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如何更改 pandas dataframe 中两列的位置

yipeiwu_com7年前 (2020-03-06)Python基础

如何更改 pandas dataframe 中两列的位置：

把其中的某列移到第一列的位置。

原来的 df 是：

df = pd.read_csv('I:/Papers/consumer/codeandpaper/TmallData/result01.csv')
               Net  Upper  Lower Mid Zsore
Answer option                        
More than once a day     0%  0.22% -0.12%  2  65 
Once a day          0%  0.32% -0.19%  3  45
Several times a week     2%  2.45%  1.10%  4  78
Once a week          1%  1.63% -0.40%  6  65

要将 Mid 这一列移动到第一列?

  　　　　　　　　　　　　　　　Mid  Upper  Lower Net Zsore
Answer option                        
More than once a day     2  0.22% -0.12%  0%  65 
Once a day          3  0.32% -0.19%  0%  45
Several times a week     4  2.45%  1.10%  2%  78
Once a week          6  1.63% -0.40%  1%  65

解决办法：（使用 ix ）

法一：

In [27]:
# get a list of columns
cols = list(df)
# move the column to head of list using index, pop and insert
cols.insert(0, cols.pop(cols.index('Mid')))
cols
Out[27]:
['Mid', 'Net', 'Upper', 'Lower', 'Zsore']
In [28]:
# use ix to reorder
df = df.ix[:, cols]
df
Out[28]:
           Mid Net Upper  Lower Zsore
Answer_option                   
More_than_once_a_day  2 0% 0.22% -0.12%   65
Once_a_day       3 0% 0.32% -0.19%   45
Several_times_a_week  4 2% 2.45%  1.10%   78
Once_a_week       6 1% 1.63% -0.40%   65

法二：

In [39]:
mid = df['Mid']
df.drop(labels=['Mid'], axis=1,inplace = True)
df.insert(0, 'Mid', mid)
df
Out[39]:
           Mid Net Upper  Lower Zsore
Answer_option                   
More_than_once_a_day  2 0% 0.22% -0.12%   65
Once_a_day       3 0% 0.32% -0.19%   45
Several_times_a_week  4 2% 2.45%  1.10%   78
Once_a_week       6 1% 1.63% -0.40%   65

#### full data
df = pd.read_csv('I:/Papers/consumer/codeandpaper/TmallData/result01.csv')
def func(x):
  return str(x['time_stamp'])+str(x['user_id'])
df['session_id'] = df.apply(func, axis=1)
del df['time_stamp']

sessionID=df['session_id']
df.drop(labels=['session_id'],axis=1,inplace=True)
df.insert(0,'session_id',sessionID)
df.to_csv('I:/Papers/consumer/codeandpaper/TmallData/result02.csv')

最终的处理结果：