Pandas提供了各种工具(功能)，可以轻松地将Series，DataFrame和Panel对象组合在一起。
pd.concat(objs,axis=0,join='outer',join_axes=None,
ignore_index=False)其中，
objs- 这是 Series ， DataFrame 或 Panel 对象的序列或映射。axis-{0，1，...}，默认为0，这是连接的轴。join-{'inner', 'outer'}，默认inner。如何处理其他轴上的索引。外部用于联合，内部用于交叉。ignore_index− 布尔值，默认为False。如果指定为True， 则不要使用连接轴上的索引值。结果轴将被标记为：0, ..., n-1。join_axes- 这是 Index 对象的列表。用于其他(n-1)轴的特定索引，而不是执行内部/外部集逻辑。
In [2]:
import pandas as pd
In [3]:
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
In [4]:
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
In [5]:
rs = pd.concat([one,two])
In [6]:
print(rs)
Name subject_id Marks_scored 1 Alex sub1 98 2 Amy sub2 90 3 Allen sub4 87 4 Alice sub6 69 5 Ayoung sub5 78 1 Billy sub2 89 2 Brian sub4 80 3 Bran sub3 79 4 Bryce sub6 97 5 Betty sub5 88
假设想把特定的键与每个碎片的 DataFrame 关联起来。可以通过使用键参数来实现这一点：
In [7]:
import pandas as pd
In [8]:
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
In [9]:
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
In [10]:
rs = pd.concat([one,two],keys=['x','y'])
In [11]:
print(rs)
Name subject_id Marks_scored x 1 Alex sub1 98 2 Amy sub2 90 3 Allen sub4 87 4 Alice sub6 69 5 Ayoung sub5 78 y 1 Billy sub2 89 2 Brian sub4 80 3 Bran sub3 79 4 Bryce sub6 97 5 Betty sub5 88
结果的索引被复制； 每个索引都是重复的。
如果想要生成的对象必须遵循自己的索引，请将 ignore_index 设置为 True 。
参考以下示例代码：
In [12]:
import pandas as pd
In [13]:
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
In [14]:
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
In [15]:
rs = pd.concat([one,two],keys=['x','y'],ignore_index=True)
In [16]:
print(rs)
Name subject_id Marks_scored 0 Alex sub1 98 1 Amy sub2 90 2 Allen sub4 87 3 Alice sub6 69 4 Ayoung sub5 78 5 Billy sub2 89 6 Brian sub4 80 7 Bran sub3 79 8 Bryce sub6 97 9 Betty sub5 88
索引完全改变，键也被覆盖。
如果需要沿 axis=1 添加两个对象，则会添加新列。
In [17]:
import pandas as pd
In [18]:
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
In [19]:
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
In [20]:
rs = pd.concat([one,two],axis=1)
In [21]:
print(rs)
Name subject_id Marks_scored Name subject_id Marks_scored 1 Alex sub1 98 Billy sub2 89 2 Amy sub2 90 Brian sub4 80 3 Allen sub4 87 Bran sub3 79 4 Alice sub6 69 Bryce sub6 97 5 Ayoung sub5 78 Betty sub5 88
In [22]:
import pandas as pd
In [23]:
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
In [24]:
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
In [25]:
# rs = one.append(two)
rs = pd.concat([one, two])
In [26]:
print(rs)
Name subject_id Marks_scored 1 Alex sub1 98 2 Amy sub2 90 3 Allen sub4 87 4 Alice sub6 69 5 Ayoung sub5 78 1 Billy sub2 89 2 Brian sub4 80 3 Bran sub3 79 4 Bryce sub6 97 5 Betty sub5 88
append() 函数也可以带多个对象：
In [27]:
import pandas as pd
In [28]:
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
In [29]:
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
In [30]:
# rs = one.append([two,one,two])
rs = pd.concat([one, two, one, two])
In [31]:
print(rs)
Name subject_id Marks_scored 1 Alex sub1 98 2 Amy sub2 90 3 Allen sub4 87 4 Alice sub6 69 5 Ayoung sub5 78 1 Billy sub2 89 2 Brian sub4 80 3 Bran sub3 79 4 Bryce sub6 97 5 Betty sub5 88 1 Alex sub1 98 2 Amy sub2 90 3 Allen sub4 87 4 Alice sub6 69 5 Ayoung sub5 78 1 Billy sub2 89 2 Brian sub4 80 3 Bran sub3 79 4 Bryce sub6 97 5 Betty sub5 88
In [32]:
import pandas as pd
# print(pd.datetime.now())
时间戳数据是时间序列数据的最基本类型，它将数值与时间点相关联。 对于Pandas对象来说，意味着使用时间点。举个例子：
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import pandas as pd
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time = pd.Timestamp('2018-11-01')
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print(time)
2018-11-01 00:00:00
也可以转换整数或浮动时期。这些的默认单位是纳秒(因为这些是如何存储时间戳的)。 然而，时代往往存储在另一个可以指定的单元中。 再举一个例子：
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import pandas as pd
In [37]:
time = pd.Timestamp(1588686880,unit='s')
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print(time)
2020-05-05 13:54:40
In [39]:
import pandas as pd
In [40]:
time = pd.date_range("12:00", "23:59", freq="30min").time
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print(time)
[datetime.time(12, 0) datetime.time(12, 30) datetime.time(13, 0) datetime.time(13, 30) datetime.time(14, 0) datetime.time(14, 30) datetime.time(15, 0) datetime.time(15, 30) datetime.time(16, 0) datetime.time(16, 30) datetime.time(17, 0) datetime.time(17, 30) datetime.time(18, 0) datetime.time(18, 30) datetime.time(19, 0) datetime.time(19, 30) datetime.time(20, 0) datetime.time(20, 30) datetime.time(21, 0) datetime.time(21, 30) datetime.time(22, 0) datetime.time(22, 30) datetime.time(23, 0) datetime.time(23, 30)]
In [42]:
import pandas as pd
In [43]:
time = pd.date_range("12:00", "23:59", freq="H").time
/tmp/ipykernel_2436/3543899096.py:1: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
time = pd.date_range("12:00", "23:59", freq="H").time
In [44]:
print(time)
[datetime.time(12, 0) datetime.time(13, 0) datetime.time(14, 0) datetime.time(15, 0) datetime.time(16, 0) datetime.time(17, 0) datetime.time(18, 0) datetime.time(19, 0) datetime.time(20, 0) datetime.time(21, 0) datetime.time(22, 0) datetime.time(23, 0)]
In [45]:
import pandas as pd
In [46]:
time = pd.to_datetime(pd.Series(['Jul 31, 2009','2019-10-10', None]),
format='mixed')
In [47]:
print(time)
0 2009-07-31 1 2019-10-10 2 NaT dtype: datetime64[ns]
NaT 表示不是一个时间的值(相当于 NaN )
举一个例子：
In [48]:
time = pd.to_datetime(['2009/11/23', '2019.12.31', None],
format='mixed')
In [49]:
time
Out[49]:
DatetimeIndex(['2009-11-23', '2019-12-31', 'NaT'], dtype='datetime64[ns]', freq=None)