pandas.DataFrame.loc[] is a property that is used to access a group of rows and columns by label(s) or a boolean array. 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.
pandas DataFrame loc key Points
- loc is used to select/filter rows and columns by labels.
- When using to select rows, you need to provide row indices label.
- It also provides a way to select rows and columns between ranges, every alternate e.t.c
pandas iloc[] is another property of DataFrame that is used to operate on column position and row indices. For a better understanding of these two learn the differences and similarities between pandas loc[] vs iloc[]
1. pandas.DataFrame.loc[] Syntax & Usage
loc is used to select rows and columns by names/labels of pandas DataFrame. One of the main advantages of DataFrame is its ease of use. You can see this yourself when you use pandas.DataFrame.loc[]
attribute to select or filter DataFrame rows or columns. This is mostly used attribute in pandas DataFrame.

START
 is the name of the row/column labelSTOP
 is the name of the last row/column label to take, andÂSTEP
 as the number of indices to advance after each extraction
Key points
- 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
Let’s create a DataFrame and explore how to use pandas loc[].
# Pandas.DataFrame.loc[] Syntax & 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)
# Output:
# 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 Row & Column By Label using loc[]
By using pandas loc[] you can select the rows and columns by name. This also supports selecting multiple rows and columns, records between two rows, between two columns e.t.c The below example demonstrates how to select row by label. Alternatively, you can also select rows using DataFrame.query() method
# Select Single Row by Label
print(df.loc['r2'])
# Output:
# Courses PySpark
# Fee 25000
# Duration 40days
# Discount 2300
# Name: r2, dtype: object
In order to select column by label.
# Select Single Column by label
print(df.loc[:, "Courses"])
# Output:
# Courses
# r1 Spark
# r2 PySpark
# r3 Hadoop
# r4 Python
# r5 pandas
3. Select Multiple Rows & Columns
Now, let’s see how to select multiple rows and columns by labels using DataFrame.loc[] property
# Select Multiple Rows by Label
print(df.loc[['r2','r3']])
# Output:
# 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"]])
# Output:
# 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 Between Two Rows or Columns
loc[] also supports 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
.
# Select Rows Between two Index Labels
# Includes both r1 and r4 rows
print(df.loc['r1':'r4'])
# Output:
# 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 between two column names. The below example selects all columns between Fee
and Discount
column labels.
# Select Columns between two Labels
# Include both 'Fee' and 'Discount' columns
print(df.loc[:,'Fee':'Discount'])
# Output:
# 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])
# Output:
# 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])
# Output:
# Fee Discount
# r1 20000 1000
# r2 25000 2300
# r3 26000 1200
# r4 22000 2500
# r5 24000 2000
6. Using Conditions with pandas loc
By using loc select DataFrame rows with conditions.
# Using Conditions
print(df.loc[df['Fee'] >= 24000])
# Output:
# Courses Fee Duration Discount
# r2 PySpark 25000 40days 2300
# r3 Hadoop 26000 35days 1200
# r5 pandas 24000 60days 2000
7. Complete Examples of pandas DataFrame loc
# Examples of pandas DataFrame loc
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)
# Select single Row
print(df.loc['r2'])
# Select Single Column by label
print(df.loc[:, "Courses"])
# Select Multiple Rows by Label
print(df.loc[['r2','r3']])
# Select Multiple Columns by labels
print(df.loc[:, ["Courses","Fee","Discount"]])
# Select Rows Between two Index Labels
# Includes both r1 and r4 rows
print(df.loc['r1':'r4'])
# Select Columns between two Labels
# Includes both 'Fee' and 'Discount' columns
print(df.loc[:,'Fee':'Discount'])
# Select Alternate rows By indeces
print(df.loc['r1':'r4':2])
# Select Alternate Columns between two Labels
print(df.loc[:,'Fee':'Discount':2])
# Using Conditions
print(df.loc[df['Fee'] >= 24000])
Conclusion
In this article, you have learned the syntax, usage, and examples of pandas DataFrame loc property. 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.
Happy Learning !!
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