Python：Pandas学习

 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)