<p>你可以试试这个</p>
<pre><code>N = 4
df_new = pd.DataFrame(df_original.values.reshape(-1, N))
df_new.columns = ['slotNew{:}'.format(i + 1) for i in range(N)]
</code></pre>
<p>代码将数据提取到<code>numpy.ndarray</code>,对其进行整形,并创建所需维度的新数据集。你知道吗</p>
<p>示例:</p>
<pre><code>import numpy as np
import pandas as pd
df0 = pd.DataFrame(np.arange(48 * 3).reshape(-1, 48))
df0.columns = ['slot{:}'.format(i + 1) for i in range(48)]
print(df0)
# slot1 slot2 slot3 slot4 ... slot45 slot46 slot47 slot48
# 0 0 1 2 3 ... 44 45 46 47
# 1 48 49 50 51 ... 92 93 94 95
# 2 96 97 98 99 ... 140 141 142 143
#
# [3 rows x 48 columns]
N = 4
df = pd.DataFrame(df0.values.reshape(-1, N))
df.columns = ['slotNew{:}'.format(i + 1) for i in range(N)]
print(df.head())
# slotNew1 slotNew2 slotNew3 slotNew4
# 0 0 1 2 3
# 1 4 5 6 7
# 2 8 9 10 11
# 3 12 13 14 15
# 4 16 17 18 19
</code></pre>
<hr/>
<p>另一种方法</p>
<pre><code>N = 4
df1 = df0.stack().reset_index()
df1['i'] = df1['level_1'].str.replace('slot', '').astype(int) // N
df1['j'] = df1['level_1'].str.replace('slot', '').astype(int) % N
df1['i'] -= (df1['j'] == 0) - df1['level_0'] * 48 / N
df1['j'] += (df1['j'] == 0) * N
df1['j'] = 'slotNew' + df1['j'].astype(str)
df1 = df1[['i', 'j', 0]]
df = df1.pivot(index='i', columns='j', values=0)
</code></pre>