Calculate the mode of a PySpark DataFrame column?

Question

Ultimately what I want is the mode of a column, for all the columns in the DataFrame. For other summary statistics, I see a couple of options: use DataFrame aggregation, or map the columns of the DataFrame to an RDD of vectors (something I'm also having trouble doing) and use colStats from MLlib. But I don't see mode as an option there.

score 8 · Accepted Answer · answered Jan 05 '16 at 08:54

8

A problem with mode is pretty much the same as with median. While it is easy to compute, computation is rather expensive. It can be done either using sort followed by local and global aggregations or using just-another-wordcount and filter:

import numpy as np
np.random.seed(1)

df = sc.parallelize([
    (int(x), ) for x in np.random.randint(50, size=10000)
]).toDF(["x"])

cnts = df.groupBy("x").count()
mode = cnts.join(
    cnts.agg(max("count").alias("max_")), col("count") == col("max_")
).limit(1).select("x")
mode.first()[0]
## 0

Either way it may require a full shuffle for each column.

answered Jan 05 '16 at 08:54

zero323

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It gives me error : AttributeError: 'str' object has no attribute 'alias' – Neo May 05 '17 at 20:32
@Ajinkya This means you're using `builtins.max` not `pyspark.sql.functions.max`. – zero323 May 06 '17 at 08:50
can you please tell how to handle the case where mode of a column is null/missing value. Then we should take 2nd highest ocurring value. Thanks – Neo May 08 '17 at 21:00
@Ajinkya Perpend this with `na.drop("column_name")` – zero323 May 09 '17 at 06:36

score 3 · Answer 2 · edited Mar 14 '21 at 03:19

This line will give you the mode of "col" in spark data frame df:

df.groupby("col").count().orderBy("count", ascending=False).first()[0]

For a list of modes for all columns in df use:

[df.groupby(i).count().orderBy("count", ascending=False).first()[0] for i in df.columns]

To add names to identify which mode for which column, make 2D list:

[[i,df.groupby(i).count().orderBy("count", ascending=False).first()[0]] for i in df.columns]

score 0 · Answer 3 · edited Mar 30 '18 at 09:33

You can calculate column mode using Java code as follows:

            case MODE:
                Dataset<Row> cnts = ds.groupBy(column).count();
                Dataset<Row> dsMode = cnts.join(
                        cnts.agg(functions.max("count").alias("max_")),
                        functions.col("count").equalTo(functions.col("max_")
                        ));
                Dataset<Row> mode = dsMode.limit(1).select(column);
                replaceValue = ((GenericRowWithSchema) mode.first()).values()[0];
                ds = replaceWithValue(ds, column, replaceValue);
                break;

private static Dataset<Row> replaceWithValue(Dataset<Row> ds, String column, Object replaceValue) {
    return ds.withColumn(column,
            functions.coalesce(functions.col(column), functions.lit(replaceValue)));
}

Your code example seems to start in the middle of a 'switch' block, and have peculiar indenting. Is there something at the beginning of your example? — rwp, Mar 30 '18 at 09:26

vamsikrishnat · Answer 4 · 2018-05-10T10:09:18.430

>>> df=newdata.groupBy('columnName').count()
>>> mode = df.orderBy(df['count'].desc()).collect()[0][0]

See My result

>>> newdata.groupBy('var210').count().show()
+------+-----+
|var210|count|
+------+-----+
|  3av_|   64|
|  7A3j|  509|
|  g5HH| 1489|
|  oT7d|  109|
|  DM_V|  149|
|  uKAI|44883|
+------+-----+

# store the above result in df
>>> df=newdata.groupBy('var210').count()
>>> df.orderBy(df['count'].desc()).collect()
[Row(var210='uKAI', count=44883),
Row(var210='g5HH', count=1489),
Row(var210='7A3j', count=509),
Row(var210='DM_V', count=149),
Row(var210='oT7d', count=109),
Row(var210='3av_', count=64)]

# get the first value using collect()
>>> mode = df.orderBy(df['count'].desc()).collect()[0][0]
>>> mode
'uKAI'

using groupBy() function getting count of each category in column. df is my result data frame has two columns var210,count. using orderBy() with column name 'count' in descending order give the max value in 1st row of data frame. collect()[0][0] is used to get the 1 tuple in data frame

subhrajit mohanty · Answer 5 · 2021-01-27T10:50:29.930

The following method can help you to get mode of all columns of an input dataframe

from pyspark.sql.functions import monotonically_increasing_id

def get_mode(df):
    column_lst = df.columns
    res = [df.select(i).groupby(i).count().orderBy("count", ascending=False) for i in column_lst]
    df_mode = res[0].limit(1).select(column_lst[0]).withColumn("temp_name_monotonically_increasing_id", monotonically_increasing_id())
    
    for i in range(1, len(res)):
        df2 = res[i].limit(1).select(column_lst[i]).withColumn("temp_name_monotonically_increasing_id", monotonically_increasing_id())
        df_mode = df_mode.join(df2, (df_mode.temp_name_monotonically_increasing_id == df2.temp_name_monotonically_increasing_id)).drop(df2.temp_name_monotonically_increasing_id)
        
    return df_mode.drop("temp_name_monotonically_increasing_id")

Calculate the mode of a PySpark DataFrame column?

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