Creating a PySpark DataFrame with Timestamp Column for a Given Range of Dates: Two Methods

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This article explains two ways one can write a PySpark DataFrame with timestamp column for a given range of time.

A) Plain way

Here are the steps to create a PySpark DataFrame with a timestamp column using the range of dates:

1. Import libraries:

from pyspark.sql import SparkSession
from pyspark.sql.functions import expr, to_date, lit
from pyspark.sql.types import TimestampType

2. Start a PySpark session:

spark = SparkSession.builder.appName("CreateDFWithTimestamp").getOrCreate()

3. Define the start and end dates for the time period:

start_date = '2022-11-01'
end_date = '2022-11-30'

4. Create a PySpark DataFrame with the start and end dates:

df = spark.createDataFrame([(start_date, end_date)], ["start_date", "end_date"])

4. Generate a range of dates as a column:

df = df.selectExpr("explode(sequence(to_date(start_date), to_date(end_date))) as date")

5. Convert the dates to timestamps:

df = df.select(expr("cast(date as timestamp)").alias("timestamp"))

6. Show the DataFrame containing the timestamps:

display(df)

All above line:

from pyspark.sql import SparkSession
from pyspark.sql.functions import expr, to_date, lit
from pyspark.sql.types import TimestampType

spark = SparkSession.builder.appName("CreateDFWithTimestamp").getOrCreate()

# Define the start and end dates for the time period
start_date = '2022-11-01'
end_date = '2022-11-30'

# Create a PySpark DataFrame with the start and end dates
df = spark.createDataFrame([(start_date, end_date)], ["start_date", "end_date"])

# Generate a range of dates as a column
df = df.selectExpr("explode(sequence(to_date(start_date), to_date(end_date))) as date")

# Convert the dates to timestamps
df = df.select(expr("cast(date as timestamp)").alias("timestamp"))

# Show the DataFrame
display(df)

B) Using Pandas library

1. Import libraries:

from pyspark.sql import SparkSession
from pyspark.sql.types import TimestampType
import pandas as pd

2. Start a PySpark session:

spark = SparkSession.builder.appName("CreateDFWithTimestamp").getOrCreate()

3. Define the start and end dates for the time period:

start_date = '2022-11-01'
end_date = '2022-11-30'

4. Generate a range of dates using pandas:

dates = pd.date_range(start=start_date, end=end_date)

5. Convert the dates to Python datetime objects:

datetimes = [date.to_pydatetime() for date in dates]

6. Create a PySpark DataFrame from the list of datetime objects:

df = spark.createDataFrame(datetimes, TimestampType())

7. Show the DataFrame containing the timestamps:

display(df)

All above line:

from pyspark.sql import SparkSession
from pyspark.sql.types import TimestampType
import pandas as pd

spark = SparkSession.builder.appName("CreateDFWithTimestamp").getOrCreate()

# Define the start and end dates for the time period
start_date = '2022-11-01'
end_date = '2022-11-30'

# Generate a range of dates using pandas
dates = pd.date_range(start=start_date, end=end_date)

# Convert the dates to Python datetime objects
datetimes = [date.to_pydatetime() for date in dates]

# Create a PySpark DataFrame from the list of datetime objects
df = spark.createDataFrame(datetimes, TimestampType())

# Show the DataFrame
df.show()

In conclusion, this article explains two methods of writing a PySpark DataFrame with a timestamp column for a given range of time. The first method uses PySpark functions such as “sequence”, “explode”, and “cast” to create the DataFrame, while the second method uses the Pandas library to generate a range of dates and then convert them to Python datetime objects before creating a PySpark DataFrame. Both methods produce a DataFrame containing timestamps for the specified range of dates.