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One common approach to handling missing data is to drop rows containing NaN values using pandas. Below are some methods that can be used: ## Method 1: ****Using**** **`dropna()`** The [dropna() method](https://www.geeksforgeeks.org/python/python-pandas-dataframe-dropna/) is the most straightforward way to remove rows with missing values. It scans through the DataFrame and drops any row that contains at least one `NaN value`. This method helps in maintaining data integrity as only complete records are used in analysis. Python `` ``` import pandas as pd ``` ``` import numpy as np ``` ``` ​ ``` ``` data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],'Age': [25, np.nan, 35, 40],'City': ['New York', 'San Francisco', 'Los Angeles', np.nan],'Salary': [5000, 6000, 5500, 6200]} ``` ``` df = pd.DataFrame(data) ``` ``` ​ ``` ``` a=df.dropna() ``` ``` print(a) ``` ****Output:****  using dropna Here we removed all NaN value rows from dataset. ## ****Method 2: Drop Rows Based on Specific Columns**** The `subset` parameter allows you to specify columns that should be checked for NaN values allowing you to maintain rows that have missing values in less important columns. Python `` ``` import pandas as pd ``` ``` import numpy as np ``` ``` ​ ``` ``` data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', np.nan, np.nan],'Age': [25, np.nan, 35, 40, np.nan, np.nan],'City': ['New York', 'San Francisco', 'Los Angeles', np.nan, np.nan, np.nan],'Salary': [5000, 6000, 5500, 6200, np.nan, np.nan]} ``` ``` df = pd.DataFrame(data) ``` ``` ​ ``` ``` df_clean = df.dropna(subset=['Name', 'Age']) ``` ``` print(df_clean) ``` ****Output:****  Drop rows based on specific coloumn Here we removed rows with NaN values present in Name and Age column. ## ****Method 3: In-Place Modification with**** **`inplace=True`** By default `dropna()` returns a new DataFrame and leaves the original dataframe unchanged. Use**`inplace=True`** to modify the original DataFrame directly. This approach save memory and processing time when working with large datasets. Python `` ``` import pandas as pd ``` ``` import numpy as np ``` ``` ​ ``` ``` data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', np.nan, np.nan],'Age': [25, np.nan, 35, 40, np.nan, np.nan],'City': ['New York', 'San Francisco', 'Los Angeles', np.nan, np.nan, np.nan],'Salary': [5000, 6000, 5500, 6200, np.nan, np.nan]} ``` ``` df = pd.DataFrame(data) ``` ``` ​ ``` ``` df.dropna(inplace=True) ``` ``` print(df) ``` ****Output:****  In place modification The `dropna()` function offers various parameters that provide flexibility in handling missing data. 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``` import pandas as pd ``` ``` import numpy as np ``` ``` ​ ``` ``` data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', np.nan, np.nan],'Age': [25, np.nan, 35, 40, np.nan, np.nan],'City': ['New York', 'San Francisco', 'Los Angeles', np.nan, np.nan, np.nan],'Salary': [5000, 6000, 5500, 6200, np.nan, np.nan]} ``` ``` df = pd.DataFrame(data) ``` ``` ​ ``` ``` df_clean = df.dropna(subset=['Name', 'Age']) ``` ``` print(df_clean) ```