Dropping Multiple Columns from a Pandas DataFrame
Data manipulation is an essential aspect of data analysis. Pandas, a popular Python library, is widely used to manipulate and analyze data.
In this article, we explore how to drop multiple columns from a Pandas DataFrame.
Method 1: Drop Multiple Columns by Name
Dropping multiple columns by name is a simple process in Pandas.
The drop method is used to remove columns from the DataFrame. It requires the name(s) of the column(s) to be dropped as an argument.
Example 1:
Suppose we have a DataFrame with columns ‘A’, ‘B’, ‘C’, ‘D’, ‘E’. We want to drop columns ‘C’ and ‘E’.
df.drop(['C','E'], axis=1, inplace=True)The axis parameter is set to 1, indicating that the columns are to be dropped. The inplace parameter is set to True, indicating that the DataFrame should be updated in place.
Method 2: Drop Columns in Range by Name
Dropping columns in a range by name is similar to dropping columns by name. It requires the range of the column names to be dropped as an argument.
The range of column names can be specified using slice notation.
Example 2:
Suppose we have a DataFrame with columns ‘A’, ‘B’, ‘C’, ‘D’, ‘E’.
We want to drop columns ‘B’ and ‘C’. We use the drop method with slice notation as follows:
df.drop(df.loc[:, 'B':'C'].columns, axis=1, inplace=True)The loc method is used to select the range of columns.
Next, the columns attribute is used to access the specified columns, and the axis and inplace parameters are set as before to drop the columns.
Method 3: Drop Multiple Columns by Index
Dropping columns by index requires the index of the column(s) to be dropped as an argument.
The index of a column is its position in the DataFrame, starting from zero.
Example 3:
Consider a DataFrame with columns ‘A’, ‘B’, ‘C’, ‘D’, and ‘E’.
We want to drop columns at index positions 1 and 4. We can use the drop method as follows:
df.drop(df.columns[[1, 4]], axis=1, inplace=True)The columns attribute is used to access the specified columns using index positions.
The axis and inplace parameters are set as before to drop the columns.
Method 4: Drop Columns in Range by Index
Dropping columns in a range by index is similar to dropping columns by index.
It requires the range of the column indices to be dropped as an argument. The range of column indices can be specified using slice notation.
Example 4:
Consider a DataFrame with columns ‘A’, ‘B’, ‘C’, ‘D’, and ‘E’. We want to drop columns at indices 1 and 2.
df.drop(df.iloc[:, 1:3].columns, axis=1, inplace=True)The iloc method is used to select the range of columns using index positions. Next, the columns attribute is used to access the specified columns, and the axis and inplace parameters are set as before to drop the columns.
Conclusion
In this article, we explored how to drop multiple columns from a Pandas DataFrame. We used four methods to drop columns by name or index, either individually or in a range.
These methods provide a lot of flexibility when working with large datasets. They can also help to clean or simplify the data prior to further analysis.
Knowing how to drop multiple columns can be a useful addition to a data analyst’s toolbox.