# How to Calculate the Sum of a Column in PySpark

You can use the following methods to calculate the sum of a column in a PySpark DataFrame:

Method 1: Calculate Sum for One Specific Column

to sum the values across multiple columns in a PySpark DataFrame:

```from pyspark.sql import functions as F

#calculate sum of column named 'game1'
df.agg(F.sum('game1')).collect()
```

Method 2: Calculate Sum for Multiple Columns

```from pyspark.sql.functions import sum

#calculate sum for game1, game2 and game3 columns
df.select(sum(df.game1), sum(df.game2), sum(df.game3)).show()```

The following examples show how to use each method in practice with the following PySpark DataFrame:

```from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()

#define data
data = [['Mavs', 25, 11, 10],
['Nets', 22, 8, 14],
['Hawks', 14, 22, 10],
['Kings', 30, 22, 35],
['Bulls', 15, 14, 12],
['Blazers', 10, 14, 18]]

#define column names
columns = ['team', 'game1', 'game2', 'game3']

#create dataframe using data and column names
df = spark.createDataFrame(data, columns)

#view dataframe
df.show()

+-------+-----+-----+-----+
|   team|game1|game2|game3|
+-------+-----+-----+-----+
|   Mavs|   25|   11|   10|
|   Nets|   22|    8|   14|
|  Hawks|   14|   22|   10|
|  Kings|   30|   22|   35|
|  Bulls|   15|   14|   12|
|Blazers|   10|   14|   18|
+-------+-----+-----+-----+```

## Example 1: Calculate Sum for One Specific Column

We can use the following syntax to calculate the sum of values in the game1 column of the DataFrame only:

```from pyspark.sql import functions as F

#calculate sum of column named 'game1'
df.agg(F.sum('game1')).collect()

116```

The sum of values in the game1 column turns out to be 116.

We can verify this is correct by manually calculating the sum of values in this column:

Sum of values in game1: 25 + 22 + 14 + 30 + 15 + 10 = 116

## Example 2: Calculate Sum for Multiple Columns

We can use the following syntax to calculate the sum of values for the game1, game2 and game3 columns of the DataFrame:

```from pyspark.sql.functions import sum

#calculate sum for game1, game2 and game3 columns
df.select(sum(df.game1), sum(df.game2), sum(df.game3)).show()

+----------+----------+----------+
|sum(game1)|sum(game2)|sum(game3)|
+----------+----------+----------+
|       116|        91|        99|
+----------+----------+----------+
```

From the output we can see:

• The sum of values in the game1 column is 116.
• The sum of values in the game2 column is 91.
• The sum of values in the game3 column is 99.

Note: If there are null values in the column, the sum function will ignore these values by default.