<p>制作块后使用<code>pandas.explode</code>。给定<code>df</code>:</p>
<pre><code>import pandas as pd
df = pd.DataFrame([np.arange(1, 49)], columns=['slot%s' % i for i in range(1, 49)])
print(df)
slot1 slot2 slot3 slot4 slot5 slot6 slot7 slot8 slot9 slot10 ... \
0 1 2 3 4 5 6 7 8 9 10 ...
slot39 slot40 slot41 slot42 slot43 slot44 slot45 slot46 slot47 \
0 39 40 41 42 43 44 45 46 47
slot48
0 48
</code></pre>
<p>用<code>chunks</code>除:</p>
<pre><code>def chunks(l, n):
"""Yield successive n-sized chunks from l.
Source: https://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks
"""
n_items = len(l)
if n_items % n:
n_pads = n - n_items % n
else:
n_pads = 0
l = l + [np.nan for _ in range(n_pads)]
for i in range(0, len(l), n):
yield l[i:i + n]
N = 4
new_df = pd.DataFrame(list(df.apply(lambda x: list(chunks(list(x), N)), 1).explode()))
print(new_df)
</code></pre>
<p>输出:</p>
<pre><code> 0 1 2 3
0 1 2 3 4
1 5 6 7 8
2 9 10 11 12
3 13 14 15 16
4 17 18 19 20
...
</code></pre>
<p>与<code>numpy.reshape</code>相比,这种方法的优势在于它可以处理<code>N</code>不是一个因子的情况:</p>
<pre><code>N = 7
new_df = pd.DataFrame(list(df.apply(lambda x: list(chunks(list(x), N)), 1).explode()))
print(new_df)
</code></pre>
<p>输出:</p>
<pre><code> 0 1 2 3 4 5 6
0 1 2 3 4 5 6 7.0
1 8 9 10 11 12 13 14.0
2 15 16 17 18 19 20 21.0
3 22 23 24 25 26 27 28.0
4 29 30 31 32 33 34 35.0
5 36 37 38 39 40 41 42.0
6 43 44 45 46 47 48 NaN
</code></pre>