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    <p>This can be accomplished quite simply with the DataFrame method <code>apply</code>.</p> <pre><code>In[1]: import pandas as pd; import numpy as np In[2]: df = pd.DataFrame(np.arange(40.).reshape((8, 5)), columns=list('abcde')); df Out[2]: a b c d e 0 0 1 2 3 4 1 5 6 7 8 9 2 10 11 12 13 14 3 15 16 17 18 19 4 20 21 22 23 24 5 25 26 27 28 29 6 30 31 32 33 34 7 35 36 37 38 39 In[3]: ser = pd.Series(np.arange(8) * 10); ser Out[3]: 0 0 1 10 2 20 3 30 4 40 5 50 6 60 7 70 </code></pre> <p>Now that we have our <code>DataFrame</code> and <code>Series</code> we need a function to pass to <code>apply</code>.</p> <pre><code>In[4]: func = lambda x: np.asarray(x) * np.asarray(ser) </code></pre> <p>We can pass this to <code>df.apply</code> and we are good to go </p> <pre><code>In[5]: df.apply(func) Out[5]: a b c d e 0 0 0 0 0 0 1 50 60 70 80 90 2 200 220 240 260 280 3 450 480 510 540 570 4 800 840 880 920 960 5 1250 1300 1350 1400 1450 6 1800 1860 1920 1980 2040 7 2450 2520 2590 2660 2730 </code></pre> <p><code>df.apply</code> acts column-wise by default, but it can can also act row-wise by passing <code>axis=1</code> as an argument to <code>apply</code>.</p> <pre><code>In[6]: ser2 = pd.Series(np.arange(5) *5); ser2 Out[6]: 0 0 1 5 2 10 3 15 4 20 In[7]: func2 = lambda x: np.asarray(x) * np.asarray(ser2) In[8]: df.apply(func2, axis=1) Out[8]: a b c d e 0 0 5 20 45 80 1 0 30 70 120 180 2 0 55 120 195 280 3 0 80 170 270 380 4 0 105 220 345 480 5 0 130 270 420 580 6 0 155 320 495 680 7 0 180 370 570 780 </code></pre> <p>This could be done more concisely by defining the anonymous function inside <code>apply</code></p> <pre><code>In[9]: df.apply(lambda x: np.asarray(x) * np.asarray(ser)) Out[9]: a b c d e 0 0 0 0 0 0 1 50 60 70 80 90 2 200 220 240 260 280 3 450 480 510 540 570 4 800 840 880 920 960 5 1250 1300 1350 1400 1450 6 1800 1860 1920 1980 2040 7 2450 2520 2590 2660 2730 In[10]: df.apply(lambda x: np.asarray(x) * np.asarray(ser2), axis=1) Out[10]: a b c d e 0 0 5 20 45 80 1 0 30 70 120 180 2 0 55 120 195 280 3 0 80 170 270 380 4 0 105 220 345 480 5 0 130 270 420 580 6 0 155 320 495 680 7 0 180 370 570 780 </code></pre>
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