Question

我不太清楚如何解释我的问题但我需要通过插入几乎空的行来修改DataFrame 对于软件格式兼容性问题。

这是一个例子：

我需要更改此类Dataframe：

df = pd.DataFrame({"line1": [200, 400, 800], 
                   "line2": [400, 900, 700], 
                   "line3": [800, 700, 966], 
                   "name": ["bla", "bloo", "bloom"})
print df

   line1  line2  line3   name
0    200    400    800    bla
1    400    900    700   bloo
2    800    700    966  bloom

对于这样的事情：

   line_name  line1  line2  line3
0  ID
1  name 
2  bla        200     400    800 
3  bloo       400     900    700
4  bloom      800     700    966

当然，真正的数据帧有更多的行和列。所以我正在寻找一种方法，可以处理可变数量的列，而无需手动添加＆＃34;空白＆＃34;在线列下一个接一个。

我尝试了一些Groupby方法以及制作了2个数据框（一个只有line，ID，name结构，另一个只有{{1} }和names然后将它们合并但没有成功。

任何想法都将不胜感激。

Answer 1

不确定这正是您想要的。根据给出的示例数据框，您可以尝试：

df = pd.DataFrame({"line1": [200, 400, 800], "line2": [400, 900, 700], "line3": [800, 700, 966], "name": ["bla", "bloo", "bloom"]})
dftemp=pd.DataFrame(columns=df.columns)
dftemp.loc[0]=(len(df.columns)-1)*['']+['ID']
dftemp.loc[1]=(len(df.columns)-1)*['']+['name']
dfnew= dftemp.append(df,ignore_index=True)
dfnew.rename(columns={'name':'line_name'}, inplace=True)
cols = dfnew.columns.tolist()
cols = cols[-1:]+cols[:-1]
dfnew = dfnew[cols]
print(dfnew)

Output:
      line_name line1 line2 line3
0        ID                  
1      name                  
2       bla   200   400   800
3      bloo   400   900   700
4     bloom   800   700   966

Answer 2

您可以使用Setting With Enlargement尝试解决方案：

import pandas as pd
import numpy as np

df = pd.DataFrame({"line1": [200, 400, 800],
                   "line2": [400, 900, 700], 
                   "line3": [800, 700, 966], 
                   "name": ["bla", "bloo", "bloom"]})
print df

   line1  line2  line3   name
0    200    400    800    bla
1    400    900    700   bloo
2    800    700    966  bloom
#create empty lists with last item name and ID by length of dataframe
#add to df two lines
df.loc[-1] = [np.nan for i in range(df.shape[1] - 1) ] + ['name']
df.loc[-2] = [np.nan for i in range(df.shape[1] - 1) ] + ['ID']
print df

    line1  line2  line3   name
 0    200    400    800    bla
 1    400    900    700   bloo
 2    800    700    966  bloom
-1    NaN    NaN    NaN   name
-2    NaN    NaN    NaN     ID

#sort and reset index, rename column and fill nan to empty string
df = df.sort_index().reset_index(drop=True).rename(columns={'name':'line_name'}).fillna('')
#reorder columns
df = df[['line_name','line1','line2','line3']]

print df

  line_name line1 line2 line3
0        ID                  
1      name                  
2       bla   200   400   800
3      bloo   400   900   700
4     bloom   800   700   966

Answer 3

df = pd.DataFrame({"line1": [200, 400, 800], "line2": [400, 900, 700], "line3": [800, 700, 966], "name": ["bla", "bloo", "bloom"]}) df.loc[-1] = [np.nan for i in range(df.shape[1] - 1) ] + ['name'] df.loc[-2] = [np.nan for i in range(df.shape[1] -1)] + ['ID'] df = df.fillna('') df=df.sort_index() df=df.reset_index() df.loc[:,['name','line1','line2','line3']]

Pandas：如何通过插入具有空值的行来更改DataFrame的结构

3 个答案: