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    Polars DataFrame count() Function

    Polars DataFrame count() Function

    In Polars, the count() function is used to count the number of non-null values in each column of a DataFrame. It does not return the total number of rows but instead counts non-null values per column. Null values are automatically excluded, making it a fast and efficient method for computing counts.

    In this article, I will explain the count() function of a Polars DataFrame, covering its syntax, parameters, and usage and how we can return a new DataFrame with a single row, where each column displays the count of non-null values.

    Key Points –

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

    Polars DataFrame count() Introduction

    Let’s know the syntax of the polars DataFrame.count() function.

    
# Syntax of count()
polars.count(*columns: str) → Expr

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    Parameters of the Polars DataFrame.count()

    It allows only one parameter.

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    Return Value

    This function returns a new DataFrame with a single row containing the count of non-null values for each column.

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    Usage of Polars DataFrame count() Function

    The DataFrame.count() function in Polars counts the number of non-null (non-None) values in each column of a DataFrame. It returns a new DataFrame with a single row, where each column represents the count of its non-null values. It is useful for understanding data completeness and ensuring data integrity.

    To run some examples of the Polars DataFrame count() function, let’s create a Polars DataFrame using data from a dictionary.

    
import polars as pl

technologies= ({
    'Courses':["Spark","PySpark","Hadoop",None,"Python","Pandas"],
    'Courses Fee' :[22000,25000,None,23000,24000,26000],
    'Duration':['30days',None,'50days','30days',None,None],
    'Discount':[1000,None,2300,None,1200,2500]
              })
df = pl.DataFrame(technologies)
print("Original DataFrame:\n", df)

    Yields below output.

    When you call the df.count() function, polars counts the number of non-null values in each column of the DataFrame. This is useful for identifying missing data.

    
# Count non-null values in each column
df2 = df.count()
print("Count of Non-Null Values in Each Column:\n", df2)

    Here,

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    Count Non-Null Values in a Specific Column

    To count non-null values in a specific column of a Polars DataFrame, use the count() function along with the select() method. This approach automatically ignores null values and returns the number of non-null entries.

    
# Count non-null values in the "Courses Fee" column
df2 = df.select(pl.col("Courses Fee").count())
print("Count of Non-Null Values in 'Courses Fee':\n",df2)

# Count non-null values in the "Courses Fee" column
df2 = df.select(pl.count("Courses Fee"))
print("Count of Non-Null Values in 'Courses Fee':\n",df2)

# Output:
# Count of Non-Null Values in 'Courses Fee':
# shape: (1, 1)
┌─────────────┐
│ Courses Fee │
│ ---         │
│ u32         │
╞═════════════╡
│ 5           │
└─────────────┘

    Here,

    <a id="h-count-non-null-values-in-multiple-columns"></a>

    Count Non-Null Values in Multiple Columns

    You can also count non-null values in multiple columns of a Polars DataFrame, we use the count() function along with the select() method.

    
# Count non-null values in "Courses" and "Discount" columns
df2 = df.select(pl.count("Courses", "Discount"))
print("Count of Non-Null Values in Selected Columns:\n", df2)

# Output:
# Count of Non-Null Values in Selected Columns:
# shape: (1, 2)
┌─────────┬──────────┐
│ Courses ┆ Discount │
│ ---     ┆ ---      │
│ u32     ┆ u32      │
╞═════════╪══════════╡
│ 5       ┆ 4        │
└─────────┴──────────┘

    Here,

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    Count Total Rows in the DataFrame

    To count the total number of rows in a Polars DataFrame (including those with None values), you can use the df.height property.

    
# Get total number of rows in the DataFrame
df2= df.height
print("Total number of rows in the DataFrame:", df2)

print("Total Rows using len():", len(df))

# Output:
# Total number of rows in the DataFrame: 6

    Here,

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    Count Rows Where a Condition is Met

    To count the number of rows that meet a specific condition in a Polars DataFrame, use the filter() method along with count(). This filters the DataFrame based on the condition and returns the count of matching rows.

    
# Count rows where "Courses Fee" is greater than 23000
df2 = df.filter(pl.col("Courses Fee") > 23000).height
print("Number of rows where 'Courses Fee' is greater than 23000:", df2)

# Output:
# Number of rows where 'Courses Fee' is greater than 23000: 3

    Here,

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    Conclusion

    In conclusion, the Polars DataFrame count() function is a powerful tool for counting non-null values across columns. Additionally, you can use height to count total rows, and filter().height to count rows based on conditions.

    Happy Learning!!

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    Related Articles

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    References