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.
# 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.
# 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.
# 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.
# 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.
