Python | Pandas Series.div()

Python | Pandas Series.ix

Last Updated : 28 Jan, 2019

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.ix attribute is a primarily label-location based indexer, with integer position fallback. It takes the label as input and returns the value corresponding to that label.

Syntax:Series.ix

Parameter : None

Returns : value

Example #1: Use Series.ix attribute to return a value lying at the specified label in 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.ix attribute to return the value lying corresponding to the ‘City 4’ label.

# return the value 
sr.ix['City 4'] 

Output :

As we can see in the output, the Series.ix attribute has returned ‘Lisbon’ as the value corresponding to the ‘City 4’ label in the given Series object.

Example #2 : Use Series.ix attribute to return a value lying at the specified label in 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.ix attribute to return the value lying corresponding to the ‘Day 3’ label.

# return the value 
sr.ix['Day 3'] 

Output :

As we can see in the output, the Series.ix attribute has returned ‘3/1/2018’ as the value corresponding to the ‘Day 3’ label in the given Series object.

Python | Pandas Series.div()

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