Basic DataFrame info (shape, dtypes, describe) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the number of rows and columns in the DataFrame.

Data Analysis Python

print(df.[1])

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

Acolumns

Bshape

Cinfo

Ddtypes

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'columns' returns only the column names, not the shape.

Using 'dtypes' returns data types, not the size.

The shape attribute gives the number of rows and columns as a tuple.

2fill in blank

medium

Complete the code to see the data types of each column in the DataFrame.

Data Analysis Python

print(df.[1])

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

Adtypes

Bshape

Cdescribe

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'shape' returns size, not data types.

Using 'describe' gives statistics, not data types.

The dtypes attribute shows the data type of each column.

3fill in blank

hard

Fix the error in the code to get summary statistics of the DataFrame.

Data Analysis Python

summary = df.[1]()

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

Adtypes

Bshape

Cinfo

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'info' gives info about the DataFrame but not statistics.

Using 'shape' is an attribute, not a method.

The describe() method returns summary statistics like mean, count, and std.

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 3 letters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' filters wrong words.

Using 'word' as value stores the word, not its length.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

Data Analysis Python

result = {{'{'}[1]: [2] for k, v in data.items() if v [3] 0{{'}'}}

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k.lower()' makes keys lowercase, not uppercase.

Using '<' instead of '>' filters wrong values.

The dictionary comprehension uses uppercase keys with k.upper(), keeps values v, and filters values greater than 0.

Practice

1. What does df.shape tell you about a DataFrame?

easy

A. The data types of each column

B. The number of rows and columns in the DataFrame

C. Summary statistics like mean and median

D. The first few rows of the DataFrame

Solution

1. Step 1: Understand what df.shape returns

   df.shape gives a tuple with two numbers: rows and columns count.

2. Step 2: Match the description to the options

   The number of rows and columns in the DataFrame correctly describes this as the number of rows and columns.

3. Final Answer:

   The number of rows and columns in the DataFrame -> Option B

4. Quick Check:

   Shape = (rows, columns) [OK]

Hint: Remember shape shows size: rows and columns [OK]

Common Mistakes:

• Confusing shape with data types
• Thinking shape shows data summary
• Mixing shape with head() output

2. Which of the following is the correct way to check the data types of columns in a DataFrame named df?

easy

A. df.dtypes

B. df.columns.types

C. df.describe()

D. df.types()

Solution

1. Step 1: Recall the attribute for column data types

   The correct attribute to check data types is df.dtypes, not a method.

2. Step 2: Verify the options

   df.dtypes uses df.dtypes correctly. Others are invalid or do not exist.

3. Final Answer:

   df.dtypes -> Option A

4. Quick Check:

   dtypes attribute shows column types [OK]

Hint: Use df.dtypes without parentheses to check types [OK]

Common Mistakes:

• Adding parentheses like df.dtypes()
• Using non-existent methods like df.types()
• Confusing describe() with dtypes

3. Given the DataFrame df below:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4.0, 5.5, 6.1], 'C': ['x', 'y', 'z']}
df = pd.DataFrame(data)
print(df.describe())

What will be the output of df.describe()?

medium

A. Summary statistics for all columns including C

B. An error because column C is non-numeric

C. Summary statistics for columns A and B only, excluding C

D. Only the count of rows

Solution

1. Step 1: Understand what df.describe() does

   It provides summary stats (count, mean, std, min, max, quartiles) for numeric columns only by default.

2. Step 2: Check the DataFrame columns

   Columns A and B are numeric (int and float), C is string (non-numeric), so C is excluded.

3. Final Answer:

   Summary statistics for columns A and B only, excluding C -> Option C

4. Quick Check:

   describe() skips non-numeric columns by default [OK]

Hint: describe() shows stats only for numeric columns by default [OK]

Common Mistakes:

• Expecting describe() to include string columns
• Thinking describe() throws error on strings
• Assuming describe() shows only count

4. You run the code below and get an error:

import pandas as pd
data = {'X': [10, 20, 30], 'Y': [5, 15, 25]}
df = pd.DataFrame(data)
print(df.dtypes())

What is the cause of the error?

medium

A. dtypes is an attribute, not a method, so parentheses cause error

B. DataFrame columns have mixed types

C. DataFrame is empty

D. Missing import statement for pandas

Solution

1. Step 1: Identify the error source

   The code calls df.dtypes() with parentheses, but dtypes is an attribute, not a method.

2. Step 2: Understand correct usage

   Correct usage is df.dtypes without parentheses to access data types.

3. Final Answer:

   dtypes is an attribute, not a method, so parentheses cause error -> Option A

4. Quick Check:

   Use df.dtypes without () [OK]

Hint: Remember dtypes is attribute, no parentheses [OK]

Common Mistakes:

• Adding parentheses to dtypes
• Assuming error is from missing import
• Thinking DataFrame is empty

5. You have a DataFrame df with columns: Age (int), Salary (float), and Department (string). You want to quickly check how many rows and columns it has, the data types of each column, and summary statistics for numeric columns. Which sequence of commands gives you all this information?

hard

A. df.columns; df.dtypes; df.tail()

B. df.describe(); df.shape; df.columns

C. df.info(); df.head(); df.describe()

D. df.shape; df.dtypes; df.describe()

Solution

1. Step 1: Identify commands for each info type

   df.shape gives rows and columns count, df.dtypes shows data types, and df.describe() gives summary stats for numeric columns.

2. Step 2: Check options for correct sequence

   df.shape; df.dtypes; df.describe() lists these three commands in order, so it provides all required info.

3. Final Answer:

   df.shape; df.dtypes; df.describe() -> Option D

4. Quick Check:

   shape, dtypes, describe cover size, types, stats [OK]

Hint: Use shape, dtypes, then describe for full quick info [OK]

Common Mistakes:

• Using df.columns instead of df.dtypes for types
• Confusing df.info() with df.describe()
• Missing shape to check size