Difference between apply(), map(), and applymap()?

🔹 map()

• Works only on a Pandas Series
• Applies a function to each individual value (element-wise)
• Mostly used for simple transformations or value mapping

👉 Example:

df["Gender"] = df["Gender"].map({"M": "Male", "F": "Female"})

✔ Best for: replacing or transforming values in a single column

🔹 apply()

• Works on both Series and DataFrame
• Can apply a function:
• Element-wise (on Series)
• Row-wise or column-wise (on DataFrame)

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👉 Example (Series):

df["Salary"].apply(lambda x: x * 1.1)

👉 Example (DataFrame row-wise):

df.apply(lambda row: row["A"] + row["B"], axis=1)

✔ Best for: custom logic or more complex operations

🔹 applymap()

• Works only on DataFrame
• Applies a function to every single cell (element-wise)

👉 Example:

df.applymap(lambda x: x * 2)

✔ Best for: transforming entire DataFrame values

🔹 Key Idea to Remember

• map() → single column, simple value mapping
• apply() → flexible, row/column logic
• applymap() → every cell in DataFrame