python - 如何将 pyspark 数据框列中的值与 pyspark 中的另一个数据框进行比较

Question

我有一个 pyspark dataframe(df1)第一行如下:

[Row(_c0='{"type":"Fi","values":[0.20100994408130646,1.172734797000885,0.06788740307092667,0.2314232587814331,0.2012220323085785]}', _c1='0')]

我想将“值”列表与下面的第一列 dataframe(df2) 进行比较值如下所示:

0    0.57581    1.25461    0.68694    0.974580    1.54789    0.23646
1    0.98745    0.23655    2.58970    4.587580    0.89756    1.25678
2    0.45780    5.78940    0.65986    2.125400    0.98745    1.23658
3    2.56834    0.25698    4.26587    0.569872    0.36987    0.68975
4    0.25678    1.23654    5.68320    0.986230    0.87563    2.58975

同样，我在 df1 中有很多行，我必须查看 df1 中的哪些值“值”列表大于 df2 中的相应列.我需要找到满足上述条件的那些索引并将其作为列表存储在另一列中到df1 .

例如1.172737 > 0.98745所以它的索引是1 .因此我将在df1 named(indices) 中有另一列其中包含 value1，如果出现另一个值，则必须附加相同的值。

比较是在各自的列和行之间进行的。上面显示的 df1 行是第 1 行，因此必须与 df2 中的第一列进行比较。

如果我没有强调某事，请在评论中告诉我。

Answer 1

此代码适用于 Python 2.7 和 Spark 2.3.2:

from pyspark.sql import functions as F
from pyspark.sql.types import ArrayType, IntegerType

# Create test dataframes
df1 = spark.createDataFrame([
        ['{"type":"Fi","values":[0.20100994408130646,1.172734797000885,0.06788740307092667,0.2314232587814331,0.2012220323085785]}', '0'],
        ['{"type":"Fi","values":[0.6, 0.8, 0.5, 2.1, 0.4]}', '0']
    ],['_c0','_c1'])
df2 = spark.createDataFrame([
        [0, 0.57581, 1.25461, 0.68694, 0.974580, 1.54789, 0.23646],
        [1, 0.98745, 0.23655, 2.58970, 4.587580, 0.89756, 1.25678],
        [2, 0.45780, 5.78940, 0.65986, 2.125400, 0.98745, 1.23658],
        [3, 2.56834, 0.25698, 4.26587, 0.569872, 0.36987, 0.68975],
        [4, 0.25678, 1.23654, 5.68320, 0.986230, 0.87563, 2.58975]
    ],['id','v1', 'v2', 'v3', 'v4', 'v5', 'v6'])

# Get schema and load json correctly
json_schema = spark.read.json(df1.rdd.map(lambda row: row._c0)).schema
df1 = df1.withColumn('json', F.from_json('_c0', json_schema))

# Get column 1 values to compare
values = [row['v1'] for row in df2.select('v1').collect()]

# Define udf to compare values
def cmp_values(lst):
    list_cmp = map(lambda t: t[0] > t[1], zip(lst, values))  # Boolean list
    return [idx for idx, cond in enumerate(list_cmp) if cond]  # Indices of satisfying elements

udf_cmp_values = F.udf(cmp_values, ArrayType(IntegerType()))

# Apply udf on array
df1 = df1.withColumn('indices', udf_cmp_values(df1.json['values']))
df1.show()

+--------------------+---+--------------------+---------+
|                 _c0|_c1|                json|  indices|
+--------------------+---+--------------------+---------+
|{"type":"Fi","val...|  0|[Fi, [0.201009944...|      [1]|
|{"type":"Fi","val...|  0|[Fi, [0.6, 0.8, 0...|[0, 2, 4]|
+--------------------+---+--------------------+---------+