5 Best Ways to Create a Pandas DataFrame Column Based on a Given Condition in Python

πŸ’‘ Problem Formulation: Python developers often encounter the need to create new columns in a pandas DataFrame based on a given condition. For example, you might want to create a new column that categorizes rows based on the numerical range of an existing column. If you have a DataFrame with a ‘temperature’ column, you might want to create a ‘state’ column that contains ‘solid’, ‘liquid’, or ‘gas’ depending on the temperature value.

Method 1: Using Apply with a Custom Function

One common approach to create a new DataFrame column based on conditions is using the apply() method with a custom function. This allows you to apply a complex set of conditions and operations to each row.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Custom function to categorize temperature
def state_of_matter(temp):
    if temp <= 0:
        return 'solid'
    elif temp < 100:
        return 'liquid'
    else:
        return 'gas'

# Applying the custom function
df['state'] = df['temperature'].apply(state_of_matter)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This code snippet creates a custom function state_of_matter() that returns a state based on the temperature. We then apply this function to each row of the ‘temperature’ column in the DataFrame using apply(), resulting in a new ‘state’ column.

Method 2: Using Vectorized Operations with NumPy

Another efficient way to generate a new DataFrame column based on conditions is to use NumPy’s vectorized operations. This method is typically faster than apply() as it operates on the entire array at once.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Vectorized operation using NumPy
conditions = [
    (df['temperature'] <= 0),
    (df['temperature'] > 0) & (df['temperature'] < 100),
    (df['temperature'] >= 100)
]
choices = ['solid', 'liquid', 'gas']

df['state'] = np.select(conditions, choices)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This code snippet creates a custom function state_of_matter() that returns a state based on the temperature. We then apply this function to each row of the ‘temperature’ column in the DataFrame using apply(), resulting in a new ‘state’ column.

Method 2: Using Vectorized Operations with NumPy

Another efficient way to generate a new DataFrame column based on conditions is to use NumPy’s vectorized operations. This method is typically faster than apply() as it operates on the entire array at once.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Vectorized operation using NumPy
conditions = [
    (df['temperature'] <= 0),
    (df['temperature'] > 0) & (df['temperature'] < 100),
    (df['temperature'] >= 100)
]
choices = ['solid', 'liquid', 'gas']

df['state'] = np.select(conditions, choices)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This code snippet creates a custom function state_of_matter() that returns a state based on the temperature. We then apply this function to each row of the ‘temperature’ column in the DataFrame using apply(), resulting in a new ‘state’ column.

Method 2: Using Vectorized Operations with NumPy

Another efficient way to generate a new DataFrame column based on conditions is to use NumPy’s vectorized operations. This method is typically faster than apply() as it operates on the entire array at once.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Vectorized operation using NumPy
conditions = [
    (df['temperature'] <= 0),
    (df['temperature'] > 0) & (df['temperature'] < 100),
    (df['temperature'] >= 100)
]
choices = ['solid', 'liquid', 'gas']

df['state'] = np.select(conditions, choices)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This code snippet creates a custom function state_of_matter() that returns a state based on the temperature. We then apply this function to each row of the ‘temperature’ column in the DataFrame using apply(), resulting in a new ‘state’ column.

Method 2: Using Vectorized Operations with NumPy

Another efficient way to generate a new DataFrame column based on conditions is to use NumPy’s vectorized operations. This method is typically faster than apply() as it operates on the entire array at once.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Vectorized operation using NumPy
conditions = [
    (df['temperature'] <= 0),
    (df['temperature'] > 0) & (df['temperature'] < 100),
    (df['temperature'] >= 100)
]
choices = ['solid', 'liquid', 'gas']

df['state'] = np.select(conditions, choices)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This code snippet creates a custom function state_of_matter() that returns a state based on the temperature. We then apply this function to each row of the ‘temperature’ column in the DataFrame using apply(), resulting in a new ‘state’ column.

Method 2: Using Vectorized Operations with NumPy

Another efficient way to generate a new DataFrame column based on conditions is to use NumPy’s vectorized operations. This method is typically faster than apply() as it operates on the entire array at once.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Vectorized operation using NumPy
conditions = [
    (df['temperature'] <= 0),
    (df['temperature'] > 0) & (df['temperature'] < 100),
    (df['temperature'] >= 100)
]
choices = ['solid', 'liquid', 'gas']

df['state'] = np.select(conditions, choices)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This code snippet creates a custom function state_of_matter() that returns a state based on the temperature. We then apply this function to each row of the ‘temperature’ column in the DataFrame using apply(), resulting in a new ‘state’ column.

Method 2: Using Vectorized Operations with NumPy

Another efficient way to generate a new DataFrame column based on conditions is to use NumPy’s vectorized operations. This method is typically faster than apply() as it operates on the entire array at once.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Vectorized operation using NumPy
conditions = [
    (df['temperature'] <= 0),
    (df['temperature'] > 0) & (df['temperature'] < 100),
    (df['temperature'] >= 100)
]
choices = ['solid', 'liquid', 'gas']

df['state'] = np.select(conditions, choices)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This code snippet creates a custom function state_of_matter() that returns a state based on the temperature. We then apply this function to each row of the ‘temperature’ column in the DataFrame using apply(), resulting in a new ‘state’ column.

Method 2: Using Vectorized Operations with NumPy

Another efficient way to generate a new DataFrame column based on conditions is to use NumPy’s vectorized operations. This method is typically faster than apply() as it operates on the entire array at once.

Here’s an example:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Vectorized operation using NumPy
conditions = [
    (df['temperature'] <= 0),
    (df['temperature'] > 0) & (df['temperature'] < 100),
    (df['temperature'] >= 100)
]
choices = ['solid', 'liquid', 'gas']

df['state'] = np.select(conditions, choices)
print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In this example, we define an array of conditions and corresponding choices, then use the np.select() function to apply these conditions all at once, creating the ‘state’ column very efficiently.

Method 3: Using pandas’ where() Method

The where() method provided by pandas can be used to create a new column based on conditionals. It’s a straightforward approach for situations where you want to set values based on one condition.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying the where method
df['state'] = 'gas'
df['state'] = df['state'].where(df['temperature'] >= 100, 'liquid')
df['state'] = df['state'].where(df['temperature'] > 0, 'solid')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This snippet initializes the ‘state’ column with ‘gas’ and subsequently uses where() to reassign ‘liquid’ to rows where the temperature is below 100, and ‘solid’ for temperatures below or equal to 0.

Method 4: Using lambdas with the apply() Method

Using a lambda function with the apply() method is a quick and often less verbose way to create a new column based on conditionals. It’s similar to using a custom function but can be more concise for simple conditions.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# Applying a lambda function
df['state'] = df['temperature'].apply(lambda temp: 'solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas')

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

In the above code, a lambda function is applied to each value in ‘temperature’. It’s a one-liner that assigns the state based on the temperature, using conditional expressions.

Bonus One-Liner Method 5: Using List Comprehensions

List comprehensions offer a Pythonic way to create lists and can also be utilized to create DataFrame columns efficiently and with concise syntax.

Here’s an example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'temperature': [0, 25, 100, -5, 150]})

# List comprehension to create a new column
df['state'] = ['solid' if temp <= 0 else 'liquid' if temp < 100 else 'gas' for temp in df['temperature']]

print(df)

Output:

   temperature   state
0            0   solid
1           25  liquid
2          100     gas
3           -5   solid
4          150     gas

This concise one-liner uses a list comprehension to iterate over the ‘temperature’ column and generate the corresponding ‘state’ values for the new column.

Summary/Discussion

  • Method 1: Apply with a Custom Function. Adaptable to complex logic. May be slower for large datasets.
  • Method 2: Vectorized Operations with NumPy. Faster performance on large datasets. Less intuitive for readers unfamiliar with vectorization.
  • Method 3: Using pandas’ where() Method. Simple for single conditions. Multiple conditions may require multiple lines.
  • Method 4: Lambdas with apply(). Quick and concise for simple functions. Can be less readable for complex logic.
  • Bonus Method 5: Using List Comprehensions. Pythonic and compact. May not be as fast as vectorized operations for very large datasets.