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.append()
function is used to concatenate two or more series object.
Syntax: Series.append(to_append, ignore_index=False, verify_integrity=False)
Parameter :
to_append : Series or list/tuple of Series
ignore_index : If True, do not use the index labels.
verify_integrity : If True, raise Exception on creating index with duplicates
Returns : appended : Series
Example #1: Use Series.append()
function to append the passed series object at the end of this series object.
import pandas as pd
sr1 = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' ])
index_1 = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
sr1.index = index_1
sr2 = pd.Series([ 'Chicage' , 'Shanghai' , 'Beijing' , 'Jakarta' , 'Seoul' ])
index_2 = [ 'City 6' , 'City 7' , 'City 8' , 'City 9' , 'City 10' ]
sr2.index = index_2
print (sr1)
print (sr2)
|
Output :
City 1 New York
City 2 Chicago
City 3 Toronto
City 4 Lisbon
City 5 Rio
dtype: object
City 6 Chicage
City 7 Shanghai
City 8 Beijing
City 9 Jakarta
City 10 Seoul
dtype: object
Now we will use Series.append()
function to append sr2 at the end of sr1 series.
result = sr1.append(sr2)
print (result)
|
Output :
City 1 New York
City 2 Chicago
City 3 Toronto
City 4 Lisbon
City 5 Rio
City 6 Chicage
City 7 Shanghai
City 8 Beijing
City 9 Jakarta
City 10 Seoul
dtype: object
As we can see in the output, the Series.append()
function has successfully append the sr2 object at the end of sr1 object.
Example #2: Use Series.append()
function to append the passed series object at the end of this series object. Ignore the original index of the two series objects.
import pandas as pd
sr1 = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' ])
index_1 = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
sr1.index = index_1
sr2 = pd.Series([ 'Chicage' , 'Shanghai' , 'Beijing' , 'Jakarta' , 'Seoul' ])
index_2 = [ 'City 6' , 'City 7' , 'City 8' , 'City 9' , 'City 10' ]
sr2.index = index_2
print (sr1)
print (sr2)
|
Output :
City 1 New York
City 2 Chicago
City 3 Toronto
City 4 Lisbon
City 5 Rio
dtype: object
City 6 Chicage
City 7 Shanghai
City 8 Beijing
City 9 Jakarta
City 10 Seoul
dtype: object
Now we will use Series.append()
function to append sr2 at the end of sr1 series. We are going to ignore the index of the given series object.
result = sr1.append(sr2, ignore_index = True )
print (result)
|
Output :
0 New York
1 Chicago
2 Toronto
3 Lisbon
4 Rio
5 Chicage
6 Shanghai
7 Beijing
8 Jakarta
9 Seoul
dtype: object
As we can see in the output, the Series.append()
function has successfully append the sr2 object at the end of sr1 object and it has also ignored the index.
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Last Updated :
27 Feb, 2019
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