Merge a list of dataframes to create one dataframe in python

Merge a list of dataframes to create one dataframe in python

You can merge a list of DataFrames to create a single DataFrame using the pd.concat() function from the pandas library in Python. This function concatenates DataFrames along a specified axis (usually rows or columns) to create a new DataFrame.

Here's how you can merge a list of DataFrames into one DataFrame:

import pandas as pd # Sample data dataframes = [ pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}), pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]}) ] # Concatenate DataFrames merged_dataframe = pd.concat(dataframes, ignore_index=True) print(merged_dataframe)

In this example, the dataframes list contains two DataFrames. The pd.concat() function is used to concatenate these DataFrames along the rows (axis=0) to create a single DataFrame merged_dataframe. The ignore_index=True argument ensures that the resulting DataFrame has a new continuous index.

You can also use the keys parameter of pd.concat() to create a multi-level index if you want to keep track of the original DataFrames. For example:

merged_dataframe = pd.concat(dataframes, keys=['df1', 'df2'])

Keep in mind that when concatenating DataFrames, make sure their column names and data types are compatible to avoid any issues.

Examples

  1. "Python merge dataframes" Description: This query suggests a general interest in merging dataframes using Python.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]}) # Merge dataframes merged_df = pd.concat([df1, df2]) print(merged_df)

  2. "Python concatenate dataframes" Description: This query indicates an interest in concatenating dataframes in Python, which is often used interchangeably with merging.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]}) # Concatenate dataframes concatenated_df = pd.concat([df1, df2]) print(concatenated_df)

  3. "Pandas merge multiple dataframes" Description: This query indicates a need to merge multiple dataframes using the Pandas library in Python.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]}) # Merge multiple dataframes merged_df = pd.concat([df1, df2]) print(merged_df)

  4. "Python merge dataframes with different columns" Description: This query suggests a requirement to merge dataframes with differing column structures.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]}) # Merge dataframes with different columns merged_df = pd.concat([df1, df2], axis=1) print(merged_df)

  5. "Python merge dataframes with duplicate columns" Description: This query indicates a need to merge dataframes that contain duplicate column names.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]}) # Merge dataframes with duplicate columns merged_df = pd.concat([df1, df2], ignore_index=True) print(merged_df)

  6. "Python join dataframes" Description: This query suggests an interest in using the join operation to merge dataframes.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'A': [7, 8, 9], 'C': [10, 11, 12]}) # Join dataframes joined_df = df1.join(df2, lsuffix='_df1', rsuffix='_df2') print(joined_df)

  7. "Python merge dataframes by column" Description: This query indicates a need to merge dataframes based on specific columns.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Alice', 'Bob', 'Charlie']}) df2 = pd.DataFrame({'ID': [2, 3, 4], 'Age': [25, 30, 35]}) # Merge dataframes by column merged_df = pd.merge(df1, df2, on='ID') print(merged_df)

  8. "Pandas merge dataframes horizontally" Description: This query indicates a need to merge dataframes horizontally, i.e., adding columns side by side.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]}) # Merge dataframes horizontally merged_df = pd.concat([df1, df2], axis=1) print(merged_df)

  9. "Python merge dataframes by index" Description: This query indicates a need to merge dataframes based on their indices.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3]}, index=['a', 'b', 'c']) df2 = pd.DataFrame({'B': [4, 5, 6]}, index=['a', 'b', 'c']) # Merge dataframes by index merged_df = pd.concat([df1, df2], axis=1) print(merged_df)

  10. "Pandas merge dataframes by row" Description: This query indicates a need to merge dataframes by stacking them on top of each other.

    Code:

    import pandas as pd # Example dataframes df1 = pd.DataFrame({'A': [1, 2, 3]}) df2 = pd.DataFrame({'A': [4, 5, 6]}) # Merge dataframes by row merged_df = pd.concat([df1, df2], ignore_index=True) print(merged_df)

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