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Is there any alternative for
df[100, c("column")] in scala spark data frames. I want to select specific row from a column of spark data frame.
100th row in above R equivalent code
Firstly, you must understand that
DataFrames are distributed, that means you can't access them in a typical procedural way, you must do an analysis first. Although, you are asking about
Scala I suggest you to read the Pyspark Documentation, because it has more examples than any of the other documentations.
However, continuing with my explanation, I would use some methods of the
RDD API cause all
DataFrames have one
RDD as attribute. Please, see my example bellow, and notice how I take the 2nd record.
df = sqlContext.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["letter", "name"]) myIndex = 1 values = (df.rdd.zipWithIndex() .filter(lambda ((l, v), i): i == myIndex) .map(lambda ((l,v), i): (l, v)) .collect()) print(values) # (u'b', 2)
Hopefully, someone gives another solution with fewer steps.
[100% Working Code], get specific row from spark dataframe apache-spark apache-spark-sql. Is there any alternative for df[100, c(“column”)] in scala spark data get specific row from spark dataframe apache-spark apache-spark-sql Is there any alternative for df[100, c(“column”)] in scala spark data frames. I want to select specific row from a column of spark data frame. for example 100th row in above R equivalent codeThe getrows() function below should get the specific rows you want.
This is how I achieved the same in Scala. I am not sure if it is more efficient than the valid answer, but it requires less coding
val parquetFileDF = sqlContext.read.parquet("myParquetFule.parquet") val myRow7th = parquetFileDF.rdd.take(7).last
Pyspark: Dataframe Row & Columns, If you've used R or even the pandas library with Python you are probably already familiar with the concept of DataFrames. Spark DataFrame Here, I’ve explained how to get the first row, minimum, maximum of each group in Spark DataFrame using Spark SQL window functions and Scala example. Though I’ve explained here with Scala, the same method could be used to working with PySpark and Python. Preparing Data & DataFrame
getrows() function below should get the specific rows you want.
For completeness, I have written down the full code in order to reproduce the output.
# Create SparkSession from pyspark.sql import SparkSession spark = SparkSession.builder.master('local').appName('scratch').getOrCreate() # Create the dataframe df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["letter", "name"]) # Function to get rows at `rownums` def getrows(df, rownums=None): return df.rdd.zipWithIndex().filter(lambda x: x in rownums).map(lambda x: x) # Get rows at positions 0 and 2. getrows(df, rownums=[0, 2]).collect() # Output: #> [(Row(letter='a', name=1)), (Row(letter='c', name=3))]
DataFrame, DataFrame is a data abstraction or a domain-specific language (DSL) for working with In Spark 2.0.0 DataFrame is a mere type alias for Dataset[Row] . But suppose I just want one specific field in the Row, say the user gender, how would I obtain it? python apache-spark dataframe pyspark apache-spark-sql share | improve this question
In PySpark, if your dataset is small (can fit into memory of driver), you can do
df is the DataFrame object, and
n is the Row of interest. After getting said Row, you can do
row["myColumn"] to get the contents, as spelled out in the API docs.
get specific row from spark dataframe - apache-spark - html, Is there any alternative for df[100, c("column")] in scala spark data frames. I want to select specific row from a column of spark data frame. for example 100th row Use rdd.collect on top of your Dataframe. The row variable will contain each row of Dataframe of rdd row type. To get each element from a row, use row.mkString(",") which will contain value of each row in comma separated values. Using split function (inbuilt function) you can access each column value of rdd row with index.
you can simply do that by using below single line of code
val arr = df.select("column").collect()(99)
Spark Tutorials, Create DataFrame from Tuples; Get DataFrame column names To find all rows matching a specific column value, you can use the filter() Pyspark: Dataframe Row & Columns. If you've used R or even the pandas library with Python you are probably already familiar with the concept of DataFrames. Spark DataFrame expand on a lot of these concepts, allowing you to transfer that knowledge easily by understanding the simple syntax of Spark DataFrames.
How to select particular column in Spark(pyspark)?, Either you convert it to a dataframe and then apply select or do a map operation Assuming you have an RDD each row of which is of the form (passenger_ID, That would only columns 2005, 2008, and 2009 with all their rows. Extracting specific rows of a pandas dataframe ¶ df2[1:3] That would return the row with index 1, and 2. The row with index 3 is not included in the extract because that’s how the slicing syntax works. Note also that row with index 1 is the second row.
Get value of a particular cell in Spark Dataframe : apachespark, I have a Spark dataframe which has 1 row and 3 columns, namely start_date, end_date, end_month_id. I want to retrieve the value from first cell into a variable and Select multiple row & columns by Labels in DataFrame using loc To select multiple rows & column, pass lists containing index labels and column names i.e. It will return a subset DataFrame with given rows and columns i.e. Only Rows with index label ‘b’ & ‘c’ and Columns with names ‘Age’, ‘Name’ are in returned DataFrame object.
Solved: pyspark get row value from row object, Solved: Using .collect method I am able to create a row object my_list which is as shown below my_list Row(Specific Name/Path (to be. If you really do have one value that you want to get, from a dataframe of one row, and you are filtering one dataframe once only, then sure, go ahead and use the collect method. But if your use case if bigger than this, if were considering doing this in a loop for example,
- Possible duplicate of How to read specific lines from sparkContext
- This is about DataFrames, and How to read specific lines from sparkContext is about RDDs
- Will the output change depending on how many nodes the data is clustered across?