Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Learn Navigating Data | Pandas Foundations
Introduction to Pandas with AI

bookNavigating Data

AI in Action


              
123456789101112

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

print(ages["Carol"])          # From Series
print(students.loc[2, "Age"]) # From DataFrame
copy
Note
Note

All three access styles in pandas - [], .loc[], and .iloc[] - support:

  • selecting by a single value;
  • selecting by a range (slice);
  • selecting by a list.

Accessing Data with []

Series

With Series, [] can be used to select both by label and by position. Positional selection still works, but is not recommended:


              
1234567891011

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

# Single
print(ages["Bob"])
# Range
print(ages["Bob":"Carol"])
print(ages[1:3])
# List
print(ages[["Alice", "Dan"]])
copy

Label-based slices in Series include both endpoints. Positional slices follow normal Python slicing rules (end excluded).

DataFrame

For DataFrames, [] is used only for column selection:


              
123456789101112

            
import pandas as pd

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

# Single
print(students["Age"])
# List
print(students[["Name", "Grade"]])
copy

df["column"] returns a Series, while df[["column"]] returns a DataFrame.

Accessing Data with .loc[]

.loc[] selects by label - for both rows and columns.

Series


              
12345678910

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

# Single
print(ages.loc["Alice"])
# Range
print(ages.loc["Bob":"Dan"])
# List
print(ages.loc[["Alice", "Dan"]])
copy

.loc[] includes the end label in slices.

DataFrame


              
1234567891011121314

            
import pandas as pd

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

# Single
print(students.loc[0, "Grade"])
# Range
print(students.loc[0:2, "Name":"Age"])
# List
print(students.loc[[0, 2], ["Name", "Grade"]])
copy

The first argument selects rows; the second selects columns. You can pass labels, slices, or lists to either.

Note
Note

Even though the range in 0:2 from the example is written with numbers, it is still interpreted as a labeled range, not a positional one.

Accessing Data with .iloc[]

.iloc[] selects by position - for both rows and columns.

Series


              
12345678910

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

# Single
print(ages.iloc[1])
# Range
print(ages.iloc[1:3])
# List
print(ages.iloc[[0, 2, 3]])
copy

.iloc[] excludes the end position in slices.

DataFrame


              
1234567891011121314

            
import pandas as pd

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

# Single
print(students.iloc[1, 2])
# Range
print(students.iloc[0:2, 0:2])
# List
print(students.iloc[[0, 1], [0, 2]])
copy

Just like with .loc[], the first argument selects rows; the second selects columns. You can pass positions, slices, or lists to either.

1. What does df["Age"] return if df is a DataFrame?

2. Which statements about .loc[] are correct?

3. Which statements about .iloc[] are correct?

question mark

What does df["Age"] return if df is a DataFrame?

Select the correct answer

question mark

Which statements about .loc[] are correct?

Select the correct answer

question mark

Which statements about .iloc[] are correct?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 4

Ask AI

expand

Ask AI

ChatGPT

Ask anything or try one of the suggested questions to begin our chat

Awesome!

Completion rate improved to 10

bookNavigating Data

Swipe to show menu

AI in Action


              
123456789101112

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

print(ages["Carol"])          # From Series
print(students.loc[2, "Age"]) # From DataFrame
copy
Note
Note

All three access styles in pandas - [], .loc[], and .iloc[] - support:

  • selecting by a single value;
  • selecting by a range (slice);
  • selecting by a list.

Accessing Data with []

Series

With Series, [] can be used to select both by label and by position. Positional selection still works, but is not recommended:


              
1234567891011

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

# Single
print(ages["Bob"])
# Range
print(ages["Bob":"Carol"])
print(ages[1:3])
# List
print(ages[["Alice", "Dan"]])
copy

Label-based slices in Series include both endpoints. Positional slices follow normal Python slicing rules (end excluded).

DataFrame

For DataFrames, [] is used only for column selection:


              
123456789101112

            
import pandas as pd

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

# Single
print(students["Age"])
# List
print(students[["Name", "Grade"]])
copy

df["column"] returns a Series, while df[["column"]] returns a DataFrame.

Accessing Data with .loc[]

.loc[] selects by label - for both rows and columns.

Series


              
12345678910

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

# Single
print(ages.loc["Alice"])
# Range
print(ages.loc["Bob":"Dan"])
# List
print(ages.loc[["Alice", "Dan"]])
copy

.loc[] includes the end label in slices.

DataFrame


              
1234567891011121314

            
import pandas as pd

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

# Single
print(students.loc[0, "Grade"])
# Range
print(students.loc[0:2, "Name":"Age"])
# List
print(students.loc[[0, 2], ["Name", "Grade"]])
copy

The first argument selects rows; the second selects columns. You can pass labels, slices, or lists to either.

Note
Note

Even though the range in 0:2 from the example is written with numbers, it is still interpreted as a labeled range, not a positional one.

Accessing Data with .iloc[]

.iloc[] selects by position - for both rows and columns.

Series


              
12345678910

            
import pandas as pd

ages = pd.Series([24, 30, 27, 22], index=["Alice", "Bob", "Carol", "Dan"])

# Single
print(ages.iloc[1])
# Range
print(ages.iloc[1:3])
# List
print(ages.iloc[[0, 2, 3]])
copy

.iloc[] excludes the end position in slices.

DataFrame


              
1234567891011121314

            
import pandas as pd

students = pd.DataFrame({
    "Name": ["Alice", "Bob", "Carol", "Dan"],
    "Age": [24, 30, 27, 22],
    "Grade": [85, 62, 90, 78]
})

# Single
print(students.iloc[1, 2])
# Range
print(students.iloc[0:2, 0:2])
# List
print(students.iloc[[0, 1], [0, 2]])
copy

Just like with .loc[], the first argument selects rows; the second selects columns. You can pass positions, slices, or lists to either.

1. What does df["Age"] return if df is a DataFrame?

2. Which statements about .loc[] are correct?

3. Which statements about .iloc[] are correct?

question mark

What does df["Age"] return if df is a DataFrame?

Select the correct answer

question mark

Which statements about .loc[] are correct?

Select the correct answer

question mark

Which statements about .iloc[] are correct?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 4
some-alt