Let us see how to add a Pandas series to another series in Python. This can be done using 2 ways:
Method 1: Using the append() function: It appends one series object at the end of another series object and returns an appended series. The attribute, ignore_index=True is used when we do not use index values on appending, i.e., the resulting index will be 0 to n-1. By default, the value of the ignore_index attribute is False.
Python3
# importing the module import pandas as pd # create 2 series objects series_1 = pd.Series([ 2 , 4 , 6 , 8 ]) series_2 = pd.Series([ 10 , 12 , 14 , 16 ]) # adding series_2 to series_1 using the append() function series_1 = series_1.append(series_2, ignore_index = True ) # displaying series_1 print (series_1) |
Output:
Appending two Series without using ignore_index attribute:
Python3
# importing the module import pandas as pd # create 2 series objects series_1 = pd.Series([ 2 , 4 , 6 , 8 ]) series_2 = pd.Series([ 10 , 12 , 14 , 16 ]) # adding series_2 to series_1 using the append() function series_1 = series_1.append(series_2) # displaying series_1 print (series_1) |
Output:
0 2
1 4
2 6
3 8
0 10
1 12
2 14
3 16
dtype: int64
Note: If we don’t use ignore_index attribute, then the second series will use its own index upon appending operation.
Method 2: Using the concat() function: It takes a list of series objects that are to be concatenated as an argument and returns a concatenated series along an axis, i.e., if axis=0, it will concatenate row-wise and if axis=1, the resulting Series will be concatenated column-wise.
Python3
# importing the module import pandas as pd # create 2 series objects series_1 = pd.Series([ 2 , 4 , 6 , 8 ]) series_2 = pd.Series([ 10 , 12 , 14 , 16 ]) # adding series_2 to series_1 using the concat() function series_1 = pd.concat([series_1, series_2], axis = 0 ) # displaying series_1 print (series_1) |
Output:
Concatenating two Series column-wise:
Python3
# importing the module import pandas as pd # create 2 series objects series_1 = pd.Series([ 2 , 4 , 6 , 8 ]) series_2 = pd.Series([ 10 , 12 , 14 , 16 ]) # adding series_2 to series_1 using the concat() function series_1 = pd.concat([series_1, series_2],axis = 1 ) # displaying series_1 print (series_1) |
Output:
0 1
0 2 10
1 4 12
2 6 14
3 8 16
Note: When concatenation done on two Series column-wise, then the result will be a DataFrame.
type(series_1) pandas.core.frame.DataFrame