我认为您需要使用 header=None
参数来设置默认列名为 0-7
,因为文件没有标题。此外,如果有分隔符 tab
,可以指定它。
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称指定
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')
你只会得到12列的数据,因为两个数据框的第一行中有些值是相同的,因此这些值被用来创建列名。在使用 concat
合并数据后,只有这些列的位置才会对齐。如果值不同,则不会对齐,你将得到 NaN
。
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