StepSetGo tricks gives free rewards free product

 

PANDAS SERIES – DIFFERENCE-BASED QUESTIONS (Class XII)

---

✍️ VERY SHORT / SHORT ANSWER (2–3 MARKS)

Q1. Difference between Pandas Series and Python List

Feature	Pandas Series	Python List
Indexing	Labeled index	No label, only position-based
Data type	Can store heterogeneous but usually single dtype	Can store heterogeneous
Operations	Supports vectorized operations	Requires loops for element-wise operations
Missing values	Can handle NaN	Cannot handle NaN directly
Methods	Built-in statistical & data analysis functions	Very limited functions

---

Q2. Difference between loc[] and iloc[]

Feature	loc[]	iloc[]
Indexing	Label-based	Integer position-based
Slicing	Includes last element	Excludes last element
Example	s.loc['a']	s.iloc[0]

---

Q3. Difference between size and count()

Feature	size	count()
Counts	Total number of elements	Only non-NaN elements
Ignores NaN	No	Yes
Example	s.size → 5	s.count() → 4

---

Q4. Difference between fillna() and dropna()

Feature	fillna()	dropna()
Purpose	Replace missing values	Remove missing values
Modifies data	Yes	Yes
Example	s.fillna(0)	s.dropna()

---

Q5. Difference between unique() and value_counts()

Feature	unique()	value_counts()
Purpose	Returns unique values	Returns frequency of each value
Output type	Array	Series
Example	[10,20,30]	10:2, 20:3

---

Q6. Difference between head() and tail()

Feature	head()	tail()
Purpose	Returns first n elements	Returns last n elements
Default n	5	5
Example	s.head(3)	s.tail(3)

---

Q7. Difference between Pandas Series and DataFrame

Feature	Series	DataFrame
Dimension	1-D	2-D
Index	Single index	Row and column index
Data type	Usually single dtype	Can have multiple dtypes
Operations	Element-wise	Row-wise & column-wise

---

---

Q8. Difference between s.index and s.values

Feature	s.index	s.values
Purpose	Returns index labels	Returns data values
Type	Index object	NumPy array
Example	s.index → ['a','b','c']	s.values → [10,20,30]

---

Q9. Difference between isnull() and notnull()

Feature	isnull()	notnull()
Purpose	Checks for missing values	Checks for non-missing values
Output	Boolean Series	Boolean Series
Example	[True, False]	[False, True]

Q10. Difference between Pandas Series and DataFrame

Feature	Pandas Series	Pandas DataFrame
Dimension	One-dimensional (1-D)	Two-dimensional (2-D)
Data storage	Stores a single column of data	Stores multiple columns of data (tabular form)
Index	Single index	Row and column index
Data type	Usually stores a single data type (homogeneous)	Can store multiple data types (heterogeneous)
Representation	A single labeled array	Table with rows and columns
Operations	Element-wise operations	Can perform row-wise and column-wise operations
Example	pd.Series([10,20,30], index=['a','b','c'])	pd.DataFrame({'Name':['A','B'],'Marks':[90,85]})