Basic DataFrame info (shape, dtypes, describe) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the shape attribute of a DataFrame tell you?

The shape attribute shows the number of rows and columns in the DataFrame as a tuple (rows, columns).

beginner

How can you find the data types of each column in a DataFrame?

Use the dtypes attribute to see the data type of each column in the DataFrame.

beginner

What information does the describe() method provide for a DataFrame?

The describe() method gives summary statistics like count, mean, std (standard deviation), min, max, and quartiles for numeric columns.

beginner

If you want to quickly check how many rows and columns your data has, which attribute do you use?

You use the shape attribute to quickly see the number of rows and columns.

beginner

Why is it useful to check dtypes before analysis?

Checking dtypes helps you understand what kind of data each column holds, so you can choose the right analysis or fix data issues early.

What does df.shape return for a DataFrame df?

A Summary statistics of numeric columns

B The data types of each column

C A tuple with (number of rows, number of columns)

D The first 5 rows of the DataFrame

df.shape returns a tuple showing the number of rows and columns.

Which attribute shows the data type of each column in a DataFrame?

A shape

B dtypes

C describe()

D info()

dtypes shows the data type of each column.

What kind of information does df.describe() provide?

A Summary statistics like mean, min, max for numeric columns

B Number of rows and columns

C Data types of columns

D The column names

describe() gives summary statistics for numeric columns.

If you want to know how many columns your DataFrame has, which is the easiest way?

A Use <code>df.dtypes</code>

B Use <code>df.head()</code>

C Use <code>df.describe()</code>

D Use <code>df.shape[1]</code>

df.shape[1] gives the number of columns.

Why is it important to check dtypes before starting data analysis?

A To understand the kind of data in each column for correct analysis

B To get summary statistics

C To see how many rows the data has

D To rename columns

Knowing data types helps choose the right analysis and catch errors early.

Explain how you would quickly check the size and data types of a DataFrame.

Think about attributes that give you quick info about rows, columns, and types.

Describe what kind of summary information the describe() method provides and why it is useful.

Focus on what describe() tells you about numeric columns.

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