apply()
apply runs a function on every value, row or column of your data.
In this page:
Syntax
df["column"].apply(function_name)
df.apply(function_name, axis=0)apply()
Series.apply(func) calls func on each value. DataFrame.apply(func, axis=0) runs func on each column, and axis=1 runs it on each row. It is flexible but slower than vectorized operations, so use it when no built-in method exists.
Note:
Prefer vectorized operations such as df["a"] * 2 over apply whenever possible.
Example: apply()
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3], "b": [10, 20, 30]})
print(df["a"].apply(lambda x: x ** 2))
print(df.apply(sum))
print(df.apply(lambda row: row["a"] + row["b"], axis=1))
# Output:
# 0 1
# 1 4
# 2 9
# Name: a, dtype: int64
# a 6
# b 60
# dtype: int64
# 0 11
# 1 22
# 2 33
# dtype: int64Related Topics
Common Mistakes
- Using apply where a vectorized operation exists
- Forgetting axis=1 for row-wise work
- Returning inconsistent types from the function
Chapter Summary
- apply runs a function over values
- axis=0 is per column, axis=1 per row
- It is slower than vectorized code
- Use it when no built-in fits
🔒
Chapter Quiz — Complete all 7 topics to unlock
0/7 topics done
Complete these topics first: