pandas Series.dot() Method Implementations
Importing necessary libraries
Python3
# importing pandas moduleimport pandas as pd # importing numpy moduleimport numpy as np |
1. With Series Data
In this example, two series are created from Python lists using Pandas Series() method. The method is then called on series1 and series2 is passed as a parameter. The result is then stored in a variable and displayed.
Python3
# creating series 1series1 = pd.Series([7, 5, 6, 4, 9]) # creating series 2series2 = pd.Series([1, 2, 3, 10, 2])# storing in new variable# calling .dot() methodans = series1.dot(series2)# displayprint('Dot product = {}'.format(ans)) |
Output:
Dot product = 93
Explanation:
The elements in the caller series are multiplied by the element at the same index in the past series. All the multiplied values are then added to get the dot product. As in the above example, the series are:
[7, 5, 6, 4, 9] [1, 2, 3, 10, 2]
Dot product = 7*1 + 5*2 + 6*3 + 4*10 + 9*2 = 7 + 10 + 18 + 40 + 18 = 93
2. With Dataframe
Python3
series = pd.Series([5, 3, 7, 4], name='Series')# Creating a DataFrame df1df1 = pd.DataFrame([[0, 1, -9, 1], [9, 1, 0, 1], [1, 3, 1, -1], [1, 1, 8, 1]])# Calculating dot product with each row in the DataFrameresult_series = series.dot(df1)# Printing the resulting Seriesprint(result_series) |
Output:
0 38
1 33
2 -6
3 5
Name: Series, dtype: int64
Explanation:
The resulting Series will have values obtained by taking the dot product of the Series [5, 3, 7, 4] with each column in the DataFrame df1.
- For the first row of
df1: 0⋅5+1⋅3+(−9)⋅7+1⋅4=38 - For the second row of
df1: 9⋅5+1⋅3+0⋅7+1⋅4=33 - For the third row of
df1: 1⋅5+3⋅3+1⋅7+(−1)⋅4=−6 - For the fourth row of
df1: 1⋅5+1⋅3+8⋅7+1⋅4=5
Note: You do not need to take the transpose in this case. The dot product operation between a Series and a DataFrame in pandas is designed to handle this situation without the need for transposing.
3. With Numpy Array
Python3
# Given NumPy array or array-likenumpy_array = np.array([1, 2, 3, 4])# Creating a pandas Seriesseries = pd.Series([5, 3, 7, 4], name='Series')# Performing element-wise multiplicationresult_series = series * numpy_array# Printing the resulting Seriesprint(result_series) |
Output:
0 5
1 6
2 21
3 16
Name: Series, dtype: int64
Output is a pandas Series where each element is the product of the corresponding elements in the original Series and NumPy array, i.e [5 * 1, 3 * 2, 7 * 3, 4 * 4], resulting in [5, 6, 21, 16].
Contact Us