IndexError: single positional indexer is out-of-bounds [Fix]

# IndexError: single positional indexer is out-of-bounds [Fix]

The Pandas "IndexError: single positional indexer is out-of-bounds " occurs when you try to index a column or a row but specify an index that is out of bounds.

To solve the error, make sure to specify an index that is not larger than the dimensions of your DataFrame.

Here is an example of how the error occurs.

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

print(df)

print('-' * 50)

# ⛔️ IndexError: single positional indexer is out-of-bounds
print(df.iloc[:, 4])

index error single positional indexer is out of bounds

The code sample tries to access the fifth column of the DataFrame, however, an error is raised because the DataFrame only has 4 columns.

shell

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    name  experience  salary
0  Alice          11   175.1
1  Bobby          14   180.2
2   Carl          16   190.3
3    Dan          18   210.4

Notice that the DataFrame has 3 columns and 4 rows.

Python indexes are zero-based, so the index of the first column is 0 and the index of the last column is -1.

You might also get the error when trying to access a row at an index that is out of bounds.

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

print(df)

print('-' * 50)

# ⛔️ IndexError: single positional indexer is out-of-bounds
print(df.iloc[[4]])

We tried to access the fifth row (index 4) and got the error because the DataFrame only has 4 rows.

You can get the number of rows and columns your DataFrame has by using df.index and df.shape[1].

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

print(df)

print('-' * 50)

row_count = len(df.index)
print(row_count)  # 👉️ 4

column_count = df.shape[1]
print(column_count)  # 👉️ 3

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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    name  experience  salary
0  Alice          11   175.1
1  Bobby          14   180.2
2   Carl          16   190.3
3    Dan          18   210.4
--------------------------------------------------
4
3

print dataframe number of rows and columns

As shown in the screenshot, the DataFrame has 4 rows and 3 columns.

In other words, the last row index is 3 and the last column index is 2 (because indexes are zero-based).

main.py

Copied!
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

print(df)

print('-' * 50)

# 👇️ Print 3rd column
print(df.iloc[:, 2])

print('-' * 50)

# 👇️ Print 4th row
print(df.iloc[[3]])

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice          11   175.1
1  Bobby          14   180.2
2   Carl          16   190.3
3    Dan          18   210.4
--------------------------------------------------
0    175.1
1    180.2
2    190.3
3    210.4
Name: salary, dtype: float64
--------------------------------------------------
  name  experience  salary
3  Dan          18   210.4

The following line prints the 3rd column (index 2).

main.py

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# 0    175.1
# 1    180.2
# 2    190.3
# 3    210.4
# Name: salary, dtype: float64
print(df.iloc[:, 2])

And the following line prints the 4th row (index 3).

main.py

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#   name  experience  salary
# 3  Dan          18   210.4
print(df.iloc[[3]])

# Accessing the last row or column in the DataFrame with an index of `-1`

If you need to access the last row or column in the DataFrame, use an index of -1.

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

print(df)

print('-' * 50)

# 👇️ print last column
print(df.iloc[:, -1])

print('-' * 50)

# 👇️ print last row
print(df.iloc[[-1]])

The code for this article is available on GitHub

The code sample above produces the following output.

shell

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#     name  experience  salary
# 0  Alice          11   175.1
# 1  Bobby          14   180.2
# 2   Carl          16   190.3
# 3    Dan          18   210.4
# --------------------------------------------------
# 0    175.1
# 1    180.2
# 2    190.3
# 3    210.4
# Name: salary, dtype: float64
# --------------------------------------------------
#   name  experience  salary
# 3  Dan          18   210.4

get last column and row of dataframe

You can achieve the same result by specifying an index of row_count - 1 and column_count - 1.

main.py

Copied!
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

print(df)

print('-' * 50)

column_count = df.shape[1]
print(column_count)  # 👉️ 3

# 👇️ print 3rd column
print(df.iloc[:, column_count - 1])

print('-' * 50)

row_count = len(df.index)
print(row_count)  # 👉️ 4

# 👇️ print 4th row
print(df.iloc[[row_count - 1]])

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice          11   175.1
1  Bobby          14   180.2
2   Carl          16   190.3
3    Dan          18   210.4
--------------------------------------------------
3
0    175.1
1    180.2
2    190.3
3    210.4
Name: salary, dtype: float64
--------------------------------------------------
4
  name  experience  salary
3  Dan          18   210.4

# Using a `try/except` statement to handle the error

Alternatively, you can use a try/except statement to handle the error.

main.py

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import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

print(df)

print('-' * 50)

try:
    print(df.iloc[:, 3])
except IndexError:
    print('Specified column index out of bounds')

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name  experience  salary
0  Alice          11   175.1
1  Bobby          14   180.2
2   Carl          16   190.3
3    Dan          18   210.4
--------------------------------------------------
Specified column index out of bounds

We try to get the fourth column (index 4) from the DataFrame, however, it only contains 3 columns, so an IndexError is raised and is then handled by the except block.

# Make sure your CSV file is not empty

If you got the error when reading a CSV file and converting it to a DataFrame, make sure the .csv file is not empty.

The same goes for your DataFrame object. If you try to access an empty DataFrame at any index, the IndexError` exception is raised.

If you get the error IndexError: index 0 is out of bounds for axis 0 with size 0, click on the link and follow the instructions.

# Additional Resources

You can learn more about the related topics by checking out the following tutorials:

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IndexError: single positional indexer is out-of-bounds [Fix]