How to Split a Pandas DataFrame into Multiple DataFrames Based on Column Values Using Groupby?

Splitting Pandas Dataframe Based on Column Values Using Groupby

In Python, it is possible to split a Pandas dataframe into multiple dataframes based on the values in a specific column. This technique is commonly utilized to create separate dataframes for each unique category or group within the column.

For instance, consider the following dataframe with a column named "ZZ":

df = 
        N0_YLDF  ZZ        MAT
    0  6.286333   2  11.669069
    1  6.317000   6  11.669069
    2  6.324889   6  11.516454
    3  6.320667   5  11.516454
    4  6.325556   5  11.516454
    5  6.359000   6  11.516454
    6  6.359000   6  11.516454
    7  6.361111   7  11.516454
    8  6.360778   7  11.516454
    9  6.361111   6  11.516454

The goal is to split this dataframe into four new dataframes, with each dataframe containing the rows where "ZZ" has a specific value: 2, 5, 6, and 7. To achieve this, we can leverage the Pandas "groupby" function.

gb = df.groupby('ZZ')    
[gb.get_group(x) for x in gb.groups]

The "groupby" function creates a GroupBy object, which groups the dataframe rows based on the values in the specified column ("ZZ" in this case). Each unique value of "ZZ" becomes a group within the GroupBy object.

The subsequent line uses a list comprehension to iterate over the groups and retrieve each group as a separate dataframe. The "get_group" method is called with each group's name (i.e., the unique values of "ZZ") to extract the corresponding dataframe.

As a result, this code generates four new dataframes, each representing a subset of the original dataframe rows that share the same value in the "ZZ" column.

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