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VERY SHORT ANSWER (1 MARK)  

Q61. What does shape attribute return for a Series?

Ans: It returns a tuple showing number of elements in the Series.

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Q62. Which function returns the minimum value in a Series?

Ans: min()

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Q63. Write the function used to find standard deviation of a Series.

Ans: std()

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Q64. What does sum() function do?

Ans: It returns the sum of all elements of the Series.

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Q65. Name the function used to sort Series values.

Ans: sort_values()

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Q66. What does idxmax() return?

Ans: It returns the index of maximum value.

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Q67. What is the output type of isnull()?

Ans: Boolean Series.

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Q68. Write one example of creating an empty Series.

Ans:

pd.Series()

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Q69. Can a Series have duplicate index values?

Ans: Yes, duplicate index values are allowed.

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Q70. Which function returns unique values of a Series?

Ans: unique()

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✍️ SHORT ANSWER (2 MARKS)

Q71. Explain unique() and nunique() functions.

Ans:

• unique() returns unique values

• nunique() returns number of unique values

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Q72. What is the use of copy() method?

Ans: It creates a duplicate copy of the Series.

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Q73. Explain value_counts() with one use.

Ans:
It counts frequency of values, useful in data analysis.

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Q74. What is the difference between head(n) and tail(n)?

Ans:

• head(n) → First n elements

• tail(n) → Last n elements

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Q75. What is reindexing? Why is it used?

Ans:
Reindexing changes index order and is used for data alignment.

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Q76. How do you check whether a Series is empty?

Ans: Using empty attribute.

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Q77. Explain astype() with example.

Ans:

s = s.astype(float)

Used to change data type.

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Q78. What are vectorized operations?

Ans:
Operations applied simultaneously on all elements.

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Q79. Write two limitations of Pandas Series.

Ans:

• One-dimensional only

• Cannot store tabular data

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Q80. Differentiate between dropna() and fillna().

Ans:

• dropna() removes missing values

• fillna() replaces missing values

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✍️ SHORT ANSWER (3 MARKS)

Q81. Explain creation of Series from NumPy array.

Ans:

import numpy as np
import pandas as pd

arr = np.array([10, 20, 30])
s = pd.Series(arr)

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Q82. Explain sorting by index and by values.

Ans:

• sort_index() → Sorts by index

• sort_values() → Sorts by values

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Q83. Explain Boolean indexing with suitable example.

Ans:

s = pd.Series([10, 20, 30])
print(s[s > 15])

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Q84. Write a program to display count, sum and mean of Series.

Ans:

import pandas as pd
s = pd.Series([5, 10, 15])
print(s.count())
print(s.sum())
print(s.mean())

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Q85. Explain alignment of data in Series operations.

Ans:
Operations align data based on index labels, not positions.

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✍️ LONG ANSWER (5 MARKS)

Q86. Explain different attributes of Pandas Series.

Ans:
Important attributes:

• index – index labels

• values – data values

• dtype – data type

• size – number of elements

• shape – structure

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Q87. Explain various methods used to analyze data in Series.

Ans:

• sum() – total

• mean() – average

• min() / max() – lowest/highest

• std() – standard deviation

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Q88. Write a program to demonstrate indexing, slicing and Boolean indexing.

Ans:

import pandas as pd
s = pd.Series([10, 20, 30, 40], index=['a','b','c','d'])

print(s['b'])       # Indexing
print(s[1:3])       # Slicing
print(s[s > 20])    # Boolean indexing

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Q89. Explain advantages of Pandas Series in data analysis.

Ans:

• Labeled indexing

• Fast computations

• Missing value handling

• Built-in functions

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Q90. Write a complete program to manage sales data using Pandas Series.

Ans:

import pandas as pd
sales = pd.Series([1200, 1500, None, 1800])

print("Total Sales:", sales.sum())
print("Average Sales:", sales.mean())
print("Highest Sales:", sales.max())
print("After removing missing values:")
print(sales.dropna())