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Introduction to Pandas with AI

bookUnderstanding DataFrames

AI in Action


              
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import pandas as pd  

products = pd.DataFrame({  
    "Name": ["Book", "Pen", "Notebook", "Pencil", "Eraser", "Ruler", "Marker"],  
    "Price": [12.5, 1.5, 4.0, 0.8, 0.5, 2.0, 1.2],  
    "Quantity": [5, 20, 12, 30, 50, 15, 25]  
})  

print(products.head())
print(products.info())
copy
Note
Definition

A DataFrame is the core pandas data structure: a two-dimensional table with labeled rows and columns. You can think of it as a complete spreadsheet or SQL table inside Python, where each column is a Series.

Creating DataFrames

Just like Series, there's more than one way to build a DataFrame.

From a Dictionary of Lists


              
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import pandas as pd  

data = {  
    "Name": ["Alice", "Bob"],  
    "Age": [25, 30],  
    "City": ["New York", "Chicago"]  
}  

df = pd.DataFrame(data)  
print(df)
copy

Each key becomes a column name, and the values form the column data.

From a List of Dictionaries


              
123456789

            
import pandas as pd  

people = [  
    {"Name": "Alice", "Age": 25, "City": "New York"},  
    {"Name": "Bob", "Age": 30, "City": "Chicago"}  
]  

df = pd.DataFrame(people)  
print(df)
copy

Each dictionary represents one row of data.

A Quick Look at a DataFrame

When working with real data, you often don't want to print the whole table - especially if it has thousands of rows. Pandas gives you a few handy methods for quick checks:


              
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import pandas as pd  

df = pd.DataFrame({  
    "Name": ["Alice", "Bob", "Carol", "Dan", "Eve", "Frank", "Grace"],  
    "Age": [25, 30, 27, 22, 29, 31, 28],  
    "City": ["New York", "Chicago", "Boston", "Seattle", "Austin", "Denver", "Miami"]  
})

print(df.head())
print(df.tail())
print(df.sample(3))
copy
  • .head(): shows the first rows (default 5);
  • .tail(): shows the last rows (default 5);
  • .sample(): shows a random selection of rows (default 1).

Metadata

A DataFrame also carries information about itself:


              
1234567891011121314

            
import pandas as pd

products = pd.DataFrame({
    "Name": ["Book", "Pen", "Notebook", "Pencil", "Eraser", "Ruler", "Marker"],
    "Price": [12.5, 1.5, 4.0, 0.8, 0.5, 2.0, 1.2],
    "Quantity": [5, 20, 12, 30, 50, 15, 25]
})

print(products.columns)
print(products.index)
print(products.dtypes)
print(products.shape)
print(products.size)
print(products.info())
copy
  • .columns: labels for columns;
  • .index: labels for rows;
  • .dtypes: data type of each column;
  • .shape: number of rows and columns;
  • .size: total number of elements;
  • .info(): a summary of the DataFrame's metadata.

1. What is the main difference between a Series and a DataFrame?

2. By default, how many rows does df.head() display?

3. Which method provides a summary of the DataFrame's metadata?

question mark

What is the main difference between a Series and a DataFrame?

Select the correct answer

question mark

By default, how many rows does df.head() display?

Select the correct answer

question mark

Which method provides a summary of the DataFrame's metadata?

Select the correct answer

¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 3

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Suggested prompts:

Can you explain what the output of products.info() means?

What does the .shape attribute tell me about the DataFrame?

How can I access a specific column or row in the DataFrame?

bookUnderstanding DataFrames

Desliza para mostrar el menú

AI in Action


              
12345678910

            
import pandas as pd  

products = pd.DataFrame({  
    "Name": ["Book", "Pen", "Notebook", "Pencil", "Eraser", "Ruler", "Marker"],  
    "Price": [12.5, 1.5, 4.0, 0.8, 0.5, 2.0, 1.2],  
    "Quantity": [5, 20, 12, 30, 50, 15, 25]  
})  

print(products.head())
print(products.info())
copy
Note
Definition

A DataFrame is the core pandas data structure: a two-dimensional table with labeled rows and columns. You can think of it as a complete spreadsheet or SQL table inside Python, where each column is a Series.

Creating DataFrames

Just like Series, there's more than one way to build a DataFrame.

From a Dictionary of Lists


              
12345678910

            
import pandas as pd  

data = {  
    "Name": ["Alice", "Bob"],  
    "Age": [25, 30],  
    "City": ["New York", "Chicago"]  
}  

df = pd.DataFrame(data)  
print(df)
copy

Each key becomes a column name, and the values form the column data.

From a List of Dictionaries


              
123456789

            
import pandas as pd  

people = [  
    {"Name": "Alice", "Age": 25, "City": "New York"},  
    {"Name": "Bob", "Age": 30, "City": "Chicago"}  
]  

df = pd.DataFrame(people)  
print(df)
copy

Each dictionary represents one row of data.

A Quick Look at a DataFrame

When working with real data, you often don't want to print the whole table - especially if it has thousands of rows. Pandas gives you a few handy methods for quick checks:


              
1234567891011

            
import pandas as pd  

df = pd.DataFrame({  
    "Name": ["Alice", "Bob", "Carol", "Dan", "Eve", "Frank", "Grace"],  
    "Age": [25, 30, 27, 22, 29, 31, 28],  
    "City": ["New York", "Chicago", "Boston", "Seattle", "Austin", "Denver", "Miami"]  
})

print(df.head())
print(df.tail())
print(df.sample(3))
copy
  • .head(): shows the first rows (default 5);
  • .tail(): shows the last rows (default 5);
  • .sample(): shows a random selection of rows (default 1).

Metadata

A DataFrame also carries information about itself:


              
1234567891011121314

            
import pandas as pd

products = pd.DataFrame({
    "Name": ["Book", "Pen", "Notebook", "Pencil", "Eraser", "Ruler", "Marker"],
    "Price": [12.5, 1.5, 4.0, 0.8, 0.5, 2.0, 1.2],
    "Quantity": [5, 20, 12, 30, 50, 15, 25]
})

print(products.columns)
print(products.index)
print(products.dtypes)
print(products.shape)
print(products.size)
print(products.info())
copy
  • .columns: labels for columns;
  • .index: labels for rows;
  • .dtypes: data type of each column;
  • .shape: number of rows and columns;
  • .size: total number of elements;
  • .info(): a summary of the DataFrame's metadata.

1. What is the main difference between a Series and a DataFrame?

2. By default, how many rows does df.head() display?

3. Which method provides a summary of the DataFrame's metadata?

question mark

What is the main difference between a Series and a DataFrame?

Select the correct answer

question mark

By default, how many rows does df.head() display?

Select the correct answer

question mark

Which method provides a summary of the DataFrame's metadata?

Select the correct answer

¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 3
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