How to Combine Dataframes in Python?

To combine dataframes in Python, you can use the concat() function or the merge() function from the pandas library. Here’s an example of each method:

1. Using the concat() function:

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2, 3],
                    'B': ['a', 'b', 'c']})

df2 = pd.DataFrame({'A': [4, 5, 6],
                    'B': ['d', 'e', 'f']})

combined_df = pd.concat([df1, df2], ignore_index=True)

print(combined_df)

In this example, df1 and df2 are two dataframes with the same column names (‘A’ and ‘B’). The concat() function is used to combine the dataframes vertically, resulting in combined_df. The ignore_index=True parameter is used to reindex the combined dataframe.

2. Using the merge() function:

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2, 3],
                    'B': ['a', 'b', 'c']})

df2 = pd.DataFrame({'A': [2, 3, 4],
                    'C': ['x', 'y', 'z']})

combined_df = pd.merge(df1, df2, on='A')

print(combined_df)

In this example, df1 and df2 are two dataframes with a common column ‘A’. The merge() function is used to combine the dataframes based on the common column ‘A’, resulting in combined_df. The on='A' parameter specifies the common column to merge on.

You can choose the method (concat() or merge()) based on whether you want to combine the dataframes vertically (concatenation) or based on a common column (merge). Adjust the parameters and column names according to your specific requirements.