pandas loc vs iloc

Pandas is a Python library used widely in the field of data science and machine learning. It helps manipulate and prepare numerical data to pass to the machine learning models. Pandas provides us with loc and iloc functions to select rows and columns from a pandas DataFrame.

In this Answer, we will look into the ways we can use both of the functions to select data from a DataFrame and highlight the key differences between them.

loc vs iloc

In this section, we will explore a side-by-side difference between both functions.


loc[]

iloc[]

Abbreviation

loc stands for "location".

iloc stands for "integer location".

Definition

Selects rows and columns based on the labels (indexes and name).

Select rows and columns based on index numbers rather than column labels.

Single value

df.loc["A"] OR df.loc[1]

df.iloc[1]

List

df.loc[["A", "B", "E"]]

df.iloc[[1, 2, 4]]

Slicing

df.loc["A" : "E", "W" : "Z"]

*includes both labels (int or str) of the slice operator

df.loc[included : included]

df.iloc[1 : 5 , 1 : 3]

*includes the first but excludes the second label (int) of the slice operator

df.iloc[included:excluded]

Condition

df.loc[condition]

Cannot apply condition in the operator.

Now that we have a clear understanding, let's see an illustration of selecting a DataFrame row using both functions.

Illustration of accessing the second row using loc[] and iloc[]
Illustration of accessing the second row using loc[] and iloc[]

In the illustration above, we access the second row of the DataFrame using the loc and iloc functions. Using the iloc function, we provide the position of the row i.e., 1 in the square brackets [] of the function, whereas for the loc function we provide the label of the row i.e., "B".

Example data

Throughout the Answer, we will be using the following example data to apply the loc and iloc accessors in our coding examples:

Name

Age

Country

John

20

USA

James

30

Canada

Alex

23

Brazil

Sara

13

Argentina

Andrew

42

Australia

Albert

12

England

Coding examples : iloc

iloc becomes useful when we want to access data using the numerical position of the rows and columns. Below, we will see some of the use cases where we can use the iloc function:

Accessing a specific row and column

The syntax to access columns and rows from a DataFrame is:

DataFrame.iloc[ row , column ]
Syntax to access specific row and column of a DataFrame

In the syntax above, we specify the DataFrame from which we want to access the rows and columns.

  • row: We insert the row number that is to be accessed. The row number of a DataFrame starts from the 0 index.

  • column: We insert the column number that is to be accessed. The column number of a DataFrame also starts from the 0 index.

Below is a code example to access specific rows and columns from a DataFrame.

import pandas as pd

person_data = pd.DataFrame({
  "Name": ["Albert", "James", "Alex", "Bob", "Sara", "Bill", "Daniel", "John"],
  "Country": ["USA", "USA", "USA", "Canada", "Canada", "Germany", "Germany", "Egypt"],
  "Age": [12, 14, 20, 25, 29, 45, 32, 60]
})

print(person_data)

print("Accessing 3rd row of the DataFrame:")
print(person_data.iloc[3])
print("######################################")

print("Accessing 3rd row and 1st column of the DataFrame:")
print(person_data.iloc[3 , 0])
print("######################################")

print("Accessing 3rd,5th and 6th rows and 1st column of the DataFrame:")
print(person_data.iloc[[3,5,6]])
Example to access a row and a column

Code example

  • Line 1: We import the pandas library so that we can create a DataFrame and apply the iloc function on it.

  • Line 2: We create a DataFrame and store it in the person_data variable.

  • Line 9: We print the complete DataFrame.

  • Line 12: We access row 3 of the DataFrame.

  • Line 16: We access row 3 and column 0 of the DataFrame.

  • Line 20: We access multiple rows by passing in a list of indices [3,5,6] .

Accessing a range of rows and columns

To access a range of columns and rows, we use a colon : operator in the rows and columns. The syntax is given below:

DataFrame.iloc[rowstart : rowend , colstart : colend]
Syntax to access a range of rows and columns

  • rowstart: The starting index of the row range from where we want to access the rows.

  • rowend: The ending index of the row range. The ending row index is not accessed; rather, the rows till rowend-1 indices are accessed.

  • colstart: The starting index of the column range from where we want to access the columns.

  • colend: The ending index of the column range. The ending column index is not accessed. Rather, the columns till colend-1 indices are accessed.

Below is a coding example to access a range of rows and columns.

import pandas as pd

person_data = pd.DataFrame({
  "Name": ["Albert", "James", "Alex", "Bob", "Sara", "Bill", "Daniel", "John"],
  "Country": ["USA", "USA", "USA", "Canada", "Canada", "Germany", "Germany", "Egypt"],
  "Age": [12, 14, 20, 25, 29, 45, 32, 60]
})

print("Accessing range of rows from the DataFrame:")
print(person_data.iloc[3:5])
print("######################################")

print("Accessing range of rows and columns from the DataFrame:")
print(person_data.iloc[3:5 , 0:2])
Example to access a range of rows and columns

Code explanation

  • Line 10: We select a range of rows starting from index 3, ending till index 4 = colend - 1

  • Line 14: We select a range of rows and columns.

Coding examples: loc

loc function is handy when accessing rows and columns based on meaningful labels rather than integer positions. Below, we will see some of the use cases where we can use the loc function:

Accessing specific rows and columns

The syntax to access rows and columns is:

DataFrame.loc[rowlabel , collabel]
Syntax to access row and column using loc

rowlabel: We define the row label we want to select from the DataFrame.

collabel: We define the column label we want to select from the DataFrame.

An example code for selecting specific rows and columns is given below:

import pandas as pd

person_data = pd.DataFrame({
  "Name": ["Albert", "James", "Alex", "Bob", "Sara", "Bill", "Daniel", "John"],
  "Country": ["USA", "USA", "USA", "Canada", "Canada", "Germany", "Germany", "Egypt"],
  "Age": [12, 14, 20, 25, 29, 45, 32, 60]
}, index=["A" ,"B", "C", "D", "E", "F", "G", "H"])

print("Accessing row with label B:")
print(person_data.loc["B"])
print("######################################")

print("Accessing row with label B and column with label Country:")
print(person_data.loc["B" , "Country"])
print("######################################")

print("Accessing A, B and C labeled rows and column with Name and Age labels:")
print(person_data.loc[["A" , "B" , "D"] , ["Name" , "Age"]])
Example to access a row and a column

Code explanation

  • Line 7: We give custom string indices to the DataFrame rows using the index parameter.

  • Line 10: We access the row with the label B.

  • Line 14: We access the cell containing the row with the label B and the column with the label Country.

  • Line 18: We access specific rows and columns by passing in the label names in a list.

Accessing a range of rows and columns

To select a range of rows and columns using the loc function, we use column and row splitting using the colon : operator similar to the iloc function.

The coding example is given below:

import pandas as pd

person_data = pd.DataFrame({
  "Name": ["Albert", "James", "Alex", "Bob", "Sara", "Bill", "Daniel", "John"],
  "Country": ["USA", "USA", "USA", "Canada", "Canada", "Germany", "Germany", "Egypt"],
  "Age": [12, 14, 20, 25, 29, 45, 32, 60]
}, index=["A" ,"B", "C", "D", "E", "F", "G", "H"])

print("Accessing range of rows from the DataFrame:")
print(person_data.loc["B" :"E"])
print("######################################")

print("Accessing range of rows and columns from the DataFrame:")
print(person_data.loc["B":"E" , "Name":"Country"])
Example to access a range of rows and columns

Code explanation

  • Line 10: We pass a range of rows, starting from the row with the label B and ending at the row with the label E.

  • Line 14: We pass a range of rows and columns.

Applying a filter on a DataFrame

To apply a filter on a DataFrame, we can pass in a condition in the bracket [] of the loc function. To explain this, we can see a coding example below:

import pandas as pd

person_data = pd.DataFrame({
  "Name": ["Albert", "James", "Alex", "Bob", "Sara", "Bill", "Daniel", "John"],
  "Country": ["USA", "USA", "USA", "Canada", "Canada", "Germany", "Germany", "Egypt"],
  "Age": [12, 14, 20, 25, 29, 45, 32, 60]
}, index=["A" ,"B", "C", "D", "E", "F", "G", "H"])

print(person_data)

print("Applying filter of person's age greater than 40")
print(person_data.loc[person_data["Age"] > 40])
Example to access data by applying a filter on a column

Code explanation

  • Line 12: We pass in the filter, person_data["Age"] > 40 inside the brackets []. Each value of the column with the label Age is checked. If the condition is true, then the value is selected.

Conclusion

loc and iloc are powerful data selection tools that pandas DataFrame provides. We use loc for accessing labeled data, whereas we use iloc to access data based on position.

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