[Python] How to delete rows and columns in a table (list of drop method options)

[Python] How to delete rows and columns in a table (list of drop method options)

How to delete a table element in a Pandas DataFrame. A summary of the drop method options based on actual usage.

It is complicated because there is the same processing with different descriptions, You can delete a matrix by remembering "columns = label" and "index = label".

Click here for a list of options ▶ [Drop method option list quick reference table](# 1 drop method option list quick reference table)

1. [Drop method / option list quick reference table](# 1 drop method option list quick reference table)
2. [Delete matrix with drop method](# 2 Delete matrix with drop method)
   1. drop(label, axis)
3. [axis = 0 (optional)](# 1-1-axis0) 2. axis='index' 3. axis=1 4. axis='columns' 2. drop(index=label) 3. drop(columns=label)
4. [Other options for drop method](# 3 Other options for drop method)
5. [Allow overwrite](# Allow overwrite)
6. [Hide error](#Hide error)

--"Df": Variable with table --"Label": row or column name --Create another object without overwriting the default --Matrix can be specified only with either "index =" and "columns =" or "axis = 1".

Table code

import pandas as pd

listA = ['AAA', 'BBB', 'CCC', 'DDD']
listB = ['EEE', 'FFF', 'GGG', 'HHH']
listC = ['III', 'JJJ', 'KKK', 'LLL']
listD = ['MMM', 'NNN', 'OOO', 'PPP']

df = pd.DataFrame(listA)
df[1] = listB
df[2] = listC
df[3] = listD

df.columns = ['aaa', 'bbb', 'ccc', 'ddd']
df.index = [111,222,333,444]

df

①drop(label, axis) ②drop(index=label) ③drop(columns=label)

** Basic type ** df.drop(label, axis=A) └ "df": Table data (DataFrame type) └ "drop ()": Drop method └ "label": Row / column name to delete └ "axis = A": Specify row or column with A

** ▼ Point ** --Specify either row or column with axis. --There are 4 patterns for specifying axsi. --A new DataFrame object is created with the original table intact. --Can be restored even if erased by mistake

** ▼ How to specify 4 axes **

Delete by line name

The following two axes (three including omissions) can be described.

① axis = 0 (optional) ②axis='index'

(1-1) axis=0 df.drop([label1,label2,,,], axis=0) df.drop([label1,label2,,,])

Even if axis = 0 is ** omitted **, the same processing will be performed.

** ▼ Specify only one column **

（axis=0) Omitted

df.drop(333)

#output

	aaa	bbb	ccc	ddd
111	AAA	EEE	III	MMM
222	BBB	FFF	JJJ	NNN
444	DDD	HHH	LLL	PPP

（axis=0) No omission

df.drop(333, axis=0)

#output

	aaa	bbb	ccc	ddd
111	AAA	EEE	III	MMM
222	BBB	FFF	JJJ	NNN
444	DDD	HHH	LLL	PPP

Multiple column names

df.drop([111,333])

#output

	aaa	bbb	ccc	ddd
222	BBB	FFF	JJJ	NNN
444	DDD	HHH	LLL	PPP

df.drop([label1,label2,,,], axis='index')

axis='index'

df.drop([111,333], axis='index')

#output

	aaa	bbb	ccc	ddd
222	BBB	FFF	JJJ	NNN
444	DDD	HHH	LLL	PPP

①axis=1 ②axis='columns'

(1-3) axis=1

df.drop([label1,label2,,,], axis=1) └ “1” of axis = 1 points to the row of column name.

** ▼ Specify only one column ** In the case of one column, the same processing is performed even if there is no [].

df.drop(['AAA'], axis=1) df.drop('AAA', axis=1)

（axis=1）

df.drop('bbb', axis=1)

#output

	aaa	ccc	ddd
111	AAA	III	MMM
222	BBB	JJJ	NNN
333	CCC	KKK	OOO
444	DDD	LLL	PPP

Multiple column names

df.drop(['bbb','ddd'], axis=1)

#output

	aaa	ccc
111	AAA	III
222	BBB	JJJ
333	CCC	KKK
444	DDD	LLL

df.drop([label1,label2,,,], axis='columns')

axis='columns'

df.drop(['bbb','ddd'], axis='columns')

#output

	aaa	ccc
111	AAA	III
222	BBB	JJJ
333	CCC	KKK
444	DDD	LLL

df.drop(index=label) └ Describe the line name you want to delete in "label".

index=label

df.drop(index=[222,444])

#output

	aaa	bbb	ccc	ddd
111	AAA	EEE	III	MMM
333	CCC	GGG	KKK	OOO

The process is the same as the following three. df.drop(label) df.drop(label, axis=0) df.drop(label, axis='index')

df.drop(columns=label) └ "label": Describe the column name you want to delete

index=label

df.drop(columns=['ddd','bbb'])

#output

	aaa	ccc
111	AAA	III
222	BBB	JJJ
333	CCC	KKK
444	DDD	LLL

Same as the following two. df.drop(label, axis=1) df.drop(label, axis='columns')

① Allow overwriting

The default does not change the original table and creates a new object. (It can be restored even if it is deleted by mistake)

Option to turn off this feature. inplace=True └ Default is "False"

Allow overwrite

df.drop(columns=['ddd','bbb'], inplace=True)

df

#output
	aaa	ccc
111	AAA	III
222	BBB	JJJ
333	CCC	KKK
444	DDD	LLL

The original table (df) has been rewritten.

Delete non-existent column

df.drop(columns=['xxx'], errors='ignore')

Nothing is displayed.

If there is no description of "errors ='ignore'", it will be displayed as follows. ⇒「KeyError: "['xxx'] not found in axis"」