How to Drop Specific Rows from a Pandas Dataframe?

Dropping Specific Rows from a Pandas Dataframe

When working with a Pandas dataframe, it often becomes necessary to remove certain rows based on specific criteria. One common requirement is to drop rows that correspond to a list of sequential numbers. This article tackles this problem and presents a comprehensive solution.

In the example provided, we have a dataframe called 'df' with the following data:

                  sales  discount  net_sales    cogs
STK_ID RPT_Date                                     
600141 20060331   2.709       NaN      2.709   2.245
       20060630   6.590       NaN      6.590   5.291
       20060930  10.103       NaN     10.103   7.981
       20061231  15.915       NaN     15.915  12.686
       20070331   3.196       NaN      3.196   2.710
       20070630   7.907       NaN      7.907   6.459

Suppose we want to drop rows 1, 2, and 4 from this dataframe. To achieve this, we can utilize the 'DataFrame.drop' method. This method takes a 'Series' object as an argument, which contains the index labels of the rows we want to remove.

The following code snippet illustrates how to drop rows 1, 2, and 4 from our dataframe:

drop_list = [1, 2, 4]
df.drop(index=drop_list, inplace=True)

Here, we create a list called 'drop_list' containing the index labels of the rows to be dropped. We then pass this list to the 'DataFrame.drop' method, specifying the 'index' parameter to indicate that we want to drop rows. Finally, the 'inplace=True' argument ensures that the dataframe is modified in place, without the need to assign it to a new variable.

After executing the above code, our dataframe will be updated as follows:

                  sales  discount  net_sales    cogs
STK_ID RPT_Date                                     
600141 20060331   2.709       NaN      2.709   2.245
       20061231  15.915       NaN     15.915  12.686
       20070630   7.907       NaN      7.907   6.459

As you can see, rows 1, 2, and 4 have been successfully removed from the dataframe. This method is highly effective for dropping specific rows based on index labels or other criteria and can be easily customized to meet your specific data manipulation requirements.

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