Creating Series from list and dictionary in Pandas - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

Anumbers

Bdict

Cpd.DataFrame

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Passing 'dict' or 'pd.DataFrame' instead of the list variable.

Passing the string 'list' instead of the variable.

We pass the list 'numbers' directly to pd.Series to create a Series.

2fill in blank

medium

Complete the code to create a pandas Series from a dictionary with custom index labels.

Pandas

import pandas as pd
scores = {'Alice': 85, 'Bob': 90, 'Carol': 78}
series = pd.Series([1], index=['Bob', 'Carol', 'Alice'])
print(series)

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

A['Alice', 'Bob', 'Carol']

Blist(scores.values())

C{'Bob': 90, 'Carol': 78, 'Alice': 85}

Dscores

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list of keys instead of the dictionary.

Passing only the values without keys.

We pass the dictionary 'scores' to pd.Series and specify a custom index order.

3fill in blank

hard

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

Pandas

import pandas as pd
values = [100, 200, 300]
series = pd.Series(values, index=[1])
print(series)

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

A['a', 'b']

B['x', 'y', 'z']

C['1', '2', '3', '4']

D['one', 'two']

Attempts:

3 left

💡 Hint

Common Mistakes

Using an index list with fewer or more labels than data items.

Using numeric strings that don't match data length.

The index length must match the data length. Here, 3 values need 3 index labels.

4fill in blank

hard

Fill both blanks to create a Series from a dictionary and select values greater than 80.

Pandas

import pandas as pd
grades = {'Math': 75, 'Science': 88, 'English': 92}
series = pd.Series([1])
high_scores = series[series [2] 80]
print(high_scores)

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

Agrades

B>

C<

D[80]

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator like '<' or '[80]'.

Passing the wrong data type to pd.Series.

We create the Series from 'grades' and filter values greater than 80 using '>'.

5fill in blank

hard

Fill all three blanks to create a Series from a list, assign custom index, and filter values less than 50.

Pandas

import pandas as pd
values = [45, 60, 30, 80]
series = pd.Series([1], index=[2])
filtered = series[series [3] 50]
print(filtered)

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

Avalues

B['a', 'b', 'c', 'd']

C<

D['x', 'y', 'z']

Attempts:

3 left

💡 Hint

Common Mistakes

Using an index list with wrong length.

Using wrong comparison operator like '>' instead of '<'.

We pass the list 'values', assign a matching index list, and filter values less than 50 using '<'.

Practice

1. What happens when you create a pandas Series from a dictionary?

easy

A. The dictionary keys become the Series index labels automatically.

B. The dictionary values become the index labels.

C. The Series will have numeric index ignoring the dictionary keys.

D. It raises an error because Series cannot be created from dictionaries.

Solution

1. Step 1: Understand Series creation from dictionary

   When creating a Series from a dictionary, pandas uses the dictionary keys as the index labels.

2. Step 2: Identify the role of dictionary keys and values

   The dictionary values become the data in the Series, and keys become the index labels automatically.

3. Final Answer:

   The dictionary keys become the Series index labels automatically. -> Option A

4. Quick Check:

   Dictionary keys = index labels [OK]

Hint: Keys become index labels automatically when using dict [OK]

Common Mistakes:

• Confusing keys with values as index
• Thinking Series always uses numeric index
• Assuming error occurs with dict input

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

easy

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

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

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

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

Solution

1. Step 1: Recall Series constructor parameters

   The correct parameters are data first, then index for labels.

2. Step 2: Match correct syntax

   pd.Series([10, 20, 30], index=['a', 'b', 'c']) uses pd.Series([10, 20, 30], index=['a', 'b', 'c']) which is correct syntax.

3. Final Answer:

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

4. Quick Check:

   Use index= for labels in Series constructor [OK]

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

Common Mistakes:

• Using labels= instead of index=
• Swapping data and index parameters
• Passing index before data

3. What is the output of this code?

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

medium

A. 5

B. 10

C. 15

D. KeyError

Solution

1. Step 1: Create Series from dictionary

   The Series has index labels 'x', 'y', 'z' with values 5, 10, 15 respectively.

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

   Accessing s['y'] returns the value 10.

3. Final Answer:

   10 -> Option B

4. Quick Check:

   s['y'] = 10 [OK]

Hint: Access Series by index label to get corresponding value [OK]

Common Mistakes:

• Confusing index label with position
• Expecting numeric index access
• Mistaking value for key

4. Identify the error in this code snippet:

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

medium

A. TypeError because index must be integers.

B. SyntaxError due to missing parentheses.

C. No error; code runs fine.

D. Index length does not match data length causing ValueError.

Solution

1. Step 1: Check data and index lengths

   Data has 3 elements but index has only 2 labels.

2. Step 2: Understand pandas behavior

   Pandas raises ValueError if index length does not match data length.

3. Final Answer:

   Index length does not match data length causing ValueError. -> Option D

4. Quick Check:

   Data length != index length causes ValueError [OK]

Hint: Ensure index length matches data length when creating Series [OK]

Common Mistakes:

• Assuming pandas auto-adjusts index length
• Confusing ValueError with SyntaxError
• Thinking index must be integers

5. You have a dictionary {'a': 1, 'b': 0, 'c': 3, 'd': 0}. How do you create a pandas Series from it that only includes keys with values greater than zero?

hard

A. pd.Series(d).loc[d > 0]

B. pd.Series(d).filter(lambda x: x > 0)

C. pd.Series({k: v for k, v in d.items() if v > 0})

D. pd.Series(d).dropna()

Solution

1. Step 1: Filter dictionary items with values > 0

   Use dictionary comprehension to keep only items where value > 0.

2. Step 2: Create Series from filtered dictionary

   Pass the filtered dictionary to pd.Series to create the Series with desired keys and values.

3. Final Answer:

   pd.Series({k: v for k, v in d.items() if v > 0}) -> Option C

4. Quick Check:

   Filter dict first, then create Series [OK]

Hint: Filter dict before Series creation to exclude unwanted keys [OK]

Common Mistakes:

• Using filter() which is not a Series method
• Using dropna() which removes NaNs, not zeros
• Trying to index Series with dict directly