Pandas offers .loc for selecting data using labels and .iloc for selection using integer positions. While both are powerful tools, it is important to understand their distinct roles and why mixing label-based and position-based indexing directly within a single accessor (like .loc or .iloc) is generally avoided.
While it might seem intuitive to want to specify a row by its label and a column by its integer position (or vice-versa) simultaneously, Pandas intentionally keeps these methods separate to maintain clarity and predictability.
The primary reason is ambiguity. Let's consider why trying to mix doesn't work as expected:
.loc Expects Labels: The .loc accessor is designed exclusively for label-based indexing. If you provide an integer, like df.loc['row_label', 0], .loc will interpret 0 as a column label, not an integer position. This will only work if you happen to have a column actually named 0. Otherwise, it will typically raise a KeyError. The only exception is when the index itself consists of integers; in that case, df.loc[0] would select the row labeled 0. But even then, the interpretation is based on the label, not the position.
.iloc Expects Integers: Conversely, .iloc is designed exclusively for integer-position-based indexing. It operates based on the zero-based position of rows and columns, regardless of their labels. If you provide a label, like df.iloc[0, 'column_label'], .iloc won't understand the string label and will raise a TypeError.
.ixIn older versions of Pandas, there was an indexer called .ix that attempted to handle both label and integer-based indexing, allowing for mixed types. However, this led to subtle bugs and confusing code because its behavior could change depending on whether the DataFrame's index contained integers or not. For example, df.ix[0] might select by label if the index contained integer labels, but by position if it didn't. This ambiguity made code harder to read and debug.
Due to these issues, .ix was deprecated. The explicit separation between .loc (labels only) and .iloc (integers only) provides a much clearer and more maintainable way to select data.
So, how do you select data when you know the label for one axis (rows) and the integer position for the other (columns), or vice versa? You don't mix them in a single call to .loc or .iloc. Instead, you use one accessor consistently and potentially convert the identifier for the other axis, or you chain the operations.
Let's use a sample DataFrame:
import pandas as pd
import numpy as np
data = {'Score': [85, 92, 78, 88, 95],
'Attempts': [1, 3, 2, 3, 1],
'Grade': ['B', 'A', 'C', 'B', 'A']}
index_labels = ['Student1', 'Student2', 'Student3', 'Student4', 'Student5']
df = pd.DataFrame(data, index=index_labels)
print(df)
Output:
Score Attempts Grade
Student1 85 1 B
Student2 92 3 A
Student3 78 2 C
Student4 88 3 B
Student5 95 1 A
Imagine you want the data for Student3 (label) from the second column (position 1, which is 'Attempts').
Option A: Use .loc (Convert Position to Label)
Find the label of the column at position 1 first.
# Get the column label at integer position 1
col_label = df.columns[1]
print(f"Column label at position 1: {col_label}")
# Use .loc with both labels
value = df.loc['Student3', col_label]
print(f"Value for Student3, column position 1: {value}")
Output:
Column label at position 1: Attempts
Value for Student3, column position 1: 2
Option B: Use .iloc (Convert Label to Position)
Find the integer position of the row label Student3 first.
# Get the integer position of row label 'Student3'
row_pos = df.index.get_loc('Student3')
print(f"Row position for Student3: {row_pos}")
# Use .iloc with both integer positions
value = df.iloc[row_pos, 1]
print(f"Value for Student3, column position 1: {value}")
Output:
Row position for Student3: 2
Value for Student3, column position 1: 2
Option C: Chained Selection Select the row by label first, then select the element from the resulting Series by position. This can sometimes be less efficient for large DataFrames but is often readable.
value = df.loc['Student3'].iloc[1]
print(f"Value using chained selection: {value}")
Output:
Value using chained selection: 2
Now, let's say you want data from the fourth row (position 3) and the column labeled 'Grade'.
Option A: Use .iloc (Convert Label to Position)
Find the integer position of the column label 'Grade'.
# Get the integer position of column label 'Grade'
col_pos = df.columns.get_loc('Grade')
print(f"Column position for Grade: {col_pos}")
# Use .iloc with both integer positions
value = df.iloc[3, col_pos]
print(f"Value for row position 3, column Grade: {value}")
Output:
Column position for Grade: 2
Value for row position 3, column Grade: B
Option B: Use .loc (Convert Position to Label)
Find the label of the row at position 3.
# Get the row label at integer position 3
row_label = df.index[3]
print(f"Row label at position 3: {row_label}")
# Use .loc with both labels
value = df.loc[row_label, 'Grade']
print(f"Value for row position 3, column Grade: {value}")
Output:
Row label at position 3: Student4
Value for row position 3, column Grade: B
Option C: Chained Selection Select the column by label first, then select the element from the resulting Series by position.
value = df['Grade'].iloc[3]
print(f"Value using chained selection: {value}")
Output:
Value using chained selection: B
While the idea of mixing label and integer indexing seems convenient, Pandas enforces separation through .loc and .iloc for good reasons, primarily clarity and predictability. When you need to select data using a mix of label and positional information, choose the accessor (.loc or .iloc) that matches your primary identifier and use helper methods like df.columns.get_loc(), df.index.get_loc(), df.columns[], or df.index[] to convert the other identifier as needed, or use chained selection. This explicit approach makes your code easier to understand and less prone to errors.
Was this section helpful?
© 2026 ApX Machine LearningEngineered with