Question

The column Indicator Value of the new Emissions_df contains a bunch of NaN values. Additionally, the Year column contains an Unnamed: 64 value. What procedure should we follow to clean these missing values in our DataFrame? Proceed with your suggested cleaning process. Answer.

Answer 1

To clean the Emissions_df DataFrame, remove any erroneous 'Unnamed: 64' values from the Year column and handle NaN values in the Indicator Value column by either filling them with a specific value, using statistics like mean or median, or dropping the rows entirely.

Step-by-step explanation:

To clean the missing values in the Emissions_df DataFrame, you can follow these steps:

• Remove any non-numeric Unnamed: 64 values from the Year column using a filtering method or by manually deleting the erroneous entry.
• Decide how to handle the NaN values in the Indicator Value column. You have multiple options, such as filling the NaNs with a specific value, using the mean or median of the column, or dropping rows with NaN values altogether.
• If replacing NaNs with a computed value, use the fillna() function with the desired statistic. If dropping rows, use the dropna() function specifying the Indicator Value column.

Before proceeding, ensure the cleaning procedure aligns with your data analysis goals, as different methods can impact the integrity and interpretation of your data.

To clean the missing values in the Emissions_df DataFrame, you can use the fillna() method to replace the NaN values with appropriate values. For example, you could fill the NaN values in the Indicator Value column with the mean or median of the column. For the Unnamed: 64 value in the Year column, you can drop the entire row using the dropna() method