dropna() データを欠損させ、その行を削除

<span class="cm-keyword">import</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">as</span> <span class="cm-variable">np</span>
<span class="cm-keyword">from</span> <span class="cm-variable">numpy</span> <span class="cm-keyword">import</span> <span class="cm-variable">nan</span> <span class="cm-keyword">as</span> <span class="cm-variable">NA</span>
<span class="cm-keyword">import</span> <span class="cm-variable">pandas</span> <span class="cm-keyword">as</span> <span class="cm-variable">pd</span>

<span class="cm-variable">sample_data_frame</span> = <span class="cm-variable">pd</span>.<span class="cm-property">DataFrame</span>(<span class="cm-variable">np</span>.<span class="cm-property">random</span>.<span class="cm-property">rand</span>(<span class="cm-number">10</span>,<span class="cm-number">4</span>))

<span class="cm-comment">#一部のデータをわざと欠損</span>
<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">iloc</span>[<span class="cm-number">1</span>,<span class="cm-number">0</span>] = <span class="cm-variable">NA</span>


<span class="cm-variable">sample_data_frame</span>

import numpy as np

from numpy import nan as NA

import pandas as pd

sample_data_frame = pd.DataFrame(np.random.rand(10,4))

#一部のデータをわざと欠損

sample_data_frame.iloc[1,0] = NA

sample_data_frame

dropnaを用いると、NaNのある行を全て取り除く

<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">dropna</span>()

1 2	<span class="cm-variable">sample_data_frame</span>.<span class="cm-property">dropna</span>()

ペアワイズ削除

<span class="cm-variable">sample_data_frame</span>[[<span class="cm-number">0</span>,<span class="cm-number">1</span>]].<span class="cm-property">dropna</span>()
</code><code class="cm-s-ipython language-python">