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
我认为这个函数更简单。您只需在开始或结束处或同时指定列的子集:
def reorder_df_columns(df, start=None, end=None):
"""
This function reorder columns of a DataFrame.
It takes columns given in the list `start` and move them to the left.
Its also takes columns in `end` and move them to the right.
"""
if start is None:
start = []
if end is None:
end = []
assert isinstance(start, list) and isinstance(end, list)
cols = list(df.columns)
for c in start:
if c not in cols:
start.remove(c)
for c in end:
if c not in cols or c in start:
end.remove(c)
for c in start + end:
cols.remove(c)
cols = start + cols + end
return df[cols]
其他回答
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
在您的情况下,
df = df.reindex(columns=['mean',0,1,2,3,4])
会做你想做的事。
在我的情况下(一般形式):
df = df.reindex(columns=sorted(df.columns))
df = df.reindex(columns=(['opened'] + list([a for a in df.columns if a != 'opened']) ))
书中最黑客的方法
df.insert(0, "test", df["mean"])
df = df.drop(columns=["mean"]).rename(columns={"test": "mean"})
您需要按所需的顺序创建一个新的列列表,然后使用df=df[cols]以新的顺序重新排列列。
cols = ['mean'] + [col for col in df if col != 'mean']
df = df[cols]
您也可以使用更通用的方法。在本例中,最后一列(由-1表示)作为第一列插入。
cols = [df.columns[-1]] + [col for col in df if col != df.columns[-1]]
df = df[cols]
如果DataFrame中存在列,也可以使用此方法按所需顺序重新排序列。
inserted_cols = ['a', 'b', 'c']
cols = ([col for col in inserted_cols if col in df]
+ [col for col in df if col not in inserted_cols])
df = df[cols]
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)