How to Drop Specific Rows from a Pandas DataFrame Using a List of Indices?

Dropping Specific Rows from a Pandas Dataframe using a List of Indices

To remove rows from a Pandas dataframe based on specific indices, the DataFrame.drop function is commonly utilized. Let's delve into its usage:

Consider a dataframe df with data on sales, discount, net sales, and COGS:

<code class="python">>>> df
                  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</code>

Now, suppose we want to drop the rows with sequence numbers 1, 2, and 4:

<code class="python">list_of_indices = [1, 2, 4]</code>

To achieve this, we can apply the DataFrame.drop function along with a Series of indices:

<code class="python">df = df.drop(index=list_of_indices)</code>

As a result, the modified df will only contain the remaining rows:

<code class="python">>>> df
                  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</code>

This approach allows us to selectively remove rows from a dataframe based on a specified list of indices, making it a versatile tool for data manipulation in Pandas.

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