python **Quiz Question: How would you drop the row with NaN value in the column "price"?*** Options: * a. `df.dropna(subset=["price"], axis=0, inplace=True)` * b. `df.dropna(df["price"], axis=0, inplace=True)` * c. `df.dropna(subset=["price"], axis=1, inplace=True)` * d. `df.dropna(df["price"], axis=1, inplace=True)`### Explanation:In this question, the goal is to identify the correct line of code that will remove any rows in a DataFrame (`df`) where the value in the column named "price" is NaN (Not a Number, indicating missing data).- **Option a:** `df.dropna(subset=["price"], axis=0, inplace=True)` - This is the correct answer. The `subset=["price"]` parameter specifies that we are considering missing values only in the "price" column. The `axis=0` parameter indicates that we are removing rows (had it been `axis=1`, we would be removing columns). The `inplace=True` parameter ensures that the DataFrame `df` is modified in place and the changes are not returned as a new DataFrame. - **Option b:** `df.dropna(df["price"], axis=0, inplace=True)` - This option is incorrect. While `df["price"]` accesses the "price" column, it is not the correct way to specify which rows to consider for NaN values within the `dropna` function.- **Option c:** `df.dropna(subset=["price"], axis=1, inplace=True)` - This option is incorrect. Using `axis=1` would mean we want to drop columns that contain NaN values in the "price" row, but not rows with NaN values in the "price" column.- **Option d:** `df.dropna(df["price"], axis=1, inplace=True)` - This option is also incorrect, for the same reasons as in Option b. Additionally, `axis=1` suggests it would drop columns, not rows.Understanding and correctly using data cleaning functions like `dropna` is crucial in data preprocessing steps. This example helps reinforce the correct syntax and usage of parameters to achieve the desired data transformation in pandas.

Use the dropna () function to drop lines with NaN / None values in pandas DataFrame. Python does…

How would you drop the row with NaN value in the column price? a. b. df.dropna(subset=["price"], axis=0, inplace=True) df.dropna(df["price"], axis=0, inplace=True) C. df.dropna(subset=["price"], axis-1, inplace=True) d. df.dropna(df["price"], axis=1, inplace=True)

How would you drop the row with NaN value in the column price? a. b. df.dropna(subset=["price"], axis=0, inplace=True) df.dropna(df["price"], axis=0, inplace=True) C. df.dropna(subset=["price"], axis-1, inplace=True) d. df.dropna(df["price"], axis=1, inplace=True)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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