The main difference between pandas loc[] vs iloc[] is loc gets DataFrame rows & columns by labels/names and iloc[] gets by integer Index/position. For loc[], if the label is not present it gives a key error. For iloc[], if the position is not present it gives an index error. In this article, I will cover the difference and similarities between loc[] and iloc[] in Pandas DataFrame by exploring with examples.
Pandas DataFrame is a two-dimensional tabular data structure with labeled axes. i.e. columns and rows. Selecting columns from DataFrame results in a new DataFrame containing only specified selected columns from the original DataFrame.
Let’s see the differences and similarities between loc[] vs iloc[] by using the below topics with examples.
- Pandas loc vs iloc Usage
- Select Single Value
- Select Multiple Values
- Select Range of Values
- Select Alternate Rows & Columns
- Using Conditions
1. Difference Between loc[] vs iloc[] in pandas DataFrame
The difference between loc[] vs iloc[] is described by how you select rows and columns from pandas DataFrame.
loc[]is used to select rows and columns by Names/Labelsiloc[]is used to select rows and columns by Integer Index/Position. zero based index position.
One of the main advantages of pandas DataFrame is the ease of use. You can see this yourself when you use loc[] or iloc[] attributes to select or filter DataFrame rows or columns. These are mostly used attributes in pandas DataFrame. Let’s see the usage of these before jumping into differences and similarities.
1.1 pandas.DataFrame.loc[] Usage
DataFrame.loc[] is label-based to select rows and/or columns in pandas. It accepts single labels, multiple labels from the list, indexes by a range (between two indexes labels), and many more.
STARTis the name of the row/column labelSTOPis the name of the last row/column label to take, andSTEPas the number of indices to advance after each extraction
Some point to note about loc[].
- By not providing a start row/column, loc[] selects from the beginning.
- By not providing stop, loc[] selects all rows/columns from the start label.
- Providing both start and stop, selects all rows/columns in between
1.2 pandas.DataFrame.iloc[] usage
DataFrame.iloc[] is a index-based to select rows and/or columns in pandas. It accepts a single index, multiple indexes from the list, indexes by a range, and many more.
STARTis the integer index of the row/column.STOPis the integer index of the last row/column where you wanted to stop the selection, andSTEPas the number of indices to advance after each extraction.
Some point to note about iloc[].
- By not providing a start index, iloc[] selects from the first row/column.
- By not providing stop, iloc[] selects all rows/columns from the start index.
- Providing both start and stop, selects all rows/columns in between.
Let’s create a DataFrame and explore the differences of loc[] and iloc[].
# Pandas.DataFrame.iloc[] usage
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Hadoop","Python","pandas"],
'Fee' :[20000,25000,26000,22000,24000],
'Duration':['30day','40days','35days','40days','60days'],
'Discount':[1000,2300,1200,2500,2000]
}
index_labels=['r1','r2','r3','r4','r5']
df = pd.DataFrame(technologies,index=index_labels)
print(df)
# Outputs:
# r1 Spark 20000 30day 1000
# r2 PySpark 25000 40days 2300
# r3 Hadoop 26000 35days 1200
# r4 Python 22000 40days 2500
# r5 pandas 24000 60days 2000
2. Select Single Value Using loc[] vs iloc[]
By using loc[] and iloc[] you can select the single row and column by name and index respectively. The below example demonstrates how to select row by label and index.
# Select Single Row by Index Label
print(df.loc['r2'])
# Select Single Row by Index
print(df.iloc[1])
# Outputs:
# Courses PySpark
# Fee 25000
# Duration 40days
# Discount 2300
# Name: r2, dtype: object
In order to select column by label and Index use below.
# Select Single Column by label
print(df.loc[:, "Courses"])
# Select Single Column by Index
print(df.iloc[:, 0])
# Outputs:
# Courses
# r1 Spark
# r2 PySpark
# r3 Hadoop
# r4 Python
# r5 pandas
3. Select Multiple Rows/Columns using loc[] vs iloc[]
To select multiple rows and columns, use the labels or integer index as a list to loc[] and iloc[] attributes. Below is an example of how to select rows by label and index.
# Select Multiple Rows by Label
print(df.loc[['r2','r3']])
# Select Multiple Rows by Index
print(df.iloc[[1,2]])
# Outputs:
# Courses Fee Duration Discount
# r2 PySpark 25000 40days 2300
# r3 Hadoop 26000 35days 1200
Similarly to select multiple columns from pandas DataFrame.
# Select Multiple Columns by labels
print(df.loc[:, ["Courses","Fee","Discount"]])
# Select Multiple Columns by Index
print(df.iloc[:, [0,1,3]])
# Outputs:
# Courses Fee Discount
# r1 Spark 20000 1000
# r2 PySpark 25000 2300
# r3 Hadoop 26000 1200
# r4 Python 22000 2500
# r5 pandas 24000 2000
4. Select Range of Values Between Two Rows or Columns
By using loc[] and iloc[], you can also select rows and columns by range. For example all items between two rows/columns. all items starting from e.t.c. The below example selects rows in between r1 and r4 row indices.
# Select Rows Between two Index Labels
# Includes both r1 and r4 rows
print(df.loc['r1':'r4'])
# Select Rows Between two Indexs
# Includes Index 0 & Execludes 4
print(df.iloc[0:4])
# Outputs:
# Courses Fee Duration Discount
# r1 Spark 20000 30day 1000
# r2 PySpark 25000 40days 2300
# r3 Hadoop 26000 35days 1200
# r4 Python 22000 40days 2500
To select columns between two column names. The below example selects all columns between Fee and Discount column labels.
# Select Columns between two Labels
# Includes both 'Fee' and 'Discount' columns
print(df.loc[:,'Fee':'Discount'])
# Select Columns between two Indexes
# Includes Index 1 & Execludes 4
print(df.iloc[:,1:4])
# Outputs:
# Fee Duration Discount
# r1 20000 30day 1000
# r2 25000 40days 2300
# r3 26000 35days 1200
# r4 22000 40days 2500
# r5 24000 60days 2000
5. Select Alternate Rows or Columns
Similarly, by using ranges you can also select every alternate row from DataFrame.
# Select Alternate rows By indeces
print(df.loc['r1':'r4':2])
# Select Alternate rows By Index
print(df.iloc[0:4:2])
# Outputs:
# Courses Fee Duration Discount
# r1 Spark 20000 30day 1000
# r3 Hadoop 26000 35days 1200
To select alternate columns use
# Select Alternate Columns between two Labels
print(df.loc[:,'Fee':'Discount':2])
# Select Alternate Columns between two Indexes
print(df.iloc[:,1:4:2])
# Output:
# Fee Discount
# r1 20000 1000
# r2 25000 2300
# r3 26000 1200
# r4 22000 2500
# r5 24000 2000
6. Using Conditions with loc[] vs iloc[]
By using loc[] and iloc[] you can also select rows by conditions from pandas DataFrame.
# Using Conditions
print(df.loc[df['Fee'] >= 24000])
print(df.iloc[list(df['Fee'] >= 24000)])
# Output:
# Courses Fee Duration Discount
# r2 PySpark 25000 40days 2300
# r3 Hadoop 26000 35days 1200
# r5 pandas 24000 60days 2000
Conclusion
In this article, you have learned the difference and similarities between loc and iloc in pandas DataFrame using examples. DataFrame.loc[] is label-based to select rows and/or columns in pandas. It accepts single label, multiple labels from the list, by a range (between two indexes labels), and many more. DataFrame.iloc[] is index-based to select rows and/or columns in pandas. it accepts a single index, multiple indexes from the list, indexes by a range, and many more.
Happy Learning !!
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