pd.eval()
pd.eval and df.eval evaluate expressions as strings, which can be faster and cleaner for big arithmetic.
In this page:
Syntax
df.eval("new_column = column1 + column2")
pd.eval("result = df.column1 + df.column2")pd.eval()
df.eval("c = a + b") creates a column from an expression string and can avoid temporary arrays on large data. It supports arithmetic, comparisons and referencing local variables with @. Gains show mostly on large DataFrames.
Note:
For small DataFrames plain Python is just as fast.
Example: pd.eval()
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3], "b": [10, 20, 30]})
df.eval("c = a + b * 2", inplace=True)
factor = 3
print(df.eval("a * @factor").tolist())
print(df)
# Output:
# [3, 6, 9]
# a b c
# 0 1 10 21
# 1 2 20 42
# 2 3 30 63Related Topics
Common Mistakes
- Expecting speed-ups on tiny data
- Forgetting @ for local variables
- Using unsupported functions in expressions
Chapter Summary
- eval evaluates string expressions
- Assigns new columns
- @var references locals
- Best on large data
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