Basic DataFrame info (shape, dtypes, describe) in Data Analysis Python

Introduction

We use basic DataFrame info to quickly understand the size, types, and summary of our data. This helps us know what we have before analyzing.

When you first load a dataset and want to see how many rows and columns it has.

When you want to check the type of data in each column (numbers, text, dates).

When you want a quick summary of statistics like mean, min, max for numeric columns.

When you want to find missing or unusual values by looking at data summaries.

When preparing data for cleaning or visualization and need a quick overview.

Syntax

Data Analysis Python

df.shape

df.dtypes

df.describe()

df.shape returns a tuple with (rows, columns).

df.dtypes shows the data type of each column.

df.describe() gives summary stats for numeric columns by default.

Examples

Prints the number of rows and columns as a tuple.

Data Analysis Python

print(df.shape)

Shows the data type of each column, like int64, float64, object (text).

Data Analysis Python

print(df.dtypes)

Displays count, mean, std, min, max, and quartiles for numeric columns.

Data Analysis Python

print(df.describe())

Shows summary statistics for all columns, including text columns.

Data Analysis Python

print(df.describe(include='all'))

Sample Program

This code creates a small table with Age, Name, and Salary columns. It then prints the size, data types, and summary stats for the numeric columns.

Data Analysis Python

import pandas as pd

# Create a simple DataFrame
 data = {
     'Age': [25, 30, 22, 40, 28],
     'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
     'Salary': [50000, 60000, 45000, 80000, 52000]
 }

df = pd.DataFrame(data)

# Print shape
print('Shape:', df.shape)

# Print data types
print('\nData types:')
print(df.dtypes)

# Print summary statistics
print('\nSummary statistics:')
print(df.describe())

Important Notes

Remember: describe() by default only summarizes numeric columns.

You can use df.info() for a quick overview including non-null counts and memory usage.

Summary

Use df.shape to see how big your data is.

Use df.dtypes to check what kind of data each column holds.

Use df.describe() to get quick stats on numeric data.

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