Question

我在两个单独的DataFrame列中有时间序列数据，它们引用相同的参数但长度不同。

在数据仅存在于一列中的日期，我希望将此值放在我的新列中。在两列都有条目的日期，我想得到平均值。（我想使用索引加入，这是一个日期时间值）

有人可以建议我可以合并我的两个列吗？感谢。

Edit2：我编写了一些应该合并来自我的两列数据的代码，但是当我尝试使用从我的第一个df具有值的行生成的索引设置新值时，我得到KeyError但是我的第二个df没有。这是代码：

def merge_func(df):
    null_index = df[(df['DOC_mg/L'].isnull() == False) & (df['TOC_mg/L'].isnull() == True)].index
    df['TOC_mg/L'][null_index] = df[null_index]['DOC_mg/L']
    notnull_index = df[(df['DOC_mg/L'].isnull() == True) & (df['TOC_mg/L'].isnull() == False)].index
    df['DOC_mg/L'][notnull_index] = df[notnull_index]['TOC_mg/L']

    df.insert(len(df.columns), 'Mean_mg/L', 0.0)
    df['Mean_mg/L'] = (df['DOC_mg/L'] + df['TOC_mg/L']) / 2
    return df

merge_func(sve)

这是错误：

KeyError: "['2004-01-14T01:00:00.000000000+0100' '2004-03-04T01:00:00.000000000+0100'\n '2004-03-30T02:00:00.000000000+0200' '2004-04-12T02:00:00.000000000+0200'\n '2004-04-15T02:00:00.000000000+0200' '2004-04-17T02:00:00.000000000+0200'\n '2004-04-19T02:00:00.000000000+0200' '2004-04-20T02:00:00.000000000+0200'\n '2004-04-22T02:00:00.000000000+0200' '2004-04-26T02:00:00.000000000+0200'\n '2004-04-28T02:00:00.000000000+0200' '2004-04-30T02:00:00.000000000+0200'\n '2004-05-05T02:00:00.000000000+0200' '2004-05-07T02:00:00.000000000+0200'\n '2004-05-10T02:00:00.000000000+0200' '2004-05-13T02:00:00.000000000+0200'\n '2004-05-17T02:00:00.000000000+0200' '2004-05-20T02:00:00.000000000+0200'\n '2004-05-24T02:00:00.000000000+0200' '2004-05-28T02:00:00.000000000+0200'\n '2004-06-04T02:00:00.000000000+0200' '2004-06-10T02:00:00.000000000+0200'\n '2004-08-27T02:00:00.000000000+0200' '2004-10-06T02:00:00.000000000+0200'\n '2004-11-02T01:00:00.000000000+0100' '2004-12-08T01:00:00.000000000+0100'\n '2011-02-21T01:00:00.000000000+0100' '2011-03-21T01:00:00.000000000+0100'\n '2011-04-04T02:00:00.000000000+0200' '2011-04-11T02:00:00.000000000+0200'\n '2011-04-14T02:00:00.000000000+0200' '2011-04-18T02:00:00.000000000+0200'\n '2011-04-21T02:00:00.000000000+0200' '2011-04-25T02:00:00.000000000+0200'\n '2011-05-02T02:00:00.000000000+0200' '2011-05-09T02:00:00.000000000+0200'\n '2011-05-23T02:00:00.000000000+0200' '2011-06-07T02:00:00.000000000+0200'\n '2011-06-21T02:00:00.000000000+0200' '2011-07-04T02:00:00.000000000+0200'\n '2011-07-18T02:00:00.000000000+0200' '2011-08-31T02:00:00.000000000+0200'\n '2011-09-13T02:00:00.000000000+0200' '2011-09-28T02:00:00.000000000+0200'\n '2011-10-10T02:00:00.000000000+0200' '2011-10-25T02:00:00.000000000+0200'\n '2011-11-08T01:00:00.000000000+0100' '2011-11-28T01:00:00.000000000+0100'\n '2011-12-20T01:00:00.000000000+0100' '2012-01-19T01:00:00.000000000+0100'\n '2012-02-14T01:00:00.000000000+0100' '2012-03-13T01:00:00.000000000+0100'\n '2012-03-27T02:00:00.000000000+0200' '2012-04-02T02:00:00.000000000+0200'\n '2012-04-10T02:00:00.000000000+0200' '2012-04-17T02:00:00.000000000+0200'\n '2012-04-26T02:00:00.000000000+0200' '2012-04-30T02:00:00.000000000+0200'\n '2012-05-03T02:00:00.000000000+0200' '2012-05-07T02:00:00.000000000+0200'\n '2012-05-10T02:00:00.000000000+0200' '2012-05-14T02:00:00.000000000+0200'\n '2012-05-22T02:00:00.000000000+0200' '2012-06-05T02:00:00.000000000+0200'\n '2012-06-19T02:00:00.000000000+0200' '2012-07-03T02:00:00.000000000+0200'\n '2012-07-17T02:00:00.000000000+0200' '2012-07-31T02:00:00.000000000+0200'\n '2012-08-14T02:00:00.000000000+0200' '2012-08-28T02:00:00.000000000+0200'\n '2012-09-11T02:00:00.000000000+0200' '2012-09-25T02:00:00.000000000+0200'\n '2012-10-10T02:00:00.000000000+0200' '2012-10-24T02:00:00.000000000+0200'\n '2012-11-21T01:00:00.000000000+0100' '2012-12-18T01:00:00.000000000+0100'] not in index"

Answer 1

您很接近，但在使用isnull（）函数时，您实际上不需要遍历行。默认情况下

df[(df['DOC_mg/L'].isnull() == False) & (df['TOC_mg/L'].isnull() == True)].index

将只返回DOC_mg/L不为空且TOC_mg/L为空的行的索引。

现在您可以执行以下操作来设置TOC_mg / L的值：

null_index = df[(df['DOC_mg/L'].isnull() == False) & \
                (df['TOC_mg/L'].isnull() == True)].index
df['TOC_mg/L'][null_index] = df['DOC_mg/L'][null_index] # EDIT To switch the index position.

这将使用TOC_mg / L为空且DOC_mg / L不为空的行的索引，并将TOC_mg / L的值设置为相同行中DOC_mg / L中的值。

注意： 这不是使用索引设置值的可接受方式，但这是我一直在做的方式。只需确保在设置值时，等式的左边是df['col_name'][index]。如果切换col_name和index，您会将值设置为永远不会重新设置为原始副本的副本。

现在要设置均值，您可以创建一个新列，我们将调用此Mean_mg/L并设置值= 0.0。然后将此新列设置为两列的平均值：

# Insert a new col at the end of the dataframe columns name 'Mean_mg/L' 
#     with default value 0.0
df.insert(len(df.columns), 'Mean_mg/L', 0.0)
# Set this columns value to the average of DOC_mg/L and TOC_mg/L
df['Mean_mg/L'] = (df['DOC_mg/L'] + df['TOC_mg/L']) / 2

在我们使用相应列值填充空值的列中，平均值将与值相同。

将两个数据帧列的数据合并为一列

1 个答案: