🧠

10. Pandas Series: Function Mapping & Transformation

Status

Done

TABLE OF CONTENT

1. Element-wise Operations

df['series'].map(arg) → Map values using a dict/Series/function (element-wise)
df['series'].apply(func) → Apply a function along the Series (element-wise or aggregate)

2. Aggregation Methods

df['series'].agg(func) → Aggregate using one or multiple operations (alias: aggregate())
Common funcs: 'sum', 'mean', 'min', 'max', or custom functions

3. Transformation Methods

df['series'].transform(func) → Return transformed values (same shape as input)
Differs from agg(): maintains original dimensions

4. Series Combination

df['series'].combine(other, func) → Combine with another Series using a function
df['series'].combine_first(other) → Fill nulls using another Series

Key Differences:

Method	Best For	Returns
`map()`	Simple value replacements	Same length
`apply()`	Complex element-wise operations	Same length
`agg()`	Summarizing data	Scalar/value
`transform()`	Group-aware transformations	Same length

Example Usage:

s1 = pd.Series([1, 2, None])
s2 = pd.Series([10, None, 30])

# Element-wise
s1.map({1: 'a', 2: 'b'})          # → ['a', 'b', None]
s1.apply(lambda x: x*2 if pd.notnull(x) else 0) # → [2, 4, 0]

# Aggregation
s1.agg(['sum', 'mean'])            # → Returns Series with both metrics

# Transformation
s1.transform(lambda x: x - x.mean()) # → Center data

# Combination
s1.combine(s2, max)                # → [10, 2, 30]
s1.combine_first(s2)               # → [1, 2, 30] (fills nulls)

When to Use:

Use map() for simple value lookups
Use apply() for custom complex operations
Use agg() for summaries, transform() for group-wise standardization
Use combine() for element-wise logic between Series


import pandas as pd
import seaborn as sns

# Load Titanic dataset
df = sns.load_dataset('titanic')

  survived  pclass     sex     age  sibsp  parch     fare embarked  class     who  adult_male deck  embark_town    alive  alone
0         0       3     male   22.0      1      0   7.2500        S  Third     man        True  NaN  Southampton      no  False
1         1       1   female   38.0      1      0  71.2833        C  First   woman       False    C    Cherbourg     yes  False
2         1       3   female   26.0      0      0   7.9250        S  Third   woman       False  NaN  Southampton     yes   True
3         1       1   female   35.0      1      0  53.1000        S  First   woman       False    C  Southampton     yes  False
4         0       3     male   35.0      0      0   8.0500        S  Third     man        True  NaN  Southampton      no   True

Function Mapping & Transformation in Pandas Series

‣

1. Element-wise Operations

‣

2. Aggregation Methods

‣

3. Transformation Methods

‣

4. Series Combination

‣

Performance Considerations

‣

10. Pandas Series: Function Mapping & Transformation

TABLE OF CONTENT

Function Mapping & Transformation in Pandas Series

1. Element-wise Operations

2. Aggregation Methods

3. Transformation Methods

4. Series Combination

Performance Considerations

Real-World Use Cases