I am trying to replace 'yyyy-MM' with 'yyyy-MM'+'-01' below is my code and I am not getting it right. Note, I am working on databricks:
from pyspark.sql.functions import col, concat, lit, when
# Show the DataFrame
new_df.show(5)
from pyspark.sql.functions import col, concat, lit, when
# Create new columns with replaced values
new_df = clinicaltrial_df.withColumn(
'Start_new',
when(
col('Start').contains('-'),
col('Start')
).otherwise(
concat(col('Start'), lit('-01'))
)
).withColumn(
'Complete_new',
when(
col('Completion').contains('-'),
col('Completion')
).otherwise(
concat(col('Completion'), lit('-01'))
)
)
# Show the DataFrame
new_df.show(5)
Your code is aimed at appending '-01' to values in the Start and Completion columns of a DataFrame
if they do not already contain a '-'. However, it seems you want to specifically target strings formatted
as 'yyyy-MM' and ensure they become 'yyyy-MM-01'.
To achieve this, you need to identify strings that precisely match the 'yyyy-MM' format.
You could use the regexp_replace
function from PySpark's sql.functions module.
This function can search for a regular expression pattern and
replace matching parts of the string with a specified replacement string.
For your case, you can look for strings that match the pattern of a year and month ('yyyy-MM')
and do not end with a day.
Then, append '-01' to these strings to standardize them as full dates ('yyyy-MM-01').
Here's how you can adjust your code:
from pyspark.sql.functions import regexp_replace
# Adjust the DataFrame
new_df = clinicaltrial_df.withColumn(
'Start_new',
regexp_replace('Start', r'^(\d{4}-\d{2})$', concat(col('Start'), lit('-01')))
).withColumn(
'Complete_new',
regexp_replace('Completion', r'^(\d{4}-\d{2})$', concat(col('Completion'), lit('-01')))
)
# Show the modified DataFrame
new_df.show(5)
The approach shown above ensures that only strings formatted exactly as 'yyyy-MM' are altered,
accurately targeting the described requirement and leveraging the powerful pattern matching capabilities
of regular expressions to implement the desired transformation.
Here's an example of the above solution for some dummy data:
from pyspark.sql import SparkSession
from pyspark.sql.functions import regexp_replace, col, concat, lit, when
from pyspark.sql.types import StructType, StructField, StringType
# Initialize SparkSession (not necessary if you're running this in Databricks as it's already initialized)
spark = SparkSession.builder.appName("example").getOrCreate()
# Define schema for the DataFrame
schema = StructType([
StructField("ID", StringType(), True),
StructField("Start", StringType(), True),
StructField("Completion", StringType(), True)
])
# Sample data
data = [
("001", "2022-01", "2022-12-31"),
("002", "2022-05-01", "2023-05"),
("003", "2023", "2023-11"),
("004", "2022-07", "2022-09-15")
]
# Create DataFrame
clinicaltrial_df = spark.createDataFrame(data, schema=schema)
# Showing original DataFrame
clinicaltrial_df.show()
# +---+----------+----------+
# | ID| Start|Completion|
# +---+----------+----------+
# |001| 2022-01|2022-12-31|
# |002|2022-05-01| 2023-05|
# |003| 2023| 2023-11|
# |004| 2022-07|2022-09-15|
# +---+----------+----------+
# Apply the solution
new_df = clinicaltrial_df.withColumn(
'Start_new',
regexp_replace('Start', r'^(\d{4}-\d{2})$', concat(col('Start'), lit('-01')))
).withColumn(
'Complete_new',
regexp_replace('Completion', r'^(\d{4}-\d{2})$', concat(col('Completion'), lit('-01')))
)
# Show the modified DataFrame
new_df.show(truncate=False)
# +---+----------+----------+----------+------------+
# |ID |Start |Completion|Start_new |Complete_new|
# +---+----------+----------+----------+------------+
# |001|2022-01 |2022-12-31|2022-01-01|2022-12-31 |
# |002|2022-05-01|2023-05 |2022-05-01|2023-05-01 |
# |003|2023 |2023-11 |2023 |2023-11-01 |
# |004|2022-07 |2022-09-15|2022-07-01|2022-09-15 |
# +---+----------+----------+----------+------------+
Note: the dates only get appended if both the year and month are present on the string
in that respective order. If you also have dates that only contain the year ('YYYY'),
dates that have orderings of year/month other than 'YYYY-MM', or dates that do not use
'-' as year/month separator (e.g., 'YYYY/MM'), these values will remain unmodified.
If you're not familiar with regex string patterns, here's a breakdown of the pattern being used in the code above:
regexp_replace is used with a regular expression pattern r'^(\d{4}-\d{2})$':
^ asserts the start of the string.(\d{4}-\d{2}) matches and captures a group consisting of four digits (representing the year),
followed by a hyphen, and then two digits (representing the month).$ asserts the end of the string.