Challenge: Drop Rows with Missing Data
When working with real-world datasets, you often encounter missing values represented as NaN (not a number). Deciding when to drop rows with missing data depends on the context and the importance of the missing information. Dropping rows is appropriate when the dataset is large enough that removing some rows will not significantly impact your analysis, or when the missing data is scattered randomly and does not represent a systematic issue. However, this approach can lead to loss of valuable information, especially if missing values are concentrated in a particular group or if the dataset is small. Always consider whether dropping rows could introduce bias or reduce the representativeness of your data.
1234567891011import pandas as pd import numpy as np data = { "name": ["Alice", "Bob", "Charlie", "David"], "age": [25, np.nan, 30, 22], "city": ["New York", "Los Angeles", np.nan, "Chicago"] } df = pd.DataFrame(data) print(df)
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Write a function that returns a new DataFrame with all rows containing any missing values removed. The function should not modify the original DataFrame. Use only the provided parameters and variables.
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Completion rate improved to 5.56Challenge: Drop Rows with Missing Data
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When working with real-world datasets, you often encounter missing values represented as NaN (not a number). Deciding when to drop rows with missing data depends on the context and the importance of the missing information. Dropping rows is appropriate when the dataset is large enough that removing some rows will not significantly impact your analysis, or when the missing data is scattered randomly and does not represent a systematic issue. However, this approach can lead to loss of valuable information, especially if missing values are concentrated in a particular group or if the dataset is small. Always consider whether dropping rows could introduce bias or reduce the representativeness of your data.
1234567891011import pandas as pd import numpy as np data = { "name": ["Alice", "Bob", "Charlie", "David"], "age": [25, np.nan, 30, 22], "city": ["New York", "Los Angeles", np.nan, "Chicago"] } df = pd.DataFrame(data) print(df)
Swipe to start coding
Write a function that returns a new DataFrame with all rows containing any missing values removed. The function should not modify the original DataFrame. Use only the provided parameters and variables.
Рішення
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