When is it appropriate to use `left_on` and `right_on` arguments in `pandas.merge`?
Explanation
`left_on` and `right_on` provide the flexibility to join two DataFrames even when the columns you want to join on do not share the same name, by specifying the key column for each DataFrame individually.
Other questions
What is the primary function of the `unstack` method when applied to a hierarchically indexed Series in pandas?
Which method is the inverse operation of `unstack` in pandas, used for pivoting columns into rows?
When using `pandas.merge` without specifying the `on` or `how` arguments, what is the default behavior?
What does the `set_index` function on a DataFrame accomplish?
When performing a many-to-many merge in pandas, how is the resulting number of rows determined for matching keys?
What is the purpose of the `suffixes` argument in the `pandas.merge` function?
What is the primary difference between the DataFrame's `join` method and `pandas.merge`?
What does the `combine_first` method do when used on two pandas Series or DataFrames?
When using `pandas.concat` to combine several Series objects with `axis="columns"`, what do the `keys` provided in the `keys` argument become in the resulting DataFrame?
What is the effect of passing `ignore_index=True` to the `pandas.concat` function?
What is the purpose of the DataFrame `pivot` method?
Which method is described as the inverse operation to `pivot` for DataFrames, transforming data from a wide to a long format?
In the `pandas.melt` function, what is the purpose of the `id_vars` argument?
When sorting a hierarchically indexed object, what is the significance of the index being lexicographically sorted?
How can you aggregate a DataFrame by a specific index level for summary statistics?
What is the result of applying the `stack` method to the DataFrame created by `data.unstack()` in the code snippet `data = pd.Series([0.9, 0.2, 0.6, 0.7], index=[['a', 'a', 'b', 'b'],[1, 2, 1, 2]])`?
When merging two DataFrames, `df1` and `df2`, on a key that results in a many-to-one join, how are the index values of the output DataFrame determined by default?
Which `how` argument value in `pandas.merge` will result in a DataFrame containing the union of keys from both input DataFrames?
If you perform an outer join on `df1` (with key 'c') and `df2` (with key 'd'), what values will appear in the columns corresponding to the non-matching DataFrame?
To merge a DataFrame `lefth` with columns `key1`, `key2` and a DataFrame `righth` with a hierarchical index, how must you specify the join keys?
In a DataFrame `frame` with a MultiIndex on the rows with levels named `key1` and `key2`, what does the method `frame.swaplevel("key1", "key2")` do?
If you concatenate two DataFrames with overlapping row indexes but different columns using `pd.concat([df1, df2], axis="columns")`, what is the outcome for rows that exist in one DataFrame but not the other?
When using `pandas.pivot` to reshape a DataFrame, if the specified `index` and `columns` arguments result in multiple values for a given cell, what happens?
What is a key difference between `stack` and `melt`?
Consider a DataFrame `df` with columns A, B, C, D. What is the result of `pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])`?
By default, the `unstack` method pivots which level of a MultiIndex?
If `unstacking` a level in a DataFrame results in some subgroups not having all the values present in that level, what does pandas introduce into the resulting DataFrame?
Consider the code: `df1 = pd.DataFrame({'key': ['b', 'b', 'a'], 'data1': [0, 1, 2]})` and `df2 = pd.DataFrame({'key': ['a', 'b', 'd'], 'data2': [0, 1, 2]})`. What is the number of rows in the output of `pd.merge(df1, df2, how='left')`?
What is the primary data structure returned by `pd.MultiIndex.from_arrays`?
If `frame` has a hierarchical index on its columns with levels named `state` and `color`, how would you select all columns under the `Ohio` state?
What is the key difference in output between `stack()` and `stack(dropna=False)`?
If a DataFrame `df` has columns `lkey` and `rkey` used for merging with `pd.merge(df3, df4, left_on="lkey", right_on="rkey")`, what happens to the `rkey` column in the output?
What are the three fundamental data combination operations in pandas mentioned at the beginning of Section 8.2?
If `left2` and `right2` are DataFrames with different columns but partially overlapping indexes, what is the result of `left2.join(right2, how="outer")`?
When using `pd.concat`, how can you create a hierarchical index on the concatenation axis to identify the original pieces of data?
Consider the DataFrame `data` created in In [126]. What is the shape of the output of `result = data.stack()`?
If `long_data` is a DataFrame in long format with columns `date`, `item`, and `value`, what does the code `pivoted = long_data.pivot(index="date", columns="item", values="value")` produce?
What is the key difference between the default behavior of `set_index` and `set_index(drop=False)`?
If `left` has `key1`='foo', `key2`='one' with `lval`=1 and `right` has `key1`='foo', `key2`='one' with `rval`=4 and another row with `key1`='foo', `key2`='one' with `rval`=5, what is the number of rows in the output of `pd.merge(left, right, on=["key1", "key2"], how="inner")`?
When merging `left1` on column 'key' and `right1` on its index, what arguments should be passed to `pd.merge`?
Consider the numpy array `arr = np.arange(12).reshape((3, 4))`. What is the shape of the output of `np.concatenate([arr, arr], axis=1)`?
If `s1` is a Series with index ['a', 'b'] and `s4` is a Series with index ['a', 'b', 'f', 'g'], what happens to the 'f' and 'g' labels in the output of `pd.concat([s1, s4], axis="columns", join="inner")`?
You have a list of DataFrames `[df1, df2]` where the row index does not contain relevant data. Which combination of arguments to `pd.concat` will combine them vertically and create a new, continuous integer index?
If `a` and `b` are two Series with overlapping indexes and some null values, how does `a.combine_first(b)` determine the values in the resulting Series?
If `df1` has a value of 1.0 in column 'a' at index 0, and `df2` has a value of 5.0 in column 'a' at index 0, what will be the value in column 'a' at index 0 of the result of `df1.combine_first(df2)`?
When reshaping a DataFrame using `df.unstack(level="state")`, what does the unstacked level become in the resulting DataFrame's structure?
If `long_data.pivot()` is called without the `values` argument, and there are multiple potential value columns, what is the structure of the resulting DataFrame?
When is `pandas.melt` particularly useful without specifying any `id_vars`?
A DataFrame `frame` is created with a hierarchical index with `key1` and `key2`, and columns with `state` and `color`. Given `frame.index.names = ["key1", "key2"]`, how many levels does the row index have?