How to Split Pandas DataFrame into Subsets Based on Column Value?

Splitting Pandas DataFrame Based on Column Value

In Pandas, a commonly encountered scenario is the need to split a DataFrame into multiple subsets based on the values present in a specific column. This allows for targeted data analysis and manipulation.

To achieve this, we can leverage the power of boolean indexing in Pandas. Let's consider a DataFrame with a column named "Sales" and explore how we can split it into two based on whether the "Sales" value is below or above a threshold value 's'.

Solution:

<code class="python"># Create a DataFrame with a "Sales" column
df = pd.DataFrame({'Sales':[10,20,30,40,50], 'A':[3,4,7,6,1]})
print (df)</code>

   A  Sales
0  3     10
1  4     20
2  7     30
3  6     40
4  1     50

<code class="python"># Split the DataFrame based on "Sales" values
s = 30

df1 = df[df['Sales'] >= s]
print (df1)</code>

   A  Sales
2  7     30
3  6     40
4  1     50

This creates a new DataFrame, df1, that contains the rows where the "Sales" value is greater than or equal to 's'.

<code class="python">df2 = df[df['Sales'] < s]
print (df2)

   A  Sales
0  3     10
1  4     20

df2 comprises the rows where the "Sales" value is less than 's'.

Alternative Approach Using Bitwise Negation:

Instead of using the greater-than or equal to operator, we can also use bitwise negation (~) to invert the mask:

<code class="python">mask = df['Sales'] >= s
df1 = df[mask]
df2 = df[~mask]
print (df1)</code>

   A  Sales
2  7     30
3  6     40
4  1     50

<code class="python">print (df2)</code>

   A  Sales
0  3     10
1  4     20

This approach achieves the same split but allows for a more concise and optimized coding style.

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