In Polars, adding a column with a numerical value to a DataFrame means inserting a new column where each row holds the same constant number or a value derived from existing columns. This can be achieved using the with_columns() method. By leveraging functions like pl.lit(), pl.Series(), and various column operations, you can efficiently enrich your dataset with valuable insights. In this article, I will explain how to add a column with a numerical value in Polars DataFrame.
Key Points –
- Use the
with_columns()method to efficiently add new columns to a Polars DataFrame. pl.lit(value)allows adding a column with a constant numerical value across all rows.pl.Series("ColumnName", values_list)is used to add a column with different numerical values for each row.- Utilize expressions to compute values when adding columns, such as doubling an existing value.
- Preserve data types by ensuring the new column aligns with the expected format to prevent errors.
- Conditional logic can be applied to generate new numerical columns based on existing column values.
- New columns can be created based on existing ones by applying arithmetic operations.
- Multiple columns can be added at once by passing a list of expressions inside
with_columns()
<a id="h-usage-of-add-a-column-with-numerical-value"></a>
Usage of Add a Column with Numerical Value
You can add a numerical column to a Polars DataFrame using the with_columns() method. This is a fundamental operation in data processing and analysis, commonly used to assign a constant value across all rows or derive new values from existing columns.
To run some examples of how to add a column with a numerical value in Polars, 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],
}
df = pl.DataFrame(studentdetails)
print("Original DataFrame:\n", df)
Yields below output.
To add a new column with a list of numerical values to your Polars DataFrame, use the with_columns() method. You can also include a column with varying numerical values for each row by providing a list.
# Adding a new column "English" with numerical values
df2 = df.with_columns(pl.Series("English", [78, 88, 82, 85, 90]))
print("Updated DataFrame:\n", df2)
# Output:
# Updated DataFrame:
# shape: (5, 4)
┌─────────────┬─────────────┬─────────┬─────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ English │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i64 │
╞═════════════╪═════════════╪═════════╪═════════╡
│ Ram ┆ 80 ┆ 85 ┆ 78 │
│ Sam ┆ 90 ┆ 95 ┆ 88 │
│ Scott ┆ 85 ┆ 80 ┆ 82 │
│ Ann ┆ 72 ┆ 90 ┆ 85 │
│ John ┆ 95 ┆ 92 ┆ 90 │
└─────────────┴─────────────┴─────────┴─────────┘
Here,
- Creating the DataFrame by using the existing dictionary to initialize a Polars DataFrame.
- The
with_columns()method is used to add a new column. pl.Series("English", [78, 88, 82, 85, 90]) creates a Polars Series named"English"with the specified values.
<a id="h-adding-a-column-with-a-constant-numerical-value"></a>
Adding a Column with a Constant Numerical Value
If you want to add a column where each row has the same numerical value, you can use the with_columns() method to insert a constant numerical value into your Polars DataFrame.
# Adding a new column "TotalMarks" with a constant value 100
df2 = df.with_columns(pl.lit(100).alias("TotalMarks"))
print("Updated DataFrame:\n", df2)
# Output:
# Updated DataFrame:
# shape: (5, 4)
┌─────────────┬─────────────┬─────────┬────────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ TotalMarks │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i32 │
╞═════════════╪═════════════╪═════════╪════════════╡
│ Ram ┆ 80 ┆ 85 ┆ 100 │
│ Sam ┆ 90 ┆ 95 ┆ 100 │
│ Scott ┆ 85 ┆ 80 ┆ 100 │
│ Ann ┆ 72 ┆ 90 ┆ 100 │
│ John ┆ 95 ┆ 92 ┆ 100 │
└─────────────┴─────────────┴─────────┴────────────┘
Here,
- Creating the DataFrame by converting the existing dictionary into a Polars DataFrame.
with_columns(pl.lit(100).alias("TotalMarks"))adds a new column called "TotalMarks", where every row contains the value 100.pl.lit(100)generates a constant value.alias("TotalMarks")assigns a name to the new column.
- This method ensures that all rows get the same value in the new column.
<a id="h-adding-a-column-based-on-an-existing-column"></a>
Adding a Column Based on an Existing Column
You can also create a new column by applying calculations to existing columns. To add a column based on an existing one in a Polars DataFrame, use the with_columns() method and perform operations on the existing data.
# Adding a new column "Percentage" based on Mathematics and Science
df2 = df.with_columns(((df["Mathematics"] + df["Science"]) / 2).alias("Percentage"))
print("Updated DataFrame:\n", df2)
# Output:
# Updated DataFrame:
# shape: (5, 4)
┌─────────────┬─────────────┬─────────┬────────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ Percentage │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ f64 │
╞═════════════╪═════════════╪═════════╪════════════╡
│ Ram ┆ 80 ┆ 85 ┆ 82.5 │
│ Sam ┆ 90 ┆ 95 ┆ 92.5 │
│ Scott ┆ 85 ┆ 80 ┆ 82.5 │
│ Ann ┆ 72 ┆ 90 ┆ 81.0 │
│ John ┆ 95 ┆ 92 ┆ 93.5 │
└─────────────┴─────────────┴─────────┴────────────┘
Here,
- Creating the DataFrame by transforming a dictionary into a Polars DataFrame.
- Adding a column based on existing columns
df["Mathematics"] + df["Science"]: sums the Mathematics and Science scores./2: computes the average score.alias("Percentage"): assigns the name "Percentage" to the new column.with_columns(...): incorporates the calculated column into the DataFrame.
<a id="h-adding-multiple-columns"></a>
Adding Multiple Columns
To add multiple columns at once, you can use the with_columns() method. This approach is efficient and enables you to create new columns using existing data or constant values.
# adding multiple columns at once
df2 = df.with_columns([
pl.lit(35).alias("Passing Marks"),
(df["Mathematics"] >= 85).alias("Passed_Math")
])
print("Updated DataFrame:\n", df2)
# Output:
# Updated DataFrame:
# shape: (5, 5)
┌─────────────┬─────────────┬─────────┬───────────────┬─────────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ Passing Marks ┆ Passed_Math │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i32 ┆ bool │
╞═════════════╪═════════════╪═════════╪═══════════════╪═════════════╡
│ Ram ┆ 80 ┆ 85 ┆ 35 ┆ false │
│ Sam ┆ 90 ┆ 95 ┆ 35 ┆ true │
│ Scott ┆ 85 ┆ 80 ┆ 35 ┆ true │
│ Ann ┆ 72 ┆ 90 ┆ 35 ┆ false │
│ John ┆ 95 ┆ 92 ┆ 35 ┆ true │
└─────────────┴─────────────┴─────────┴───────────────┴─────────────┘
<a id="h-add-a-column-by-summing-two-columns"></a>
Add a Column by Summing Two Columns
You can create a new column by summing two existing columns using the with_columns() function. Let’s calculate the total marks for Mathematics + Science and store it in a new column "Total Marks".
# Adding a new column by summing Mathematics and Science
df2 = df.with_columns((df["Mathematics"] + df["Science"]).alias("Total Marks"))
print("Updated DataFrame:\n", df2)
# Output:
# Updated DataFrame:
# shape: (5, 4)
┌─────────────┬─────────────┬─────────┬─────────────┐
│ Studentname ┆ Mathematics ┆ Science ┆ Total Marks │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i64 │
╞═════════════╪═════════════╪═════════╪═════════════╡
│ Ram ┆ 80 ┆ 85 ┆ 165 │
│ Sam ┆ 90 ┆ 95 ┆ 185 │
│ Scott ┆ 85 ┆ 80 ┆ 165 │
│ Ann ┆ 72 ┆ 90 ┆ 162 │
│ John ┆ 95 ┆ 92 ┆ 187 │
└─────────────┴─────────────┴─────────┴─────────────┘
Here,
with_columns()is used to add a new column.- Column-wise operations (
+) are performed directly on DataFrame columns. alias("Total Marks")renames the computed column.
<a id="h-conclusion"></a>
Conclusion
In conclusion, adding a numerical column in Polars is straightforward and highly flexible using the with_columns() method. Whether you're adding a constant value, a list of values, or performing calculations based on existing columns, Polars provides an efficient and expressive way to manipulate DataFrames. By utilizing functions like pl.lit(), pl.Series(), and column operations, you can enhance your dataset with new insights seamlessly.
Happy Learning!!
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