38. Merge Pandas DataFrames | Pandas Merge | Merge Dataframe Pandas | Python Pandas Tutorial

The merge function allows you to merge data frames using similar logic as merging SQL tables.
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Parameters
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left : DataFrame
right : DataFrame
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on: label or list
* Please remember, the column you provide in "on" must be common in both the data frames.
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If 'on' is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.
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*Object to merge with.
how : {'left', 'right', 'outer', 'inner'}, default 'inner'
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Type of merge to be performed.
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* inner: Use the intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
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* left: Use only keys from left frame, similar to a SQL left outer join; preserve key order.
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* right: Use only keys from the right frame, similar to a SQL right outer join; preserve key order.
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* outer: Use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
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- indicator: bool or str, default False
* If True, adds a column to the output DataFrame called "_merge" with information on the source of each row.
* The column can be given a different name by providing a string argument.
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left_index : bool, default False
Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.
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right_index : bool, default False
Use the index from the right DataFrame as the join key. Same as
left_index.
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*suffixes : list-like, default is ("_x", "_y")
- A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in 'left' and 'right'.
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DataFrames
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data1 = {'key':['K0','k1','k2','k3','k4'],
'Name':['Priyang','Aadhya','Vedant','Parshv','krisha']}
data2={'key':['K0','k1','k2','k3','k4'],
'Marks':[98,99,89,85,87]}

df1=pd.DataFrame(data1)
df2=pd.DataFrame(data2)
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data1 = {'key1':['K0','k1','k2','k3','k4'],
'key2':['K0','k1','k0','k1','k2'],
'Name':['Priyang','Aadhya','Vedant','Parshv','krisha']}
data2={'key1':['K0','k1','k2','k3','k4'],
'key2':['K0','k1','k0','k1','k2'],
'Marks':[98,99,89,85,87]}
df1=pd.DataFrame(data1)
df2=pd.DataFrame(data2)
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