How to Split a Pandas DataFrame into Multiple DataFrames Based on a Column's Unique Values?

Splitting a Pandas DataFrame Based on Column Values Using Groupby

This article presents a solution to the challenge of splitting a DataFrame into multiple parts based on unique values within a specific column.

Consider the following DataFrame:

import pandas as pd

df = pd.DataFrame({
    "N0_YLDF": [6.286333, 6.317000, 6.324889, 6.320667, 6.325556, 6.359000, 6.359000, 6.361111, 6.360778, 6.361111],
    "ZZ": [2, 6, 6, 5, 5, 6, 6, 7, 7, 6],
    "MAT": [11.669069, 11.669069, 11.516454, 11.516454, 11.516454, 11.516454, 11.516454, 11.516454, 11.516454, 11.516454]
})

The goal is to create a new DataFrame that has multiple columns for the "N0_YLDF" column, with each column corresponding to a unique value in the "ZZ" column. To achieve this, we can utilize the groupby() function.

grouped_df = df.groupby("ZZ")

The groupby() function creates a pandas.core.groupby.groupby.DataFrameGroupBy object, which represents the DataFrame with the groups split according to the values in the specified column. In this case, we have four groups:

print(grouped_df.groups)

# Output
{2: [0], 6: [1, 2, 5, 6, 9], 5: [3, 4], 7: [7, 8]}

To obtain the individual DataFrames for each group, we can use list comprehension:

split_dfs = [grouped_df.get_group(key) for key in grouped_df.groups]

The get_group() method returns a DataFrame that contains the rows belonging to the specified group.

The resulting split_dfs list contains four DataFrames, each representing a different value in the "ZZ" column.

For example, to access the DataFrame for the group with "ZZ" value of 6, you can use:

split_df_6 = split_dfs[1]

This will give you a DataFrame with the following rows:

   N0_YLDF   ZZ        MAT
1  6.317000   6  11.669069
2  6.324889   6  11.516454
5  6.359000   6  11.516454
6  6.359000   6  11.516454
9  6.361111   6  11.516454

By utilizing the groupby() function and the get_group() method, you can effectively split a DataFrame into multiple parts based on the values in a specified column.

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