How can I multiply a numpy array with pandas series?

Question

I have a numpy series of size (50,0)

array([1.01255569e+00, 1.04166667e+00, 1.07158165e+00, 1.10229277e+00,
       1.13430127e+00, 1.16387337e+00, 1.20365912e+00, 1.24007937e+00,
       1.27877238e+00, 1.31856540e+00, 1.35281385e+00, 1.40291807e+00,
       1.45180023e+00, 1.49700599e+00, 1.55183116e+00, 1.60051216e+00,
       1.66002656e+00, 1.73370319e+00, 1.80115274e+00, 1.87687688e+00,
       1.95312500e+00, 2.04750205e+00, 2.14961307e+00, 2.23613596e+00,
       2.34082397e+00, 2.48015873e+00, 2.61780105e+00, 2.75027503e+00,
       2.91715286e+00, 3.07881773e+00, 3.31564987e+00, 3.57142857e+00,
       3.81679389e+00, 4.17362270e+00, 4.51263538e+00, 4.95049505e+00,
       5.59284116e+00, 6.17283951e+00, 7.02247191e+00, 8.03858521e+00,
       9.72762646e+00, 1.17370892e+01, 1.47928994e+01, 2.10084034e+01,
       3.12500000e+01, 4.90196078e+01, 9.25925926e+01, 2.08333333e+02,
       5.00000000e+02, 1.25000000e+03])

And I have a pandas dataframe of length 50 as well.

            x
0   9.999740e-01
1   9.981870e-01
2   9.804506e-01
3   9.187764e-01
4   8.031568e-01
5   6.544660e-01
6   5.032716e-01
7   3.707446e-01
8   2.650768e-01
9   1.857835e-01
10  1.285488e-01
11  8.824506e-02
12  6.030141e-02
13  4.111080e-02
14  2.800453e-02
15  1.907999e-02
16  1.301045e-02
17  8.882996e-03
18  6.074386e-03
19  4.161024e-03
20  2.855636e-03
21  1.963543e-03
22  1.352791e-03
23  9.338596e-04
24  6.459459e-04
25  4.476854e-04
26  3.108912e-04
27  2.163201e-04
28  1.508106e-04
29  1.053430e-04
30  7.372442e-05
31  5.169401e-05
32  3.631486e-05
33  2.555852e-05
34  1.802129e-05
35  1.272995e-05
36  9.008454e-06
37  6.386289e-06
38  4.535381e-06
39  3.226546e-06
40  2.299394e-06
41  1.641469e-06
42  1.173785e-06
43  8.407618e-07
44  6.032249e-07
45  4.335110e-07
46  3.120531e-07
47  2.249870e-07
48  1.624726e-07
49  1.175140e-07

And I want to multiply every numpy cells with pandas cell.

Example:

1.01255569e+00*9.999740e-01
1.04166667e+00*9.981870e-01

Desired output

numpy array of same size.

Did you try anything?

John Zwinck
– John Zwinck

2018-12-09 01:09:24 +00:00
Commented Dec 9, 2018 at 1:09 — John Zwinck
– John Zwinck, Commented Dec 9, 2018 at 1:09
I tried: p.multiply(n, axis=0)

maximusdooku
– maximusdooku

2018-12-09 01:09:59 +00:00
Commented Dec 9, 2018 at 1:09 — maximusdooku
– maximusdooku, Commented Dec 9, 2018 at 1:09
Why not try *?

John Zwinck
– John Zwinck

2018-12-09 01:10:28 +00:00
Commented Dec 9, 2018 at 1:10 — John Zwinck
– John Zwinck, Commented Dec 9, 2018 at 1:10

tel · Accepted Answer · 2018-12-09 01:20:26Z

5

You can just use the .values property of the 'x' series in your Pandas dataframe:

df['x'].values * arr

where df is your dataframe and arr is your array.

The above expression will return the result as a Numpy array. If you want a Pandas DataFrame instead, you can omit the use of .values:

df['x'] * arr

edited Dec 9, 2018 at 1:20

answered Dec 9, 2018 at 1:09

tel

14k2 gold badges48 silver badges67 bronze badges

Sign up to request clarification or add additional context in comments.

3 Comments

John Zwinck Over a year ago

There is no need for .values here.

maximusdooku Over a year ago

Thank you......Without the values, I get a series. With it, I get the array in the exact size.

tel Over a year ago

@JohnZwinck Using .values makes the expression return a numpy array. Without it, you get a pandas dataframe. Whether you should use .values depends on which return type you want.

U13-Forward · Accepted Answer · 2018-12-09 01:24:33Z

0

Or np.multiply, multiply n with p['x'].values:

print(np.multiply(n,p['x'].values))

Or pd.Series.multiply:

print(np.array(p['x'].multiply(n)))

Or pd.Series.mul:

print(np.array(p['x'].mul(n)))

answered Dec 9, 2018 at 1:24

U13-Forward

71.8k15 gold badges100 silver badges125 bronze badges

Collectives™ on Stack Overflow

How can I multiply a numpy array with pandas series?

Example:

Desired output

2 Answers 2

3 Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

Example:

Desired output

2 Answers 2

3 Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related