFrame column operations using withColumn() examples.

First, let&#;s create a DataFrame to work with.

1. Change DataType using PySpark withColumn()

By using PySpark on a DataFrame, we can cast or change the data type of a column. In order to change data type, you would also need to use function along with withColumn(). The below statement changes the datatype from to for the column.

2. Update The Value of an Existing Column

PySpark function of DataFrame can also be used to change the value of an existing column. In order to change the value, pass an existing column name as a first argument and a value to be assigned as a second argument to the withColumn() function. Note that the second argument should be type . Also, see Different Ways to Update PySpark DataFrame Column.

This snippet multiplies the value of &#;salary&#; with and updates the value back to &#;salary&#; column.

3. Create a Column from an Existing

To add/create a new column, specify the first argument with a name you want your new column to be and use the second argument to assign a value by applying an operation on an existing column. Also, see Different Ways to Add New Column to PySpark DataFrame.

This snippet creates a new column &#;CopiedColumn&#; by multiplying &#;salary&#; column with value

4. Add a New Column using withColumn()

In order to create a new column, pass the column name you wanted to the first argument of transformation function. Make sure this new column not already present on DataFrame, if it presents it updates the value of that column.

On below snippet, function is used to add a constant value to a DataFrame column. We can also chain in order to add multiple columns.

5. Rename Column Name

Though you cannot rename a column using withColumn, still I wanted to cover this as renaming is one of the common operations we perform on DataFrame. To rename an existing column use function on DataFrame.

6. Drop Column From PySpark DataFrame

Use &#;drop&#; function to drop a specific column from the DataFrame.

Note: Note that all of these functions return the new DataFrame after applying the functions instead of updating DataFrame.

7. PySpark withColumn() Complete Example

The complete code can be downloaded from PySpark withColumn GitHub project

Happy Learning !!

Tags: withColumn,withColumnRenamed

NNK

SparkByExamples.com is a Big Data and Spark examples community page, all examples are simple and easy to understand and well tested in our development environment Read more ..

PySpark withColumn() Usage with Examples
Sours: https://sparkbyexamples.com/pyspark/pyspark-withcolumn/

pyspark.sql.DataFrame.withColumn¶

(colName, col)[source]¶

Returns a new by adding a column or replacing the existing column that has the same name.

The column expression must be an expression over this ; attempting to add a column from some other will raise an error.

Parameters
colNamestr

string, name of the new column.

col

a expression for the new column.

Notes

This method introduces a projection internally. Therefore, calling it multiple times, for instance, via loops in order to add multiple columns can generate big plans which can cause performance issues and even StackOverflowException. To avoid this, use with the multiple columns at once.

Examples

>>> df.withColumn('age2',df.age+2).collect()[Row(age=2, name='Alice', age2=4), Row(age=5, name='Bob', age2=7)]

pyspark.sql.DataFrame.wherepyspark.sql.DataFrame.withColumnRenamed

Sours: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.withColumn.html
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Spark is a DataFrame function that is used to add a new column to DataFrame, change the value of an existing column, convert the datatype of a column, derive a new column from an existing column, on this post, I will walk you through commonly used DataFrame column operations with Scala examples.

Spark withColumn() Syntax and Usage

Spark is a transformation function of DataFrame that is used to manipulate the column values of all rows or selected rows on DataFrame.

withColumn() function returns a new Spark DataFrame after performing operations like adding a new column, update the value of an existing column, derive a new column from an existing column, and many more.

Below is a syntax of function.

&#; specify a new column you wanted to create. use an existing column to update the value.

&#; column expression.

Since withColumn() is a transformation function it doesn&#;t execute until action is called.

Spark withColumn() method introduces a projection internally. Therefore, calling it multiple times, for instance, via loops in order to add multiple columns can generate big plans which can cause performance issues and even . To avoid this, use with the multiple columns at once.

Spark Documentation

First, let&#;s create a DataFrame to work with.

1. Add a New Column to DataFrame

To create a new column, pass your desired column name to the first argument of withColumn() transformation function. Make sure this new column not already present on DataFrame, if it presents it updates the value of the column. On the below snippet, lit() function is used to add a constant value to a DataFrame column. We can also chain in order to add multiple columns.

The above approach is fine if you are manipulating few columns, but when you wanted to add or update multiple columns, do not use the chaining withColumn() as it leads to performance issues, use select() to update multiple columns instead.

2. Change Value of an Existing Column

Spark function of DataFrame can also be used to update the value of an existing column. In order to change the value, pass an existing column name as a first argument and value to be assigned as a second column. Note that the second argument should be type .

This snippet multiplies the value of &#;salary&#; with and updates the value back to &#;salary&#; column.

3. Derive New Column From an Existing Column

To create a new column, specify the first argument with a name you want your new column to be and use the second argument to assign a value by applying an operation on an existing column.

This snippet creates a new column &#;CopiedColumn&#; by multiplying &#;salary&#; column with value

4. Change Column Data Type

By using Spark withColumn on a DataFrame and using cast function on a column, we can change datatype of a DataFrame column. The below statement changes the datatype from String to Integer for the &#;salary&#; column.

5. Add, Replace, or Update multiple Columns

When you wanted to add, replace or update multiple columns in Spark DataFrame, it is not suggestible to chain function as it leads into performance issue and recommends to use select() after creating a temporary view on DataFrame

6. Rename Column Name

Though examples in 6,7, and 8 doesn&#;t use function, I still feel like explaining how to rename, drop, and split columns as these would be useful to you.

To rename an existing column use &#;withColumnRenamed&#; function on DataFrame.

7. Drop a Column

Use function to drop a specific column from the DataFrame.

8. Split Column into Multiple Columns

Though this example doesn&#;t use function, I still feel like it&#;s good to explain on splitting one DataFrame column to multiple columns using Spark transformation function.

This snippet split name column into &#;first name&#;, &#;last name&#; and address column into &#;Address Line1&#;, &#;City&#;, &#;State&#; and &#;ZipCode&#;. Yields below output:

Note: Note that all of these functions return the new DataFrame after applying the functions instead of updating DataFrame.

9. Spark withColumn() Complete Example

The complete code can be downloaded from GitHub

Happy Learning !!

Related Articles

Tags: withColumn,withColumnRenamed

NNK

SparkByExamples.com is a Big Data and Spark examples community page, all examples are simple and easy to understand and well tested in our development environment Read more ..

Spark DataFrame withColumn
Sours: https://sparkbyexamples.com/spark/spark-dataframe-withcolumn/

A DataFrame in Spark is a dataset organized into named columns. Spark data frame is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations. When you work with Datarames, you may get a requirement to rename the column. In this article, we will check how to rename a PySpark DataFrame column, Methods to rename DF column and some examples.

Rename PySpark DataFrame Column - Methods and Examples

Rename PySpark DataFrame Column

As mentioned earlier, we often need to rename one column or multiple columns on PySpark (or Spark) DataFrame.

Note that, we are only renaming the column name. We are not replacing or converting DataFrame column data type.

Following are some methods that you can use to rename dataFrame columns in Pyspark.

  • Use Function
  • Function to Rename All Columns in DataFrame
  • Use DataFrame Column method

Now let use check these methods with an examples.

Test Data

Following is the test DataFrame that we will be using in subsequent methods and examples.

Rename DataFrame Column using withColumnRenamed

This is one of the easiest methods that you can use to rename dataFrame column.

Following example uses the Spark withColumnRenamed function to rename DataFrame column name.

AS you can see, d_id is renamed to dept_id.

Spark Function to Rename All Columns in DataFrame

The converts strongly typed collection of data to generic DataFrame with columns renamed. You can use this method to create new DataFrame with different column names.

For example, consider below example.

With using  for renaming columns in DataFrame must be careful. This method works much slower than others.

Rename DataFrame Column using Alias Method

This is one of the easiest methods and often used in many pyspark code. an is used to rename the DataFrame column while displaying its content.

For Example,

Consider following Spark SQL example that uses an alias to rename DataFrame column names.

Related Articles

Hope this helps 🙂

Tags: Apache Spark,pyspark

Sours: https://dwgeek.com/rename-pyspark-dataframe-column-steps-and-examples.html/

Column pyspark example with

5 Ways to add a new column in a PySpark Dataframe

Too much data is getting generated day by day.

Although sometimes we can manage our big data using tools like Rapids or Parallelization, Spark is an excellent tool to have in your repertoire if you are working with Terabytes of data.

In my last post on Spark, I explained how to work with PySpark RDDs and Dataframes.

Although this post explains a lot on how to work with RDDs and basic Dataframe operations, I missed quite a lot when it comes to working with PySpark Dataframes.

And it is onlywhen I required more functionality that I read up and came up with multiple solutions to do one single thing.

How to create a new column in spark?

Now, this might sound trivial, but believe me, it isn’t. With so much you might want to do with your data, I am pretty sure you will end up using most of these column creation processes in your workflow. Sometimes to utilize Pandas functionality, or occasionally to use RDDs based partitioning or sometimes to make use of the mature python ecosystem.

This post is going to be about — “Multiple ways to create a new column in Pyspark Dataframe.”

If you have PySpark installed, you can skip the Getting Started section below.

I know that a lot of you won’t have spark installed in your system to try and learn. But installing Spark is a headache of its own.

Since we want to understand how it works and work with it, I would suggest that you use Spark on Databricks here online with the community edition. Don’t worry, it is free, albeit fewer resources, but that works for us right now for learning purposes.

Once you register and login will be presented with the following screen.

You can start a new notebook here.

Select the Python notebook and give any name to your notebook.

Once you start a new notebook and try to execute any command, the notebook will ask you if you want to start a new cluster. Do it.

The next step will be to check if the sparkcontext is present. To check if the sparkcontext is present, you have to run this command:

sc

This means that we are set up with a notebook where we can run Spark.

Here, I will work on the Movielens mlk.zip dataset. , ratings from users on movies. In this zipped folder, the file we will specifically work with is the rating file. This filename is kept as “u.data”

If you want to upload this data or any data, you can click on the Data tab in the left and then Add Data by using the GUI provided.

We can then load the data using the following commands:

ratings = spark.read.load("/FileStore/tables/u.data",format="csv", sep="\t", inferSchema="true", header="false")ratings = ratings.toDF(*['user_id', 'movie_id', 'rating', 'unix_timestamp'])

Here is how it looks:

ratings.show()

Ok, so now we are set up to begin the part we are interested in finally. How to create a new column in PySpark Dataframe?

The most pysparkish way to create a new column in a PySpark DataFrame is by using built-in functions. This is the most performant programmatical way to create a new column, so this is the first place I go whenever I want to do some column manipulation.

We can use along with PySpark SQL functions to create a new column. In essence, you can find String functions, Date functions, and Math functions already implemented using Spark functions. We can import spark functions as:

import pyspark.sql.functions as F

Our first function, the function gives us access to the column. So if we wanted to multiply a column by 2, we could use as:

ratings_with_scale10 = ratings.withColumn("ScaledRating", 2*F.col("rating"))ratings_with_scaleshow()

We can also use math functions like function:

ratings_with_exp = ratings.withColumn("expRating", 2*F.exp("rating"))ratings_with_exp.show()

There are a lot of other functions provided in this module, which are enough for most simple use cases. You can check out the functions list here.

Sometimes we want to do complicated things to a column or multiple columns. This could be thought of as a map operation on a PySpark Dataframe to a single column or multiple columns. While Spark SQL functions do solve many use cases when it comes to column creation, I use Spark UDF whenever I want to use the more matured Python functionality.

To use Spark UDFs, we need to use the function to convert a regular python function to a Spark UDF. We also need to specify the return type of the function. In this example the return type is

import pyspark.sql.functions as F
from pyspark.sql.types import *def somefunc(value):
if value < 3:
return 'low'
else:
return 'high'#convert to a UDF Function by passing in the function and return type of functionudfsomefunc = F.udf(somefunc, StringType())ratings_with_high_low = ratings.withColumn("high_low", udfsomefunc("rating"))ratings_with_high_low.show()

Sometimes both the spark UDFs and SQL Functions are not enough for a particular use-case. You might want to utilize the better partitioning that you get with spark RDDs. Or you may want to use group functions in Spark RDDs. You can use this one, mainly when you need access to all the columns in the spark data frame inside a python function.

Whatever the case be, I find this way of using RDD to create new columns pretty useful for people who have experience working with RDDs that is the basic building block in the Spark ecosystem.

The process below makes use of the functionality to convert between and objects. We convert a row object to a dictionary. Work with the dictionary as we are used to and convert that dictionary back to row again.

import math
from pyspark.sql import Rowdef rowwise_function(row):
# convert row to dict:
row_dict = row.asDict()
# Add a new key in the dictionary with the new column name and value.
row_dict['Newcol'] = math.exp(row_dict['rating'])
# convert dict to row:
newrow = Row(**row_dict)
# return new row
return newrow# convert ratings dataframe to RDD
ratings_rdd = ratings.rdd
# apply our function to RDD
ratings_rdd_new = ratings_rdd.map(lambda row: rowwise_function(row))
# Convert RDD Back to DataFrame
ratings_new_df = sqlContext.createDataFrame(ratings_rdd_new)ratings_new_df.show()

This functionality was introduced in the Spark version And this allows you to use pandas functionality with Spark. I generally use it when I have to run a groupby operation on a Spark dataframe or whenever I need to create rolling features and want to use Pandas rolling functions/window functions.

The way we use it is by using the decorator. We assume here that the input to the function will be a pandas data frame. And we need to return a pandas dataframe in turn from this function.

The only complexity here is that we have to provide a schema for the output Dataframe. We can make that using the format below.

# Declare the schema for the output of our function
outSchema = StructType([StructField('user_id',IntegerType(),True),StructField('movie_id',IntegerType(),True),StructField('rating',IntegerType(),True),StructField('unix_timestamp',IntegerType(),True),StructField('normalized_rating',DoubleType(),True)])# decorate our function with pandas_udf decorator
@F.pandas_udf(outSchema, F.PandasUDFType.GROUPED_MAP)
def subtract_mean(pdf):
# pdf is a pandas.DataFrame
v = pdf.rating
v = v - v.mean()
pdf['normalized_rating'] =v
return pdfrating_groupwise_normalization = ratings.groupby("movie_id").apply(subtract_mean)rating_groupwise_normalization.show()

We can also make use of this to train multiple individual models on each spark node. For that, we replicate our data and give each replication a key and some training params like max_depth, etc. Our function then takes the pandas Dataframe, runs the required model, and returns the result. The structure would look something like below.

# 0. Declare the schema for the output of our function
outSchema = StructType([StructField('replication_id',IntegerType(),True),StructField('RMSE',DoubleType(),True)])# decorate our function with pandas_udf decorator
@F.pandas_udf(outSchema, F.PandasUDFType.GROUPED_MAP)
def run_model(pdf):
# 1. Get hyperparam values
num_trees = pdf.num_trees.values[0]
depth = pdf.depth.values[0]
replication_id = pdf.replication_id.values[0]
# 2. Train test split
Xtrain,Xcv,ytrain,ycv = train_test_split
# 3. Create model using the pandas dataframe
clf = RandomForestRegressor(max_depth = depth, num_trees=num_trees,)
clf.fit(Xtrain,ytrain)
# 4. Evaluate the model
rmse = RMSE(clf.predict(Xcv,ycv)
# 5. return results as pandas DF
res =pd.DataFrame({'replication_id':replication_id,'RMSE':rmse})
return res

results = replicated_data.groupby("replication_id").apply(run_model)

Above is just an idea and not a working code. Though it should work with minor modifications.

For people who like SQL, there is a way even to create columns using SQL. For this, we need to register a temporary SQL table and then use simple select queries with an additional column. One might also use it to do joins.

ratings.registerTempTable('ratings_table')
newDF = sqlContext.sql('select *, 2*rating as newCol from ratings_table')
newDF.show()
Sours: https://towardsdatascience.com/5-ways-to-add-a-new-column-in-a-pyspark-dataframe-4e75c2fd8c08
Spark DataFrame Operations and Transformations ❌PySpark Tutorial

Introduction to DataFrames - Python

This article demonstrates a number of common PySpark DataFrame APIs using Python.

A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. You can think of a DataFrame like a spreadsheet, a SQL table, or a dictionary of series objects. For more information and examples, see the Quickstart on the Apache Spark documentation website.

Create DataFrames

# import pyspark class Row from module sqlfrompyspark.sqlimport*# Create Example Data - Departments and Employees# Create the Departmentsdepartment1=Row(id='',name='Computer Science')department2=Row(id='',name='Mechanical Engineering')department3=Row(id='',name='Theater and Drama')department4=Row(id='',name='Indoor Recreation')# Create the EmployeesEmployee=Row("firstName","lastName","email","salary")employee1=Employee('michael','armbrust','[email protected]',)employee2=Employee('xiangrui','meng','[email protected]',)employee3=Employee('matei',None,'[email protected]',)employee4=Employee(None,'wendell','[email protected]',)employee5=Employee('michael','jackson','[email protected]',)# Create the DepartmentWithEmployees instances from Departments and EmployeesdepartmentWithEmployees1=Row(department=department1,employees=[employee1,employee2])departmentWithEmployees2=Row(department=department2,employees=[employee3,employee4])departmentWithEmployees3=Row(department=department3,employees=[employee5,employee4])departmentWithEmployees4=Row(department=department4,employees=[employee2,employee3])print(department1)print(employee2)print(departmentWithEmployees1.employees[0].email)

Create DataFrames from a list of the rows

departmentsWithEmployeesSeq1=[departmentWithEmployees1,departmentWithEmployees2]df1=spark.createDataFrame(departmentsWithEmployeesSeq1)display(df1)departmentsWithEmployeesSeq2=[departmentWithEmployees3,departmentWithEmployees4]df2=spark.createDataFrame(departmentsWithEmployeesSeq2)display(df2)

Work with DataFrames

Union two DataFrames

unionDF=df1.union(df2)display(unionDF)

Write the unioned DataFrame to a Parquet file

# Remove the file if it existsdbutils.fs.rm("/tmp/databricks-df-example.parquet",True)unionDF.write.parquet("/tmp/databricks-df-example.parquet")

Read a DataFrame from the Parquet file

parquetDF=spark.read.parquet("/tmp/databricks-df-example.parquet")display(parquetDF)

Explode the employees column

frompyspark.sql.functionsimportexplodeexplodeDF=unionDF.select(explode("employees").alias("e"))flattenDF=explodeDF.selectExpr("e.firstName","e.lastName","e.email","e.salary")flattenDF.show()
+++++|firstName|lastName|email|salary|+++++|michael|armbrust|[email protected]|||xiangrui|meng|[email protected]|||matei|null|[email protected]|||null|wendell|[email protected]|||michael|jackson|[email protected]|||null|wendell|[email protected]|||xiangrui|meng|[email protected]|||matei|null|[email protected]||+++++

Use to return the rows that match a predicate

filterDF=flattenDF.filter(flattenDF.firstName=="xiangrui").sort(flattenDF.lastName)display(filterDF)
frompyspark.sql.functionsimportcol,asc# Use `|` instead of `or`filterDF=flattenDF.filter((col("firstName")=="xiangrui")|(col("firstName")=="michael")).sort(asc("lastName"))display(filterDF)

The clause is equivalent to

whereDF=flattenDF.where((col("firstName")=="xiangrui")|(col("firstName")=="michael")).sort(asc("lastName"))display(whereDF)

Replace values with using DataFrame Na function

nonNullDF=flattenDF.fillna("--")display(nonNullDF)

Retrieve only rows with missing or

filterNonNullDF=flattenDF.filter(col("firstName").isNull()|col("lastName").isNull()).sort("email")display(filterNonNullDF)

Example aggregations using and

frompyspark.sql.functionsimportcountDistinctcountDistinctDF=nonNullDF.select("firstName","lastName")\ .groupBy("firstName")\ .agg(countDistinct("lastName").alias("distinct_last_names"))display(countDistinctDF)

Compare the DataFrame and SQL query physical plans

Tip

They should be the same.

countDistinctDF.explain()
# register the DataFrame as a temp view so that we can query it using SQLnonNullDF.createOrReplaceTempView("databricks_df_example")# Perform the same query as the DataFrame above and return ``explain``countDistinctDF_sql=spark.sql(''' SELECT firstName, count(distinct lastName) AS distinct_last_names FROM databricks_df_example GROUP BY firstName''')countDistinctDF_sql.explain()

Sum up all the salaries

salarySumDF=nonNullDF.agg({"salary":"sum"})display(salarySumDF)

Print the summary statistics for the salaries

nonNullDF.describe("salary").show()

An example using pandas and Matplotlib integration

importpandasaspdimportmatplotlib.pyplotaspltplt.clf()pdDF=nonNullDF.toPandas()pdDF.plot(x='firstName',y='salary',kind='bar',rot=45)display()

Cleanup: remove the Parquet file

dbutils.fs.rm("/tmp/databricks-df-example.parquet",True)

DataFrame FAQs

This FAQ addresses common use cases and example usage using the available APIs. For more detailed API descriptions, see the PySpark documentation.

How can I get better performance with DataFrame UDFs?

If the functionality exists in the built-in functions, using these will perform better. Example usage follows. Also see the PySpark Functions API reference. Use the built-in functions and the API to add new columns. You can also use to replace an existing column after the transformation.

frompyspark.sqlimportfunctionsasFfrompyspark.sql.typesimport*# Build an example DataFrame dataset to work with.dbutils.fs.rm("/tmp/dataframe_sample.csv",True)dbutils.fs.put("/tmp/dataframe_sample.csv","""id|end_date|start_date|location1| | |CA-SF2| | |CA-SD3| | |NY-NY4| | |NY-NY5| | |CA-SD""",True)df=spark.read.format("csv").options(header='true',delimiter='|').load("/tmp/dataframe_sample.csv")df.printSchema()
# Instead of registering a UDF, call the builtin functions to perform operations on the columns.# This will provide a performance improvement as the builtins compile and run in the platform's JVM.# Convert to a Date typedf=df.withColumn('date',F.to_date(df.end_date))# Parse out the date onlydf=df.withColumn('date_only',F.regexp_replace(df.end_date,' (\d+)[:](\d+)[:](\d+).*$',''))# Split a string and index a fielddf=df.withColumn('city',F.split(df.location,'-')[1])# Perform a date diff functiondf=df.withColumn('date_diff',F.datediff(F.to_date(df.end_date),F.to_date(df.start_date)))
df.createOrReplaceTempView("sample_df")display(sql("select * from sample_df"))

I want to convert the DataFrame back to JSON strings to send back to Kafka.

There is an underlying function that returns an RDD of JSON strings using the column names and schema to produce the JSON records.

rdd_json=df.toJSON()rdd_json.take(2)

My UDF takes a parameter including the column to operate on. How do I pass this parameter?

There is a function available called that creates a constant column.

frompyspark.sqlimportfunctionsasFadd_n=udf(lambdax,y:x+y,IntegerType())# We register a UDF that adds a column to the DataFrame, and we cast the id column to an Integer type.df=df.withColumn('id_offset',add_n(F.lit(),df.id.cast(IntegerType())))
# any constants used by UDF will automatically pass through to workersN=90last_n_days=udf(lambdax:x<N,BooleanType())df_filtered=df.filter(last_n_days(df.date_diff))display(df_filtered)

I have a table in the Hive metastore and I’d like to access to table as a DataFrame. What’s the best way to define this?

There are multiple ways to define a DataFrame from a registered table. Call or select and filter specific columns using an SQL query:

# Both return DataFrame typesdf_1=table("sample_df")df_2=spark.sql("select * from sample_df")

I’d like to clear all the cached tables on the current cluster.

There’s an API available to do this at a global level or per table.

spark.catalog.clearCache()spark.catalog.cacheTable("sample_df")spark.catalog.uncacheTable("sample_df")

I’d like to compute aggregates on columns. What’s the best way to do this?

The method takes a list of column names and expressions for the type of aggregation you’d like to compute. See pyspark.sql.DataFrame.agg. You can use built-in functions in the expressions for each column.

# Provide the min, count, and avg and groupBy the location column. Diplay the resultsagg_df=df.groupBy("location").agg(F.min("id"),F.count("id"),F.avg("date_diff"))display(agg_df)

I’d like to write out the DataFrames to Parquet, but would like to partition on a particular column.

You can use the following APIs to accomplish this. Ensure the code does not create a large number of partition columns with the datasets otherwise the overhead of the metadata can cause significant slow downs. If there is a SQL table back by this directory, you will need to call to update the metadata prior to the query.

df=df.withColumn('end_month',F.month('end_date'))df=df.withColumn('end_year',F.year('end_date'))df.write.partitionBy("end_year","end_month").parquet("/tmp/sample_table")display(dbutils.fs.ls("/tmp/sample_table"))

How do I properly handle cases where I want to filter out NULL data?

You can use and provide similar syntax as you would with a SQL query.

null_item_schema=StructType([StructField("col1",StringType(),True),StructField("col2",IntegerType(),True)])null_df=spark.createDataFrame([("test",1),(None,2)],null_item_schema)display(null_df.filter("col1 IS NOT NULL"))

How do I infer the schema using the or libraries?

There is an option flag. Providing a header ensures appropriate column naming.

adult_df=spark.read.\ format("com.spark.csv").\ option("header","false").\ option("inferSchema","true").load("dbfs:/databricks-datasets/adult/adult.data")adult_df.printSchema()

You have a delimited string dataset that you want to convert to their datatypes. How would you accomplish this?

Use the RDD APIs to filter out the malformed rows and map the values to the appropriate types. We define a function that filters the items using regular expressions.


Sours: https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html

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