How to Divide a Pandas DataFrame by a Column Value?

How to Divide a Pandas DataFrame by a Column Value

Splitting a Pandas DataFrame based on a column value can be useful for creating separate subsets of data. Suppose you have a DataFrame with a column named 'Sales' and you want to divide it into two DataFrames: one containing rows where 'Sales' is less than a specified value, and another containing rows where 'Sales' is greater than or equal to that value.

To achieve this, you can use boolean indexing with the following steps:

1. Define the splitting value: Assign the desired value to a variable, s.
2. Create boolean masks: Use boolean indexing to create two masks: df['Sales'] < s (for values less than s) and df['Sales'] >= s (for values greater than or equal to s).

3. Split the DataFrame: Apply the boolean masks to the original DataFrame to create two new DataFrames:

   • df1 = df[df['Sales'] >= s] (DataFrame with 'Sales' >= s)
   • df2 = df[df['Sales'] < s] (DataFrame with 'Sales' < s)

Alternatively, you can invert the first mask using the ~ operator:

mask = df['Sales'] >= s
df1 = df[mask]
df2 = df[~mask]<p>Here's an example to illustrate the process:</p>
<pre class="brush:php;toolbar:false"><code class="python">df = pd.DataFrame({'Sales': [10, 20, 30, 40, 50], 'A': [3, 4, 7, 6, 1]})
print(df)

s = 30

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

df2 = df[df['Sales'] < s]
print(df2)</code>

The output will be:

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

   A  Sales
2  7     30
3  6     40
4  1     50

   A  Sales
0  3     10
1  4     20

This demonstrates how to split a Pandas DataFrame into two based on a specified column value using boolean indexing.

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