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Introduction to Pandas with AI

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AI in Action


              
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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

Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 1. Capitolo 4

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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

Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 1. Capitolo 4
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