pandas Merge, join, and concatenate

合并，联合以及连接

http://pandas.pydata.org/pandas-docs/stable/merging.html

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• objs : a sequence or mapping of Series, DataFrame, or Panel objects. If a dict is passed, the sorted keys will be used as the keys argument, unless it is passed, in which case the values will be selected (see below). Any None objects will be dropped silently unless they are all None in which case a ValueError will be raised.
• axis : {0, 1, …}, default 0. The axis to concatenate along.
• join : {‘inner’, ‘outer’}, default ‘outer’. How to handle indexes on other axis(es). Outer for union and inner for intersection.
• ignore_index : boolean, default False. If True, do not use the index values on the concatenation axis. The resulting axis will be labeled 0, …, n – 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the index values on the other axes are still respected in the join.
• join_axes : list of Index objects. Specific indexes to use for the other n – 1 axes instead of performing inner/outer set logic.
• keys : sequence, default None. Construct hierarchical index using the passed keys as the outermost level. If multiple levels passed, should contain tuples.
• levels : list of sequences, default None. Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys.
• names : list, default None. Names for the levels in the resulting hierarchical index.
• verify_integrity : boolean, default False. Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation.
• copy : boolean, default True. If False, do not copy data unnecessarily.

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frames  = [ df1, df2, df3 ]

Append

Merge（根据列的值，而非index，来进行合并）

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• left: A DataFrame object
• right: Another DataFrame object
• on: Columns (names) to join on. Must be found in both the left and right DataFrame objects. If not passed and left_index and right_index are False, the intersection of the columns in the DataFrames will be inferred to be the join keys
• left_on: Columns from the left DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame
• right_on: Columns from the right DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame
• left_index: If True, use the index (row labels) from the left DataFrame as its join key(s). In the case of a DataFrame with a MultiIndex (hierarchical), the number of levels must match the number of join keys from the right DataFrame
• right_index: Same usage as left_index for the right DataFrame
• how: One of ‘left’, ‘right’, ‘outer’, ‘inner’. Defaults to inner. See below for more detailed description of each method
• sort: Sort the result DataFrame by the join keys in lexicographical order. Defaults to True, setting to False will improve performance substantially in many cases
• suffixes: A tuple of string suffixes to apply to overlapping columns. Defaults to (‘_x’, ‘_y’).
• copy: Always copy data (default True) from the passed DataFrame objects, even when reindexing is not necessary. Cannot be avoided in many cases but may improve performance / memory usage. The cases where copying can be avoided are somewhat pathological but this option is provided nonetheless.
• indicator: Add a column to the output DataFrame called _merge with information on the source of each row. _merge is Categorical-type and takes on a value of left_only for observations whose merge key only appears in ‘left’ DataFrame, right_only for observations whose merge key only appears in ‘right’ DataFrame, and both if the observation’s merge key is found in both.

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Join（从两个可能index不相同的df中，把部分列合并到一起）

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