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    Polars Sum DataFrame Columns With Examples

    Polars Sum DataFrame Columns With Examples

    In Polars, the sum() function is used to compute the sum of values across a DataFrame’s columns or within a specific column. This function performs a column-wise summation, adding the values within each column. It works similarly to sum() in Pandas but is optimized for performance in Polars. In this article, I will explain how to sum Polars DataFrame rows for given columns with examples.

    Key Points –

    <a id="h-polars-dataframe-sum-introduction"></a>

    Polars DataFrame sum() Introduction

    Let’s know the syntax of the Polars DataFrame sum() function.

    
# Syntax of sum()
polars.sum(*names: str) → Expr

    <a id="h-parameters-of-the-polars-dataframe-sum"></a>

    Parameters of the Polars DataFrame.sum()

    <a id="h-return-value"></a>

    Return Value

    This function returns an expression (Expr) that can be used in select(), with_columns(), or pl.sum_horizontal() function

    <a id="h-usage-of-polars-sum-dataframe-columns"></a>

    Usage of Polars Sum DataFrame Columns

    The sum() function in Polars is used to compute the total sum of values in one or multiple columns of a DataFrame. It efficiently handles numerical data while ignoring null values by default.

    To run some examples of the Polars sum DataFrame columns, let’s create a Polars DataFrame.

    
import polars as pl

studentdetails = {
       "Studentname":["Ram", "Sam", "Scott", "Ann", "John"],
       "Mathematics" :[80,90,85,72,95],
       "Science" :[85,95,80,90,92],
       "English" :[90,85,80,75,95]
              }
df = pl.DataFrame(studentdetails)
print("Original DataFrame:\n", df)

    Yields below output.

    The sum() function calculates the sum of numeric columns while excluding non-numeric ones (such as strings) in polars. This makes it especially useful for aggregating numerical data such as total scores, financial metrics, or other quantitative datasets.

    
# Using sum() to calculate column-wise sum
df2 = df.sum()
print("Column-wise Sum:\n", df2)

    Here,

    <a id="h-summing-a-specific-column-using-select"></a>

    Summing a Specific Column Using select()

    If you want to compute the sum of a specific column, you can use the select() function along with sum() method. The select() function allows you to apply operations on specific columns and return a new DataFrame rather than modifying the original one.

    
# Sum of only the "Mathematics" column
df2 = df.select(pl.col("Mathematics").sum())
print("Sum of Mathematics column:\n", df2)

# Summing a specific column (Mathematics)
df2 = df.select(pl.sum("Mathematics"))
print("Sum of Mathematics column:\n", df2)

# Output:
# Sum of Mathematics column:
# shape: (1, 1)
┌─────────────┐
│ Mathematics │
│ ---         │
│ i64         │
╞═════════════╡
│ 422         │
└─────────────┘

    Here,

    <a id="h-using-sum-on-multiple-columns"></a>

    Using sum() on Multiple Columns

    To sum multiple specific columns in a Polars DataFrame, you can pass multiple column names to the pl.sum() function.

    
# Summing "Mathematics" and "Science" columns
df2 = df.select(pl.sum(["Mathematics", "Science"]))
print("Sum of Mathematics & Science columns:\n", df2)

# Output:
# Sum of Mathematics & Science columns:
# shape: (1, 2)
┌─────────────┬─────────┐
│ Mathematics ┆ Science │
│ ---         ┆ ---     │
│ i64         ┆ i64     │
╞═════════════╪═════════╡
│ 422         ┆ 442     │
└─────────────┴─────────┘

    Here,

    <a id="h-row-wise-sum-using-sum-horizontal"></a>

    Row-wise Sum Using sum_horizontal()

    You can calculate the sum of multiple columns for each row using the sum_horizontal() function. This is useful when computing total scores for each student, summing financial transactions per record, or aggregating any numerical data across columns.

    Let's calculate the total marks by summing the Mathematics, Science, and English scores for each student.

    
# Adding a Total Score column
df = df.with_columns(
    pl.sum_horizontal("Mathematics", "Science", "English").alias("Total Score"))
print(df)

# Output:
# shape: (5, 5)
┌─────────────┬─────────────┬─────────┬─────────┬─────────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ English ┆ Total Score │
│ ---         ┆ ---         ┆ ---     ┆ ---     ┆ ---         │
│ str         ┆ i64         ┆ i64     ┆ i64     ┆ i64         │
╞═════════════╪═════════════╪═════════╪═════════╪═════════════╡
│ Ram         ┆ 80          ┆ 85      ┆ 90      ┆ 255         │
│ Sam         ┆ 90          ┆ 95      ┆ 85      ┆ 270         │
│ Scott       ┆ 85          ┆ 80      ┆ 80      ┆ 245         │
│ Ann         ┆ 72          ┆ 90      ┆ 75      ┆ 237         │
│ John        ┆ 95          ┆ 92      ┆ 95      ┆ 282         │
└─────────────┴─────────────┴─────────┴─────────┴─────────────┘

    Here,

    <a id="h-sum-after-filtering-based-on-a-condition"></a>

    Sum After Filtering Based on a Condition

    You can first filter the DataFrame based on a specific condition and then apply the sum() function to sum the relevant numeric columns. This is useful for scenarios such as calculating total scores for students who meet a certain criterion or aggregating sales data for specific categories.

    
# Filtering students with Science > 80 and summing Mathematics scores
df2 = df.filter(pl.col("Science") > 80).select(pl.col("Mathematics").sum())
print(df2)

# Output:
# shape: (1, 1)
┌─────────────┐
│ Mathematics │
│ ---         │
│ i64         │
╞═════════════╡
│ 337         │
└─────────────┘

    Here,

    <a id="h-sum-with-missing-values-nulls"></a>

    Sum with missing values (Nulls)

    When using sum() in Polars, missing values (nulls) can affect the results. By default, Polars ignores null values when summing. Let's explore how sum() behaves with missing data.

    
import polars as pl

# Creating a DataFrame with null (missing) values
studentdetails = {
    "Studentname": ["Ram", "Sam", "Scott", "Ann", "John"],
    "Mathematics": [80, 90, None, 72, 95],  
    "Science": [85, None, 80, 90, 92],  
    "English": [90, 85, 80, 75, None] 
}
df = pl.DataFrame(studentdetails)

# Sum of all numeric columns
print(df.sum())

# Output:
# shape: (1, 4)
┌─────────────┬─────────────┬─────────┬─────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ English │
│ ---         ┆ ---         ┆ ---     ┆ ---     │
│ str         ┆ i64         ┆ i64     ┆ i64     │
╞═════════════╪═════════════╪═════════╪═════════╡
│ null        ┆ 337         ┆ 347     ┆ 330     │
└─────────────┴─────────────┴─────────┴─────────┘

    By default, sum() ignores nulls when calculating sums. However, if you want to replace None values with 0 before summing, you can use fill_null(0).

    
# Handling missing values
df2 = df.fill_null(0).sum()
print(df2)

# Output:
# shape: (1, 4)
┌─────────────┬─────────────┬─────────┬─────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ English │
│ ---         ┆ ---         ┆ ---     ┆ ---     │
│ str         ┆ i64         ┆ i64     ┆ i64     │
╞═════════════╪═════════════╪═════════╪═════════╡
│ null        ┆ 337         ┆ 347     ┆ 330     │
└─────────────┴─────────────┴─────────┴─────────┘

    <a id="h-conclusion"></a>

    Conclusion

    In this article, I have explained how to compute the sum of Polars DataFrame columns whether for all columns, specific columns, or on a row-wise basis by using the df.sum() function, select() function, pl.sum_horizontal(), and df.with_columns() function in Polars.

    Happy Learning!!

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    References