Pandas快速入门
Pandas快速入门
这是一个Pandas快速入门教程，主要面向新用户。这里主要是为那些喜欢“短平快”的读者准备的，有兴趣的读者可通过其它教程文章来一步一步地更复杂的应用知识。
首先，假设您安装好了Anaconda，现在启动Anaconda开始学始本教程中的示例。工作界面如下所示 -
测试工作环境是否有安装好了Pandas，导入相关包如下：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
print("Hello, Pandas")
然后执行一下，看有没有问题，如果正常应该会在终端输出区看到以下结果 -
对象创建
通过传递值列表来创建一个系列，让Pandas创建一个默认的整数索引：
import pandas as pd import numpy as np s = pd.Series([1,3,5,np.nan,6,8]) print(s)
执行后输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
1.0
3.0
5.0
NaN
6.0
8.0
dtype: float64
通过传递numpy数组，使用datetime索引和标记列来创建DataFrame：
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=7)
print(dates)
print("--"*16)
df = pd.DataFrame(np.random.randn(7,4), index=dates, columns=list('ABCD'))
print(df)
执行后输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07'],
dtype='datetime64[ns]', freq='D')
--------------------------------
A B C D
2017-01-01 -0.732038 0.329773 -0.156383 0.270800
2017-01-02 0.750144 0.722037 -0.849848 -1.105319
2017-01-03 -0.786664 -0.204211 1.246395 0.292975
2017-01-04 -1.108991 2.228375 0.079700 -1.738507
2017-01-05 0.348526 -0.960212 0.190978 -2.223966
2017-01-06 -0.579689 -1.355910 0.095982 1.233833
2017-01-07 1.086872 0.664982 0.377787 1.012772
通过传递可以转换为类似系列的对象的字典来创建DataFrame。参考以下示例代码 -
import pandas as pd
import numpy as np
df2 = pd.DataFrame({ 'A' : 1.,
'B' : pd.Timestamp('20170102'),
'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
'D' : np.array([3] * 4,dtype='int32'),
'E' : pd.Categorical(["test","train","test","train"]),
'F' : 'foo' })
print(df2)
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
A B C D E F
1.0 2017-01-02 1.0 3 test foo
1.0 2017-01-02 1.0 3 train foo
1.0 2017-01-02 1.0 3 test foo
1.0 2017-01-02 1.0 3 train foo
有指定dtypes，参考以下示例代码 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
如果使用IPython，则会自动启用列名(以及公共属性)的选项完成。 以下是将要完成的属性的一个子集：
In [13]: df2.<TAB> df2.A df2.bool df2.abs df2.boxplot df2.add df2.C df2.add_prefix df2.clip df2.add_suffix df2.clip_lower df2.align df2.clip_upper df2.all df2.columns df2.any df2.combine df2.append df2.combine_first df2.apply df2.compound df2.applymap df2.consolidate df2.D
可以看到，列A，B，C和D自动标签完成。E也在一样。其余的属性为了简洁而被截短。
查看数据
查看框架的顶部和底部的数据行。参考以下示例代码 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=7)
df = pd.DataFrame(np.random.randn(7,4), index=dates, columns=list('ABCD'))
print(df.head())
print("--------------" * 10)
print(df.tail(3))
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
A B C D
2017-01-01 -0.520856 -0.555019 -2.286424 1.745681
2017-01-02 1.114030 0.861933 0.795958 0.420670
2017-01-03 -0.343605 -0.937356 0.052693 -0.540735
2017-01-04 1.587684 -0.743756 0.021738 -0.702190
2017-01-05 1.243403 0.930299 0.234343 1.604182
------------------------------------------------------------
A B C D
2017-01-05 1.243403 0.930299 0.234343 1.604182
2017-01-06 -0.087004 -0.368055 1.434022 0.464193
2017-01-07 -1.248981 0.973724 -0.288384 -0.577388
显示索引，列和底层numpy数据，参考以下代码 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=7)
df = pd.DataFrame(np.random.randn(7,4), index=dates, columns=list('ABCD'))
print("index is :" )
print(df.index)
print("columns is :" )
print(df.columns)
print("values is :" )
print(df.values)
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
index is :
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
'2017-01-05', '2017-01-06', '2017-01-07'],
dtype='datetime64[ns]', freq='D')
columns is :
Index(['A', 'B', 'C', 'D'], dtype='object')
values is :
[[ 2.23820398 0.18440123 0.08039084 -0.27751926]
[-0.12335513 0.36641304 -0.28617579 0.34383109]
[-0.85403491 0.63876989 1.26032173 -1.27382333]
[-0.07262661 -0.01788962 0.28748668 1.12715561]
[-1.14293392 -0.88263364 0.72250762 -1.64051326]
[ 0.41864083 0.40545953 -0.14591541 -0.57168728]
[ 1.01383531 -0.22793823 -0.44045634 1.04799829]]
描述显示数据的快速统计摘要，参考以下示例代码 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=7)
df = pd.DataFrame(np.random.randn(7,4), index=dates, columns=list('ABCD'))
print(df.describe())
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
A B C D
count 7.000000 7.000000 7.000000 7.000000
mean -0.675425 -0.257835 0.144049 0.275621
std 1.697957 0.793953 1.301520 1.412291
min -2.595040 -1.200401 -1.230538 -0.976166
25% -1.992393 -0.723464 -0.897041 -0.800919
50% -1.050666 -0.445612 0.004719 -0.705840
75% 0.592677 0.068574 0.874195 1.398337
max 1.717166 1.150948 2.279856 2.416514
调换数据，参考以下示例代码 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.T)
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
2017-01-01 2017-01-02 2017-01-03 2017-01-04 2017-01-05 2017-01-06
A 0.932454 -2.148503 1.398975 1.565676 -0.167527 -0.242041
B 0.584585 1.373330 -0.069801 -0.102857 1.286432 -0.703491
C -0.345119 -0.680955 1.686750 1.184996 0.016170 -0.663963
D 0.431751 0.444830 -1.524739 0.040007 0.220172 1.423627
通过轴排序，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.sort_index(axis=1, ascending=False))
`
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
D C B A
2017-01-01 0.426359 2.542352 -0.324047 0.418973
2017-01-02 -0.834625 -1.356709 0.150744 -1.690500
2017-01-03 -0.018274 0.900801 1.072851 0.149830
2017-01-04 -1.075027 -0.889379 -0.663223 -1.404002
2017-01-05 -1.273966 -1.335761 -1.356561 -1.135199
2017-01-06 -1.590793 0.693430 -0.504164 0.143386
按值排序，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.sort_values(by='B'))
`
执行上面示例代码后，输出结果如下 -
A B C D 2017-01-06 0.764517 -1.526019 0.400456 -0.182082 2017-01-05 -0.177845 -1.269836 -0.534676 0.796666 2017-01-04 -0.981485 -0.082572 -1.272123 0.508929 2017-01-02 -0.290117 0.053005 -0.295628 -0.346965 2017-01-03 0.941131 0.799280 2.054011 -0.684088 2017-01-01 0.597788 0.892008 0.903053 -0.821024
选择区块
注意虽然用于选择和设置的标准Python/Numpy表达式是直观的，可用于交互式工作，但对于生产代码，但建议使用优化的Pandas数据访问方法
.at，.iat，.loc，.iloc和.ix。
获取
选择一列，产生一个系列，相当于df.A，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df['A'])
`
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
2017-01-01 0.317460
2017-01-02 -0.933726
2017-01-03 0.167860
2017-01-04 0.816184
2017-01-05 -0.745503
2017-01-06 0.505319
Freq: D, Name: A, dtype: float64
选择通过[]操作符，选择切片行。参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df[0:3])
print("========= 指定选择日期 ========")
print(df['20170102':'20170103'])
`
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
A B C D
2017-01-01 1.103449 0.926571 -1.649978 -0.309270
2017-01-02 0.516404 -0.734076 -0.081163 -0.528497
2017-01-03 0.240356 0.231224 -1.463315 -1.157256
========= 指定选择日期 ========
A B C D
2017-01-02 0.516404 -0.734076 -0.081163 -0.528497
2017-01-03 0.240356 0.231224 -1.463315 -1.157256
按标签选择
使用标签获取横截面，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.loc[dates[0]])
`
执行上面示例代码后，输出结果如下 -
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
A -0.483292
B -0.536987
C -0.889947
D 1.250857
Name: 2017-01-01 00:00:00, dtype: float64
通过标签选择多轴，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.loc[:,['A','B']])
`
执行上面示例代码后，输出结果如下 -
A B 2017-01-01 0.479048 -0.105106 2017-01-02 0.172920 0.086570 2017-01-03 -1.302485 -0.593550 2017-01-04 -0.595438 1.304054 2017-01-05 0.154267 1.336219 2017-01-06 -0.341204 0.781300
显示标签切片，包括两个端点，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.loc['20170102':'20170104',['A','B']])
`
执行上面示例代码后，输出结果如下 -
A B 2017-01-02 1.062995 -0.108277 2017-01-03 1.962106 -0.294664 2017-01-04 -0.128576 0.717738
减少返回对象的尺寸(大小)，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.loc['20170102',['A','B']])
`
执行上面示例代码后，输出结果如下 -
A 0.252749 B 0.119747 Name: 2017-01-02 00:00:00, dtype: float64
获得标量值，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.loc[dates[0],'A'])
`
执行上面示例代码后，输出结果如下 -
-0.0839903627822
快速访问标量(等同于先前的方法)，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.at[dates[0],'A'])
`
执行上面示例代码后，输出结果如下 -
-0.0839903627822
通过位置选择
通过传递的整数的位置选择，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.iloc[3])
`
执行上面示例代码后，输出结果如下 -
A 0.944506 B 1.035781 C 0.421373 D 0.017660 Name: 2017-01-04 00:00:00, dtype: float64
通过整数切片，类似于numpy/python，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.iloc[3:5,0:2])
`
执行上面示例代码后，输出结果如下 -
A B 2017-01-04 -1.617068 0.548090 2017-01-05 -0.864247 0.419743
通过整数位置的列表，类似于numpy/python样式，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.iloc[[1,2,4],[0,2]])
`
执行上面示例代码后，输出结果如下 -
A C 2017-01-02 0.085091 0.568128 2017-01-03 0.729076 -0.451151 2017-01-05 -1.281975 -0.190119
明确切片行，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.iloc[1:3,:])
`
执行上面示例代码后，输出结果如下 -
A B C D 2017-01-02 -1.123970 -0.010969 -1.076657 -0.538908 2017-01-03 -0.314408 0.004415 -0.356924 0.337539
明确切片列，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.iloc[:,1:3])
`
执行上面示例代码后，输出结果如下 -
B C 2017-01-01 0.323663 1.027599 2017-01-02 -0.176624 -0.959683 2017-01-03 0.689698 0.622540 2017-01-04 1.864511 1.023157 2017-01-05 0.964123 2.062503 2017-01-06 -0.375143 0.231328
要明确获取值，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.iloc[1,1])
`
执行上面示例代码后，输出结果如下 -
0.829950900219
要快速访问标量(等同于先前的方法)，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df.iat[1,1])
`
执行上面示例代码后，输出结果如下 -
-0.170996002652
布尔索引
使用单列的值来选择数据，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df[df.A > 0])
`
执行上面示例代码后，输出结果如下 -
A B C D 2017-01-03 0.276486 -1.003779 0.721863 -0.558061 2017-01-04 1.177206 -0.464778 -0.116442 -0.385712 2017-01-06 0.846665 -1.398207 -0.145356 0.924342
从满足布尔条件的DataFrame中选择值。，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
print(df[df > 0])
`
执行上面示例代码后，输出结果如下 -
A B C D 2017-01-01 NaN 1.963213 0.643244 0.945643 2017-01-02 0.364237 0.917368 NaN NaN 2017-01-03 0.702624 NaN 0.088565 NaN 2017-01-04 1.274313 NaN 2.313910 NaN 2017-01-05 2.586315 0.588273 NaN 1.482597 2017-01-06 NaN 0.405928 0.309201 NaN
使用isin()方法进行过滤，参考以下示例程序 -
import pandas as pd
import numpy as np
dates = pd.date_range('20170101', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
df2 = df.copy()
df2['E'] = ['one', 'one','two','three','four','three']
print(df2)
print("============= start to filter =============== ")
print(df2[df2['E'].isin(['two','four'])])
`
执行上面示例代码后，输出结果如下 -
A B C D E
2017-01-01 0.723399 -0.369247 0.863941 -1.910875 one
2017-01-02 -0.047573 -0.609780 2.130650 -0.019281 one
2017-01-03 -0.566122 -0.850374 -0.031516 0.362023 two
2017-01-04 0.903819 -0.513673 0.118850 -0.351811 three
2017-01-05 -0.485232 -0.864457 1.396835 -1.696083 four
2017-01-06 0.272145 -0.644449 -1.319063 -0.201354 three
============= start to filter ===============
A B C D E
2017-01-03 -0.566122 -0.850374 -0.031516 0.362023 two
2017-01-05 -0.485232 -0.864457 1.396835 -1.696083 four
目录
