lesson 2

import numpy as np
import pandas as pd

text = pd.read_csv('train_chinese.csv')
text.head()

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

frame = pd.DataFrame(np.arange(8).reshape((2,4)),
                     index = ['three','one'],
                     columns = ['d','a','b','c'])

排序与排名

• 默认升序排列，降序排列设置参数为 ascending = False
• sort_inde 按行索引，参数axis = 1 按列名排序
• sort_values 按值排序； sort_values(by =[]) 按照一个或者多个列名字进行排序

frame.sort_index()

	d	a	b	c
one	4	5	6	7
three	0	1	2	3

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

	d	c	b	a
three	0	3	2	1
one	4	7	6	5

frame.sort_values(by = ['b','c'],ascending = False)

	d	a	b	c
one	4	5	6	7
three	0	1	2	3

Series 排名

• rank 排名，对数组进行从1到有效数据总数分配名次的操作,相同数据，平均排名
• 加上参数 rank(method = ‘first’),按照观察顺序排名，相同数据靠前的，排名靠前
• rank(ascending = False,method = ‘max’)

DataFrame 排名

frame.rank（axis = ‘columns’）

平级关系打破方法

• average 默认：在每个组中平均排名
• min 对整个组使用最小排名
• max 对整个组使用最大排名
• first 按照值在数据中出现的顺序分配排名

obj = pd.Series([7,-2,7,4,6,8])
obj.rank(ascending = 'False',method = 'max')

0    5.0
1    1.0
2    5.0
3    2.0
4    3.0
5    6.0
dtype: float64

text.sort_values(by = ['年龄'],ascending = False).head(10)

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0	0	0	27042	30.0000	A23	S
851	852	0	3	Svensson, Mr. Johan	male	74.0	0	0	347060	7.7750	NaN	S
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C
96	97	0	1	Goldschmidt, Mr. George B	male	71.0	0	0	PC 17754	34.6542	A5	C
116	117	0	3	Connors, Mr. Patrick	male	70.5	0	0	370369	7.7500	NaN	Q
672	673	0	2	Mitchell, Mr. Henry Michael	male	70.0	0	0	C.A. 24580	10.5000	NaN	S
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0	1	1	WE/P 5735	71.0000	B22	S
33	34	0	2	Wheadon, Mr. Edward H	male	66.0	0	0	C.A. 24579	10.5000	NaN	S
54	55	0	1	Ostby, Mr. Engelhart Cornelius	male	65.0	0	1	113509	61.9792	B30	C
280	281	0	3	Duane, Mr. Frank	male	65.0	0	0	336439	7.7500	NaN	Q

text.sort_values(by = ['年龄']).head(10)

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
803	804	1	3	Thomas, Master. Assad Alexander	male	0.42	0	1	2625	8.5167	NaN	C
755	756	1	2	Hamalainen, Master. Viljo	male	0.67	1	1	250649	14.5000	NaN	S
644	645	1	3	Baclini, Miss. Eugenie	female	0.75	2	1	2666	19.2583	NaN	C
469	470	1	3	Baclini, Miss. Helene Barbara	female	0.75	2	1	2666	19.2583	NaN	C
78	79	1	2	Caldwell, Master. Alden Gates	male	0.83	0	2	248738	29.0000	NaN	S
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.7500	NaN	S
305	306	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	113781	151.5500	C22 C26	S
827	828	1	2	Mallet, Master. Andre	male	1.00	0	2	S.C./PARIS 2079	37.0042	NaN	C
381	382	1	3	Nakid, Miss. Maria ("Mary")	female	1.00	0	2	2653	15.7417	NaN	C
164	165	0	3	Panula, Master. Eino Viljami	male	1.00	4	1	3101295	39.6875	NaN	S

从上边可以看出，忽略其他因素，年龄越大，存活率越低！

DataFrame的数据运算与对齐

df1 = pd.DataFrame(np.arange(12).reshape((3,4)),
                   index = ['three','one','a'],
                   columns = ['a','b','c','d']
                  )
df1

	a	b	c	d
three	0	1	2	3
one	4	5	6	7
a	8	9	10	11

df2 = pd.DataFrame(np.arange(12).reshape((3,4)),
                   index = ['b','one','a'],
                   columns = ['a','e','c','f']
                  )
df2

	a	e	c	f
b	0	1	2	3
one	4	5	6	7
a	8	9	10	11

df2+df1

	a	b	c	d	e	f
a	16.0	NaN	20.0	NaN	NaN	NaN
b	NaN	NaN	NaN	NaN	NaN	NaN
one	8.0	NaN	12.0	NaN	NaN	NaN
three	NaN	NaN	NaN	NaN	NaN	NaN

max(text['兄弟姐妹个数']+text['父母子女个数'])

10

text['票价'].describe()

count    891.000000
mean      32.204208
std       49.693429
min        0.000000
25%        7.910400
50%       14.454200
75%       31.000000
max      512.329200
Name: 票价, dtype: float64

text['父母子女个数'].describe()

count    891.000000
mean       0.381594
std        0.806057
min        0.000000
25%        0.000000
50%        0.000000
75%        0.000000
max        6.000000
Name: 父母子女个数, dtype: float64