学习了Pandas的一些基本语法后，通过下面的例子来初步了解一些pandas数据处理的过程，下面是三个要使用到的数据链接：

https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-abbrevs.csv
https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-areas.csv
https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-population.csv

一、读取数据并获取数据：

>>>import pandas as pd
>>>pop = pd.read_csv('state-population.csv')
>>>areas = pd.read_csv('state-areas.csv')
>>>abbrevs = pd.read_csv('state-abbrevs.csv')
>>>pop.head()
	state/region	ages	year	population
0	AL				under18	2012	1117489.0
1	AL				total	2012	4817528.0
2	AL				under18	2010	1130966.0
3	AL				total	2010	4785570.0
4	AL				under18	2011	1125763.0
>>>areas.head()
	state		area (sq. mi)
0	Alabama		52423
1	Alaska		656425
2	Arizona		114006
3	Arkansas	53182
4	California	163707
>>>abbrevs.head()
	state		abbreviation
0	Alabama		AL
1	Alaska		AK
2	Arizona		AZ
3	Arkansas	AR
4	California	CA

其中，pandas.DataFrame.head()默认读取的是前五行数据，pd.read_csv()得到的是一个DataFrame对象。
关于pandas.DataFrame.head()的官方文档如下：
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html

二、得到美国各州人口的密度排名：

虽然可以计算每张表来的到结果，但在这里试着用数据集链接解决问题：

1、把pop中的state/region和abbrevs中的abbreviation合并：

>>>merged = pd.merge(pop, abbrevs, how='outer',
                 left_on='state/region', right_on='abbreviation')
>>>merged = merged.drop('abbreviation', 1)	# 丢掉 axis=1（列）上的元素
>>>merged.head()
	state/region	ages	year	population	state
0	AL				under18	2012	1117489.0	Alabama
1	AL				total	2012	4817528.0	Alabama
2	AL				under18	2010	1130966.0	Alabama
3	AL				total	2010	4785570.0	Alabama
4	AL				under18	2011	1125763.0	Alabama

2、检查是否有缺失值并处理：

>>>merged.isnull().any()
state/region    False
ages            False
year            False
population       True
state            True
dtype: bool

可以看出，部分的population有缺失值，看是哪些数据：

>>>merged[merged['population'].isnull()].head()
	state/region	ages	year	population	state
2448	PR			under18	1990	NaN			NaN
2449	PR			total	1990	NaN			NaN
2450	PR			total	1991	NaN			NaN
2451	PR			under18	1991	NaN			NaN
2452	PR			total	1993	NaN			NaN

再看看哪些州也有缺失：

>>>merged.loc[merged['state'].isnull(), 'state/region'].unique()
array(['PR', 'USA'], dtype=object)

loc的用法：
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html?highlight=loc#pandas.DataFrame.loc
可以发现，人口数据中包含PR和USA这两项数据，但是这两项的名称并没有出现在缩写表中，我们可以补全:

>>>merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico'
>>>merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States'
>>>merged.isnull().any()
state/region    False
ages            False
year            False
population       True
state           False
dtype: bool

这下state没有缺失值了。

3、将面积数据合并，用共同的state来合并：

下面how='left’的意思就是：以merged的数据为基础，把areas的数据向merged上合并。

>>>final = pd.merge(merged, areas, on='state', how='left')
>>>final.head()
	state/region	ages	year	population	state	area (sq. mi)
0	AL				under18	2012	1117489.0	Alabama	52423.0
1	AL				total	2012	4817528.0	Alabama	52423.0
2	AL				under18	2010	1130966.0	Alabama	52423.0
3	AL				total	2010	4785570.0	Alabama	52423.0
4	AL				under18	2011	1125763.0	Alabama	52423.0

检查有没有缺失值：

>>>final.isnull().any()
state/region     False
ages             False
year             False
population        True
state            False
area (sq. mi)     True
dtype: bool

看看是哪些地区面积的缺失：

>>>final['state'][final['area (sq. mi)'].isnull()].unique()
array(['United States'], dtype=object)

是全美国的面积缺失，在这里，该数据没有什么作用，可以用pandas.DataFrame.dropna()方法去除：
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html?highlight=dropna#pandas.DataFrame.dropna

>>>final.dropna(inplace=True)
>>>final.head()

可以再检查一下缺失值，发现没有缺失值了：

>>>final.isnull().any()
state/region     False
ages             False
year             False
population       False
state            False
area (sq. mi)    False
dtype: bool

3、选择2010年的各州人口以及总人口数据：

query()方法：查询DataFrame的列
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html?highlight=query#pandas.DataFrame.query

>>>data2010 = final.query("year == 2010 & ages == 'total'")
>>>data2010.head()
			state/region	ages	year		population	state		area (sq. mi)
3				AL			total	2010		4785570.0	Alabama		52423.0
91				AK			total	2010		713868.0	Alaska		656425.0
101				AZ			total	2010		6408790.0	Arizona		114006.0
189				AR			total	2010		2922280.0	Arkansas	53182.0
197				CA			total	2010		37333601.0	California	163707.0

4、计算人口密度并按序排列：

set_index（）：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.set_index.html?highlight=set_index#pandas.DataFrame.set_index
sort_values(): https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html?highlight=sort_value

>>>data2010.set_index('state', inplace=True) # 以 state 设置为行标签
>>>density = data2010['population'] / data2010['area (sq. mi)']
>>>density.sort_values(ascending=False, inplace=True) # 按任一轴的值排序，这里 ascending=False 为降序
>>>density.head()
state
District of Columbia    8898.897059
Puerto Rico             1058.665149
New Jersey              1009.253268
Rhode Island             681.339159
Connecticut              645.600649
dtype: float64

查看最后几名：

>>>density.tail()
state
South Dakota    10.583512
North Dakota     9.537565
Montana          6.736171
Wyoming          5.768079
Alaska           1.087509
dtype: float64