pandas 入門（2）

from pandas import Series, DataFrame, Index
import numpy as np
from numpy import nan as NAobj = Series(range(3), index=['a', 'b', 'c'])
print(obj)
index = obj.index
print(index)
print(index[1:])
# index[1] = 'd'  index對象時不可以被修改的  為了安全和共享

index = Index(np.arange(3))
obj2 = Series([1.5, -2.6, 0], index=index)
print(obj2.index is index)
# 嵌套字典（字典的字典）
pop = {'nevada': {2001: 2.4,2002: 2.9},'ohio': {2000: 1.5,2001: 1.7,2002: 3.6}
}
frame3 = DataFrame(pop)
frame3.index.name = 'year'
frame3.columns.name = 'state'
print(frame3)
print('ohio' in frame3.columns)
print(2003 in frame3.index)  # index有很多的方法和屬性（有時間呢，可以摸索一下）# reindex創建適應新索引的新對象（這里我不是很懂）
obj = Series([2.3, 4.5, -23.3, 4.3], index=['d', 'b', 'a', 'c'])
print(obj)
obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])
print(obj2)  # 索引和值一一對應，根據新索引進行重排
obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)
print(obj2)  # 索引不存在，可以引入缺失值

obj3 = Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])
print(obj3)
# obj3 = obj3.reindex(range(6), method='ffill')  # 或者pad
# print(obj3)  # 向前值填充
obj3 = obj3.reindex(range(6), method='bfill')  # 或者pad
print(obj3)  # 向后值填充# 成員資格方法
data = DataFrame({'qu1': [1, 3, 4, 3, 4], 'qu2': [2, 3, 1, 2, 3], 'qu3': [1, 5, 2, 4, 4]})
print(data)# 處理缺失數據
string = Series(['aar', 'art', np.nan, 'avo'])
print(string)
print(string.isnull())# 過濾掉缺失數據
data = Series([1, NA, 3.5, NA, 7])
print(data.dropna())  # 過濾掉NA
print(data.notnull())data = DataFrame([[1, 6.5, 3], [1, NA, NA], [NA, NA, NA], [NA, 6.5, 3]])
print(data)
print(data.dropna())   # 丟棄掉含有NA的所有行
print(data.dropna(how='all'))  # 丟我掉全為NA的行
data[4] = NA
print(data)
print(data.dropna(axis=1, how='all'))   # 丟棄掉全為NA的列

df = DataFrame(np.random.randn(7, 3))
df.ix[:4, 1] = NA  # 要錢也要后
df.ix[:2, 2] = NA
print(df)
print(df.dropna(thresh=3))  # thresh對應的值是觀測的數據個數# 填充缺失數據
print(df.fillna(0))
print(df.fillna({1: 0.4}))  # 指定的列進行填充
_ = df.fillna(0, inplace=True)  # 本地填充修改， 不產生新對象
print(df)df = DataFrame(np.random.randn(6, 3))
df.ix[2:, 1] = NA  # 要錢也要后
df.ix[4:, 2] = NA
print(df)
print(df.fillna(method='ffill'))  # 向前填充
print(df.fillna(method='ffill', limit=2))  # 填充限制

data = Series([1, NA, 3.5, NA, 7])
print(data)
print(data.fillna(data.mean()))  # 用平均值填充na值

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