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How to delete rows from a pandas DataFrame based on a conditional expression?

In Pandas, removing rows that meet a specific condition typically involves creating a filtered DataFrame that excludes those rows, or explicitly dropping them using their indices. Below are a few common approaches:

1. Filtering with Boolean Indexing (Recommended)

The most straightforward way is to filter the DataFrame based on a Boolean condition. For example, suppose you have a condition: “Keep all rows where colA is greater than 10, and discard any rows where colA is less than or equal to 10.”

import pandas as pd

df = pd.DataFrame({
    "colA": [5, 12, 15, 8, 20],
    "colB": ["A", "B", "C", "D", "E"]
})

# Retain rows where colA > 10
df_filtered = df[df["colA"] > 10]
print(df_filtered)
  1. Condition: df["colA"] > 10 creates a boolean Series (True/False).
  2. Filtering: df[...] keeps rows where the condition is True.
  3. Result: A new DataFrame without rows that fail the condition. The original df is unchanged unless you reassign it, e.g. df = df[df["colA"] > 10].

2. Dropping Rows by Identifying Indices

Sometimes you might want to explicitly drop rows in place (or create a copy). Here, you:

  1. Identify the indices of rows that meet the unwanted condition.
  2. Use df.drop() to remove those rows.
# Condition: rows where colA <= 10
rows_to_drop = df[df["colA"] <= 10].index

# Drop them from the DataFrame
df_dropped = df.drop(rows_to_drop)
print(df_dropped)
  1. df[df["colA"] <= 10].index gives the index labels of all rows where colA is ≤ 10.
  2. df.drop(...) removes them.
  3. If you want to modify the original DataFrame in place, add inplace=True: 
    df.drop(rows_to_drop, inplace=True)

3. Combining Multiple Conditions

You can combine multiple conditions using:

  • & for logical AND
  • | for logical OR
  • ~ for logical NOT

For example:

# Keep rows where colA > 10 AND colB == "E"
df_filtered = df[(df["colA"] > 10) & (df["colB"] == "E")]

Performance & Best Practices

  1. Boolean Indexing is typically the most intuitive approach for removing unwanted rows in a single step.
  2. Chaining multiple operations (e.g. df[df["colA"] > 10].drop(...)) can reduce clarity. If your conditions or transformations are complex, consider breaking them into separate lines or well-named variables.
  3. In-Place vs Copy: Remember that df[...] or df.drop(...) without inplace=True returns a new DataFrame. If you need the original DataFrame to change, either reassign the result back to df or use inplace=True.

Enhance Your Python & Data Skills

For more complex data manipulation tasks, a strong Python foundation is invaluable. Grokking Python Fundamentals by DesignGurus.io can help you master Python best practices and advanced features—ensuring you can tackle all kinds of data handling scenarios with confidence.

With these approaches, you can easily remove rows that meet (or fail) any condition—helping you keep your data clean, relevant, and ready for analysis.

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