Pandas drop multiple columns by index. By understanding the different use cases for the drop() function, you can improve your data analysis skills and become more productive in working with Pandas. I am trying to drop multiple columns (column 2 and 70 in my data set, indexed as 1 and 69 respectively) by index number in a pandas data frame with the following code: df. , rows or columns), and whether or not to modify the original DataFrame in place. columns[[i, j, k]] where i, j, k are the column indices of the columns you want to drop. Mar 27, 2024 路 In this article, you have learned what is Pandas MultiIndex, how to create it, how to convert the muli index to columns, flatten MultiIndex columns, drop the index, and transform it to a Single index with examples. We can drop a column from any level of multi-index DataFrame. I need to drop various sets of columns and i'm hoping there is a way of using the old df. At times, there may be a need to simplify your data by dropping one or multiple levels from these indices. This tutorial covers various scenarios for removing DataFrame columns by index. droplevel() methods. drop(('a', 'c'), axis = 1, inplace = True) Or specify the level as shown below Mar 5, 2024 路 馃挕 Problem Formulation: When working with data in Pandas, you might encounter complex DataFrames with multi-level column indices (also known as “MultiIndex”). e. When using the Pandas DataFrame . Remove rows or columns by specifying label names and corresponding axis, or by directly specifying index or column names. provide quick and easy access to pandas data structures across a wide range of use cases. let’s create DataFrame using data from a dictionary. ). May 3, 2025 路 DataFrame with Custom Index Column. drop # DataFrame. The Python and NumPy indexing operators [] and attribute operator . columns['slices'],axis=1) I've built selections suc May 14, 2021 路 This tutorial explains how to drop one or more columns from a pandas DataFrame by index number, including several examples. You can also drop multiple columns by passing a list of column names. drop (' column_name ', axis= 1, inplace= True) #drop multiple columns by name df. columns[0] the result is: Continent So to drop the column on index 0 we can use the following syntax: df. reset_index() #make index become label Let’s see an example of how to drop multiple columns by index. com Nov 18, 2024 路 This article will discuss dropping columns in Pandas DataFrame by label names or index positions. This method works as the examples shown above, where you can either: Mar 6, 2014 路 I have struggled with this problem since I don’t know why my droplevel() function does not work. Such headers are divided into the levels where the first header is at level 0, the second header is at level 1, and so on. To drop columns by column number, pass df. drop() method, you can drop multiple columns by name by passing in a list of columns to drop. Note: the argument to droplevel is tried to be first interpreted as a label ; so if any of the levels happens to have an integer name, it will be dropped i. If you wanted to drop multiple columns by index, unfortunately, the drop() method doesn’t take an index as a param, but we can overcome this by getting column names by index using df. Example 1: Drop a single column by index Oct 6, 2024 路 Pandas DataFrame. This allows one to arbitrarily index these even with values not in the categories, similarly to how you can reindex any pandas index. inplace=True ensures the original DataFrame is modified directly. Work through several and learn that ‘a’ in your table is columns name and ‘b’, ‘c’ are index. How to Drop Multiple Pandas Columns by Names. Happy Learning !! Related Articles. To drop column(s) by index in a Pandas DataFrame, you can use the drop() method by specifying the column index/indices and setting the axis=1 parameter. columns[1:3], axis=1) print("\nDataFrame after dropping columns from index 1 to 3:") print(df4) Output: DataFrame after dropping columns from index 1 to 3: A D E 0 A1 D1 E1 1 A2 D2 E2 2 A3 D3 E3 3 A4 D4 E4 4 A5 D5 E5 Note. May 14, 2021 路 You can use the following syntax to drop one row from a pandas DataFrame by index number: #drop first row from DataFrame df = df. droplevel() you can drop single or more levels from multi-level rows/column index. Multiple levels can be dropped at once via supplying a list and if any of the index has a name, those can be used, too (as exemplified in the linked doc). drop(df Mar 9, 2023 路 Drop column from multi-index DataFrame. drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise') [source] # Drop specified labels from rows or columns. Learn how to drop multiple Pandas columns by index in Python with detailed examples. df. columns[[1, 69]]], axis=1, inplace=True) Jun 13, 2025 路 Usage of Drop Column(s) by Index in Pandas. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. Dec 5, 2024 路 Related: You can also use the drop() function to drop the first and the last column of the DataFrame. Drop Multiple Columns by Index. So to get the first column we have: df. When using a multi-index, labels on different levels can be removed by specifying the See full list on sparkbyexamples. ) is assigned to the DataFrame. To drop multiple columns by their indices pass df. Aug 24, 2020 路 In the following section, you’ll learn how to drop multiple columns in Pandas. columns. Jul 28, 2021 路 You can use the drop() function to drop one or more columns from a pandas DataFrame: #drop one column by name df. columns [[0]], axis= 1, inplace= True) # Oct 2, 2024 路 Now, let’s perform Pandas drop index level from MultiIndex by using DataFrame. columns[i] to the drop() function where i is the column index of the column you want to drop. index single label or list-like Alternative to specifying axis ( labels, axis=0 is equivalent to index=labels ). >>> idx. . drop(data. Aug 5, 2014 路 With a multi-index we have to specify the column using a tuple in order to drop a specific column, or specify the level to drop all columns with that key on that index level. This method works as the examples shown above, where you can either: May 21, 2025 路 # Drop columns from index 1 to 3 (exclusive) df4 = data. Do like this will help. columns[[1,3]], axis = 1) In the above example column with index 1 (2 nd column) and Index 3 (4 th column) is dropped. To drop a column by index we will combine: df. Drop Rows From Pandas DataFrame Examples; Drop Single & Multiple Columns From Pandas DataFrame Nov 16, 2012 路 The best way to do this in Pandas is to use drop:. So the resultant dataframe will be Drop multiple columns with index in pandas Drop columns by index. reset_index(drop=True, inplace=True) resets the index to the default 0-based integers. , not positionally: Aug 24, 2020 路 In the following section, you’ll learn how to drop multiple columns in Pandas. Aug 9, 2018 路 I have a large pandas dataframe (>100 columns). Jan 24, 2023 路 This tutorial explains how to drop multiple columns from a pandas DataFrame, including several examples. To run some examples of drop column(s) by index. drop ([' column_name1 ', ' column_name2 '], axis= 1, inplace= True) #drop one column by index df. drop (index=[0, 1, 3]) You can use it to remove one or multiple columns by name or index, remove columns with missing data, remove columns by condition or keep only one or a few columns. Dec 4, 2024 路 Example 4: Dropping Columns Using a List of Column Names. We can also drop from a specific level. columns; drop() This step is based on the previous step plus getting the name of the columns by index. Dropping columns can be achieved using methods like drop (), iloc [], loc [], and iterative approaches. droplevel() and MultiIndex. drop (index= 0) And you can use the following syntax to drop multiple rows from a pandas DataFrame by index numbers: #drop first, second, and fourth row from DataFrame df = df. drop ('green', level = 'color') MultiIndex([(0, 'purple'), (1, 'purple'), (2, 'purple')], names=['number', 'color Passing a list will return a plain-old Index; indexing with a Categorical will return a CategoricalIndex, indexed according to the categories of the passed Categorical dtype. drop(['Age', 'City'], axis=1) print(df_dropped_columns) Output: Name 0 Alice 1 Bob 2 Charlie 3 David May 28, 2025 路 The drop() function in the Python pandas library is useful for removing specified rows or columns from a DataFrame or Series. drop([df. columns[]. DataFrame. name = None df. drop (df. drop=True prevents the old index from being added as a separate column. Instead of saying drop column 'c' say drop ('a','c') as shown below: df. df = df. Explanation: custom index (student-1, student-2, etc. drop() method is used to remove the columns from the DataFrame, by default it doesn’t remove on the existing DataFrame instead it returns a new DataFrame after dropping the columns specified with the drop method. drop(df. DataFrame can have multiple column headers, such DataFrame is called a multi-index DataFrame. Or, the drop() method accepts index/columns keywords as an alternative to specifying the axis. Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’). Dec 8, 2021 路 Step 2: Drop column by index in Pandas. The function takes in several parameters, including the labels to drop, the axis (i. Using MultiIndex. ''' drop multiple columns based on column index''' df. # Drop both 'Age' and 'City' columns df_dropped_columns = df. drop('column_name', axis=1) where 1 is the axis number (0 for rows and 1 for columns. pandas. tvsstx nau szjm ebauvc hzgtq divui yevqunta ytqokb fdecq qgqsf