How to delete DataFrame row in Pandas based on column value?

Removing rows from a DataFrame based on specific column values is a common operation in data cleaning and preprocessing. Below are two straightforward methods:

1. Filtering Rows with Boolean Indexing (Most Common)

import pandas as pd

df = pd.DataFrame({
    "Name": ["Alice", "Bob", "Charlie", "David"],
    "Age": [25, 30, 35, 40],
    "City": ["New York", "Chicago", "San Francisco", "Seattle"]
})

# Suppose we want to remove rows where "City" == "Chicago"
df_filtered = df[df["City"] != "Chicago"]
print(df_filtered)

• Explanation: df["City"] != "Chicago" creates a boolean Series indicating which rows have a “City” different from “Chicago.”
• We keep only those rows by slicing df[...].
• This method returns a new DataFrame; the original df remains unchanged.

2. Dropping Rows by Index Using drop()

# Identify the rows where "City" == "Chicago"
rows_to_drop = df[df["City"] == "Chicago"].index

# Drop them from the DataFrame
df.drop(rows_to_drop, inplace=True)
print(df)

• Explanation: First, we get the index of rows we want to remove: df[df["City"] == "Chicago"].index.
• Then we call df.drop(rows_to_drop, inplace=True) to remove those rows in place.

Additional Tips

• inplace=True modifies the original DataFrame. If you prefer returning a new DataFrame and keeping the original intact, omit inplace=True and assign the result back to df or another variable.
• Chaining: If you do something like df[df["City"] == "Chicago"].drop(...), be careful—it can lead to confusion or partial data modifications. Usually, it’s clearer to separate the steps.

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Happy Data Wrangling!