Question

使用pandas merge，结果列令人困惑：

df1 = pd.DataFrame(np.random.randint(0, 100, size=(5, 5)))

df2 = pd.DataFrame(np.random.randint(0, 100, size=(5, 5)))

df2[0] = df1[0]  # matching key on the first column.

# Now the weird part.
pd.merge(df1, df2, left_on=0, right_on=0).shape
Out[96]: (5, 9)
pd.merge(df1, df2, left_index=True, right_index=True).shape
Out[102]: (5, 10)
pd.merge(df1, df2, left_on=0, right_on=1).shape
Out[107]: (0, 11)

列数不固定，列标签也不稳定，更糟糕的是这些没有清楚记录。

我想读取结果数据框的一些列，这些列有很多列（数百个）。目前我正在使用.iloc []，因为标签太多了。但我担心由于奇怪的合并结果，这很容易出错。读取合并数据框中某些列的正确方法是什么？

Python：2.7.13，Pandas：0.19.2

Answer 1

合并密钥

1.1当join-key是一列时合并键（当你在第一列上说“df2 [0] = df1 [0]＃matching key时，这是正确的解决方案。 “）

1.2当merge-key是索引时合并索引 ==＆GT;为什么你在第二次合并中得到1列的原因（pd.merge（df1，df2，left_index = True，right_index = True）.shape）是因为初始连接键现在出现两次“0_x”＆amp; '0_y'

关于列名称

在合并期间，列名称不会更改，除非两个数据框中都有相同名称的列。列改变如下，你得到：

'initial_column_name'+'_ x'（后缀'_x'被添加到左侧数据框的列（df1））
'initial_column_name'+'_ y'（后缀'_y'添加到右侧数据框的列（df2））

Answer 2

为了处理合并结果中列数的3种不同情况，我最终检查列数，然后转换列号索引以在.iloc []中使用。以下是未来搜索者的代码。

我现在知道处理大量列的最佳方法。如果有的话我会给出更好的答案。

转换列号索引的实用程序方法：

def get_merged_column_index(num_col_df, num_col_df1, num_col_df2, col_df1=[], col_df2=[], joinkey_df1=[], joinkey_df2=[]):
    """Transform the column indexes in old source dataframes to column indexes in merged dataframe. Check for different pandas merged result formats.

    :param num_col_df: number of columns in merged dataframe df.
    :param num_col_df1: number of columns in df1.
    :param num_col_df2: number of columns in df2.
    :param col_df1: (list of int) column position in df1 to keep (0-based).
    :param col_df2: (list of int) column position in df2 to keep (0-based).
    :param joinkey_df1:  (list of int) column position (0-based). Not implemented now.
    :param joinkey_df2:  (list of int) column position (0-based). Not implemented now.
    :return: (list of int) transformed column indexes, 0-based, in merged dataframe.
    """

    col_df1 = np.array(col_df1)
    col_df2 = np.array(col_df2)

    if num_col_df == num_col_df1 + num_col_df2: # merging keeps same old columns
        col_df2 += num_col_df1
    elif num_col_df == num_col_df1 + num_col_df2 + 1: # merging add column 'key_0' to the head
        col_df1 += 1
        col_df2 += num_col_df1 + 1
    elif num_col_df <= num_col_df1 + num_col_df2 - 1: # merging deletes (possibly many) duplicated "join-key" columns in df2, keep and do not change order columns in df1.
        raise ValueError('Format of merged result is too complicated.')
    else:
        raise ValueError('Undefined format of merged result.')

    return np.concatenate((col_df1, col_df2)).astype(int).tolist()

然后：

cols_toextract_df1 = []
cols_toextract_df2 = []
converted_cols = get_merged_column_index(num_col_df=df.shape[1], num_col_df1=df1.shape[1], num_col_df2=df2.shape[1], col_df1=cols_toextract_df1, col_df2=cols_toextract_df1)
extracted_df = df.iloc[:, converted_cols]

如何阅读pandas merge的结果？

2 个答案: