Series creation from lists and dicts in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Series from list with custom index

What is the output of this code creating a pandas Series from a list with a custom index?

Data Analysis Python

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

A

a    10
b    20
c    30
dtype: object

B

0    10
1    20
2    30
dtype: int64

C

a    10
b    20
c    30
Name: None, dtype: int64

D

a    10
b    20
c    30
dtype: int64

💡 Hint

Check how the index parameter changes the labels of the Series.

The Series is created with values [10, 20, 30] and index labels ['a', 'b', 'c']. The dtype is int64 because the values are integers.

❓ data_output

intermediate

Length of Series from dict with missing keys

What is the length of the Series created from this dictionary with a specified index containing keys not in the dict?

Data Analysis Python

import pandas as pd
d = {'x': 1, 'y': 2}
s = pd.Series(d, index=['x', 'y', 'z'])
print(len(s))

A 2

B 3

C KeyError

D 1

💡 Hint

Series length matches the index length, even if some keys are missing in the dict.

The Series has index ['x', 'y', 'z'], so length is 3. The missing key 'z' gets a NaN value but counts as an element.

🔧 Debug

advanced

Identify the error in Series creation from dict with list values

What error does this code raise when creating a Series from a dictionary with list values?

Data Analysis Python

import pandas as pd
d = {'a': [1, 2], 'b': [3, 4]}
s = pd.Series(d)
print(s)

A No error, prints the Series with lists as values

B ValueError: arrays must be same length

C TypeError: unhashable type: 'list'

D KeyError: 'a'

💡 Hint

Check if pandas allows lists as values in Series from dict.

Pandas allows lists as values in Series from dict. The Series will have keys as index and lists as values without error.

🧠 Conceptual

advanced

Behavior of Series creation from dict with partial index

When creating a pandas Series from a dictionary with a specified index that includes keys not in the dictionary, what value does pandas assign to those missing keys?

A It assigns NaN (Not a Number) to missing keys in the Series.

B It raises a KeyError and stops execution.

C It assigns zero (0) to missing keys automatically.

D It ignores missing keys and creates Series only with existing keys.

💡 Hint

Think about how pandas handles missing data in Series.

Pandas assigns NaN to any index label not found in the dictionary when creating a Series with a specified index.

🚀 Application

expert

Create Series from dict with mixed types and analyze output

Given this dictionary with mixed value types, what is the dtype of the resulting Series?

Data Analysis Python

import pandas as pd
d = {'a': 1, 'b': 2.5, 'c': '3'}
s = pd.Series(d)
print(s.dtype)

A int64

B float64

C object

D string

💡 Hint

Consider how pandas handles mixed data types in a Series.

When a Series has mixed types (int, float, string), pandas uses dtype 'object' to hold all types.

Practice

1. What does a pandas Series store besides the data itself?

easy

A. Multiple columns like a DataFrame

B. Labels (index) for each data element

C. Only numeric values

D. No labels, just raw data

Solution

1. Step 1: Understand what a Series is

   A Series is like a list but with labels for each element.

2. Step 2: Identify what is stored besides data

   Besides the data values, a Series stores labels called an index.

3. Final Answer:

   Labels (index) for each data element -> Option B

4. Quick Check:

   Series stores data + labels [OK]

Hint: Remember: Series = data + labels (index) [OK]

Common Mistakes:

• Thinking Series stores multiple columns like DataFrame
• Assuming Series only stores numeric data
• Believing Series has no labels

2. Which of the following is the correct way to create a pandas Series from a list [10, 20, 30] with labels ['a', 'b', 'c']?

easy

A. pd.Series([10, 20, 30], labels=['a', 'b', 'c'])

B. pd.Series(index=[10, 20, 30], data=['a', 'b', 'c'])

C. pd.Series({'a':10, 'b':20, 'c':30})

D. pd.Series([10, 20, 30], index=['a', 'b', 'c'])

Solution

1. Step 1: Recall Series creation syntax from list

   Use pd.Series(data_list, index=labels_list) to assign labels.

2. Step 2: Check each option

   pd.Series([10, 20, 30], index=['a', 'b', 'c']) uses correct syntax: data first, then index labels. Others misuse parameters or use wrong keywords.

3. Final Answer:

   pd.Series([10, 20, 30], index=['a', 'b', 'c']) -> Option D

4. Quick Check:

   Correct syntax uses index= for labels [OK]

Hint: Use index= to assign labels when creating Series from list [OK]

Common Mistakes:

• Using labels= instead of index=
• Swapping data and index parameters
• Passing dict instead of list for this question

3. What will be the output of this code?

import pandas as pd
s = pd.Series({'x': 5, 'y': 10, 'z': 15})
print(s['y'])

medium

A. 10

B. 'y'

C. KeyError

D. 5

Solution

1. Step 1: Understand Series creation from dict

   Keys become labels, values become data. So 'y' label has value 10.

2. Step 2: Access value by label 'y'

   Using s['y'] returns the value 10 stored at label 'y'.

3. Final Answer:

   10 -> Option A

4. Quick Check:

   Dict keys = labels, s['y'] = 10 [OK]

Hint: Dict keys become labels; access by label returns value [OK]

Common Mistakes:

• Expecting s['y'] to return the label 'y'
• Confusing keys and values
• Thinking it raises KeyError

4. Identify the error in this code snippet:

import pandas as pd
s = pd.Series([1, 2, 3], index=['a', 'b'])

medium

A. No error, code runs fine

B. Data must be a dictionary, not a list

C. Index length does not match data length

D. pd.Series cannot take index parameter

Solution

1. Step 1: Check lengths of data and index

   Data has 3 elements, index has 2 labels. They must be equal length.

2. Step 2: Understand pandas behavior

   Mismatch causes ValueError: Length of passed values does not match index.

3. Final Answer:

   Index length does not match data length -> Option C

4. Quick Check:

   Data and index lengths must match [OK]

Hint: Data and index must have same length [OK]

Common Mistakes:

• Assuming list data must be dict
• Thinking index parameter is invalid
• Ignoring length mismatch error

5. You have a dictionary {'a': 1, 'b': 0, 'c': 3, 'd': None}. You want to create a Series that only includes keys with truthy values (non-zero, non-None). Which code correctly creates this filtered Series?

hard

A. pd.Series({k: v for k, v in {'a':1, 'b':0, 'c':3, 'd':None}.items() if v})

B. pd.Series({'a':1, 'b':0, 'c':3, 'd':None}).dropna()

C. pd.Series({'a':1, 'b':0, 'c':3, 'd':None}).fillna(0)

D. pd.Series({'a':1, 'b':0, 'c':3, 'd':None})

Solution

1. Step 1: Understand filtering for truthy values

   Truthy means values that are not 0, None, False, or empty.

2. Step 2: Check each option's effect

   pd.Series({k: v for k, v in {'a':1, 'b':0, 'c':3, 'd':None}.items() if v}) uses dict comprehension to filter out falsy values before creating Series. pd.Series({'a':1, 'b':0, 'c':3, 'd':None}).dropna() only drops None but keeps 0. pd.Series({'a':1, 'b':0, 'c':3, 'd':None}).fillna(0) replaces None with 0, keeping falsy values. pd.Series({'a':1, 'b':0, 'c':3, 'd':None}) includes all values.

3. Final Answer:

   pd.Series({k: v for k, v in {'a':1, 'b':0, 'c':3, 'd':None}.items() if v}) -> Option A

4. Quick Check:

   Filter dict first to exclude falsy values [OK]

Hint: Filter dict with if v before Series creation [OK]

Common Mistakes:

• Using dropna() only removes None, not 0
• Filling None with 0 keeps falsy values
• Not filtering before creating Series