Series creation from lists and dicts in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a pandas Series from a list.

Data Analysis Python

import pandas as pd

numbers = [10, 20, 30, 40]
series = pd.Series([1])
print(series)

Drag options to blanks, or click blank then click option'

A[10, 20, 30, 40]

Bpd.Series

Cnumbers

D'numbers'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the list name as a string (with quotes).

Trying to pass the pd.Series function itself.

The pd.Series() function takes a list as input to create a Series. Here, we pass the variable numbers which holds the list.

2fill in blank

medium

Complete the code to create a pandas Series from a dictionary.

Data Analysis Python

import pandas as pd

scores = {'Alice': 85, 'Bob': 90, 'Cara': 78}
series = pd.Series([1])
print(series)

Drag options to blanks, or click blank then click option'

Ascores

B'scores'

C['Alice', 'Bob', 'Cara']

D[85, 90, 78]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the dictionary keys or values as a list instead of the dictionary itself.

Using quotes around the dictionary variable name.

The pd.Series() function can take a dictionary to create a Series where keys become the index and values become the data.

3fill in blank

hard

Fix the error in the code to create a Series from a list with custom index labels.

Data Analysis Python

import pandas as pd

values = [100, 200, 300]
labels = ['a', 'b', 'c']
series = pd.Series(values, index=[1])
print(series)

Drag options to blanks, or click blank then click option'

Alabels

Bvalues

C'labels'

D[1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the data list as index.

Passing the variable name as a string.

The index parameter expects a list or array of labels. Here, labels is the variable holding the list of index labels.

4fill in blank

hard

Fill both blanks to create a Series from a dictionary and set a custom name for the Series.

Data Analysis Python

import pandas as pd

data = {'x': 1, 'y': 2, 'z': 3}
series = pd.Series([1], name=[2])
print(series)

Drag options to blanks, or click blank then click option'

Adata

B'MySeries'

C'data'

D['MySeries']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the dictionary name as a string.

Passing the name as a list instead of a string.

The first blank is the dictionary variable data to create the Series. The second blank is the name of the Series as a string.

5fill in blank

hard

Fill all three blanks to create a Series from a list, set custom index labels, and assign a name.

Data Analysis Python

import pandas as pd

values = [5, 10, 15]
labels = ['p', 'q', 'r']
series = pd.Series([1], index=[2], name=[3])
print(series)

Drag options to blanks, or click blank then click option'

Avalues

Blabels

C'Numbers'

D'values'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing variable names as strings.

Mixing up values and labels.

Not using quotes for the name.

The first blank is the list of values, the second blank is the list of index labels, and the third blank is the name of the Series as a string.

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