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Python:Pandas学习

  1 import pandas as pd
  2 import numpy as np
  3 s = pd.Series([1, 3, 6, np.nan, 44, 1])
  4 
  5 df= pd.DataFrame(np.random.random((4,5)))
  6 
  7 # data frame 常用属性
  8 df.dtypes
  9 df.index
 10 df.columns
 11 df.values
 12 
 13 # data frame 常用方法
 14 df.describe()
 15 df.T
 16 df.sort_index(axis = 1, ascending = False)
 17 df.sort_values(by = 4)
 18 
 19 # 选择数据
 20 dates = pd.date_range(20160101, periods = 6)
 21 df = pd.DataFrame(np.arange(24).reshape((6,4)), index = dates,
 22                   columns = [A, B, C, D])
 23 
 24 ‘‘‘row or column‘‘‘ # 行不可隔着选择
 25 print(df[0:3])
 26 print(df[[A, D]])
 27 
 28 ‘‘‘select by label:loc‘‘‘ # 行不可隔着选择
 29 print(df.loc[20160101, :])
 30 print(df.loc[:,[A, B]])
 31 
 32 ‘‘‘select by position:iloc‘‘‘
 33 print(df.iloc[[0, 2], [0, 3]])
 34 
 35 ‘‘‘mixed selection:ix‘‘‘
 36 print(df.ix[[0, 2], [A, D]])
 37 
 38 ‘‘‘Boolean indexing‘‘‘
 39 print(df[df.B > 5])
 40 
 41 # 设置数据
 42 df.iloc[2, 2] = 111
 43 df.loc[20160101, D] = 222
 44 df.B[df.A > 5] = 0
 45 print(df)
 46 
 47 df[F] = np.nan
 48 df[E] = range(6)
 49 print(df)
 50 
 51 # 处理缺失数据
 52 df.iloc[0, 1] = np.nan
 53 df.iloc[1, 2] = np.nan
 54 print(df)
 55 print(df.dropna(axis = 0, how = all)) # how = {‘any‘, ‘all‘}
 56 print(df.fillna(value =http://www.mamicode.com/ 0))
 57 print(np.any(df.isnull()))
 58 
 59 # data frame 合并
 60 ‘‘‘concatenating‘‘‘
 61 df1 = pd.DataFrame(np.ones((3,4))*0, columns = [a, b, c, d])
 62 df2 = pd.DataFrame(np.ones((3,4))*1, columns = [a, b, c, d])
 63 df3 = pd.DataFrame(np.ones((3,4))*2, columns = [a, b, c, d])
 64 
 65 res = pd.concat([df1, df2, df3], axis = 0, ignore_index = True)
 66 res1 = pd.concat([df1, df2, df3], axis = 1)
 67 
 68 ‘‘‘join参数‘‘‘
 69 df1 = pd.DataFrame(np.ones((3,4))*0, columns = [a, b, c, d], index = [1, 2, 3])
 70 df2 = pd.DataFrame(np.ones((3,4))*1, columns = [b, c, d, e], index = [2, 3, 4])
 71 
 72 res = pd.concat([df1, df2], join = outer, ignore_index = True)
 73 res = pd.concat([df1, df2], join = inner, ignore_index = True)
 74 print(res)
 75 
 76 ‘‘‘join_axes‘‘‘
 77 res = pd.concat([df1, df2], axis = 1, join = inner)
 78 res = pd.concat([df1, df2], axis = 1, join_axes = [df1.index])
 79 
 80 # append
 81 df1 = pd.DataFrame(np.ones((3,4))*0, columns = [a, b, c, d], index = [1, 2, 3])
 82 df2 = pd.DataFrame(np.ones((3,4))*1, columns = [b, c, d, e], index = [2, 3, 4])
 83 df3 = pd.DataFrame(np.ones((3,4))*1, columns = [b, c, d, e], index = [2, 3, 4])
 84 
 85 res = df1.append([df2, df3], ignore_index = True)
 86 res1 = pd.concat([df1, df2, df3])
 87 print(res)
 88 print(res1)
 89 
 90 # data frame merge
 91 ‘‘‘merge one key‘‘‘
 92 left = pd.DataFrame({key:[K1,K2,K3],
 93                      A:[1,2,3],
 94                      B:[4,5,6]})
 95 
 96 right = pd.DataFrame({key:[K0,K1,K3],
 97                      A:[11,43,53],
 98                      D:[12,-1,0]})
 99 res = pd.merge(left, right, on = key, how = outer)
100 print(res)
101 
102 ‘‘‘merge two or more keys‘‘‘
103 left = pd.DataFrame({key0:[K1,K2,K3],
104                      key1:[X0,X2,X3],
105                      A:[1,2,3],
106                      B:[4,5,6]})
107 
108 right = pd.DataFrame({key0:[K0,K1,K3],
109                       key1:[X1,X0,K3],
110                      A:[11,43,53],
111                      D:[12,-1,0]})
112 res = pd.merge(left, right, on = [key0, key1], how = outer)
113 print(res)
114 
115 ‘‘‘merge index‘‘‘
116 left = pd.DataFrame({A:[1,2,3],
117                      B:[4,5,6]},
118                     index = [K0, K1, K2])
119 
120 right = pd.DataFrame({A:[11,43,53],
121                      D:[12,-1,0]},
122                     index = [K1, K2, K3])
123 res = pd.merge(left, right, left_index = True,
124                right_index = True)
125 print(res)
126 
127 ‘‘‘handle overlapping columns‘‘‘
128 left = pd.DataFrame({key:[K1,K2,K3],
129                      A:[1,2,3],
130                      B:[4,5,6]})
131 
132 right = pd.DataFrame({key:[K0,K1,K3],
133                      A:[11,43,53],
134                      B:[12,-1,0]})
135 res = pd.merge(left, right, on = key,
136                suffixes = [_left, _right] , how = outer)
137 print(res)
138 
139 # 作图
140 import pandas as pd
141 import numpy as np
142 import matplotlib.pyplot as plt
143 
144 ‘‘‘plot data‘‘‘
145 ‘‘‘Series‘‘‘
146 data = http://www.mamicode.com/pd.Series(np.random.randn(1000), index = np.arange(1000))
147 data =http://www.mamicode.com/ data.cumsum()
148 data.plot()
149 print(data)
150 
151 ‘‘‘Data Frame‘‘‘
152 data = http://www.mamicode.com/pd.DataFrame(np.random.randn(1000, 4), 
153                     index = np.arange(1000),
154                     columns = list("ABCD"))
155 print(data.head())
156 data =http://www.mamicode.com/ data.cumsum()
157 data.plot()
158 ax = data.plot.scatter(x = A, y = C,
159                        color = Red,
160                        label = Class 2)
161 data.plot.scatter(x = A, y = B,
162                   color = DarkGreen,
163                   label = Class 2,
164                   ax = ax)

 

Python:Pandas学习