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
对我来说,一个非常简单的解决方案是在df.columns上使用.rendex:
df = df[df.columns.reindex(['mean', 0, 1, 2, 3, 4])[0]]
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)
您可以使用可用于两个轴的重新索引:
df
# 0 1 2 3 4 mean
# 0 0.943825 0.202490 0.071908 0.452985 0.678397 0.469921
# 1 0.745569 0.103029 0.268984 0.663710 0.037813 0.363821
# 2 0.693016 0.621525 0.031589 0.956703 0.118434 0.484254
# 3 0.284922 0.527293 0.791596 0.243768 0.629102 0.495336
# 4 0.354870 0.113014 0.326395 0.656415 0.172445 0.324628
# 5 0.815584 0.532382 0.195437 0.829670 0.019001 0.478415
# 6 0.944587 0.068690 0.811771 0.006846 0.698785 0.506136
# 7 0.595077 0.437571 0.023520 0.772187 0.862554 0.538182
# 8 0.700771 0.413958 0.097996 0.355228 0.656919 0.444974
# 9 0.263138 0.906283 0.121386 0.624336 0.859904 0.555009
df.reindex(['mean', *range(5)], axis=1)
# mean 0 1 2 3 4
# 0 0.469921 0.943825 0.202490 0.071908 0.452985 0.678397
# 1 0.363821 0.745569 0.103029 0.268984 0.663710 0.037813
# 2 0.484254 0.693016 0.621525 0.031589 0.956703 0.118434
# 3 0.495336 0.284922 0.527293 0.791596 0.243768 0.629102
# 4 0.324628 0.354870 0.113014 0.326395 0.656415 0.172445
# 5 0.478415 0.815584 0.532382 0.195437 0.829670 0.019001
# 6 0.506136 0.944587 0.068690 0.811771 0.006846 0.698785
# 7 0.538182 0.595077 0.437571 0.023520 0.772187 0.862554
# 8 0.444974 0.700771 0.413958 0.097996 0.355228 0.656919
# 9 0.555009 0.263138 0.906283 0.121386 0.624336 0.859904
我想到了和Dmitriy Work一样的答案,显然是最简单的:
df["mean"] = df.mean(1)
l = list(np.arange(0,len(df.columns) -1 ))
l.insert(0,-1)
df.iloc[:,l]
我想在一个数据帧前面加上两列,我不知道所有列的确切名称,因为它们是从之前的pivot语句生成的。所以,如果你也遇到同样的情况:把你知道名字的列放在前面,然后让它们跟着“所有其他列”,我提出了以下一般解决方案:
df = df.reindex_axis(['Col1','Col2'] + list(df.columns.drop(['Col1','Col2'])), axis=1)