'Multiply column in dataframe with one row in another dataframe
I'm having problems with multiplying values in two different dataframes. Im doing a PCA regression and want to multiply all my loadings with the original values.
for example:
PCA dataframe
| PC1 | PC2 | |
|---|---|---|
| X | 0 | 1 |
| X1 | 1 | 2 |
| X2 | 2 | 1 |
| X3 | 2 | 1 |
| X4 | 3 | 2 |
| X5 | 5 | 4 |
Original dataframe:
| A | A1 | A2 | A3 | A4 | A5 | |
|---|---|---|---|---|---|---|
| 1 | 1 | 3 | 4 | 1 | 2 | 4 |
| 2 | 8 | 5 | 3 | 2 | 1 | 2 |
| 3 | 9 | 3 | 5 | 1 | 3 | 1 |
I then want to multiply PC1 with every row in the original dataframe such that:
PC1 = 0xA + 1xA1 + 2xA2 + 2xA3 + 3xA4 + 5xA5
insert first row from second dataframe: PC1 = 0x1 + 3x1 + 4x2 + 2x1 + 3x2 + 5x8 = 59 Second row: PC1 = 0x8 + 5x1 +3x2 + 2x2 + 1x3 + 5x2 = 28 Third row: PC1 = 0x9 + 1x3 + 2x5 + 2x1 + 3x3 + 1x5 = 29
new dataframe:
| PC1 | PC2 | |
|---|---|---|
| 1 | 59 | |
| 2 | 28 | |
| 3 | 29 |
And so on.
My PCA dataframe have the shape (14,4) and my value dataframe has the shape (159,14)
Solution 1:[1]
You are looking for a dot product - which you can get with np.dot
print(df)
2 3
1
X 0 1
X1 1 2
X2 2 1
X3 2 1
X4 3 2
X5 5 4
print(xf)
2 3 4 5 6 7
1
1 1 3 4 1 2 4
2 8 5 3 2 1 2
3 9 3 5 1 3 1
print(pd.DataFrame(np.dot(xf, df), columns=['PC1', 'PC2']))
PC1 PC2
0 39 32
1 28 33
2 29 31
Solution 2:[2]
If same length of first DataFrame and same length of columns names in second DataFrame is possible multiple by numpy array with DataFrame.dot with rename columns names by df1.columns:
df = df2.dot(df1.to_numpy()).rename(columns=dict(enumerate(df1.columns)))
print (df)
PC1 PC2
1 39 32
2 28 33
3 29 31
Solution 3:[3]
Use:
string = """ PC1 PC2
X 0 1
X1 1 2
X2 2 1
X3 2 1
X4 3 2
X5 5 4"""
string2 = """A A1 A2 A3 A4 A5
1 3 4 1 2 4
8 5 3 2 1 2
9 3 5 1 3 1"""
data1 = [x.split(' ') for x in string.split('\n')]
data2 = [x.split(' ') for x in string2.split('\n')]
df1 = pd.DataFrame(np.array([x[1:] for x in data1[1:]], dtype = float), columns = np.array(data1)[0,1:])
df2 = pd.DataFrame(np.array(data2[1:], dtype = float), columns = data2[0])
#Solution
import numpy as np
pd.DataFrame(np.dot(df2,df1), columns = ['PC1', 'PC2'])
Output:
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | Mortz |
| Solution 2 | |
| Solution 3 | keramat |

