How Pandas and PySpark handle adding a column with fewer rows than the existing DataFrame

Pandas:

When you add a column with fewer rows in Pandas, the remaining rows will be filled with NaN (Not a Number) to represent the missing data. Pandas DataFrames allow mixed data types and handle missing values using NaN by default.

Example:

import pandas as pd

# Create a DataFrame with 5 rows and 2 columns
df = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [10, 20, 30, 40, 50]
})

# Add a new column 'C' with only 3 rows
df['C'] = [100, 200, 300]

print(df)

Output:

   A   B      C
0  1  10  100.0
1  2  20  200.0
2  3  30  300.0
3  4  40    NaN
4  5  50    NaN

In Pandas, the last two rows for column C will be filled with NaN because there were only 3 values provided, but the DataFrame has 5 rows.

PySpark:

In PySpark, DataFrames are strongly typed and do not allow None (which represents missing data in PySpark) to be implicitly added. If you try to add a column with fewer rows, PySpark will throw an error, as the number of rows in all columns must match.

In PySpark, you must ensure that the number of rows is consistent across all columns. If you want to add fewer values, you would need to fill the rest explicitly with None (null) values.

Example:

from pyspark.sql import SparkSession
from pyspark.sql import functions as F

# Create a Spark session
spark = SparkSession.builder.appName("example").getOrCreate()

# Create a DataFrame with 5 rows and 2 columns
data = [(1, 10), (2, 20), (3, 30), (4, 40), (5, 50)]
df = spark.createDataFrame(data, ["A", "B"])

# Add a new column 'C' with only 3 values, padding with None for the rest
new_data = [100, 200, 300, None, None]
df_with_c = df.withColumn("C", F.array(*[F.lit(x) for x in new_data]))

df_with_c.show()

Output:

+---+---+---+
|  A|  B|  C|
+---+---+---+
|  1| 10|100|
|  2| 20|200|
|  3| 30|300|
|  4| 40|null|
|  5| 50|null|
+---+---+---+

Summary:

• Pandas: If you add a column with fewer rows than the DataFrame, the remaining rows will be filled with NaN.
• PySpark: You must explicitly handle missing rows by padding them with None (null) values, as Spark DataFrames require all columns to have the same number of rows.