Understanding The Pandas Concat Function

Concatenation of Pandas DataFrames Made Easy

Understanding the Pandas Concat Function

The concat function in Pandas is a powerful tool for combining multiple data frames into a single, cohesive dataset. It offers a range of options for controlling how the data is joined, including axis specification, join type, and hierarchical indexing.

Syntax

The syntax of the concat method is as follows: pandas.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=None, copy=True) ``` * **objs**: A sequence of data frames to concatenate. * **axis**: The axis along which to concatenate the data frames (0 for rows, 1 for columns). * **join**: The type of join to perform (`'inner'`, `'outer'`, or `'left'`/`'right'`). * **ignore_index**: Whether to reset the index of the concatenated data frame. * **keys**: A list of keys to use for hierarchical indexing. * **levels**: A list of levels for the hierarchical index. * **names**: A list of names for the hierarchical index. * **verify_integrity**: Whether to verify the integrity of the data frames before concatenating. * **sort**: Whether to sort the concatenated data frame. * **copy**: Whether to create a copy of the concatenated data frame.

Example

To concatenate two data frames, df1 and df2, along the rows, use the following code: ```python df3 = pd.concat([df1, df2], axis=0) ``` This will create a new data frame, df3, that contains the rows from both df1 and df2. The index of df3 will be the union of the indices of df1 and df2.