How to Divide a DataFrame Based on Column Values in Pandas?

Pandas: Dividing a DataFrame Based on Column Values

When working with Pandas DataFrames, the need arises to split the data into subsets based on specific column values. One common scenario is splitting a DataFrame based on a threshold value. Here's how it can be achieved:

Creating Boolean Masks

The simplest method involves creating a boolean mask using comparison operators. By applying the mask to the DataFrame, you can create two DataFrames with data satisfying conditions set by the mask.

For example, to split a DataFrame by a column named 'Sales' with sales values less than and greater than or equal to a specified threshold 's':

<code class="python">import pandas as pd

df = pd.DataFrame({'Sales':[10,20,30,40,50], 'A':[3,4,7,6,1]})
print(df)

s = 30

# Boolean mask for rows where Sales >= s
sales_ge_mask = df['Sales'] >= s

# DataFrame with Sales >= s
df1 = df[sales_ge_mask]
print(df1)

# Boolean mask for rows where Sales < s
sales_lt_mask = df['Sales'] < s

# DataFrame with Sales < s
df2 = df[sales_lt_mask]
print(df2)

You can invert the mask using the "~" operator to split the DataFrame based on the negation of the condition.

<code class="python"># Boolean mask for rows where Sales < s
sales_lt_mask = df['Sales'] < s

# DataFrame with Sales >= s
df1 = df[~sales_lt_mask]
print(df1)

# DataFrame with Sales < s
df2 = df[sales_lt_mask]
print(df2)</code>

This method efficiently creates subsets of DataFrames based on tailored conditions.

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