import pandas as pd

 import numpy as np

 s = pd.Series([1, 3, 6, np.nan, 44, 1])

 df= pd.DataFrame(np.random.random((4,5)))

 # data frame 常用属性

 df.dtypes

 df.index

 df.columns

 df.values

 # data frame 常用方法

 df.describe()

 df.T

 df.sort_index(axis = 1, ascending = False)

 df.sort_values(by = 4)

 # 选择数据

 dates = pd.date_range('', periods = 6)

 df = pd.DataFrame(np.arange(24).reshape((6,4)), index = dates,

                   columns = ['A', 'B', 'C', 'D'])

 '''row or column''' # 行不可隔着选择

 print(df[0:3])

 print(df[['A', 'D']])

 '''select by label:loc''' # 行不可隔着选择

 print(df.loc['', :])

 print(df.loc[:,['A', 'B']])

 '''select by position:iloc'''

 print(df.iloc[[0, 2], [0, 3]])

 '''mixed selection:ix'''

 print(df.ix[[0, 2], ['A', 'D']])

 '''Boolean indexing'''

 print(df[df.B > 5])

 # 设置数据

 df.iloc[2, 2] = 111

 df.loc['', 'D'] = 222

 df.B[df.A > 5] = 0

 print(df)

 df['F'] = np.nan

 df['E'] = range(6)

 print(df)

 # 处理缺失数据

 df.iloc[0, 1] = np.nan

 df.iloc[1, 2] = np.nan

 print(df)

 print(df.dropna(axis = 0, how = 'all')) # how = {'any', 'all'}

 print(df.fillna(value = 0))

 print(np.any(df.isnull()))

 # data frame 合并

 '''concatenating'''

 df1 = pd.DataFrame(np.ones((3,4))*0, columns = ['a', 'b', 'c', 'd'])

 df2 = pd.DataFrame(np.ones((3,4))*1, columns = ['a', 'b', 'c', 'd'])

 df3 = pd.DataFrame(np.ones((3,4))*2, columns = ['a', 'b', 'c', 'd'])

 res = pd.concat([df1, df2, df3], axis = 0, ignore_index = True)

 res1 = pd.concat([df1, df2, df3], axis = 1)

 '''join参数'''

 df1 = pd.DataFrame(np.ones((3,4))*0, columns = ['a', 'b', 'c', 'd'], index = [1, 2, 3])

 df2 = pd.DataFrame(np.ones((3,4))*1, columns = ['b', 'c', 'd', 'e'], index = [2, 3, 4])

 res = pd.concat([df1, df2], join = 'outer', ignore_index = True)

 res = pd.concat([df1, df2], join = 'inner', ignore_index = True)

 print(res)

 '''join_axes'''

 res = pd.concat([df1, df2], axis = 1, join = 'inner')

 res = pd.concat([df1, df2], axis = 1, join_axes = [df1.index])

 # append

 df1 = pd.DataFrame(np.ones((3,4))*0, columns = ['a', 'b', 'c', 'd'], index = [1, 2, 3])

 df2 = pd.DataFrame(np.ones((3,4))*1, columns = ['b', 'c', 'd', 'e'], index = [2, 3, 4])

 df3 = pd.DataFrame(np.ones((3,4))*1, columns = ['b', 'c', 'd', 'e'], index = [2, 3, 4])

 res = df1.append([df2, df3], ignore_index = True)

 res1 = pd.concat([df1, df2, df3])

 print(res)

 print(res1)

 # data frame merge

 '''merge one key'''

 left = pd.DataFrame({'key':['K1','K2','K3'],

                      'A':[1,2,3],

                      'B':[4,5,6]})

 right = pd.DataFrame({'key':['K0','K1','K3'],

                      'A':[11,43,53],

                      'D':[12,-1,0]})

 res = pd.merge(left, right, on = 'key', how = 'outer')

 print(res)

 '''merge two or more keys'''

 left = pd.DataFrame({'key0':['K1','K2','K3'],

                      'key1':['X0','X2','X3'],

                      'A':[1,2,3],

                      'B':[4,5,6]})

 right = pd.DataFrame({'key0':['K0','K1','K3'],

                       'key1':['X1','X0','K3'],

                      'A':[11,43,53],

                      'D':[12,-1,0]})

 res = pd.merge(left, right, on = ['key0', 'key1'], how = 'outer')

 print(res)

 '''merge index'''

 left = pd.DataFrame({'A':[1,2,3],

                      'B':[4,5,6]},

                     index = ['K0', 'K1', 'K2'])

 right = pd.DataFrame({'A':[11,43,53],

                      'D':[12,-1,0]},

                     index = ['K1', 'K2', 'K3'])

 res = pd.merge(left, right, left_index = True,

                right_index = True)

 print(res)

 '''handle overlapping columns'''

 left = pd.DataFrame({'key':['K1','K2','K3'],

                      'A':[1,2,3],

                      'B':[4,5,6]})

 right = pd.DataFrame({'key':['K0','K1','K3'],

                      'A':[11,43,53],

                      'B':[12,-1,0]})

 res = pd.merge(left, right, on = 'key',

                suffixes = ['_left', '_right'] , how = 'outer')

 print(res)

 # 作图

 import pandas as pd

 import numpy as np

 import matplotlib.pyplot as plt

 '''plot data'''

 '''Series'''

 data = pd.Series(np.random.randn(1000), index = np.arange(1000))

 data = data.cumsum()

 data.plot()

 print(data)

 '''Data Frame'''

 data = pd.DataFrame(np.random.randn(1000, 4),

                     index = np.arange(1000),

                     columns = list("ABCD"))

 print(data.head())

 data = data.cumsum()

 data.plot()

 ax = data.plot.scatter(x = 'A', y = 'C',

                        color = 'Red',

                        label = 'Class 2')

 data.plot.scatter(x = 'A', y = 'B',

                   color = 'DarkGreen',

                   label = 'Class 2',

                   ax = ax)