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
您可以使用一个集合,它是唯一元素的无序集合,以保持“其他列的顺序不变”:
other_columns = list(set(df.columns).difference(["mean"])) #[0, 1, 2, 3, 4]
然后,可以通过以下方式使用lambda将特定列移动到前面:
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(np.random.rand(10, 5))
In [4]: df["mean"] = df.mean(1)
In [5]: move_col_to_front = lambda df, col: df[[col]+list(set(df.columns).difference([col]))]
In [6]: move_col_to_front(df, "mean")
Out[6]:
mean 0 1 2 3 4
0 0.697253 0.600377 0.464852 0.938360 0.945293 0.537384
1 0.609213 0.703387 0.096176 0.971407 0.955666 0.319429
2 0.561261 0.791842 0.302573 0.662365 0.728368 0.321158
3 0.518720 0.710443 0.504060 0.663423 0.208756 0.506916
4 0.616316 0.665932 0.794385 0.163000 0.664265 0.793995
5 0.519757 0.585462 0.653995 0.338893 0.714782 0.305654
6 0.532584 0.434472 0.283501 0.633156 0.317520 0.994271
7 0.640571 0.732680 0.187151 0.937983 0.921097 0.423945
8 0.562447 0.790987 0.200080 0.317812 0.641340 0.862018
9 0.563092 0.811533 0.662709 0.396048 0.596528 0.348642
In [7]: move_col_to_front(df, 2)
Out[7]:
2 0 1 3 4 mean
0 0.938360 0.600377 0.464852 0.945293 0.537384 0.697253
1 0.971407 0.703387 0.096176 0.955666 0.319429 0.609213
2 0.662365 0.791842 0.302573 0.728368 0.321158 0.561261
3 0.663423 0.710443 0.504060 0.208756 0.506916 0.518720
4 0.163000 0.665932 0.794385 0.664265 0.793995 0.616316
5 0.338893 0.585462 0.653995 0.714782 0.305654 0.519757
6 0.633156 0.434472 0.283501 0.317520 0.994271 0.532584
7 0.937983 0.732680 0.187151 0.921097 0.423945 0.640571
8 0.317812 0.790987 0.200080 0.641340 0.862018 0.562447
9 0.396048 0.811533 0.662709 0.596528 0.348642 0.563092
其他回答
如果列名太长,无法键入,则可以通过整数列表指定新顺序,其中包含以下位置:
数据:
0 1 2 3 4 mean
0 0.397312 0.361846 0.719802 0.575223 0.449205 0.500678
1 0.287256 0.522337 0.992154 0.584221 0.042739 0.485741
2 0.884812 0.464172 0.149296 0.167698 0.793634 0.491923
3 0.656891 0.500179 0.046006 0.862769 0.651065 0.543382
4 0.673702 0.223489 0.438760 0.468954 0.308509 0.422683
5 0.764020 0.093050 0.100932 0.572475 0.416471 0.389390
6 0.259181 0.248186 0.626101 0.556980 0.559413 0.449972
7 0.400591 0.075461 0.096072 0.308755 0.157078 0.207592
8 0.639745 0.368987 0.340573 0.997547 0.011892 0.471749
9 0.050582 0.714160 0.168839 0.899230 0.359690 0.438500
通用示例:
new_order = [3,2,1,4,5,0]
print(df[df.columns[new_order]])
3 2 1 4 mean 0
0 0.575223 0.719802 0.361846 0.449205 0.500678 0.397312
1 0.584221 0.992154 0.522337 0.042739 0.485741 0.287256
2 0.167698 0.149296 0.464172 0.793634 0.491923 0.884812
3 0.862769 0.046006 0.500179 0.651065 0.543382 0.656891
4 0.468954 0.438760 0.223489 0.308509 0.422683 0.673702
5 0.572475 0.100932 0.093050 0.416471 0.389390 0.764020
6 0.556980 0.626101 0.248186 0.559413 0.449972 0.259181
7 0.308755 0.096072 0.075461 0.157078 0.207592 0.400591
8 0.997547 0.340573 0.368987 0.011892 0.471749 0.639745
9 0.899230 0.168839 0.714160 0.359690 0.438500 0.050582
虽然看起来我只是以不同的顺序显式键入列名,但列“mean”的事实应该清楚地表明,new_order与实际位置相关,而不是列名。
对于OP问题的具体情况:
new_order = [-1,0,1,2,3,4]
df = df[df.columns[new_order]]
print(df)
mean 0 1 2 3 4
0 0.500678 0.397312 0.361846 0.719802 0.575223 0.449205
1 0.485741 0.287256 0.522337 0.992154 0.584221 0.042739
2 0.491923 0.884812 0.464172 0.149296 0.167698 0.793634
3 0.543382 0.656891 0.500179 0.046006 0.862769 0.651065
4 0.422683 0.673702 0.223489 0.438760 0.468954 0.308509
5 0.389390 0.764020 0.093050 0.100932 0.572475 0.416471
6 0.449972 0.259181 0.248186 0.626101 0.556980 0.559413
7 0.207592 0.400591 0.075461 0.096072 0.308755 0.157078
8 0.471749 0.639745 0.368987 0.340573 0.997547 0.011892
9 0.438500 0.050582 0.714160 0.168839 0.899230 0.359690
这种方法的主要问题是多次调用同一代码将每次产生不同的结果,因此需要小心:)
这里有一种移动一个现有列的方法,它将修改现有的数据帧。
my_column = df.pop('column name')
df.insert(3, my_column.name, my_column) # Is in-place
您可以使用以下名称列表对数据帧列进行重新排序:
df=df.filter(list_of_col_name)
我尝试了创建一个order函数,您可以使用Stata的order命令对列进行重新排序/移动。最好创建一个py文件(其名称可能是order.py),并将其保存在目录中并调用它的函数
def order(dataframe,cols,f_or_l=None,before=None, after=None):
#만든이: 김완석, Stata로 뚝딱뚝딱 저자, blog.naver.com/sanzo213 운영
# 갖다 쓰시거나 수정을 하셔도 되지만 출처는 꼭 밝혀주세요
# cols옵션 및 befor/after옵션에 튜플이 가능하게끔 수정했으며, 오류문구 수정함(2021.07.12,1)
# 칼럼이 멀티인덱스인 상태에서 reset_index()메소드 사용했을 시 적용안되는 걸 수정함(2021.07.12,2)
import pandas as pd
if (type(cols)==str) or (type(cols)==int) or (type(cols)==float) or (type(cols)==bool) or type(cols)==tuple:
cols=[cols]
dd=list(dataframe.columns)
for i in cols:
i
dd.remove(i) #cols요소를 제거함
if (f_or_l==None) & ((before==None) & (after==None)):
print('f_or_l옵션을 쓰시거나 아니면 before옵션/after옵션 쓰셔야되요')
if ((f_or_l=='first') or (f_or_l=='last')) & ~((before==None) & (after==None)):
print('f_or_l옵션 사용시 before after 옵션 사용불가입니다.')
if (f_or_l=='first') & (before==None) & (after==None):
new_order=cols+dd
dataframe=dataframe[new_order]
return dataframe
if (f_or_l=='last') & (before==None) & (after==None):
new_order=dd+cols
dataframe=dataframe[new_order]
return dataframe
if (before!=None) & (after!=None):
print('before옵션 after옵션 둘다 쓸 수 없습니다.')
if (before!=None) & (after==None) & (f_or_l==None):
if not((type(before)==str) or (type(before)==int) or (type(before)==float) or
(type(before)==bool) or ((type(before)!=list)) or
((type(before)==tuple))):
print('before옵션은 칼럼 하나만 입력가능하며 리스트 형태로도 입력하지 마세요.')
else:
b=dd[:dd.index(before)]
a=dd[dd.index(before):]
new_order=b+cols+a
dataframe=dataframe[new_order]
return dataframe
if (after!=None) & (before==None) & (f_or_l==None):
if not((type(after)==str) or (type(after)==int) or (type(after)==float) or
(type(after)==bool) or ((type(after)!=list)) or
((type(after)==tuple))):
print('after옵션은 칼럼 하나만 입력가능하며 리스트 형태로도 입력하지 마세요.')
else:
b=dd[:dd.index(after)+1]
a=dd[dd.index(after)+1:]
new_order=b+cols+a
dataframe=dataframe[new_order]
return dataframe
下面的python代码是我制作的order函数的一个示例。我希望您可以使用我的order函数轻松地对列进行重新排序:)
# module
import pandas as pd
import numpy as np
from order import order # call order function from order.py file
# make a dataset
columns='a b c d e f g h i j k'.split()
dic={}
n=-1
for i in columns:
n+=1
dic[i]=list(range(1+n,10+1+n))
data=pd.DataFrame(dic)
print(data)
# use order function (1) : order column e in the first
data2=order(data,'e',f_or_l='first')
print(data2)
# use order function (2): order column e in the last , "data" dataframe
print(order(data,'e',f_or_l='last'))
# use order function (3) : order column i before column c in "data" dataframe
print(order(data,'i',before='c'))
# use order function (4) : order column g after column b in "data" dataframe
print(order(data,'g',after='b'))
# use order function (4) : order columns ['c', 'd', 'e'] after column i in "data" dataframe
print(order(data,['c', 'd', 'e'],after='i'))
要根据其他列的名称将现有列设置为右侧/左侧,请执行以下操作:
def df_move_column(df, col_to_move, col_left_of_destiny="", right_of_col_bool=True):
cols = list(df.columns.values)
index_max = len(cols) - 1
if not right_of_col_bool:
# set left of a column "c", is like putting right of column previous to "c"
# ... except if left of 1st column, then recursive call to set rest right to it
aux = cols.index(col_left_of_destiny)
if not aux:
for g in [x for x in cols[::-1] if x != col_to_move]:
df = df_move_column(
df,
col_to_move=g,
col_left_of_destiny=col_to_move
)
return df
col_left_of_destiny = cols[aux - 1]
index_old = cols.index(col_to_move)
index_new = 0
if len(col_left_of_destiny):
index_new = cols.index(col_left_of_destiny) + 1
if index_old == index_new:
return df
if index_new < index_old:
index_new = np.min([index_new, index_max])
cols = (
cols[:index_new]
+ [cols[index_old]]
+ cols[index_new:index_old]
+ cols[index_old + 1 :]
)
else:
cols = (
cols[:index_old]
+ cols[index_old + 1 : index_new]
+ [cols[index_old]]
+ cols[index_new:]
)
df = df[cols]
return df
E.g.
cols = list("ABCD")
df2 = pd.DataFrame(np.arange(4)[np.newaxis, :], columns=cols)
for k in cols:
print(30 * "-")
for g in [x for x in cols if x != k]:
df_new = df_move_column(df2, k, g)
print(f"{k} after {g}: {df_new.columns.values}")
for k in cols:
print(30 * "-")
for g in [x for x in cols if x != k]:
df_new = df_move_column(df2, k, g, right_of_col_bool=False)
print(f"{k} before {g}: {df_new.columns.values}")
输出: