Why DataFrame creation matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataFrame from a dictionary.

Pandas

import pandas as pd
data = {'name': ['Anna', 'Bob'], 'age': [25, 30]}
df = pd.[1](data)
print(df)

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

ADataFrame

BSeries

Carray

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.Series instead of pd.DataFrame

Trying to create a DataFrame with pd.list or pd.array which do not exist

We use pd.DataFrame() to create a DataFrame from a dictionary. Other options like Series or list do not create a DataFrame.

2fill in blank

medium

Complete the code to create a DataFrame with specified columns order.

Pandas

import pandas as pd
data = {'name': ['Anna', 'Bob'], 'age': [25, 30]}
df = pd.DataFrame(data, columns=[1])
print(df)

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

A['age', 'name']

B['name', 'age']

C['age']

D['name']

Attempts:

3 left

💡 Hint

Common Mistakes

Reversing the order of columns

Passing a single string instead of a list

The columns parameter takes a list of column names in the order you want them to appear. Here, ['name', 'age'] matches the data keys.

3fill in blank

hard

Fix the error in the code to create a DataFrame from a list of lists.

Pandas

import pandas as pd
data = [['Anna', 25], ['Bob', 30]]
df = pd.DataFrame([1], columns=['name', 'age'])
print(df)

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

Adata()

B{data}

C['data']

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using curly braces around the variable

Passing the variable as a string

Trying to call the variable as a function

The variable data should be passed directly to pd.DataFrame(). Using braces or quotes causes errors.

4fill in blank

hard

Fill both blanks to create a DataFrame from a list of dictionaries and set the index.

Pandas

import pandas as pd
data = [{'name': 'Anna', 'age': 25}, {'name': 'Bob', 'age': 30}]
df = pd.DataFrame([1])
df = df.set_index([2])
print(df)

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

Adata

B'name'

C'age'

D['name']

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list instead of a string for the index column

Setting index to 'age' which may not be unique

Passing data incorrectly

We pass the list of dictionaries data to create the DataFrame. Then we set the index to the 'name' column to label rows by names.

5fill in blank

hard

Fill all three blanks to create a DataFrame from a dictionary, rename columns, and select a subset.

Pandas

import pandas as pd
data = {'n': ['Anna', 'Bob'], 'a': [25, 30], 'c': ['NY', 'LA']}
df = pd.DataFrame([1])
df = df.rename(columns=[2])
df_subset = df.loc[:, [3]]
print(df_subset)

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

Adata

B{'n': 'name', 'a': 'age', 'c': 'city'}

C['name', 'age']

D['n', 'a']

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names

Passing rename columns as a list instead of a dictionary

Selecting columns by old names after renaming

First, create the DataFrame from data. Then rename columns using the dictionary mapping old to new names. Finally, select the subset of columns ['name', 'age'].

Practice

1. Why is creating a DataFrame important when working with data in pandas?

easy

A. It converts data into images.

B. It automatically cleans all data errors.

C. It organizes data into rows and columns for easy analysis.

D. It deletes duplicate data without user input.

Solution

1. Step 1: Understand DataFrame structure

   A DataFrame organizes data into rows and columns, similar to a spreadsheet.

2. Step 2: Recognize the benefit for analysis

   This structure makes it easier to analyze and manipulate data clearly and simply.

3. Final Answer:

   It organizes data into rows and columns for easy analysis. -> Option C

4. Quick Check:

   DataFrames = organized data [OK]

Hint: DataFrames = tables that make data easy to handle [OK]

Common Mistakes:

• Thinking DataFrames fix data errors automatically
• Confusing DataFrames with image data
• Assuming DataFrames delete duplicates by default

2. Which of the following is the correct way to create a pandas DataFrame from a dictionary?

easy

A. pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

B. pd.DataFrame['A': [1, 2], 'B': [3, 4]]

C. pd.DataFrame({'A' = [1, 2], 'B' = [3, 4]})

D. pd.DataFrame({'A': 1, 2, 'B': 3, 4})

Solution

1. Step 1: Check dictionary syntax

   The dictionary keys must be strings and values lists, using colons ':' to assign.

2. Step 2: Confirm DataFrame constructor usage

   pd.DataFrame() takes a dictionary with keys as column names and lists as column data.

3. Final Answer:

   pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) -> Option A

4. Quick Check:

   Correct dict + pd.DataFrame() = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) [OK]

Hint: Use curly braces and colons inside pd.DataFrame() [OK]

Common Mistakes:

• Using square brackets instead of parentheses for DataFrame
• Using '=' instead of ':' in dictionary
• Passing single values instead of lists for column data

3. What will be the output of this code?

import pandas as pd
data = {'Name': ['Anna', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df.shape)

medium

A. (2, 3)

B. (2, 2)

C. (3, 2)

D. (1, 2)

Solution

1. Step 1: Understand DataFrame shape

   DataFrame shape returns (rows, columns).

2. Step 2: Count rows and columns in data

   Data has 2 names and 2 ages, so 2 rows and 2 columns.

3. Final Answer:

   (2, 2) -> Option B

4. Quick Check:

   Rows=2, Columns=2 [OK]

Hint: Shape = (rows, columns) count [OK]

Common Mistakes:

• Confusing rows with columns count
• Assuming shape returns columns first
• Counting keys as rows

4. Identify the error in this DataFrame creation code:

import pandas as pd
data = {'X': [1, 2], 'Y': 3}
df = pd.DataFrame(data)

medium

A. Data values must be lists or arrays, not single integers.

B. Dictionary keys must be integers, not strings.

C. pd.DataFrame() cannot take a dictionary as input.

D. Missing import statement for pandas.

Solution

1. Step 1: Check data dictionary values

   Values are single integers, but DataFrame expects list-like objects for columns.

2. Step 2: Understand DataFrame input requirements

   Each column must have multiple values in a list or array to form rows.

3. Final Answer:

   Data values must be lists or arrays, not single integers. -> Option A

4. Quick Check:

   Values need to be lists [OK]

Hint: Use lists for column data, not single numbers [OK]

Common Mistakes:

• Using single values instead of lists
• Thinking keys must be integers
• Ignoring import pandas statement

5. You have nested data: {'Team': ['A', 'B'], 'Scores': [[10, 20], [15, 25]]}. How do you create a DataFrame that shows each score as a separate row with the team name repeated?

hard

A. Convert nested lists to strings before creating the DataFrame.

B. Create DataFrame directly with nested lists; pandas will flatten automatically.

C. Use pd.DataFrame({'Team': ['A', 'B'], 'Scores': [10, 20, 15, 25]}).

D. Use pd.DataFrame.explode() on the 'Scores' column after creating the DataFrame.

Solution

1. Step 1: Create DataFrame with nested lists

   Creating DataFrame with nested lists keeps 'Scores' as lists in cells.

2. Step 2: Use explode() to flatten lists into rows

   explode() expands each list element into its own row, repeating 'Team' values.

3. Final Answer:

   Use pd.DataFrame.explode() on the 'Scores' column after creating the DataFrame. -> Option D

4. Quick Check:

   explode() flattens nested lists [OK]

Hint: Use explode() to turn list elements into rows [OK]

Common Mistakes:

• Expecting pandas to flatten nested lists automatically
• Manually flattening data without explode()
• Converting lists to strings losing data structure