How to Union 3 Dataframes By Pandas?

To union 3 dataframes by pandas, you can use the concat() function. This function allows you to concatenate multiple dataframes along a specified axis (rows or columns). You can pass a list of dataframes as an argument to the function, and pandas will concatenate them together. The syntax for the concat() function is pd.concat([df1, df2, df3]), where df1, df2, and df3 are the dataframes you want to union. Make sure that the dataframes have the same column names and data types before concatenating them to avoid any issues.

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How to concatenate dataframes in pandas?

You can concatenate dataframes in pandas using the concat() function. Here is an example of how to concatenate two dataframes:

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import pandas as pd

# Create two dataframes
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

# Concatenate the two dataframes
result = pd.concat([df1, df2])

print(result)

This will concatenate the two dataframes vertically, meaning the rows from the second dataframe will be appended to the end of the first dataframe. You can also concatenate dataframes horizontally by specifying the axis parameter:

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result = pd.concat([df1, df2], axis=1)

This will concatenate the two dataframes side by side, meaning the columns from the second dataframe will be appended to the right of the columns in the first dataframe.

How to combine three dataframes using pandas?

To combine three dataframes using pandas, you can use the concat function. Here is an example:

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import pandas as pd

# Create three sample dataframes
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})
df3 = pd.DataFrame({'A': [13, 14, 15], 'B': [16, 17, 18]})

# Combine the three dataframes
combined_df = pd.concat([df1, df2, df3])

print(combined_df)

This will concatenate the three dataframes row-wise, combining them into a single dataframe.

How to merge dataframes with duplicate column names in pandas?

To merge dataframes with duplicate column names in pandas, you can use the suffixes parameter in the merge function to specify a suffix for duplicate column names.

Here's an example:

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import pandas as pd

# Create two dataframes with duplicate column names
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

# Merge the two dataframes with a suffix for duplicate column names
merged_df = pd.merge(df1, df2, on='A', suffixes=('_df1', '_df2'))

print(merged_df)

In this example, the suffixes=('_df1', '_df2') parameter specifies that the duplicate column names should be suffixed with "_df1" and "_df2" in the resulting merged dataframe. This will prevent any naming conflicts and allow you to merge dataframes with duplicate column names.