pandas drop row(s) or column(s)¶

In [74]:

 
import pandas as pd​df = pd.read_clipboard()​df.head(5)

Out[74]:

	Name	Position	Office	Age	Start date	Salary
0	Name	Position	Office	Age	Start date	Salary
1	Airi Satou	Accountant	Tokyo	33	2008/11/28	$162,700
2	Angelica Ramos	Chief Executive Officer (CEO)	London	47	2009/10/09	$1,200,000
3	Ashton Cox	Junior Technical Author	San Francisco	66	2009/01/12	$86,000
4	Bradley Greer	Software Engineer	London	41	2012/10/13	$132,000

In [75]:

 
# df.drop(df['Name'] == "Name", inplace=True)df = df[1:]​df.head(3)

Out[75]:

	Name	Position	Office	Age	Start date	Salary
1	Airi Satou	Accountant	Tokyo	33	2008/11/28	$162,700
2	Angelica Ramos	Chief Executive Officer (CEO)	London	47	2009/10/09	$1,200,000
3	Ashton Cox	Junior Technical Author	San Francisco	66	2009/01/12	$86,000

In [76]:

 
df.drop('Position', axis = 1)​df.drop(['Name','Office'], axis=1)​df.head(2)

Out[76]:

	Name	Position	Office	Age	Start date	Salary
1	Airi Satou	Accountant	Tokyo	33	2008/11/28	$162,700
2	Angelica Ramos	Chief Executive Officer (CEO)	London	47	2009/10/09	$1,200,000

In [77]:

 
df.drop(1, inplace=True)​df.head(3)

Out[77]:

	Name	Position	Office	Age	Start date	Salary
2	Angelica Ramos	Chief Executive Officer (CEO)	London	47	2009/10/09	$1,200,000
3	Ashton Cox	Junior Technical Author	San Francisco	66	2009/01/12	$86,000
4	Bradley Greer	Software Engineer	London	41	2012/10/13	$132,000

In [78]:

 
df.drop([2,4],inplace=True)​df.head(3)

Out[78]:

	Name	Position	Office	Age	Start date	Salary
3	Ashton Cox	Junior Technical Author	San Francisco	66	2009/01/12	$86,000
5	Brenden Wagner	Software Engineer	San Francisco	28	2011/06/07	$206,850
6	Brielle Williamson	Integration Specialist	New York	61	2012/12/02	$372,000

In [80]:

 
df = df[:-2]df.head()

Out[80]:

	Name	Position	Office	Age	Start date	Salary
3	Ashton Cox	Junior Technical Author	San Francisco	66	2009/01/12	$86,000

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