Python | Pandas Series.iat

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.iat attribute access a single value for a row/column pair by integer position.

Syntax: Series.iat Parameter : None Returns : return value at the specified location

Example #1: Use Series.iat attribute to return the value present at the specified location for the given Series object. Python3

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon'])

# Creating the row axis labels
sr.index = ['City 1', 'City 2', 'City 3', 'City 4'] 

# Print the series
print(sr)

Output : Now we will use Series.iat attribute to return the value lying at the 0th index. Python3 1==

# return the value at 0th index
sr.iat[0]

Output : As we can see in the output, the Series.iat attribute has returned 'New York', which is the value present at the 0th index in the given Series object.   Example #2 : Use Series.iat attribute to return the value present at the specified location for the given Series object. Python3

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(['1/1/2018', '2/1/2018', '3/1/2018', '4/1/2018'])

# Creating the row axis labels
sr.index = ['Day 1', 'Day 2', 'Day 3', 'Day 4']

# Print the series
print(sr)

Output : Now we will use Series.iat attribute to return the value lying at the 2nd index. Python3 1==

# return the value at 2nd index
sr.iat[2]

Output : As we can see in the output, the Series.iat attribute has returned '3/1/2018', which is the value present at the 2nd index in the given Series object.