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 duplicatesReturns : appended : Series
Example #1: Use Series.append() function to append the passed series object at the end of this series object.
# importing pandas as pd import pandas as pd # Creating the first Series sr1 = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) # Create the first Index index_1 = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # set the index of first series sr1.index = index_1 # Creating the second Series sr2 = pd.Series(['Chicage', 'Shanghai', 'Beijing', 'Jakarta', 'Seoul']) # Create the second Index index_2 = ['City 6', 'City 7', 'City 8', 'City 9', 'City 10'] # set the index of second series sr2.index = index_2 # Print the first series print(sr1) # Print the second series 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.
# append sr2 at the end of sr1 result = sr1.append(sr2) # Print the result 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.
# importing pandas as pd import pandas as pd # Creating the first Series sr1 = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio']) # Create the first Index index_1 = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] # set the index of first series sr1.index = index_1 # Creating the second Series sr2 = pd.Series(['Chicage', 'Shanghai', 'Beijing', 'Jakarta', 'Seoul']) # Create the second Index index_2 = ['City 6', 'City 7', 'City 8', 'City 9', 'City 10'] # set the index of second series sr2.index = index_2 # Print the first series print(sr1) # Print the second series 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.
# append sr2 at the end of sr1 # ignore the index result = sr1.append(sr2, ignore_index = True) # Print the result 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.
