DataFrame structure (index, columns, values) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the row labels of the DataFrame.

Data Analysis Python

row_labels = df.[1]

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

Avalues

Bindex

Ccolumns

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'columns' instead of 'index' to get row labels.

Trying to use 'values' which gives the data, not labels.

The index attribute gives the row labels of a DataFrame.

2fill in blank

medium

Complete the code to get the column labels of the DataFrame.

Data Analysis Python

column_labels = df.[1]

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

Akeys

Bindex

Cvalues

Dcolumns

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'index' which is for rows, not columns.

Using 'values' which gives the data, not labels.

The columns attribute gives the column labels of a DataFrame.

3fill in blank

hard

Fix the error in the code to get the data values of the DataFrame.

Data Analysis Python

data = df.[1]()

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

Ato_numpy

Bget_values

Cdata

Dvalues

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'values()' as if it were a method (it's an attribute).

Using deprecated 'get_values()' method.

The to_numpy() method returns the data values as a NumPy array. The values attribute is not a method, and get_values() is deprecated.

4fill in blank

hard

Fill both blanks to create a DataFrame with specified index and columns.

Data Analysis Python

df = pd.DataFrame(data, index=[1], columns=[2])

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

A['row1', 'row2']

B['col1', 'col2']

C['a', 'b']

D['x', 'y']

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping index and columns values.

Using labels that don't match data dimensions.

The index parameter sets row labels, and columns sets column labels. Here, ['row1', 'row2'] are row labels and ['col1', 'col2'] are column labels.

5fill in blank

hard

Fill all three blanks to create a DataFrame from a dictionary with correct index and columns.

Data Analysis Python

df = pd.DataFrame([1], index=[2], columns=[3])

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

A{'A': [1, 2], 'B': [3, 4]}

B['row1', 'row2']

C['A', 'B']

D{'X': [5, 6], 'Y': [7, 8]}

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up data dictionary and labels.

Using column labels that don't match dictionary keys.

The first blank is the data dictionary, the second is the row labels, and the third is the column labels matching the dictionary keys.

Practice

1. Which part of a DataFrame represents the labels for the rows?

easy

A. Values

B. Columns

C. Index

D. Cells

Solution

1. Step 1: Understand DataFrame parts

   A DataFrame has three main parts: index (row labels), columns (column names), and values (data).

2. Step 2: Identify row labels

   The row labels are called the index, which helps identify each row uniquely.

3. Final Answer:

   Index -> Option C

4. Quick Check:

   Row labels = Index [OK]

Hint: Rows are labeled by the index in a DataFrame [OK]

Common Mistakes:

• Confusing columns with index
• Thinking values are labels
• Mixing cells with index

2. Which of the following is the correct way to get the column names of a DataFrame named df?

easy

A. df.columns

B. df.index

C. df.values

D. df.names

Solution

1. Step 1: Recall DataFrame attributes

   To get column names, use the columns attribute of the DataFrame.

2. Step 2: Match correct syntax

   The correct syntax is df.columns. Other options refer to rows or data.

3. Final Answer:

   df.columns -> Option A

4. Quick Check:

   Columns = df.columns [OK]

Hint: Use df.columns to get column names quickly [OK]

Common Mistakes:

• Using df.index for columns
• Trying df.names which doesn't exist
• Using df.values for columns

3. Given the DataFrame df below:

   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

What will df.values[1, 2] return?

medium

A. 5

B. 2

C. 6

D. 8

Solution

1. Step 1: Understand DataFrame values and indexing

   df.values returns the data as a 2D array. The first index is the row, the second is the column.

2. Step 2: Locate the value at row 1, column 2

   Row 1 is the second row: [2, 5, 8]. Column 2 is the third column, value 8.

3. Final Answer:

   8 -> Option D

4. Quick Check:

   df.values[1, 2] = 8 [OK]

Hint: Remember indexing starts at 0 for rows and columns [OK]

Common Mistakes:

• Mixing row and column indexes
• Using df.iloc instead of df.values
• Off-by-one errors in indexing

4. What is wrong with this code to get the row labels of a DataFrame df?

row_labels = df.values

medium

A. It returns an empty list

B. It returns the data values, not the row labels

C. It returns the column names instead

D. It causes a syntax error

Solution

1. Step 1: Understand what df.values returns

   df.values returns the data inside the DataFrame as a numpy array, not the row labels.

2. Step 2: Identify correct attribute for row labels

   Row labels are stored in df.index, not df.values.

3. Final Answer:

   It returns the data values, not the row labels -> Option B

4. Quick Check:

   df.values ≠ row labels [OK]

Hint: Use df.index for rows, not df.values [OK]

Common Mistakes:

• Confusing df.values with df.index
• Expecting df.values to give labels
• Using wrong attribute names

5. You have a DataFrame df with custom row labels ["a", "b", "c"] and columns ["X", "Y"]. Which code correctly extracts the data values as a 2D array?

hard

A. df.values

B. df.index

C. df.columns

D. df.data

Solution

1. Step 1: Recall DataFrame parts

   Index holds row labels, columns hold column names, and values hold the actual data.

2. Step 2: Identify correct attribute for data values

   To get the data as a 2D array, use df.values. Other options do not return data values.

3. Final Answer:

   df.values -> Option A

4. Quick Check:

   Data values = df.values [OK]

Hint: Use df.values to get data regardless of labels [OK]

Common Mistakes:

• Using df.index or df.columns for data
• Trying df.data which doesn't exist
• Confusing labels with values