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
大多数答案都不够概括,panda reindex_axis方法有点乏味,因此我提供了一个简单的函数,可以使用字典将任意数量的列移动到任意位置,其中key=列名,value=要移动到的位置。如果数据帧很大,请将True传递给“big_data”,那么函数将返回有序的列列表。您可以使用此列表来分割数据。
def order_column(df, columns, big_data = False):
"""Re-Orders dataFrame column(s)
Parameters :
df -- dataframe
columns -- a dictionary:
key = current column position/index or column name
value = position to move it to
big_data -- boolean
True = returns only the ordered columns as a list
the user user can then slice the data using this
ordered column
False = default - return a copy of the dataframe
"""
ordered_col = df.columns.tolist()
for key, value in columns.items():
ordered_col.remove(key)
ordered_col.insert(value, key)
if big_data:
return ordered_col
return df[ordered_col]
# e.g.
df = pd.DataFrame({'chicken wings': np.random.rand(10, 1).flatten(), 'taco': np.random.rand(10,1).flatten(),
'coffee': np.random.rand(10, 1).flatten()})
df['mean'] = df.mean(1)
df = order_column(df, {'mean': 0, 'coffee':1 })
>>>
col = order_column(df, {'mean': 0, 'coffee':1 }, True)
col
>>>
['mean', 'coffee', 'chicken wings', 'taco']
# you could grab it by doing this
df = df[col]
其他回答
大多数答案都不够概括,panda reindex_axis方法有点乏味,因此我提供了一个简单的函数,可以使用字典将任意数量的列移动到任意位置,其中key=列名,value=要移动到的位置。如果数据帧很大,请将True传递给“big_data”,那么函数将返回有序的列列表。您可以使用此列表来分割数据。
def order_column(df, columns, big_data = False):
"""Re-Orders dataFrame column(s)
Parameters :
df -- dataframe
columns -- a dictionary:
key = current column position/index or column name
value = position to move it to
big_data -- boolean
True = returns only the ordered columns as a list
the user user can then slice the data using this
ordered column
False = default - return a copy of the dataframe
"""
ordered_col = df.columns.tolist()
for key, value in columns.items():
ordered_col.remove(key)
ordered_col.insert(value, key)
if big_data:
return ordered_col
return df[ordered_col]
# e.g.
df = pd.DataFrame({'chicken wings': np.random.rand(10, 1).flatten(), 'taco': np.random.rand(10,1).flatten(),
'coffee': np.random.rand(10, 1).flatten()})
df['mean'] = df.mean(1)
df = order_column(df, {'mean': 0, 'coffee':1 })
>>>
col = order_column(df, {'mean': 0, 'coffee':1 }, True)
col
>>>
['mean', 'coffee', 'chicken wings', 'taco']
# you could grab it by doing this
df = df[col]
简单地说,
df = df[['mean'] + df.columns[:-1].tolist()]
你也可以这样做:
df = df[['mean', '0', '1', '2', '3']]
您可以通过以下方式获取列列表:
cols = list(df.columns.values)
输出将产生:
['0', '1', '2', '3', 'mean']
…然后,在将其放入第一个函数之前,可以手动重新排列
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
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