How To Remove Column In Python

In this Python Pandas tutorial, we will discuss everything on Pandas Delete column and how to Drop column in DataFrameusing Pandas.

Pandas Delete Cavalcade DataFrame
Pandas Delete Column by Proper name
Pandas Delete Column by Index
Pandas Delete Column if Exists
Pandas Delete Column by Status
Pandas Delete Columns with NaN
Pandas Delete Cavalcade if all nan
Pandas Delete Column by Position
Pandas Delete Column with no Name
Pandas Delete Column Header
Pandas Delete Columns Except
Driblet first column in Pandas DataFrame
Driblet last column in Pandas DataFrame
Drop multiple columns in Pandas DataFrame
Drop duplicate columns in Pandas DataFrame
Remove first column in Pandas DataFrame
Remove column names in Pandas DataFrame
Drop column Pandas series
Driblet ane column in Pandas DataFrame
Drop listing of column in Pandas DataFrame

We have used Electric Car Dataset downloaded from Kaggle.

Pandas Delete Column DataFrame

In this department, nosotros will learn well-nigh Pandas Delete Column from DataFrame using Python.

There are 3 methods of removing column from DataFrame in Python Pandas. drop(), delete(), pop().
dop() is the more often than not used method in Python Pandas for removing rows or columns and we will be using the same.

Syntax:

This is the the syntax for drop() method in Python Pandas.

          df.drop(     labels=None,     axis: 'Centrality' = 0,     index=None,     columns=None,     level: 'Level | None' = None,     inplace: 'bool' = False,     errors: 'str' = 'raise', )

labels – provide the name(southward) of row(s) or cavalcade(southward)
centrality – ane is for row and 0 is for column, if label is column name and so provide centrality=1.
index – centrality 0 = label, provide alphabetize if working with rows
columns – axis=1, columns=labels
inplace – if set to Truthful, and so changes will take identify immediately. value reassignment will not be required.
erros – if fix to 'heighten' then error will announced when something will go wrong.

In our case on the jupyter notebook, we have demonstrated all of these methods.

Read: How to Convert Pandas DataFrame to a Dictionary

Pandas Delete Column by Proper noun

In this section, we will learn about Pandas Delete Column by Name.

Raw dataset has huge amount of data, not every information is necessary for a detail job or prediction model.
At that fourth dimension, we clean the dataset by removing unwanted rows and columns.
drop() method is used to remove the columns.
single column name or list of column proper name can be passed to delete the cavalcade past name.
In our example, we have deleted 'PlugType' column from the dataframe.
Here is the chief code that is deleting the column by name.

          df.drop('PlugType', axis=1, inplace=True)

Implementation on Jupyter Notebook

Please read the comments to understand the utilize of that code snippet.

Read: Python Pandas Drib Rows Example

Pandas Delete Column by Alphabetize

In this section, nosotros will learn most Pandas Delete Column by index.

While cleaning the data at fourth dimension, we came across column(due south) that has no proper name. At that fourth dimension, using index value nosotros tin can delete the column.
Using drop method we can provide the index number for the column. This will delete the column from the dataframe.
In our example, nosotros take delete the column on alphabetize 0. But you can modify it to whatever value merely that index but be present in the dataset.
inplace = True, means the changes will take effect immedietley. There is no need for re-consignment.
Hither is the main lawmaking responsible for deleting the column by index in Python Pandas.

          df.drop(df.columns[0], axis=one, inplace=True)

Implementation on Jupyter Notebook

Please read the comments to understand the apply of that lawmaking snippet.

Read: How to use Pandas driblet() function

Pandas Delete Column if Exists

In this section, we volition larn well-nigh Pandas delete column if exists.

If exists condition here simply means that delete the cavalcade if that cavalcade is present in the dataframe.
there are multiple ways of doing so but the nearly efficient manner is by setting the errors to 'ignore'.
If the column is present than it will be deleted otherwise nothing will happen.
In our example, we have deleted ii columns. Out of these 2 columns only one is nowadays in the dataset.
Here is the main code to delete the column if exist.

          df.drib(['Model', 'test'], axis=1, errors='ignore', inplace=True)

Implementation on Jupyter Notebook

Please refer to the examples to understand the utilize of code snippets.

Read: Groupby in Python Pandas

Pandas Delete Column by Condition

In this department, we will learn about Pandas Delete Column by Condition.

While cleaning the the dataset at times we have to remove function of data depending upon some condition.
Let's say nosotros are working on the taxation payers in Us dataset. Then nosotros volition apply a status to seperate non-tax payer based apon their annual income. After removing not-revenue enhancement payer will be left with data of taxation payers in United states.
In our example, we have removed all the cars that are below 50000 euro.
Hither is the main code to delete column by condition.

          df.drop(df[df['PriceEuro'] < 50000].index, inplace = True)

Implement on Jupyter Notebook

Read: Crosstab in Python Pandas

Pandas Delete Columns with NaN

In this section, we will learn about Pandas Delete Columns with NaN.

NaN referrs to the missing values in the dataset.
Missing values are the most mutual thing that tin be found in the dataset.
image a visitor shared a feedback class with the customer and customer submits the form without filling it or just by filling mandatory fields. Now this information will be saved as NaN in the database.
In python pandas nosotros can delete the missing values using dropna() method.
dropna() is peculiarly written to find and delete the rows or columns with the missing value(southward).
Since we want to delete columns then we will provide axis=ane in dropna() function.
In our case, we have deleted all the columns that have even one missing value.
Here is the principal code to delete the cavalcade with NaN or misisng values.

          df.dropna(axis=i, inplace=Truthful)

Implementation on Jupyter Notebook

Read: Missing Data in Pandas in Python

Pandas Delete Column if all nan

In this section, we will learn about Pandas Delete Column if all nan.

NaN referrs to the missing values in the dataset.
Missing values are the about mutual matter that can be plant in the dataset.
image a company shared a feedback form with the client and customer submits the form without filling it or just past filling mandatory fields. Now this data volition be saved as NaN in the database.
In python pandas we can delete the missing values using dropna() method.
dropna() is particularly written to discover and delete the rows or columns with the missing value(s).
Since we desire to delete column(southward) having all the missing value and then we will using df.dropna(axis=1, how='all')
how='all' checks if the all the values of column are empty. If yes then it deletes the cavalcade.
In our example, we have created new column 'Rating' with no values in it. So this column will be deleted afterwards executing this code snippet.
Here is the master code to delete the column with all NaN or misisng values.

Read: Python Pandas CSV Tutorial

Pandas Delete Column past Position

In this section, we will understand well-nigh Pandas Delete column by Position. Position can also be referred to every bit an index.

While cleaning the data at a time, we came across column(s) that has no name. At that time, using the alphabetize value we can delete the column.
Using the drib method nosotros tin can provide the index number for the column. This volition delete the column from the dataframe.
In our case, nosotros have deleted the cavalcade on index 0. Only y'all can modify it to whatever value but that alphabetize but be present in the dataset.
inplace = Truthful, which means the changes will accept effect immediately. There is no need for re-assignment.
Here is the main code responsible for deleting the column by index in Python Pandas.

Read: Pandas DataFrame Iterrows

Pandas Delete Column with no Name

In this section, we will acquire well-nigh Pandas Delete Column with No Name.

Columns with no name can controlled or operated using their index value.
In our case, nosotros whave created a new cavalcade with no proper name.
to delete that column we have used it'due south index value in pandas drop method.
Hither is the main code to Delete Column with No Proper noun.

          df.drop(df.columns[fourteen], axis=1, inplace=Truthful)

Implementation on Jupyter Notebook

Pandas Delete Column Header

In this department, we volition learn nigh Pandas delete column header in Python.

It is not possibel to remove the header from the dataset using Python Pandas simply it can hibernate in multiple means.
first method is change the header to empty string for all the columns.
second method is export to new file with header=False .
In our example, we accept demonstrated bothe ways.
Here is the principal code for removing Header in Python pandas.

          # gear up the columns to empty string df.columns = [''] * len(df.columns)  # consign file with no header df.to_csv('without_header.csv', header=Fake)

Implementation using Jupyter Notebook

Read How to Catechumen Python DataFrame to JSON

Pandas Delete Columns Except

In this section, we will learn about Pandas Delete Columns Except.

Using deviation method we can exclude columns that we don't want to delete.
In our example, we have excluded Model and PowerTrain.
Hither is the main code to delete columns with exception.

          df.driblet(df[df.columns.difference(['Model', 'PowerTrain'])], axis=i)

Implementation on Jupyter Notebook

Read How to convert floats to integer in Pandas

Drib column in Pandas DataFrame

In this Program, we will hash out how to driblet column in Pandas Dataframe.
In Python, Pandas drop columns and rows from DataFrame. You can apply the "drop" method and this part specifies labels from columns or rows. The Pandas.driblet() method deletes columns and rows by directly mentioning the column names or indexes.

Syntax:

Here is the Syntax of Pandas.drib() method

          DataFrame.drop               (                Labels=None,                centrality=0,                index=None,                columns=None,                Level=None,                inplace=False,                errors='enhance'               )

Information technology consists of few Parameters
- Labels: It is a column name to drop and by default, information technology is fix equally None.
- axis: This parameter indicates whether to driblet labels if axis=1 then it removes columns and by default, it takes 0 value.
- alphabetize: This parameter specifies the axis and provides the row label.
- Columns: It accepts a single column label and by default, it is set as None value.

Source Code:

          import pandas as pd  new_dict = { 	'val1':['m', 'o', 'a', 'p', 'z'], 	'val2':['z', 'chiliad', 'u', '10', 'y'], 	'val3':['xi', '55', '806', '22', '66'], 	'val4':['389', '32', '180', '378', '674'], 	'val5':['134', '809', '457', '293', '3939'] }  df = pd.DataFrame(new_dict) C= df.drop(['val2'], axis = 1) print(C)

In the to a higher place lawmaking showtime, nosotros have imported a Pandas package and then create a dictionary in which nosotros have inserted five fields in each column.

Now we want to catechumen the dictionary into a dataframe past using the pd.Dataframe() method.

Now in this example, we want to remove the column proper name 'val2'. To do this we can use the df.drop() method to remove columns for the dataframe.

You can refer to the below Screenshot

Drop column in Pandas DataFrame — Drib column in Pandas DataFrame

Read How to Get first North rows of Pandas DataFrame in Python

Drop commencement column in Pandas DataFrame

Here we can see how to drop the first column of Pandas DataFrame in Python.
By using the df.iloc() method we can select a part of the Pandas DataFrame based on the indexing. In Python Pandas the iloc() method is used to select a specific prison cell of the Dataset and this method accepts only integer values and as well nosotros cannot laissez passer boolean values as an index.

Syntax:

Hither is the Syntax of DataFrame.iloc() method

          DataFrame.iloc()

Example:

Let'southward have an case and sympathise how to drop the get-go column from Dataframe.

          import pandas as pd  new_lis = [('Micheal',  245, 'Newzealand',   34) ,             ('John',    968, 'Switzerland' ,  25) ,             ('Geroge',  1678, 'Australia',   36) ,             ('Oliva',   3789, 'Bangladesh' ,  19)]  df = pd.DataFrame(  new_lis,                      columns=['Stu_name', 'Stu_id', 'Stu_city', 'Stu_age'])  new_data = df.iloc[: , 1:] impress("Updated Dataframe after removing outset col : ") impress(new_data)

Hither is the Screenshot of the following given code

Drop first column in Pandas DataFrame — Drop the outset column in Pandas DataFrame

In the above Screenshot every bit you can see the output of the first column 'stu_name' has been removed from DataFrame.

Read Pandas replace nan with 0

Drop the first column from DataFrame

By using the df.drop() method we can perform this item task and in this example, nosotros volition use parameter axis=1 and inplace =Truthful.

Source Lawmaking:

          import pandas equally pd  new_dict = { 	'col1':['167', '267', '390', '789', '129'], 	'col2':['209', '108', '329', '458', '788'], 	'col3':['267', '589', '349', '1589', '2789'], 	'col4':['44', '99', '31', 'xi', '23'], 	'col5':['72', '44', '68', '908', '502'] }   df = pd.DataFrame(new_dict) df.drib(df.columns[[0]], axis = i, inplace = True) impress(df)

In the above code, we have dropped the commencement column based on the column index. In this case, nosotros have mentioned the index number along with the axis and inplace parameter in the df.drop() method.

In one case you will print 'df' then the output volition show the modifying dataframe.

Here is the implementation of the following given code

Read How to Add a Column to a DataFrame in Python Pandas

Drop last cavalcade in Pandas DataFrame

Let united states see how to drop the last column of Pandas DataFrame.
Past using the del keyword we can easily drop the terminal column of Pandas DataFrame. In Python, the del keyword is used to remove the variable from namespace and delete an object similar lists and information technology does not return whatsoever blazon of value.
To drop the last cavalcade of the dataframe start, we set the position at -1 and and then select the column bypassing the column name in the del method.

Syntax:

          del obj_name

Instance:

Let's take an case and check how to drop the final column of the DataFrame

          import pandas as pd  new_lis = [('Micheal',  245, 'Newzealand',   34) ,             ('John',    968, 'Switzerland' ,  25) ,             ('Geroge',  1678, 'Commonwealth of australia',   36) ,             ('Oliva',   3789, 'Bangladesh' ,  19)]  df = pd.DataFrame(  new_lis,                      columns=['Stu_name', 'Stu_id', 'Stu_city', 'Stu_age']) del df[df.columns[-i]] impress("Updated Dataframe afterward driblet final column : ") impress(df)

In the higher up code, nosotros accept created a listing of tuples then create a Dataframe object. Now we want to drib the last cavalcade of the dataframe we can but utilize the df.columns[-one] method in the del keyword.

Hither is the execution of the following given code

Drop last column in Pandas DataFrame — Drop concluding column in Pandas DataFrame

Read How to Convert Pandas DataFrame to NumPy Array in Python

Drop the concluding column of Pandas DataFrame

By using the df.drop() method we can solve this problem and in this example, we have mentioned the index number along with the axis and inplace parameter in the df.drop() method.

Source Code:

          import pandas as pd  new_dict = { 	'val1':['457', '289', '180', '743', '119'], 	'val2':['278', '140', '678', '712', '8924'], 	'val3':['267', '589', '349', '1589', '2789'], 	'val4':['44', '99', '31', '11', '23'], 	'val5':['72', '44', '68', '908', '502'] }   df = pd.DataFrame(new_dict) df.drop(df.columns[[4]], centrality = 1, inplace = Truthful) print(df)

Here is the Screenshot of the following given code

As you tin see in the Screenshot the last cavalcade 'val5' has been removed from Pandas DataFrame.

Read How to Find Duplicates in Python DataFrame

Drib multiple columns in Pandas DataFrame

Let us see how to drop multiple columns in Pandas DataFrame.
In this example nosotros will employ the method df.drop() on the dataframe to drop multiple columns. We volition use an array of column labels and select alphabetize column numbers for dropping.

Source Code:

          import pandas as pd  stu_info = {'stu_name': ['Chris', 'Hemsworth', 'Hayden', 'Adam'], 	'stu_id': [178, 924, 1290, 6234], 	'Desgination': ['Programmer', 'tester', 'Gamer', 'Q.a'], 	'stu_age': [17, 19, 21, 32]}   df = pd.DataFrame(stu_info) df.drib(df.columns[[three,0,1]], axis = i, inplace = True) print("Updated dataframe after drop multile cols:") print(df)

In the above code, we accept created a dataframe and so use the driblet() part on the Pandas DataFrame to remove multiple columns.

Once you lot will impress 'df' then the output volition display the updated dataframe that contains only a specific 'designation' column.

You lot can refer to the beneath Screenshot

Drop multiple columns in Pandas DataFrame — Driblet multiple columns in Pandas DataFrame

Read Add row to Dataframe Python Pandas

How to drib multiple columns in Pandas

By using the Python Pandas df.popular() method we can perform this detail task and this function is used to remove a specific column.

Syntax:

Here is the Syntax of DataFrame.popular() method

          DataFrame.pop(detail)

Source Lawmaking:

          import pandas as pd  new_val_lis = [('Elijah', 1829, 'China',   32) ,             ('Potter',    3449, 'France' ,  17) ,             ('James',    9234, 'Newzealand', 19) ,             ('George',    13490, 'Germany' ,   21)]  df = pd.DataFrame( new_val_lis,                      columns=['Stu_name', 'Stu_id', 'Stu_city', 'Stu_age'])  df.pop(df.columns[-1]) df.pop(df.columns[-2]) print("Updated Dataframe:") print(df)

Here is the Screenshot of the post-obit given code

Drop duplicate columns in Pandas DataFrame

In this program, we will hash out how to drop duplicate columns in Pandas DataFrame.
In Python Pandas.drop_duplicate() method volition assist the user to remove duplicate columns or rows from the Pandas DataFrame.

Syntax:

Here is the Syntax of Pandas.DataFrame.indistinguishable() method

          DataFrame.dropduplicates                         (                          subset=None,                          go on='first',                          inplace=False,                          ignore_index=False                         )

Information technology consists of few parameters
- Subset: This parameter indicates the list of column labels and by default information technology is fix as None value.
- keep: By default information technology takes as 'starting time' value that means drop the duplicate values except for the get-go element.

Source Lawmaking:

          import pandas as pd import numpy as np new_val = np.random.randint(0,10, (iv,3))   df = pd.DataFrame(np.hstack([new_val, new_val]), columns=['z', 'd', 'b', 'x', 'b', 'z'] ) impress(df) print("Updated dataframe:") print(df.T.drop_duplicates().T)

Here is the implementation of the following given code

Drop duplicate columns in Pandas DataFrame

Remove beginning column in Pandas DataFrame

In this topic, you can use the drop() function, and also you lot can refer to the to a higher place topic Drop the first cavalcade in Pandas DataFrame.

Remove column names in Pandas DataFrame

Let u.s.a. encounter how to remove a column name in Pandas DataFrame.
In this example, we have selected the 'val2' column name to remove from Pandas dataframe. To exercise this task we have to utilise the df.drop() method and this office will assist yous to driblet specific column names from the dataframe.

Source Code:

          import pandas equally pd  new_dict = { 	'val1':['k', 'o', 'a', 'p', 'z'], 	'val2':['z', 'thousand', 'u', 'x', 'y'], 	'val3':['11', '55', '806', '22', '66'], 	'val4':['389', '32', '180', '378', '674'], 	'val5':['134', '809', '457', '293', '3939'] }  df = pd.DataFrame(new_dict) d= df.drop(['val2'], axis = 1) print(d)

Here is the implementation of the following given lawmaking

Driblet column Pandas series

Here nosotros tin run into how to drop the cavalcade in Pandas Serial.
Past using DataFrame.drop() method we can easily solve this problem and this method will drop elements of a serial based on particular the alphabetize labels.

Syntax:

Here is the Syntax of Pandas.Series.driblet() method

          Series.drib            (             labels=None,             axis=0,             alphabetize=None,             columns=None,             Level=None,             inplace=False,             errors='enhance'

Example:

          import numpy as np import pandas as pd  new_dat = pd.Serial(data=np.arange(three), index=['China', 'Japan', 'Australia']) b=new_dat.drop(labels=['Japan', 'Australia']) impress(b)

In the above code showtime, we accept imported numpy and Pandas library and and then create a Pandas serial by using pd. series and assign a number of values by applying np.arange() function.

Here is the Output of the following given code

Drop column Pandas series — Drop cavalcade Pandas series

Drop ane column in Pandas DataFrame

Here we can see how to drop one cavalcade in Pandas DataFrame.
By using the df.drop() method we can perform this particular task and in this example first, we take created a dictionary and contains fundamental-value pair elements. Now use the df.drib() method and assign a specific column value.

Case:

          import pandas as pd  new_dt = { 	'val3':['93', '921', '1889', '3765', '126'], 	'val4':['1021', '446', '9578', '129', '389'], 	'col2':['467', '13456', '489', '356', '221'], 	'col3':['Elijah', 'George', 'Micheal', 'oliva', '390'] }  df = pd.DataFrame(new_dt)  b= df.driblet(['col2'], axis = i) print(b)

You lot tin can refer to the below Screenshot

Drop one column in Pandas DataFrame — Drop 1 column in Pandas DataFrame

Drop list of column in Pandas DataFrame

In the Program, we will discuss how to driblet listing of column in Pandas DataFrame.
In Python Pandas the iloc() method is used to select a specific jail cell of the Dataset and this method accepts only integer values and also we cannot pass boolean values equally an index.

Example:

          import pandas as pd  new_data = [('Stever',  782, 'Malayasia',   12) ,             ('Roger',   230, 'Newzealand' ,  23) ,             ('Geroge',  119, 'Australia',   36) ,             ('Oliva',   534, 'Bangladesh' ,  19)]  df = pd.DataFrame(  new_data,                      columns=['Stu_name', 'Stu_id', 'Stu_city', 'Stu_age'])  new_val = df.iloc[: , ane:] new_val = df.iloc[: , two:] print("Updated Dataframe after removing beginning col : ") print(new_val)

In the above code, nosotros take removed 2 specific columns that are 'Stu_name' and 'Stu_id'. Once yous volition impress 'new_val' and then the output volition display the updated dataframe.

Here is the execution of the following given code

Drop list of column in Pandas DataFrame — Drib list of columns in Pandas DataFrame

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In this tutorial, we take learned about Pandas Delete Column. As well, we accept covered these topics.

Pandas Delete Column DataFrame
Pandas Delete Cavalcade by Name
Pandas Delete Column by Index
Pandas Delete Column if Exists
Pandas Delete Column by Condition
Pandas Delete Columns with NaN
Pandas Delete Column if all nan
Pandas Delete Column by Position
Pandas Delete Column with no Name
Pandas Delete Column Header
Pandas Delete Columns Except
Drop starting time cavalcade in Pandas DataFrame
Drop last column in Pandas DataFrame
Drib multiple columns in Pandas DataFrame
Driblet indistinguishable columns in Pandas DataFrame
Remove offset cavalcade in Pandas DataFrame
Remove column names in Pandas DataFrame
Drib column Pandas serial
Drop one column in Pandas DataFrame
Drib list of cavalcade in Pandas DataFrame