pandas 熊猫连接失败
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Pandas concat failing
提问by user308827
I am trying to concat dataframes based on the foll. 2 csv files:
我正在尝试根据 foll 连接数据帧。2个csv文件：
df_a: https://www.dropbox.com/s/slcu7o7yyottujl/df_current.csv?dl=0
df_a: https://www.dropbox.com/s/slcu7o7yyottujl/df_current.csv?dl=0
df_b: https://www.dropbox.com/s/laveuldraurdpu1/df_climatology.csv?dl=0
df_b: https://www.dropbox.com/s/laveuldraurdpu1/df_climatology.csv?dl=0
Both of these have the same number and names of columns. However, when I do this:
这两者具有相同的列数和名称。但是，当我这样做时：
pandas.concat([df_a, df_b])
I get the error:
我收到错误：
AssertionError: Number of manager items must equal union of block items
# manager items: 20, # tot_items: 21
How to fix this?
如何解决这个问题？
回答by phil_20686
I believe that this error occurs if the following two conditions are met:
我相信如果满足以下两个条件就会出现这个错误：
- The data frames have different columns. (i.e.
(df1.columns == df2.columns)isFalse - The columns has a repeated value.
- 数据框有不同的列。（即
(df1.columns == df2.columns)是False - 列具有重复值。
Basically if you concatdataframes with columns [A,B,C]and [B,C,D]it can work out to make one series for each distinct column name. So if I try to join a third dataframe [B,B,C]it does not know which column to append and ends up with fewer distinct columns than it thinks it needs.
基本上，如果您concat使用带有列的数据框，[A,B,C]并且[B,C,D]它可以为每个不同的列名称制作一个系列。因此，如果我尝试加入第三个数据框，[B,B,C]它不知道要附加哪一列，最终得到的不同列比它认为需要的要少。
If your dataframes are such that df1.columns == df2.columnsthen it will work anyway. So you can join [B,B,C]to [B,B,C], but not to [C,B,B], as if the columns are identical it probably just uses the integer indexes or something.
如果您的数据帧是这样的，df1.columns == df2.columns那么无论如何它都会起作用。所以你可以加入[B,B,C]到[B,B,C]，但不能加入到，[C,B,B]好像列是相同的，它可能只使用整数索引或其他东西。
回答by kmader
You can get around this issue with a 'manual' concatenation, in this case your
您可以通过“手动”连接来解决此问题，在这种情况下，您的
list_of_dfs = [df_a, df_b]
And instead of running
而不是跑步
giant_concat_df = pd.concat(list_of_dfs,0)
You can use turn all of the dataframes to a list of dictionaries and then make a new data frame from these lists (merged with chain)
您可以使用将所有数据框转换为字典列表，然后从这些列表中创建一个新数据框（与链合并）
from itertools import chain
list_of_dicts = [cur_df.T.to_dict().values() for cur_df in list_of_dfs]
giant_concat_df = pd.DataFrame(list(chain(*list_of_dicts)))
回答by Karatheodory
Unfortunately, the source files are already unavailable, so I can't check my solution in your case. In my case the error occurred when:
不幸的是，源文件已经不可用，所以我无法在你的情况下检查我的解决方案。在我的情况下，错误发生在：
- Data frames have two columns with the same name (I've had
IDandidcolumns, which I then converted to lower case, so they become the same) - Value types of the same-named columns are different
- 数据框有两列同名（我有
ID和id列，然后我将其转换为小写，因此它们变得相同） - 同名列的值类型不同
Here is an example which gives me the error in question:
这是一个示例，它给了我有问题的错误：
df1 = pd.DataFrame(data=[
['a', 'b', 'id', 1],
['a', 'b', 'id', 2]
], columns=['A', 'B', 'id', 'id'])
df2 = pd.DataFrame(data=[
['b', 'c', 'id', 1],
['b', 'c', 'id', 2]
], columns=['B', 'C', 'id', 'id'])
pd.concat([df1, df2])
>>> AssertionError: Number of manager items must equal union of block items
# manager items: 4, # tot_items: 5
Removing / renaming one of the columns makes this code work.
删除/重命名其中一列使此代码起作用。
