pandas之数据选择

pandas中有三种索引方法：.loc，.iloc和[]，注意：.ix的用法在0.20.0中已经不建议使用了

import pandas as pd
import numpy as np

In [5]:

dates = pd.date_range("20170101",periods=6)
df1 = pd.DataFrame(np.arange(24).reshape(6,4),index=dates,columns=["A","B","C","D"])
df1

Out[5]:

	A	B	C	D
2017-01-01	0	1	2	3
2017-01-02	4	5	6	7
2017-01-03	8	9	10	11
2017-01-04	12	13	14	15
2017-01-05	16	17	18	19
2017-01-06	20	21	22	23

In [6]:

将dataframe的列获取为一个series

df1["A"]#将dataframe的列获取为一个series

Out[6]:

2017-01-01     0
2017-01-02     4
2017-01-03     8
2017-01-04    12
2017-01-05    16
2017-01-06    20
Freq: D, Name: A, dtype: int32

In [7]:

df1.A#另一种获取

Out[7]:

2017-01-01     0
2017-01-02     4
2017-01-03     8
2017-01-04    12
2017-01-05    16
2017-01-06    20
Freq: D, Name: A, dtype: int32

In [8]:

切片，获取前2行

df1[0:2]#切片，获取前2行

Out[8]:

	A	B	C	D
2017-01-01	0	1	2	3
2017-01-02	4	5	6	7

In [9]:

通过索引获取指定行

df1["20170102":"20170104"]#通过索引获取指定行

Out[9]:

	A	B	C	D
2017-01-02	4	5	6	7
2017-01-03	8	9	10	11
2017-01-04	12	13	14	15

In [11]:

通过标签选择数据

#通过标签选择数据
df1.loc["20170102"]

Out[11]:

A    4
B    5
C    6
D    7
Name: 2017-01-02 00:00:00, dtype: int32

In [12]:

提取某个行的指定列

df1.loc["20170102",["A","C"]]#提取某个行的指定列

Out[12]:

A    4
C    6
Name: 2017-01-02 00:00:00, dtype: int32

In [13]:

df1.loc[:,["A","B"]]

Out[13]:

	A	B
2017-01-01	0	1
2017-01-02	4	5
2017-01-03	8	9
2017-01-04	12	13
2017-01-05	16	17
2017-01-06	20	21

In [14]:

通过位置选择数据

#通过位置选择数据
df1.iloc[2]#提取第二行

Out[14]:

A     8
B     9
C    10
D    11
Name: 2017-01-03 00:00:00, dtype: int32

In [15]:

df1.iloc[1:3,2:4]

Out[15]:

	C	D
2017-01-02	6	7
2017-01-03	10	11

In [18]:

提取不连续的行和列

#提取不连续的行和列
df1.iloc[[1,2,4],[1,3]]

Out[18]:

	B	D
2017-01-02	5	7
2017-01-03	9	11
2017-01-05	17	19

In [20]:

#混合标签位置选择
df1.ix[2:4,["A","C"]]

c:\users\wuzs\appdata\local\programs\python\python36-32\lib\site-packages\ipykernel_launcher.py:2: FutureWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated

c:\users\wuzs\appdata\local\programs\python\python36-32\lib\site-packages\pandas\core\indexing.py:808: FutureWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
  retval = getattr(retval, self.name)._getitem_axis(key, axis=i)

Out[20]:

	A	C
2017-01-03	8	10
2017-01-04	12	14

In [23]:

df1.ix["20170102":"20170104",2:4]

c:\users\wuzs\appdata\local\programs\python\python36-32\lib\site-packages\ipykernel_launcher.py:1: FutureWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
  """Entry point for launching an IPython kernel.

Out[23]:

	C	D
2017-01-02	6	7
2017-01-03	10	11
2017-01-04	14	15

In [24]:

判断某一行的值大小

#判断某一行的值大小
df1.A >6

Out[24]:

2017-01-01    False
2017-01-02    False
2017-01-03     True
2017-01-04     True
2017-01-05     True
2017-01-06     True
Freq: D, Name: A, dtype: bool

In [25]:

df1[df1.A>6]#根据判断组成新的DataFrame

Out[25]:

	A	B	C	D
2017-01-03	8	9	10	11
2017-01-04	12	13	14	15
2017-01-05	16	17	18	19
2017-01-06	20	21	22	23

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