Let's say I have a dataframe with two columns, and I would like to filter the values of the second column based on different thresholds that are determined by the values of the first column. Such thresholds are defined in a dictionary, whose keys are the first column values, and the dict values are the thresholds. There will be also a default value to match columns that do not have any of the specified values.
So for example:
thresholds_dict = {"A": 5, "B": 2, "C": 4, "default": 0}
sample_dataframe =
| Column1 | Column2 |
| A | 3 |
| A | 6 |
| B | 4 |
| B | 1 |
| C | 2 |
| D | 0 |
//Get threshold from dict based on value of Column1 on ...
result_dataframe = sample_dataframe[sample_dataframe[Column2] >= ...]
result_dataframe =
| Column1 | Column2 |
| A | 6 |
| B | 4 |
| D | 0 |
What would be the best way to achieve this? (Not sure what to write in... part).
PySpark version.
Your dataframe:
from pyspark.sql import functions as F
sample_dataframe = spark.createDataFrame(
[("A", 3),
("A", 6),
("B", 4),
("B", 1),
("C", 2),
("D", 0)],
["Column1", "Column2"]
)
thresholds_dict = {"A": 5, "B": 2, "C": 4, "default": 0}
Script:
comparison = F.when(F.lit(False), None)
for k, v in thresholds_dict.items():
comparison = comparison.when(F.col("Column1") == k, v)
comparison = comparison.otherwise(thresholds_dict["default"])
result_dataframe = sample_dataframe.filter(F.col("Column2") >= comparison)
result_dataframe.show()
# +-------+-------+
# |Column1|Column2|
# +-------+-------+
# | A| 6|
# | B| 4|
# | D| 0|
# +-------+-------+
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