1、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)
#index为主键，为索引值；columns为列的label值；
结果为：
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
2、通过传递可以转换为类似系列的对象的字典来创建DataFrame
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) Python #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 0 1.0 2017-01-02 1.0 3 test foo 1 1.0 2017-01-02 1.0 3 train foo 2 1.0 2017-01-02 1.0 3 test foo 3 1.0 2017-01-02 1.0 3 train foo
3、
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) print('-'*20) print(df.head(3))#选取前3行数据 print("--------------" * 10) print(df.tail(3))#选取后3行数据 #结果为： A B C D 2017-01-01 -0.793688 2.181523 0.557200 -0.279401 2017-01-02 0.671325 -0.756951 1.081400 1.008983 2017-01-03 0.302179 0.567060 0.006960 -0.091598 2017-01-04 0.510611 0.389851 2.271782 -0.700009 2017-01-05 0.393021 0.306979 1.935575 0.867504 2017-01-06 -1.306705 0.324890 -0.271238 1.234804 2017-01-07 1.090559 -0.427427 0.902197 0.157098 -------------------- A B C D 2017-01-01 -0.793688 2.181523 0.55720 -0.279401 2017-01-02 0.671325 -0.756951 1.08140 1.008983 2017-01-03 0.302179 0.567060 0.00696 -0.091598 -------------------------------------------------------------------------------------------------------------------------------------------- A B C D 2017-01-05 0.393021 0.306979 1.935575 0.867504 2017-01-06 -1.306705 0.324890 -0.271238 1.234804 2017-01-07 1.090559 -0.427427 0.902197 0.157098
4、
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)#获取主键值 print("columns is :" ) print(df.columns)#获取column值 print("values is :" ) print(df.values)#获取数据值
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]]
5、描述显示数据的快速统计摘要
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()) ################################## A B C D count 7.000000 7.000000 7.000000 7.000000 mean 0.254545 -0.124449 0.466692 0.076727 std 0.701449 1.038154 1.058242 0.633414 min -0.826816 -1.775741 -1.025271 -0.885684 25% -0.202499 -0.649271 -0.173390 -0.275273 50% 0.401212 0.030409 0.588951 0.211761 75% 0.724367 0.556147 1.094887 0.411284 max 1.163683 1.060436 1.860166 0.938987
6、调换数据 行列转换 类似矩阵转置
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)################ Python 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
7、通过轴排序
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:") print(df) print("axis=1 ascending=False: ") print(df.sort_index(axis=1, ascending=False)) print("axis=0 ascending=False: ") print(df.sort_index(axis=0, ascending=False)) print("axis=1 ascending=True: ") print(df.sort_index(axis=1, ascending=True)) print("axis=0 ascending=True: ") print(df.sort_index(axis=0, ascending=True)) df: A B C D 2017-01-01 1.383892 -0.723536 0.335398 0.450175 2017-01-02 -0.614480 1.076641 -1.102721 -1.418669 2017-01-03 1.337403 -0.176513 0.992887 0.094828 2017-01-04 0.897667 0.488634 1.648647 0.056338 2017-01-05 -0.241695 1.560610 0.391279 0.361618 2017-01-06 1.052111 -1.719268 0.341144 2.130635 axis=1 ascending=False: D C B A 2017-01-01 0.450175 0.335398 -0.723536 1.383892 2017-01-02 -1.418669 -1.102721 1.076641 -0.614480 2017-01-03 0.094828 0.992887 -0.176513 1.337403 2017-01-04 0.056338 1.648647 0.488634 0.897667 2017-01-05 0.361618 0.391279 1.560610 -0.241695 2017-01-06 2.130635 0.341144 -1.719268 1.052111 axis=0 ascending=False: A B C D 2017-01-06 1.052111 -1.719268 0.341144 2.130635 2017-01-05 -0.241695 1.560610 0.391279 0.361618 2017-01-04 0.897667 0.488634 1.648647 0.056338 2017-01-03 1.337403 -0.176513 0.992887 0.094828 2017-01-02 -0.614480 1.076641 -1.102721 -1.418669 2017-01-01 1.383892 -0.723536 0.335398 0.450175 axis=1 ascending=True: A B C D 2017-01-01 1.383892 -0.723536 0.335398 0.450175 2017-01-02 -0.614480 1.076641 -1.102721 -1.418669 2017-01-03 1.337403 -0.176513 0.992887 0.094828 2017-01-04 0.897667 0.488634 1.648647 0.056338 2017-01-05 -0.241695 1.560610 0.391279 0.361618 2017-01-06 1.052111 -1.719268 0.341144 2.130635 axis=0 ascending=True: A B C D 2017-01-01 1.383892 -0.723536 0.335398 0.450175 2017-01-02 -0.614480 1.076641 -1.102721 -1.418669 2017-01-03 1.337403 -0.176513 0.992887 0.094828 2017-01-04 0.897667 0.488634 1.648647 0.056338 2017-01-05 -0.241695 1.560610 0.391279 0.361618 2017-01-06 1.052111 -1.719268 0.341144 2.130635
8、
#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'))
或者 print(df.sort_values('B'))
#` #Python #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
9、
#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']) ` Python 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']) ` Python 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:") print(df) print('-'*50) print(df.loc[dates[0]]) df: A B C D 2017-01-01 0.592135 -0.763021 0.420110 -0.766594 2017-01-02 -0.203704 0.471745 -1.770516 1.100931 2017-01-03 0.717300 0.299209 -0.856983 1.163530 2017-01-04 -0.581382 -0.633760 -0.012644 -0.058334 2017-01-05 -0.081392 0.091552 1.159507 0.802206 2017-01-06 1.126909 2.306718 0.511462 -0.864211 -------------------------------------------------- A 0.592135 B -0.763021 C 0.420110 D -0.766594 Name: 2017-01-01 00:00:00, dtype: float64
8、排序算法
#排序算法 #sort_values()提供了从mergeesort，heapsort和quicksort中选择算法的一个配置。Mergesort是唯一稳定的算法。参考以下示例代码 - import pandas as pd import numpy as np unsorted_df = pd.DataFrame({'col1':[2,1,1,1],'col2':[1,3,2,4]}) sorted_df = unsorted_df.sort_values(by='col1' ,kind='mergesort') print (sorted_df)