Basic DataFrame info (shape, dtypes, describe) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Basic DataFrame info (shape, dtypes, describe)

Create DataFrame

↓

Check shape

↓

Check dtypes

↓

Run describe()

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Review summary statistics

Start with a DataFrame, then check its size, data types, and summary statistics step-by-step.

Execution Sample

Data Analysis Python

import pandas as pd

data = {'Age': [25, 30, 22], 'Name': ['Ann', 'Bob', 'Cal'], 'Score': [88.5, 92.0, 85.0]}
df = pd.DataFrame(data)

print(df.shape)
print(df.dtypes)
print(df.describe())

This code creates a DataFrame and shows its shape, data types, and summary statistics.

Execution Table

Step	Action	Output Type	Output Value
1	Create DataFrame from dictionary	DataFrame	{'Age': [25,30,22], 'Name': ['Ann','Bob','Cal'], 'Score': [88.5,92.0,85.0]}
2	Check shape	tuple	(3, 3)
3	Check dtypes	Series	Age int64 Name object Score float64 dtype: object
4	Run describe()	DataFrame	Age Score count 3.000000 3.000000 mean 25.666667 88.500000 std 4.041452 3.511885 min 22.000000 85.000000 25% 23.500000 86.250000 50% 25.000000 88.500000 75% 27.500000 90.250000 max 30.000000 92.000000

💡 All DataFrame info methods executed successfully.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4
df	None	DataFrame with 3 rows and 3 columns	Same	Same	Same
shape	None	None	(3, 3)	(3, 3)	(3, 3)
dtypes	None	None	None	Age int64, Name object, Score float64	Age int64, Name object, Score float64
describe	None	None	None	None	Summary stats DataFrame for numeric columns

Key Moments - 3 Insights

Why does describe() only show statistics for numeric columns?

describe() by default summarizes numeric columns only, so text columns like 'Name' are excluded. See execution_table step 4 where only 'Age' and 'Score' stats appear.

What does the shape (3, 3) mean exactly?

Shape (3, 3) means 3 rows and 3 columns in the DataFrame, as shown in execution_table step 2.

Why is 'Name' dtype object and not string?

In pandas, text columns are stored as 'object' dtype, which means general Python objects, including strings. This is shown in execution_table step 3.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what does the shape (3, 3) represent?

A 3 rows and 3 columns

B 3 columns and 3 rows

C 3 rows and 2 columns

D 3 columns and 2 rows

💡 Hint

Check the 'Output Value' column in execution_table step 2.

At which step do we see the data types of each column?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look for 'Check dtypes' in the 'Action' column of execution_table.

If the DataFrame had a new column with text data, how would describe() output change?

A It would include summary stats for the new text column

B It would ignore the new text column by default

C It would cause an error

D It would convert text to numbers automatically

💡 Hint

Refer to key_moments about describe() behavior on text columns.

Concept Snapshot

Basic DataFrame info:
- df.shape shows rows and columns count
- df.dtypes shows data type per column
- df.describe() summarizes numeric columns
Use these to quickly understand your data size and types.

Full Transcript

We start by creating a DataFrame from a dictionary with three columns: Age, Name, and Score. Then, we check the shape using df.shape, which tells us the DataFrame has 3 rows and 3 columns. Next, df.dtypes shows the data type of each column: Age is integer, Name is object (text), and Score is float. Finally, df.describe() gives summary statistics like count, mean, and min for numeric columns only, excluding the text column. This step-by-step helps us understand the size, types, and basic stats of our data.