Python数据处理进阶——pandas

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# 函数反应和映射
df = DataFrame(np.random.randn(4,3), columns= list("bde"),
index= ["Utah", "Ohio", "Texas", "Oregon"])
# print df
# print np.abs(df)
# 将函数应用到各列或行所形成的一维数组上。
f = lambda x : x.max() - x.min()
# 每一列的最大值减最小值
# print df.apply(f, axis=0)
# 每一行的最大值减最小值
# print df.apply(f, axis=1)
# 返回值由多个值组成的Series
def f(x):
return Series([x.min(), x.max()], index=["min","max"])
# print df.apply(f)
# 保留两位小数点
format = lambda x : "%.2f" % x
# print df.applymap(format)
# print df["e"].map(format)# 排序和排名
obj = Series(np.arange(4.), index=["b","a","d","c"])
# print obj.sort_index()
frame = DataFrame(np.arange(8).reshape((2,4)),index=["three","one"],
columns=["d",'a','b','c'])
# 按照索引的行进行排序
# print frame.sort_index(axis=1)
# 按照索引的列进行排序
# print frame.sort_index(axis=0)
# 按照值的列进行排序（必须传入一个列的索引且只能排列一组）
# print frame.sort_values('b', axis=0, ascending=False)
# 按照值的行进行排序(必须传入一个行的索引且只能排列一组)
# print frame.sort_values("one", axis=1, ascending=False)
# 根据多个列进行排序
# print frame.sort_index(by=["a","b"])# 排名
obj1 = Series([7,-5,7,4,2,0,4])
# print obj1.rank()# 加减乘除 add代表加，sub代表减， div代表除法， mul代表乘法
df1 = DataFrame(np.arange(12).reshape((3,4)), columns=list("abcd"))
df2 = DataFrame(np.arange(20).reshape((4,5)), columns=list("abcde"))# print df1 + df2
# 将缺失值用0代替
# print df1.add(df2, fill_value=0)
# 再进行重新索引时，也可以指定一个填充值
# print df1.reindex(columns=df2.columns, fill_value=0)data = {"state": ["Ohio","Ohio","Ohio","Nevada","Nevada"],
"year" : [2000, 2001, 2002, 2001, 2002],
"pop" : [1.5, 1.7, 3.6, 2.4, 2.9]}
frame = DataFrame(data)
# print frame
# 矩阵的横坐标
# print frame.columns
# 矩阵的纵坐标
# print frame.index
# 获取列通过类似字典标记的方式或属性的方式，可以将DataFrame的列获取为一个Series：
# print frame["state"]
# print frame.year
# 获取行也通过类似字典标记的方式或属性的方式，比如用索引字段ix
# print frame.ix[3]
# 修改列的内容
frame["debt"] = 16.5
# print frame
# 精准匹配
val = Series([-1.2, -1.5, -1.7], index=["two", "four", "five"])
frame.index = Series(['one', 'two', 'three', 'four', 'five'])
frame.debt = val
# print frame
# 为不存在的列赋值存在列中的某个值会创建出一个布尔列。关键字del用于删除列。
frame["eastern"] = frame.state == "Ohio"
# print frame
del frame["eastern"] # 只能这样表示
# print frame
# 嵌套字典
pop = { "Nevada" : {2001 : 2.4, 2002 : 2.9},
"Ohio" : {2000 : 1.5, 2001 : 1.7, 2002 : 3.6}
}
# 传给DataFrame，它会被解释为：外层字典的键作为列，内层键则作为行索引
frame2 = DataFrame(pop)
# print frame2
# 对该结果进行转置
# print frame2.T
# 内层字典的键会被合并、排序以形成最终的索引。
frame3 = DataFrame(pop, index=[2001, 2002, 2003])
# print frame3
frame3.index.name = "year"; frame3.columns.name = "state"
# print frame3# 重新索引
obj = Series([4.5, 7.2, -5.3, 3.6], index=["d", "b", "a", "c"])
# reindex将会根据新索引进行重排。
obj2 = obj.reindex(["a", "b", "c", "d", "e"])
# print obj2
# 将缺失值用0代替
obj2 = obj.reindex(["a", "b", "c", "d", "e"], fill_value= 0)
# print obj2# 插值处理--Series
obj3 = Series(["blue", "purple", "yellow"], index=[0,2,4])
# 前向填充ffill或pad
a = obj3.reindex(xrange(6), method="ffill")
# print a
# 后向填充bfill或backfill
b = obj3.reindex(xrange(6), method="bfill")
# print b# 插值处理--DataFrame
import numpy as np
f = DataFrame(np.arange(9).reshape((3,3)), index=["a","c","d"],
columns=["Ohio", "Texas", "California"])
# 改变行的索引
f2 = f.reindex(["a","b","c","d"], fill_value=9)
# print f2
# 改变列的索引
col = ["Texas", "Utah", "California"]
f3 = f.reindex(columns=col)
# print f3
# 同时改变列和行的索引
f4 = f.reindex(["a","b","c","d"], method="ffill",
columns=["Texas", "Utah", "California"])
# print f4# 丢弃指定轴上的项--Series
mys = Series(np.arange(5.), index=["a","b","c","d","e"])
# print mys
# drop（）删除某个索引以及对应的值
mys_new = mys.drop("c")
# print mys_new
mys_new1 = mys.drop(["c","e"])
# print mys_new1# 丢弃指定轴上的项--DataFrame
data = DataFrame(np.arange(16).reshape((4,4)),
index=["Ohio", "Colorado", "Utah", "New York"],
columns=["one", "two", "three", "four"])
# 删除某行轴上的值
data1 = data.drop(["Ohio","Utah"], axis=0) # axis=0代表行
# print data1
# 删除某列轴上的值
data2 = data.drop(["one","three"], axis=1) # axis=1代表列
# print data2obj = Series(range(5), index=['a', 'a', 'b', 'b', 'c'])
# 使用is_unique属性可以知道他的值是否是唯一的
print obj.index.is_unique
# obj['a']
df = DataFrame(np.random.randn(4, 3), index=['a', 'b', 'a', 'b'])
print df.ix["b", 1]
print df[1]

from pandas import Series, DataFrame
import pandas as pd
import numpy as np
# 索引、选取和过滤--Series
obj = Series(np.arange(4), index=["a","b","c","d"])
# print obj["b"]
# print obj[1]
# print obj[2:4]
# print obj[["b","a","d"]]
# print obj[[1,3]]
# print obj[obj < 2]
# 利用标签的切片运算与普通的python切片运算不同，其末端是包含的
# print obj["b":"c"]
obj["b":"c"] = 5
# print obj# 索引、选取和过滤--DataFrame
data = DataFrame(np.arange(16).reshape((4, 4)),
index=["Ohio", "Colorado", "Utah", "New York"],
columns=["one", "two", "three", "four"])
# 选取某列的值
# print data["two"]
# 选取多个列的值
# print data[["two","one"]]
# 通过切片或布尔型数组选取行的值
a = data[:2]
b = data[data["three"] > 5]
# data[data < 5] = 0
# print data
# 选取出列和行的值，用ix[行，列]
c = data.ix["Ohio","two"]
# print c, data
# print data.ix["Ohio",["two","three"]]
# 可以用数字代替列的轴
# print data.ix[["Ohio","Colorado"],[3,0,1]]
# 也可以用数字代替行的轴
# print data.ix[[0,1],[3,0,1]]
d = data.ix[:"Utah", "two"]
# 行中每个值大于5且前三列的值
e = data.ix[data.three > 5, :3]
# print e# Series的字符串表现形式为：索引在左边，值在右边。如果没有指定索引，那么默认从0到（N-1）的整数型索引。
# 可以通过values和index属性获取数组的形式和索引。
obj = Series([2,3,-6,7])
# print obj
# print obj.values
# print obj.index
obj2 = Series([2,3,-6,7],index=["d","b","a","c"])
# print obj2.index
# print obj2["a"]
obj2["d"] = 6
# print obj2[["c","a","d"]]
# print obj2[obj2 > 0 ]
# print obj2 * 2
# print np.exp(obj2)
sdata = {"Ohio" : 35000, "Texas" : 71000, "Oregon" : 16000, "Utah" : 5000}
# 直接用字典建立数组
obj3 = Series(sdata)
# 如果只传入一个字典，则结果Series中的索引就是原字典的键。
states = ["California","Ohio","Oregon","Texas"]
obj4 = Series(sdata, index=states)
# 上述obj4中California在对应的sdata中找不到对应值，所以用NaN表示（缺失值）
# 检测是否有缺失值。
pd.isnull(obj4)
pd.notnull(obj4)
obj4.isnull()
# Series最重要的一个功能是：它在算术运算中会自动对齐不同的索引的数据。
# print obj3 + obj4# Series对象和索引都有一个name属性，该属性跟pandas其他的关键功能关系非常密切：
obj4.name = "population"
obj4.index.name = "state"
# print obj4
# Series的索引可以通过赋值的方式就地修改
obj.index = ["Bob","Steve","Jeff","Ryan"]
print obj

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
df = DataFrame([[1.4, np.nan], [7.1, -4.5],
[np.nan, np.nan],[0.75, -1.3]],
index=['a','b','c','d'],
columns=["one","two"])
# print df.sum()
# 传入axis=1将会按行进行求和运算
# print df.sum(axis=1)
# NA值会自动被排除，除非整个切片是NA值。可以通过skipna选项禁止这种功能
d = df.mean(axis=1, skipna=False)
f = lambda x : "%.2f" % x
# print d.apply(f)# 统计
# 间接统计
# print df.idxmax()
# 累计型统计(前一项加后一项)
# print df.cumsum()
# 一次性汇总统计
# print df.describe()
# print df.min(axis=1)# 计算相关系数和协方差obj = DataFrame(np.random.randn(5,4),
index=["2009-12-24","2009-12-28","2009-12-29","2009-12-30","2009-12-31"],
columns=["AAPL","GOOG","IBM","MSFT"])
obj.index.name = "Data"
# print obj
# index 代表行， columns 代表列
# corr方法用于计算两个Series中重叠的、非NA的、按索引对齐的值的相对系数。cov用于计算协方差：
# print obj.MSFT.corr(obj.IBM)
# print obj.MSFT.cov(obj.IBM)
# 用于DataFrame的corr和cov
# 相关系数
# print obj.corr()
# 协方差
# print obj.cov()
# 按列或行跟一个Series或Data Frame之间的相关系数
# axis=1进行行进行计算
# print obj.corrwith(obj.IBM)# 唯一值
obj1 = Series(["c",'a','d','a','a','b','b','c','c'])
uniques = obj1.unique()
# 加排序
# print uniques.sort()
# 计算出现的频率
print obj1.value_counts()