Pandas 使用 append 创建新列

Question

我正在尝试将多个文本文件编译成一个数据框。但是，当我使用 Pandas Concat 函数加入数据框时，生成的数据框的形状会添加新列。在下面的代码示例中，数据框 3 有 12 列而不是 8 列。为什么？

**Input:**
import pandas as pd

df1 = pd.read_csv('2011-12-01-data.txt',sep = None, engine = 'python')
df2 = pd.read_csv('2011-12-02-data.txt',sep = None, engine = 'python')
df3= pd.concat([df1, df2])

print(df1.shape)
print(df2.shape)
print(df3.shape)

**Output:** 
df1 shape = (26986, 8)
df1 shape =(27266, 8)
df3 shape =(54252, 12)

我正在使用http://lunadong.com/datasets/clean_flight.zip提供的航班数据

Answer 1

我认为默认列名称0-7 需要header=None 参数，因为文件没有标题。另外，如果有分隔符tab，也可以指定。

df1 = pd.read_csv('2011-12-01-data.txt',sep = '\t', engine = 'python', header=None)
df2 = pd.read_csv('2011-12-02-data.txt',sep = '\t', engine = 'python', header=None)
df3= pd.concat([df1, df2])

print(df1.shape)
print(df2.shape)
print(df3.shape)
(26987, 8)
(27267, 8)
(54254, 8)

print(df1.columns)
Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64')
print(df2.columns)
Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64')
print(df3.columns)
Int64Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64')

另一种解决方案是为新列名指定names 参数：

names= ['col1','col2','col3','col4','col5','col6','col7','col8']
df1 = pd.read_csv('2011-12-01-data.txt',sep = '\t', engine = 'python', names=names)
df2 = pd.read_csv('2011-12-02-data.txt',sep = '\t', engine = 'python', names=names)
df3= pd.concat([df1, df2])

print(df1.shape)
print(df2.shape)
print(df3.shape)
(26987, 8)
(27267, 8)
(54254, 8)

print(df1.columns)
print(df2.columns)
print(df3.columns)
Index(['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8'], dtype='object')
Index(['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8'], dtype='object')
Index(['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7', 'col8'], dtype='object')

---

您只得到 12 列，因为两个数据框的第一行中的某些值是相同的，因此从中创建了列名。 concat 之后的列仅针对此列对齐。如果值不同，则没有对齐，您会得到NaNs。

print(df1.columns)
Index(['aa', 'AA-1007-TPA-MIA', '12/01/2011 01:55 PM', '12/01/2011 02:07 PM',
       'F78', '12/01/2011 03:00 PM', '12/01/2011 02:57 PM', 'D5'],
      dtype='object')

print(df2.columns)
Index(['aa', 'AA-1007-TPA-MIA', '12/02/2011 01:55 PM', '12/02/2011 02:13 PM',
       'F78', '12/02/2011 03:00 PM', '12/02/2011 03:05 PM', 'D5'],
      dtype='object')

print(df3.columns)
Index(['12/01/2011 01:55 PM', '12/01/2011 02:07 PM', '12/01/2011 02:57 PM',
       '12/01/2011 03:00 PM', '12/02/2011 01:55 PM', '12/02/2011 02:13 PM',
       '12/02/2011 03:00 PM', '12/02/2011 03:05 PM', 'AA-1007-TPA-MIA', 'D5',
       'F78', 'aa'],
      dtype='object')

---

print(df3.head())
  12/01/2011 01:55 PM       12/01/2011 02:07 PM       12/01/2011 02:57 PM  \
0                 NaN      12/1/2011 2:07PM EST      12/1/2011 2:51PM EST   
1                 NaN  12/1/11 2:06 PM (-05:00)  12/1/11 2:51 PM (-05:00)   
2                 NaN  12/1/11 2:06 PM (-05:00)  12/1/11 2:51 PM (-05:00)   
3                 NaN  12/1/11 2:06 PM (-05:00)  12/1/11 2:51 PM (-05:00)   
4                 NaN  12/1/11 2:06 PM (-05:00)  12/1/11 2:51 PM (-05:00)   

  12/01/2011 03:00 PM 12/02/2011 01:55 PM 12/02/2011 02:13 PM  \
0                 NaN                 NaN                 NaN   
1                 NaN                 NaN                 NaN   
2                 NaN                 NaN                 NaN   
3                 NaN                 NaN                 NaN   
4                 NaN                 NaN                 NaN   

  12/02/2011 03:00 PM 12/02/2011 03:05 PM  AA-1007-TPA-MIA   D5  F78  \
0                 NaN                 NaN  AA-1007-TPA-MIA  NaN  NaN   
1                 NaN                 NaN  AA-1007-TPA-MIA  NaN  NaN   
2                 NaN                 NaN  AA-1007-TPA-MIA  NaN  NaN   
3                 NaN                 NaN  AA-1007-TPA-MIA  NaN  NaN   
4                 NaN                 NaN  AA-1007-TPA-MIA  NaN  NaN   

                aa  
0   flightexplorer  
1  airtravelcenter  
2       myrateplan  
3      helloflight  
4        flytecomm 

---

print(df3.tail())
      12/01/2011 01:55 PM 12/01/2011 02:07 PM 12/01/2011 02:57 PM  \
27261                 NaN                 NaN                 NaN   
27262                 NaN                 NaN                 NaN   
27263                 NaN                 NaN                 NaN   
27264                 NaN                 NaN                 NaN   
27265                 NaN                 NaN                 NaN   

      12/01/2011 03:00 PM     12/02/2011 01:55 PM     12/02/2011 02:13 PM  \
27261                 NaN        Dec 02 - 10:20pm        Dec 02 - 10:23pm   
27262                 NaN             10:20pDec 2             10:23pDec 2   
27263                 NaN     2011-12-02 10:20 PM                     NaN   
27264                 NaN     2011-12-02 10:20 pm                     NaN   
27265                 NaN  2011-12-02 10:20PM CST  2011-12-02 10:31PM CST   

          12/02/2011 03:00 PM     12/02/2011 03:05 PM  AA-1007-TPA-MIA    D5  \
27261        Dec 02 - 11:59pm       Dec 02 - 11:51pm*  AA-2059-DFW-SLC    A3   
27262             11:43pDec 2                     NaN  AA-2059-DFW-SLC    A3   
27263     2011-12-02 11:59 PM                     NaN  AA-2059-DFW-SLC   NaN   
27264                     NaN                     NaN  AA-2059-DFW-SLC   NaN   
27265  2011-12-02 11:35PM MST  2011-12-02 11:43PM MST  AA-2059-DFW-SLC   A3    

         F78           aa  
27261  C20/C  travelocity  
27262    C20       orbitz  
27263    NaN      weather  
27264    C20          dfw  
27265   C20    flightwise 

Answer 2

用户 jezrael 的回答解决了这个问题。但是，让我尝试解释一下为什么 pandas 会在您的连接数据框中添加新列以及出了什么问题。

pandas 误读标题

当您设置 header = None 时，pandas 会读取文件的第一行作为标题，并将其默认设置为每列的名称。根据您的代码，如果header = None，这些是每个数据框可以获得的两组列。

df1: ['啊'， 'AA-1007-TPA-MIA', '12/01/2011 01:55 PM', '12/01/2011 02:07 PM', 'F78', '2011 年 12 月 1 日下午 3:00', '12/01/2011 02:57 PM', 'D5']

df2: ['啊'， 'AA-1007-TPA-MIA', '12/02/2011 01:55 PM', '12/02/2011 02:13 PM', 'F78', '2011 年 12 月 2 日下午 03:00', '12/02/2011 03:05 PM', 'D5']

附加的非唯一列

最后，当您连接两个数据框时，所有不属于 df1 和 df2 的列都被附加为单独的列。 'aa'、'AA-1007-TPA-MIA'、'F78' 和 'D5' 是 df1 和 df2 独有的，而其他所有内容都附加到列列表中。

这导致 4(df1&df2) + 4(df1) + 4(df2) = 12 列