<p>如果列名是类似<code>None</code>的<code>NoneType</code>或类似<code>None</code>的字符串,则在第一次通过比较<code>0</code>匹配值之前,使用解决方案对其进行比较,并使用比较值对<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.cumsum.html" rel="nofollow noreferrer">^{<cd4>}</a>进行比较,最后通过<code>:</code>对^{a2进行传递,以便通过掩码获取所有行和列:</p>
<pre><code>d = {
'ID': [1,2,34],
'Sr. No': [13,23,343],
'Col_1': [1,23,4345],
'None': [None, None, None]
}
df = pd.DataFrame(d)
mask = (df.columns.isna() | (df.columns == 'None')).cumsum() == 0
df1 = df.loc[:, mask]
print (df1)
ID Sr. No Col_1
0 1 13 1
1 2 23 23
2 34 343 4345
</code></pre>
<hr/>
<pre><code>d = {
'ID': [1,2,34],
'Sr. No': [13,23,343],
'Col_1': [1,23,4345],
None: [None, None, None]
}
df = pd.DataFrame(d)
mask = (df.columns.isna() | (df.columns == 'None')).cumsum() == 0
df1 = df.loc[:, mask]
print (df1)
ID Sr. No Col_1
0 1 13 1
1 2 23 23
2 34 343 4345
</code></pre>
<hr/>
<pre><code>d = {
'ID': [1,2,34],
'Sr. No': [13,23,343],
'Col_1': [1,23,4345],
'col_z': [None, None, None]
}
df = pd.DataFrame(d)
mask = (df.columns.isna() | (df.columns == 'None')).cumsum() == 0
df1 = df.loc[:, mask]
print (df1)
ID Sr. No Col_1 col_z
0 1 13 1 None
1 2 23 23 None
2 34 343 4345 None
</code></pre>
<p>编辑:</p>
<pre><code>d = {
'ID': [1,2,34],
'Sr. No': [13,23,343],
'Col_1': [1,23,4345]
}
df = pd.DataFrame(d, index=[1,None,5])
mask = (df.index.isna() | (df.index== 'None')).cumsum() == 0
df1 = df.loc[mask]
print (df1)
ID Sr. No Col_1
1 1 13 1
</code></pre>