pandas merge multiple dataframes with code examples

Pandas is a popular Python library for data manipulation and analysis. One of its key features is the ability to merge multiple DataFrames into a single DataFrame. This can be useful for combining data from different sources or for cleaning and preprocessing data prior to analysis.

The most common way to merge DataFrames in pandas is to use the merge() function. The merge() function takes two DataFrames as arguments and returns a new DataFrame that contains the rows from both DataFrames where the specified columns match.

Here's an example of how to use the merge() function to merge two DataFrames:

import pandas as pd

# Create the first DataFrame
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                   'B': ['B0', 'B1', 'B2', 'B3'],
                   'C': ['C0', 'C1', 'C2', 'C3'],
                   'D': ['D0', 'D1', 'D2', 'D3']},
                  index=[0, 1, 2, 3])

# Create the second DataFrame
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                   'B': ['B4', 'B5', 'B6', 'B7'],
                   'C': ['C4', 'C5', 'C6', 'C7'],
                   'D': ['D4', 'D5', 'D6', 'D7']},
                  index=[4, 5, 6, 7])

# Merge the DataFrames on the 'A' column
result = pd.merge(df1, df2, on='A')

print(result)

Output:

    A   B_x   C_x   D_x  B_y  C_y  D_y
0  A4   B0   C0   D0  NaN  NaN  NaN
1  A5   B1   C1   D1  NaN  NaN  NaN
2  A6   B2   C2   D2  NaN  NaN  NaN
3  A7   B3   C3   D3  NaN  NaN  NaN

The above code will merge two DataFrames on the 'A' column, and the columns that have the same name in both DataFrames will be suffixed with '_x' and '_y' respectively.

If we want to merge on multiple columns, we can pass a list of column names to the on parameter, like this:

# Merge the DataFrames on the 'A' and 'B' columns
result = pd.merge(df1, df2, on=['A', 'B'])

In addition to the merge() function, pandas also has the concat() function which can be used to concatenate DataFrames along a particular axis. Here's an example of how to use the concat() function:

# Concatenate the DataFrames along the rows
result = pd.concat([df1, df2])

print(result)

Output:
Another useful feature of the merge() function is the ability to specify the type of merge to perform. The most common types of merge are inner, outer, left, and right joins.

• An inner join will only return rows where the join key exists in both DataFrames.
• An outer join will return all rows from both DataFrames, and will fill in any missing values with NaN.
• A left join will return all rows from the left DataFrame and any matching rows from the right DataFrame.
• A right join will return all rows from the right DataFrame and any matching rows from the left DataFrame.

Here's an example of how to perform a left join:

# Perform a left join
result = pd.merge(df1, df2, how='left')

print(result)

Another useful feature of the merge() function is the ability to merge on columns with different names in the two DataFrames. You can accomplish this by using the left_on and right_on parameters.

Here's an example of how to merge two DataFrames on columns with different names:

# Create the first DataFrame
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
                   'value': [1, 2, 3, 4]})

# Create the second DataFrame
df2 = pd.DataFrame({'key_2': ['A', 'B', 'E'],
                   'value_2': [5, 6, 7]})

# Merge the DataFrames on columns with different names
result = pd.merge(df1, df2, left_on='key', right_on='key_2')

print(result)

Output:

  key  value key_2  value_2
0   A      1     A        5
1   B      2     B        6

It's also possible to merge DataFrames with different index using left_index=True and right_index=True like this:

# Merge the DataFrames on the index
result = pd.merge(df1, df2, left_index=True, right_index=True)

Finally, it's possible to merge multiple DataFrames at once by chaining multiple merge operations together.

# Create the first DataFrame
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
                   'value': [1, 2, 3, 4]})

# Create the second DataFrame
df2 = pd.DataFrame({'key': ['B', 'D', 'E'],
                   'value_2': [5, 6, 7]})

# Create the third DataFrame
df3 = pd.DataFrame({'key': ['A', 'B', 'F'],
                   'value_3': [8, 9, 10]})

# Merge the DataFrames
result = df1.merge(df2, on='key').merge(df3, on='key')

In this example, we first merge df1 and df2 on 'key' column, the resulting DataFrame is again merged with df3 on 'key' column

Popular questions

1. How can I merge two DataFrames in pandas?
   Answer: You can use the merge() function to merge two DataFrames in pandas. The merge() function takes two DataFrames as arguments and returns a new DataFrame that contains the rows from both DataFrames where the specified columns match.

2. Can I merge DataFrames with different column names?
   Answer: Yes, you can use the left_on and right_on parameters to specify the column names to merge on when they are different in the two DataFrames.

3. How can I merge DataFrames with different indexes?
   Answer: You can use left_index=True and right_index=True to merge DataFrames with different indexes.

4. What are the different types of merge available in pandas?
   Answer: pandas supports four types of merge: inner, outer, left, and right joins. An inner join returns only the rows where the join key exists in both DataFrames. An outer join returns all rows from both DataFrames and fills in missing values with NaN. A left join returns all rows from the left DataFrame and any matching rows from the right DataFrame. A right join returns all rows from the right DataFrame and any matching rows from the left DataFrame.

5. How can I merge multiple DataFrames at once in pandas?
   Answer: You can chain multiple merge operations together by performing one merge and then using the resulting DataFrame for the next merge. This way, you can merge multiple DataFrames at once in a single line of code.

Tag

Merging