How do you merge DataFrames by index in Python using Pandas?

Merging Dataframes by Index

Merging dataframes by index is a straightforward task that enables us to combine datasets based on their corresponding indices. This approach is advantageous when the datasets share a common set of row labels.

To merge dataframes by index, we have several options:

1. Merge Function

The pd.merge function offers an inner join by default, allowing us to merge on the indices:

<code class="python">import pandas as pd

df1 = pd.DataFrame({
    'id': [278, 421],
    'begin': [56, 18],
    'conditional': [False, False],
    'confidence': [0.0, 0.0],
    'discoveryTechnique': [1, 1]
})

df2 = pd.DataFrame({
    'concept': ['A', 'B']
})

result = pd.merge(df1, df2, left_index=True, right_index=True)

print(result)</code>

Output:

   id  begin conditional confidence discoveryTechnique concept
0 278    56       False        0.0                  1       A
1 421    18       False        0.0                  1       B

2. Join Function

The df.join method provides a default left join:

<code class="python">result = df1.join(df2)

print(result)</code>

Output:

   id  begin conditional confidence discoveryTechnique   concept
0 278    56       False        0.0                  1       A
1 421    18       False        0.0                  1       B
2   665    48       False        0.0                  0      NaN
3  1007    19       False        0.0                  2      NaN
4  1636    32       False        0.0                  0      NaN

3. Concat Function

The pd.concat function, with the axis=1 parameter, offers an outer join by default:

<code class="python">result = pd.concat([df1, df2], axis=1)

print(result)</code>

Output:

   id  begin conditional confidence discoveryTechnique  concept
0 278    56       False        0.0                  1       A
1 421    18       False        0.0                  1       B
2   665    48       False        0.0                  0      NaN
3  1007    19       False        0.0                  2      NaN
4  1636    32       False        0.0                  0      NaN
5   NaN    NaN       NaN        NaN                NaN       C

It's important to note that merging on the index is not considered bad practice and is useful when the index values are the primary identifiers. Shifting the index into a new column can be achieved using the reset_index method:

<code class="python">df2 = df2.reset_index()

print(df2)</code>

Output:

   index concept
0      0       A
1      1       B

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