我有以下DataFrame(df):
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 5))
我通过分配添加更多列:
df['mean'] = df.mean(1)
如何将列的意思移到前面,即将其设置为第一列,而其他列的顺序保持不变?
我有以下DataFrame(df):
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 5))
我通过分配添加更多列:
df['mean'] = df.mean(1)
如何将列的意思移到前面,即将其设置为第一列,而其他列的顺序保持不变?
当前回答
我有一个在panda中重新排序列名的非常具体的用例。有时我在基于现有列的数据帧中创建一个新列。默认情况下,panda将在末尾插入我的新列,但我希望新列插入到它派生的现有列旁边。
def rearrange_list(input_list, input_item_to_move, input_item_insert_here):
'''
Helper function to re-arrange the order of items in a list.
Useful for moving column in pandas dataframe.
Inputs:
input_list - list
input_item_to_move - item in list to move
input_item_insert_here - item in list, insert before
returns:
output_list
'''
# make copy for output, make sure it's a list
output_list = list(input_list)
# index of item to move
idx_move = output_list.index(input_item_to_move)
# pop off the item to move
itm_move = output_list.pop(idx_move)
# index of item to insert here
idx_insert = output_list.index(input_item_insert_here)
# insert item to move into here
output_list.insert(idx_insert, itm_move)
return output_list
import pandas as pd
# step 1: create sample dataframe
df = pd.DataFrame({
'motorcycle': ['motorcycle1', 'motorcycle2', 'motorcycle3'],
'initial_odometer': [101, 500, 322],
'final_odometer': [201, 515, 463],
'other_col_1': ['blah', 'blah', 'blah'],
'other_col_2': ['blah', 'blah', 'blah']
})
print('Step 1: create sample dataframe')
display(df)
print()
# step 2: add new column that is difference between final and initial
df['change_odometer'] = df['final_odometer']-df['initial_odometer']
print('Step 2: add new column')
display(df)
print()
# step 3: rearrange columns
ls_cols = df.columns
ls_cols = rearrange_list(ls_cols, 'change_odometer', 'final_odometer')
df=df[ls_cols]
print('Step 3: rearrange columns')
display(df)
其他回答
下面是一个超级简单的方法示例。如果您要从excel复制标题,请使用.split('\t')
df = df['FILE_NAME DISPLAY_PATH SHAREPOINT_PATH RETAILER LAST_UPDATE'.split()]
假设您有列为A、B、C的df。
最简单的方法是:
df = df.reindex(['B','C','A'], axis=1)
这里有一个函数可以对任意数量的列执行此操作。
def mean_first(df):
ncols = df.shape[1] # Get the number of columns
index = list(range(ncols)) # Create an index to reorder the columns
index.insert(0,ncols) # This puts the last column at the front
return(df.assign(mean=df.mean(1)).iloc[:,index]) # new df with last column (mean) first
熊猫>=1.3(2022年编辑):
df.insert(0, 'mean', df.pop('mean'))
怎么样(对于熊猫<1.3,原始答案)
df.insert(0, 'mean', df['mean'])
https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-选择添加删除
只需按所需顺序分配列名:
In [39]: df
Out[39]:
0 1 2 3 4 mean
0 0.172742 0.915661 0.043387 0.712833 0.190717 1
1 0.128186 0.424771 0.590779 0.771080 0.617472 1
2 0.125709 0.085894 0.989798 0.829491 0.155563 1
3 0.742578 0.104061 0.299708 0.616751 0.951802 1
4 0.721118 0.528156 0.421360 0.105886 0.322311 1
5 0.900878 0.082047 0.224656 0.195162 0.736652 1
6 0.897832 0.558108 0.318016 0.586563 0.507564 1
7 0.027178 0.375183 0.930248 0.921786 0.337060 1
8 0.763028 0.182905 0.931756 0.110675 0.423398 1
9 0.848996 0.310562 0.140873 0.304561 0.417808 1
In [40]: df = df[['mean', 4,3,2,1]]
现在,“mean”列出现在前面:
In [41]: df
Out[41]:
mean 4 3 2 1
0 1 0.190717 0.712833 0.043387 0.915661
1 1 0.617472 0.771080 0.590779 0.424771
2 1 0.155563 0.829491 0.989798 0.085894
3 1 0.951802 0.616751 0.299708 0.104061
4 1 0.322311 0.105886 0.421360 0.528156
5 1 0.736652 0.195162 0.224656 0.082047
6 1 0.507564 0.586563 0.318016 0.558108
7 1 0.337060 0.921786 0.930248 0.375183
8 1 0.423398 0.110675 0.931756 0.182905
9 1 0.417808 0.304561 0.140873 0.310562