Pandas split dataframe by unique column value

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Pandas Split Dataframe by Unique Column Value

In Pandas, you can split a dataframe based on unique values in a specific column using the groupby() function. The groupby() function groups the dataframe by the unique values in the specified column, allowing you to perform operations on each group separately.

Example:

Let’s say we have a dataframe named df with the following data:

Col1 Col2
A 1
B 2
C 3
A 4
B 5

Now, if we want to split the dataframe into separate dataframes based on the unique values in Col1, we can use the following code:

grouped = df.groupby('Col1')
dfs = []

for key, group in grouped:
    dfs.append(group)
    
# dfs will now contain separate dataframes for each group

In this example, the groupby() function groups the dataframe df by the values in Col1. The loop then iterates over each group, and each group is appended to the list dfs.

After running this code, dfs will contain the following dataframes:

Dataframe 1:

Col1 Col2
A 1
A 4

Dataframe 2:

Col1 Col2
B 2
B 5

Dataframe 3:

Col1 Col2
C 3

Each dataframe in dfs represents a group of rows with the same value in Col1. You can now perform operations on each individual dataframe if needed.

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