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
import numpy as np
import pandas as pd
df = pd.DataFrame()
column_names = ['x','y','z','mean']
for col in column_names:
df[col] = np.random.randint(0,100, size=10000)
您可以尝试以下解决方案:
解决方案1:
df = df[ ['mean'] + [ col for col in df.columns if col != 'mean' ] ]
解决方案2:
df = df[['mean', 'x', 'y', 'z']]
解决方案3:
col = df.pop("mean")
df = df.insert(0, col.name, col)
解决方案4:
df.set_index(df.columns[-1], inplace=True)
df.reset_index(inplace=True)
解决方案5:
cols = list(df)
cols = [cols[-1]] + cols[:-1]
df = df[cols]
解决方案6:
order = [1,2,3,0] # setting column's order
df = df[[df.columns[i] for i in order]]
时间比较:
解决方案1:
CPU时间:用户1.05 ms,sys:35µs,总计:1.08 ms壁时间:995µs
解决方案2:
CPU时间:用户933µs,系统:0 ns,总计:933µ壁时间:800µs
解决方案3:
CPU时间:用户0 ns,sys:1.35 ms,总计:1.35 ms壁时间:1.08 ms
解决方案4:
CPU时间:用户1.23毫秒,系统:45µs,总计:1.27毫秒壁时间:986µs
解决方案5:
CPU时间:用户1.09 ms,系统:19µs,总计:1.11 ms壁时间:949µs
解决方案6:
CPU时间:用户955µs,系统:34µs,总计:989µs壁时间:859µs
其他回答
一种简单的方法是用列列表重新分配数据帧,根据需要重新排列。
这就是你现在拥有的:
In [6]: df
Out[6]:
0 1 2 3 4 mean
0 0.445598 0.173835 0.343415 0.682252 0.582616 0.445543
1 0.881592 0.696942 0.702232 0.696724 0.373551 0.670208
2 0.662527 0.955193 0.131016 0.609548 0.804694 0.632596
3 0.260919 0.783467 0.593433 0.033426 0.512019 0.436653
4 0.131842 0.799367 0.182828 0.683330 0.019485 0.363371
5 0.498784 0.873495 0.383811 0.699289 0.480447 0.587165
6 0.388771 0.395757 0.745237 0.628406 0.784473 0.588529
7 0.147986 0.459451 0.310961 0.706435 0.100914 0.345149
8 0.394947 0.863494 0.585030 0.565944 0.356561 0.553195
9 0.689260 0.865243 0.136481 0.386582 0.730399 0.561593
In [7]: cols = df.columns.tolist()
In [8]: cols
Out[8]: [0L, 1L, 2L, 3L, 4L, 'mean']
按任意方式重新排列列。这是我将最后一个元素移动到第一个位置的方式:
In [12]: cols = cols[-1:] + cols[:-1]
In [13]: cols
Out[13]: ['mean', 0L, 1L, 2L, 3L, 4L]
然后重新排序数据帧,如下所示:
In [16]: df = df[cols] # OR df = df.ix[:, cols]
In [17]: df
Out[17]:
mean 0 1 2 3 4
0 0.445543 0.445598 0.173835 0.343415 0.682252 0.582616
1 0.670208 0.881592 0.696942 0.702232 0.696724 0.373551
2 0.632596 0.662527 0.955193 0.131016 0.609548 0.804694
3 0.436653 0.260919 0.783467 0.593433 0.033426 0.512019
4 0.363371 0.131842 0.799367 0.182828 0.683330 0.019485
5 0.587165 0.498784 0.873495 0.383811 0.699289 0.480447
6 0.588529 0.388771 0.395757 0.745237 0.628406 0.784473
7 0.345149 0.147986 0.459451 0.310961 0.706435 0.100914
8 0.553195 0.394947 0.863494 0.585030 0.565944 0.356561
9 0.561593 0.689260 0.865243 0.136481 0.386582 0.730399
DataFrame.sort_index(axis=1)非常干净。请在此处检查文档。然后凹入
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
只需键入要更改的列名,然后为新位置设置索引。
def change_column_order(df, col_name, index):
cols = df.columns.tolist()
cols.remove(col_name)
cols.insert(index, col_name)
return df[cols]
对于您的情况,这将是:
df = change_column_order(df, 'mean', 0)
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: