5 Best Ways to Find NaN Indexes in a Python Series

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πŸ’‘ Problem Formulation: You’re dealing with a series of data in Python, and you need to find the locations of not-a-number (NaN) values. Such values often represent missing data and can affect the accuracy of your calculations or analysis. For a given series, e.g., pd.Series([1, np.nan, 3, np.nan, 5]), your goal is to locate the index positions of the NaNs, which in this case would be [1, 3].

Method 1: Using Pandas isna() Function

This method involves using the isna() function provided by the pandas library. The function returns a boolean series indicating whether each value is missing (NaN). By combining this with the where() function, you get the indices of all NaN values. This method is clear and pandas-native, which makes it a popular choice among data scientists.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Using isna() to find NaN values and then getting their indices
nan_indices = series.isna().where(lambda x: x).dropna().index.tolist()

print(nan_indices)

Output:

[1, 3]

This code first marks all NaN values as True using isna(), then filters to keep only those True values using where(). Finally, it removes all non-existent entries (if any) with dropna() and collects the indices into a list.

Method 2: Using List Comprehension with math.isnan()

If you’re not working within the pandas framework, you can use Python’s math library in conjunction with a list comprehension to identify NaN values. This method is suitable for any iterable and does not require pandas, making it versatile for use with lists and other sequences.

Here’s an example:

import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Using isna() to find NaN values and then getting their indices
nan_indices = series.isna().where(lambda x: x).dropna().index.tolist()

print(nan_indices)

Output:

[1, 3]

This code first marks all NaN values as True using isna(), then filters to keep only those True values using where(). Finally, it removes all non-existent entries (if any) with dropna() and collects the indices into a list.

Method 2: Using List Comprehension with math.isnan()

If you’re not working within the pandas framework, you can use Python’s math library in conjunction with a list comprehension to identify NaN values. This method is suitable for any iterable and does not require pandas, making it versatile for use with lists and other sequences.

Here’s an example:

import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Using isna() to find NaN values and then getting their indices
nan_indices = series.isna().where(lambda x: x).dropna().index.tolist()

print(nan_indices)

Output:

[1, 3]

This code first marks all NaN values as True using isna(), then filters to keep only those True values using where(). Finally, it removes all non-existent entries (if any) with dropna() and collects the indices into a list.

Method 2: Using List Comprehension with math.isnan()

If you’re not working within the pandas framework, you can use Python’s math library in conjunction with a list comprehension to identify NaN values. This method is suitable for any iterable and does not require pandas, making it versatile for use with lists and other sequences.

Here’s an example:

import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Using isna() to find NaN values and then getting their indices
nan_indices = series.isna().where(lambda x: x).dropna().index.tolist()

print(nan_indices)

Output:

[1, 3]

This code first marks all NaN values as True using isna(), then filters to keep only those True values using where(). Finally, it removes all non-existent entries (if any) with dropna() and collects the indices into a list.

Method 2: Using List Comprehension with math.isnan()

If you’re not working within the pandas framework, you can use Python’s math library in conjunction with a list comprehension to identify NaN values. This method is suitable for any iterable and does not require pandas, making it versatile for use with lists and other sequences.

Here’s an example:

import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Using isna() to find NaN values and then getting their indices
nan_indices = series.isna().where(lambda x: x).dropna().index.tolist()

print(nan_indices)

Output:

[1, 3]

This code first marks all NaN values as True using isna(), then filters to keep only those True values using where(). Finally, it removes all non-existent entries (if any) with dropna() and collects the indices into a list.

Method 2: Using List Comprehension with math.isnan()

If you’re not working within the pandas framework, you can use Python’s math library in conjunction with a list comprehension to identify NaN values. This method is suitable for any iterable and does not require pandas, making it versatile for use with lists and other sequences.

Here’s an example:

import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Using isna() to find NaN values and then getting their indices
nan_indices = series.isna().where(lambda x: x).dropna().index.tolist()

print(nan_indices)

Output:

[1, 3]

This code first marks all NaN values as True using isna(), then filters to keep only those True values using where(). Finally, it removes all non-existent entries (if any) with dropna() and collects the indices into a list.

Method 2: Using List Comprehension with math.isnan()

If you’re not working within the pandas framework, you can use Python’s math library in conjunction with a list comprehension to identify NaN values. This method is suitable for any iterable and does not require pandas, making it versatile for use with lists and other sequences.

Here’s an example:

import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.
import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Using isna() to find NaN values and then getting their indices
nan_indices = series.isna().where(lambda x: x).dropna().index.tolist()

print(nan_indices)

Output:

[1, 3]

This code first marks all NaN values as True using isna(), then filters to keep only those True values using where(). Finally, it removes all non-existent entries (if any) with dropna() and collects the indices into a list.

Method 2: Using List Comprehension with math.isnan()

If you’re not working within the pandas framework, you can use Python’s math library in conjunction with a list comprehension to identify NaN values. This method is suitable for any iterable and does not require pandas, making it versatile for use with lists and other sequences.

Here’s an example:

import math

# A non-pandas series (list)
series = [2, np.nan, 5, np.nan, 7]

# Using list comprehension with math.isnan() to find indices of NaNs
nan_indices = [index for index, value in enumerate(series) if math.isnan(value)]

print(nan_indices)

Output:

[1, 3]

This snippet loops through the series with enumerate(), which yields both the index and value. It then uses math.isnan() to check for NaN and keep the index if the value is NaN. It’s a concise method but requires caution with types since math.isnan() won’t handle non-float values.

Method 3: Using NumPy’s argwhere() Combined with isnan()

NumPy provides a function isnan() which, like pandas’ isna(), returns a boolean array. However, by combining this with argwhere(), you get the indices directly as a NumPy array. This approach is efficient and makes use of NumPy’s optimized operations.

Here’s an example:

import numpy as np

# Creating a NumPy array with NaN values
array = np.array([2, np.nan, 5, np.nan, 7])

# Using NumPy's isnan() along with argwhere() to find indices of NaNs
nan_indices = np.argwhere(np.isnan(array)).flatten()

print(nan_indices)

Output:

[1 3]

The code utilizes NumPy’s isnan() to find NaNs in the array, then argwhere() to extract their positions. The resulting array is flattened to get a one-dimensional array of indices.

Method 4: Using Pandas index with Boolean Masking

Another pandas-centric method employs boolean masking directly to the series’ index property. This takes advantage of the fact that pandas indexes can be used to filter series and dataframes just like arrays.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# Applying boolean masking to get indices of NaN values
nan_indices = series.index[series.isna()]

print(nan_indices.tolist())

Output:

[1, 3]

Here, series.isna() creates a boolean mask, and when applied to series.index, it delivers the indices of NaNs. This method is straightforward and idiomatic when working with pandas.

Bonus One-Liner Method 5: Using filter() with pd.isna()

A concise one-liner that uses Python’s built-in filter() function combined with pd.isna() can yield NaN indices without the need for explicit loops or comprehensions. This is a Pythonic way to handle this search and reads nicely in code.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a pandas Series with NaN values
series = pd.Series([2, np.nan, 5, np.nan, 7])

# One-liner using filter() and pd.isna()
nan_indices = list(filter(lambda idx: pd.isna(series[idx]), range(len(series))))

print(nan_indices)

Output:

[1, 3]

This approach uses filter() to iterate over each index and applies lambda to check if the value at that index is NaN, using pd.isna(). This effectively filters out non-NaN indices, leaving only the indices of NaNs.

Summary/Discussion

  • Method 1: Pandas isna() with where(). Strengths: Native to pandas, readable, and convenient. Weaknesses: Dependent on pandas, not suitable for native Python lists.
  • Method 2: List Comprehension with math.isnan(). Strengths: Versatile and straightforward, works outside pandas. Weaknesses: Can raise type errors if non-float values are present.
  • Method 3: NumPy’s argwhere() with isnan(). Strengths: Efficient with NumPy’s optimized operations. Weaknesses: Requires conversion to NumPy array, added dependency on the NumPy library.
  • Method 4: Pandas Boolean Masking. Strengths: Clean and idiomatic for pandas users. Weaknesses: Limited to pandas data structures.
  • Method 5: Using filter() with pd.isna(). Strengths: Pythonic one-liner, clear intent. Weaknesses: May be less intuitive for those new to functional programming paradigms.