How to Drop Multiple Columns in Pandas
You can use the .drop() method on a pandas DataFrame to drop multiple columns at once. The .
drop() method takes two arguments:
the list of column names to drop, and an axis keyword specifying if you want to drop columns (axis=1) or rows (axis=0).
- Load the pandas library into your Python environment
- Import your data set into a pandas DataFrame
- Use the
- drop() method to drop multiple columns from your DataFrame, passing in a list of column names to drop as the first argument
- Specify the axis=1 argument to tell Pandas you want to drop columns, not rows
- Save or output your modified DataFrame as needed
Pandas Drop Multiple Columns by Index
If you’re working with a dataframe in pandas, there may be times when you want to drop multiple columns by their index. This can be done using the .drop() method.
To use this method, you need to specify the indexes of the columns you want to drop. You can do this by passing in a list of column indexes:
df.drop(columns=[0,1])
This will drop the first and second columns from your dataframe. Of course, you can also specify column names instead of indexes:
Drop Columns Pandas
Pandas is a powerful Python data analysis toolkit that provides a wide range of functionality for working with data. One common task when working with data is to drop columns from a DataFrame. This can be done in several ways, but the most straightforward way is to use the drop() method.
The drop() method takes two arguments: the label of the column to be dropped, and the axis on which the column should be dropped (0 for rows, 1 for columns). For example, to drop the column ‘foo’ from a DataFrame named df, you would do:
df.drop(‘foo’, axis=1)
If you wanted to drop multiple columns at once, you could pass in a list of column labels:
Pandas Drop Multiple Columns by Name
If you have a dataframe with a lot of columns and want to remove more than one column at a time, the easiest way to do it is to use the .drop() method. All you need to do is pass in a list of column names and set the axis parameter equal to 1.
Here’s an example:
How to Drop Multiple Rows in Pandas
If you need to drop multiple rows from a pandas DataFrame, there are a few ways to do it.
One way is to use the drop() method. You can specify which rows you want to drop by passing in a list of row indexes.
For example, if we wanted to drop the first, third, and fourth rows from our DataFrame, we could do it like this:
“`python
df = df.drop([0, 2, 3])
“`
Another way is to use the indexing operator (i.e., square brackets) with the ‘!’ character. This allows you to select all of the rows except for those that you specify in your indexing list.
So, if we wanted to accomplish the same thing as above using this technique, we would do it like this:
“`python
df = df[[‘a’, ‘b’]] # select all columns except for ‘c’ and ‘d’ which will be dropped
Drop Columns by Index Pandas
When working with data in Pandas, there may be times when you want to drop columns from your DataFrame by their index. To do this, you can use the .drop() method and specify the column indexes that you want to drop.
For example, let’s say we have a DataFrame with three columns:
df = pd.DataFrame({'A':[1,2,3], 'B':[4,5,6], 'C':[7,8,9]})
df
If we wanted to drop columns ‘B’ and ‘C’, we could do so by specifying their column indexes:
A B C
0 1 4 7
1 2 5 8
2 3 6 9df.drop([1,2])
A
0 1
Credit: machinelearninghd.com
Can We Drop Multiple Columns in Pandas?
Yes, we can drop multiple columns in pandas. To do this, we simply pass a list of column names to the drop() function. For example, if we have a dataframe with columns A, B, C and D, and we want to drop columns B and C, we would do the following:
dataframe.drop(['B', 'C'], axis=1)
This would leave us with only column A and D remaining in our dataframe.
How Do You Drop a Bunch of Columns in Pandas?
If you have a pandas DataFrame and you want to remove some columns from it, there are a couple of ways to do it.
The first way is to use the .drop() method.
This method takes two arguments: the name of the columns to be dropped, and an axis keyword argument. The axis keyword specifies whether you want to drop rows or columns. The default value for axis is 0, which means that by default, .
drop() will drop rows. If you want to drop columns instead, you need to set the axis keyword argument to 1.
The second way to remove columns from a DataFrame is by using the del statement.
This method can only be used on one column at a time though, so if you want to remove multiple columns then you would need to use del multiple times.
Here's an example of how to use each of these methods:
import pandas as pd
# Create a dataframe
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
# Remove column using .
drop() method
df = df.drop('col2', axis=1) # or df = df.drop(['col2'], axis=1) # both work
How Do I Drop 10 Columns in Pandas?
If you have a dataframe with 100 columns, and want to remove 10 of them, there are a few ways to do it. One way would be to hard-code the names of the columns you want to keep:
df = df[['column1', 'column2', 'column3']]
This will work fine if you know the names of all the columns you want to keep ahead of time. But what if you want to drop 10 random columns? Or 10 specific columns based on some criteria?
Here's one way to do it:
First, create a list of the column names:
cols = df.columns.tolist()
Then, use the list comprehension technique to remove the column names you don't want:
cols = [c for c in cols if c not in ['column1', 'column2', 'column3']]
Finally, pass the remaining column names back into your dataframe:
How Do I Drop 5 Columns in Pandas?
There are a few ways to drop columns in pandas. One way is to use the .drop() method.
The .drop() method takes two arguments, the first is the name of the column you want to drop, and the second is the axis argument. The axis argument specifies whether you want to drop rows or columns.
By default, the axis is set to 0, which means it will drop rows. To drop columns, you need to set the axis argument to 1.
Another way to drop columns in pandas is by using the del keyword.
The del keyword can be used on a dataframe like so: del df['column_name']. This will delete the column from the dataframe permanently.
You can also use indexing and slicing when dropping columns in pandas.
. For instance, if you wanted to only keep certain columns, you could do something like this: df = df[['column1', 'column2']]. This would only keep column1 and column2 while getting rid of all other columns in your dataframe.
It's important to note that when using methods like .drop() or del on a dataframe, these changes are not permanent unless you reassign the variable back (like we did with df = df[['column1', 'column2']]).
Dropping multiple columns from a dataframe
Conclusion
If you want to drop multiple columns from your DataFrame, you can use the df.drop() method. You just need to specify the names of the columns you want to drop as a list:
df = df.drop(['column_name1', 'column_name2'], axis=1)
You can also use the inplace parameter to make this change permanent without having to reassign the DataFrame:
