# setting up a dummy dataframe
raw_data ={'name':['Willard Morris','Al Jennings','Omar Mullins','Spencer McDaniel'],'age':[20,19,22,21],'favorite_color':['blue','red','yellow',"green"],'grade':[88,92,95,70]}
df = pd.DataFrame(raw_data, index =['Willard Morris','Al Jennings','Omar Mullins','Spencer McDaniel'])
df
#now 'age' will appear at the end of our df
df = df[['favorite_color','grade','name','age']]
df.head()
#old df columns
df.columns
Index(['A','B','C','D'],dtype='***')#new column format that we want to rearange
new_col =['D','C','B','A']#list of column name in order that we want
df = df[new_col]
df.columns
Index(['D','C','B','A'],dtype='***')#new column order
# Get column list in ['item1','item2','item3'] format
df.columns
# [0]output:
Index(['item1','item2','item3'], dtype='object')# Copy just the list portion of the output and rearrange the columns
cols =['item3','item1','item2']# Resave dataframe using new column order
df = df[cols]
You could also do something like this:
df = df[['mean','0','1','2','3']]
You can get the list of columns with:
cols =list(df.columns.values)
The output will produce:['0','1','2','3','mean']