删除 Pandas 数据帧中的 NaN/NULL 列？

是的。请参阅 http://pandas.pydata.org/pandas-docs/stable/missing_data.html 和文档字符串：dropnaDataFrame.dropna

Definition: DataFrame.dropna(self, axis=0, how='any', thresh=None, subset=None)
Docstring:
Return object with labels on given axis omitted where alternately any
or all of the data are missing

Parameters
----------
axis : {0, 1}
how : {'any', 'all'}
    any : if any NA values are present, drop that label
    all : if all values are NA, drop that label
thresh : int, default None
    int value : require that many non-NA values
subset : array-like
    Labels along other axis to consider, e.g. if you are dropping rows
    these would be a list of columns to include

Returns
-------
dropped : DataFrame

要运行的特定命令为：

df=df.dropna(axis=1,how='all')

用于从数据框中删除所有空列的函数：

def Remove_Null_Columns(df):
    dff = pd.DataFrame()
    for cl in fbinst:
        if df[cl].isnull().sum() == len(df[cl]):
            pass
        else:
            dff[cl] = df[cl]
    return dff 

此函数将从 df 中删除所有 Null 列。

这是一个简单的函数，您可以通过传递数据帧和阈值来直接使用

df
'''
     pets   location     owner     id
0     cat  San_Diego     Champ  123.0
1     dog        NaN       Ron    NaN
2     cat        NaN     Brick    NaN
3  monkey        NaN     Champ    NaN
4  monkey        NaN  Veronica    NaN
5     dog        NaN      John    NaN
'''

---

def rmissingvaluecol(dff,threshold):
    l = []
    l = list(dff.drop(dff.loc[:,list((100*(dff.isnull().sum()/len(dff.index))>=threshold))].columns, 1).columns.values)
    print("# Columns having more than %s percent missing values:"%threshold,(dff.shape[1] - len(l)))
    print("Columns:\n",list(set(list((dff.columns.values))) - set(l)))
    return l


rmissingvaluecol(df,1) #Here threshold is 1% which means we are going to drop columns having more than 1% of missing values

#output
'''
# Columns having more than 1 percent missing values: 2
Columns:
 ['id', 'location']
'''

现在创建不包括这些列的新数据框

l = rmissingvaluecol(df,1)
df1 = df[l]

PS：您可以根据需要更改阈值

奖励步骤

您可以找到每列缺失值的百分比（可选）

def missing(dff):
    print (round((dff.isnull().sum() * 100/ len(dff)),2).sort_values(ascending=False))

missing(df)

#output
'''
id          83.33
location    83.33
owner        0.00
pets         0.00
dtype: float64
'''

另一种解决方案是在非 null 位置创建一个具有 True 值的布尔数据帧，然后获取至少具有一个 True 值的列。这将删除具有所有 NaN 值的列。

df = df.loc[:,df.notna().any(axis=0)]

如果要删除至少缺少一个 （NaN） 值的列;

df = df.loc[:,df.notna().all(axis=0)]

此方法在删除包含空字符串、零或基本上任何给定值的列时特别有用。例如;

df = df.loc[:,(df!='').all(axis=0)]

删除至少包含一个空字符串的列。