python数据分析（pandas入门）
1、pandas数据结构之DataFrame
DataFrame生成方式：1、从另一个DataFrame创建。2、从具有二维形状的NumPy数组或数组的复合结构生成。3、使用Series创建。4、从CSV之类文件生成。下面介绍DataFrame的简单用法：
a)：读取文件
代码：
from pandas.io.parsers import read_csv
df=read_csv("H:\Python\data\WHO.csv")
print "DataFrame:",dfDataFrame: Country CountryID Continent \
0 Afghanistan 1 1
1 Albania 2 2
2 Algeria 3 3
3 Andorra 4 2
4 Angola 5 3
代码：
print "Shape:",df.shape #大小
print "Length:",len(df) #长度Shape: (202, 358)
Length: 202
代码：
print "Column Headers",df.columns #得到每列的标题
print "Data type",df.dtypes #得到每列数据的类型Column Headers Index([u'Country', u'CountryID', u'Continent',
u'Adolescent fertility rate (%)', u'Adult literacy rate (%)',
u'Gross national income per capita (PPP international $)',
u'Net primary school enrolment ratio female (%)',
u'Net primary school enrolment ratio male (%)',
u'Population (in thousands) total',
u'Population annual growth rate (%)',
...
u'Total_CO2_emissions', u'Total_income', u'Total_reserves',
u'Trade_balance_goods_and_services', u'Under_five_mortality_from_CME',
u'Under_five_mortality_from_IHME', u'Under_five_mortality_rate',
u'Urban_population', u'Urban_population_growth',
u'Urban_population_pct_of_total'],
dtype='object', length=358)
Data type Country object
CountryID int64
Continent int64
Adolescent fertility rate (%) float64
Adult literacy rate (%) float64
Gross national income per capita (PPP international $) float64
Net primary school enrolment ratio female (%) float64
Net primary school enrolment ratio male (%) float64
代码：
print "Index:",df.indexIndex: RangeIndex(start=0, stop=202, step=1)
代码：
print "Vales:",df.valuesVales: [['Afghanistan' 1L 1L ..., 5740436.0 5.44 22.9]
['Albania' 2L 2L ..., 1431793.9 2.21 45.4]
['Algeria' 3L 3L ..., 20800000.0 2.61 63.3]
...,
['Yemen' 200L 1L ..., 5759120.5 4.37 27.3]
['Zambia' 201L 3L ..., 4017411.0 1.95 35.0]
['Zimbabwe' 202L 3L ..., 4709965.0 1.9 35.9]]
2、pandas数据结构之Series
pandas的Series数据结构是由不同类型的元素组成的一维数组，该数据结构也具有标签，创建方式有：由Python字典创建；由numpy数组创建；由单个标量值创建。
a)：类型。当选中DataFrame的一列时，得到的是一个Series型的数据。
代码：
country_df=df["Country"]
print "Type df:",type(df)
print "Type country_df:",type(country_df)Type df:
Type country_df:
代码：
print "Series Shape:",country_df.shape #获取列的形状
print "Series index:",country_df.index #获取索引
print "Series values:",country_df.values #获取该列的所有值
print "Series name:",country_df.name #获取列名（标题）Series Shape: (202L,)
Series index: RangeIndex(start=0, stop=202, step=1)
Series values: ['Afghanistan' 'Albania' 'Algeria' 'Andorra' 'Angola' 'Antigua and Barbuda'
'Argentina' 'Armenia' 'Australia' 'Austria' 'Azerbaijan' 'Bahamas'
'Bahrain' 'Bangladesh' 'Barbados' 'Belarus' 'Belgium' 'Belize' 'Benin'
'Bermuda' 'Bhutan' 'Bolivia' 'Bosnia and Herzegovina' 'Botswana' 'Brazil'
'Brunei Darussalam' 'Bulgaria' 'Burkina Faso' 'Burundi' 'Cambodia'
'Cameroon' 'Canada' 'Cape Verde' 'Central African Republic' 'Chad' 'Chile'
'China' 'Colombia' 'Comoros' 'Congo, Dem. Rep.' 'Congo, Rep.'
'Cook Islands' 'Costa Rica' "Cote d'Ivoire" 'Croatia' 'Cuba' 'Cyprus'
'Czech Republic' 'Denmark' 'Djibouti' 'Dominica' 'Dominican Republic'
'Ecuador' 'Egypt' 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Estonia'
'Ethiopia' 'Fiji' 'Finland' 'France' 'French Polynesia' 'Gabon' 'Gambia'
'Georgia' 'Germany' 'Ghana' 'Greece' 'Grenada' 'Guatemala' 'Guinea'
'Guinea-Bissau' 'Guyana' 'Haiti' 'Honduras' 'Hong Kong, China' 'Hungary'
'Iceland' 'India' 'Indonesia' 'Iran (Islamic Republic of)' 'Iraq'
'Ireland' 'Israel' 'Italy' 'Jamaica' 'Japan' 'Jordan' 'Kazakhstan' 'Kenya'
'Kiribati' 'Korea, Dem. Rep.' 'Korea, Rep.' 'Kuwait' 'Kyrgyzstan'
"Lao People's Democratic Republic" 'Latvia' 'Lebanon' 'Lesotho' 'Liberia'
'Libyan Arab Jamahiriya' 'Lithuania' 'Luxembourg' 'Macao, China'
'Macedonia' 'Madagascar' 'Malawi' 'Malaysia' 'Maldives' 'Mali' 'Malta'
'Marshall Islands' 'Mauritania' 'Mauritius' 'Mexico'
'Micronesia (Federated States of)' 'Moldova' 'Monaco' 'Mongolia'
'Montenegro' 'Morocco' 'Mozambique' 'Myanmar' 'Namibia' 'Nauru' 'Nepal'
'Netherlands' 'Netherlands Antilles' 'New Caledonia' 'New Zealand'
'Nicaragua' 'Niger' 'Nigeria' 'Niue' 'Norway' 'Oman' 'Pakistan' 'Palau'
'Panama' 'Papua New Guinea' 'Paraguay' 'Peru' 'Philippines' 'Poland'
'Portugal' 'Puerto Rico' 'Qatar' 'Romania' 'Russia' 'Rwanda'
'Saint Kitts and Nevis' 'Saint Lucia' 'Saint Vincent and the Grenadines'
'Samoa' 'San Marino' 'Sao Tome and Principe' 'Saudi Arabia' 'Senegal'
'Serbia' 'Seychelles' 'Sierra Leone' 'Singapore' 'Slovakia' 'Slovenia'
'Solomon Islands' 'Somalia' 'South Africa' 'Spain' 'Sri Lanka' 'Sudan'
'Suriname' 'Swaziland' 'Sweden' 'Switzerland' 'Syria' 'Taiwan'
'Tajikistan' 'Tanzania' 'Thailand' 'Timor-Leste' 'Togo' 'Tonga'
'Trinidad and Tobago' 'Tunisia' 'Turkey' 'Turkmenistan' 'Tuvalu' 'Uganda'
'Ukraine' 'United Arab Emirates' 'United Kingdom'
'United States of America' 'Uruguay' 'Uzbekistan' 'Vanuatu' 'Venezuela'
'Vietnam' 'West Bank and Gaza' 'Yemen' 'Zambia' 'Zimbabwe']
Series name: Country代码：
print "Last 2 countries:",country_df[-2:]
print "Last 2 countries type:",type(country_df[-2:])Last 2 countries: 200 Zambia
201 Zimbabwe
Name: Country, dtype: object
Last 2 countries type:
3、利用Pandas查询数据
a)：head()和tail()函数：
代码：
sunspots=read_csv("H:\Python\data\sunspots.csv")
print "Head 2：",sunspots.head(2) #查看前两行
print "Tail 2：",sunspots.tail(2) #查看后两行Head 2： Date Yearly Mean Total Sunspot Number
0 2016/12/31 39.8
1 2015/12/31 69.8
Tail 2： Date Yearly Mean Total Sunspot Number
316 1701-12-31 18.3
317 1700-12-31 8.3
代码：
last_date=sunspots.index[-1]
print "Last value:\n",sunspots.loc[last_date]last_date=sunspots.index[-1]
print "Last value:\n",sunspots.loc[last_date]4、利用Pandas的DataFrame进行统计计算
pandas的DataFrame数据结构为我们提供了若干统计函数，下面给出部分方法及其简要说明。
| 方法 | 说明 |
| describe | 这个方法返回描述性统计信息 |
| count | 返回非NAN数据项的数量 |
| mad | 计算平均绝对偏差，级类似于标准差的一个有力统计工具 |
| median | 返回中位数，等价于第50百分位数的值 |
| min | 返回最小值 |
| max | 返回最大值 |
| mode | 返回众数（mod），即一组数据中出现次数最多的变量值 |
| std | 返回表示离散度的标准差，即方差的平方根 |
| var | 返回方差 |
| skew | 返回偏差系数（skewness）,该系数表示的是数据分布的对称程度 |
| kurt | 这个方法将返回峰太系数，反映数据分布曲线顶端尖峭或扁平程度 |
print "Describe:\n",sunspots.describe() print "Non NaN observations:\n",sunspots.count() print "MAD:\n",sunspots.mad() print "Median:\n",sunspots.median() print "Min:\n",sunspots.min() print "Max:\n",sunspots.max() print "Mode:\n",sunspots.mode() print "Standard Deviation:\n",sunspots.std() print "Variance:\n",sunspots.var() print "Skewness:\n",sunspots.skew() print "Kurtosis:\n",sunspots.kurt()
运行结果：
Describe:
Yearly Mean Total Sunspot Number
count 318.000000
mean 79.193396
std 61.988788
min 0.000000
25% 24.950000
50% 66.250000
75% 116.025000
max 269.300000
Non NaN observations:
Date 318
Yearly Mean Total Sunspot Number 318
dtype: int64
MAD:
Yearly Mean Total Sunspot Number 50.925104
dtype: float64
Median:
Yearly Mean Total Sunspot Number 66.25
dtype: float64
Min:
Date 1700-12-31
Yearly Mean Total Sunspot Number 0
dtype: object
Max:
Date 2016/12/31
Yearly Mean Total Sunspot Number 269.3
dtype: object
Mode:
Date Yearly Mean Total Sunspot Number
0 1985/12/31 18.3
Standard Deviation:
Yearly Mean Total Sunspot Number 61.988788
dtype: float64
Variance:
Yearly Mean Total Sunspot Number 3842.60983
dtype: float64
Skewness:
Yearly Mean Total Sunspot Number 0.808551
dtype: float64
Kurtosis:
Yearly Mean Total Sunspot Number -0.130045
dtype: float64
5、利用pandas的DataFrame实现数据聚合
a)：为numpy的随机数生成器指定种子，以确保重复运行程序时生成的数据不会走样。该数据有4列：
1、Weather(一个字符串)；
2、Food(一个字符串)；
3、Price(一个随机浮点数)；
4、Number(1~9之间的一个随机整数)。
代码：
import pandas as pd
from numpy.random import seed
from numpy.random import rand
from numpy.random import randint
import numpy as np
seed(42)
#random.rand(n),生成n个0到1间随机数
#random.random_integers(low,high=None,size=None) 生成闭区间[low,high]上离散均匀分布的整数值;若high=None，则取值区间变为[1,low]
df=pd.DataFrame({'Weather':['cold','hot','cold','hot','cold','hot','cold'],'Food':['soup','soup','icecream','chocolate','icecream','icecream','soup'],
'Price':10*rand(7),'Number':randint(1,9,size=(7,))})
print df运行结果：
Food Number Price Weather
0 soup 8 3.745401 cold
1 soup 5 9.507143 hot
2 icecream 4 7.319939 cold
3 chocolate 8 5.986585 hot
4 icecream 8 1.560186 cold
5 icecream 3 1.559945 hot
6 soup 6 0.580836 cold
b)：通过Weather列为数据分组，然后遍历各组数据
代码：
weather_group=df.groupby('Weather') #按天气分组
i=0
for name,group in weather_group:
i=i+1
print "Group ",i,name
print groupGroup 1 cold
Food Number Price Weather
0 soup 8 3.745401 cold
2 icecream 4 7.319939 cold
4 icecream 8 1.560186 cold
6 soup 6 0.580836 cold
Group 2 hot
Food Number Price Weather
1 soup 5 9.507143 hot
3 chocolate 8 5.986585 hot
5 icecream 3 1.559945 hotc)：变量Weather_group是一种特殊的pandas对象，可由groupby()生成。这个对象为我们提供了聚合函数，下面展示它的用法：
代码：
print "Weather group first:\n",weather_group.first() #展示各组第一行内容
print "Weather group last:\n",weather_group.last() #展示各组最后一行内容
print "Weather group mean:\n",weather_group.mean() #计算各组均值Weather group first:
Food Number Price
Weather
cold soup 8 3.745401
hot soup 5 9.507143
Weather group last:
Food Number Price
Weather
cold soup 6 0.580836
hot icecream 3 1.559945
Weather group mean:
Number Price
Weather
cold 6.500000 3.301591
hot 5.333333 5.684558
d)：恰如利用数据库的查询操作那样，也可以针对多列进行分组。
然后就可以用groups属性来了解所生成的数据组，以及每一组包含的行数：
代码：
wf_group=df.groupby(['Weather','Food'])
print "WF Group:\n",wf_group.groupsWF Group:
{('hot', 'chocolate'): Int64Index([3], dtype='int64'), ('cold', 'icecream'): Int64Index([2, 4], dtype='int64'), ('cold', 'soup'): Int64Index([0, 6], dtype='int64'), ('hot', 'soup'): Int64Index([1], dtype='int64'), ('hot', 'icecream'): Int64Index([5], dtype='int64')}
e)：通过agg方法，可以对数据组施加一系列的numpy函数：
代码：
print "WF Aggregated:\n",wf_group.agg([np.mean,np.median])WF Aggregated:
Number Price
mean median mean median
Weather Food
cold icecream 6 6 4.440063 4.440063
soup 7 7 2.163119 2.163119
hot chocolate 8 8 5.986585 5.986585
icecream 3 3 1.559945 1.559945
soup 5 5 9.507143 9.507143
6、DataFrame的串联与附加操作
a)：数据库中的数据表有内部连接与外部连接两种连接类型。pandas的DataFrame也有类似操作，也可以对数据进行串联和附加。
函数concat()的作用是串联DataFrame，如可以把一个由3行数据组成的DataFrame与其他行数据行串接，以便重建原DataFrame：
代码：
print "df:3\n",df[:3]
print "Contact Back together:\n",pd.concat([df[:3],df[:3]])df:3
Food Number Price Weather
0 soup 8 3.745401 cold
1 soup 5 9.507143 hot
2 icecream 4 7.319939 cold
Contact Back together:
Food Number Price Weather
0 soup 8 3.745401 cold
1 soup 5 9.507143 hot
2 icecream 4 7.319939 cold
0 soup 8 3.745401 cold
1 soup 5 9.507143 hot
2 icecream 4 7.319939 cold
代码：
print "Appending rows:\n",df[3:].append(df[5:])Appending rows:
Food Number Price Weather
3 chocolate 8 5.986585 hot
4 icecream 8 1.560186 cold
5 icecream 3 1.559945 hot
6 soup 6 0.580836 cold
5 icecream 3 1.559945 hot
6 soup 6 0.580836 cold
a)、新建两个CSV文件：dest.csv和tips.csv
代码：
dests=pd.read_csv("H:\Python\data\dest.csv")
tips=pd.read_csv("H:\Python\data\\tips.csv")
print "dests:\n",dests
print "tips:\n",tipsdests:
EmpNr Dest
0 5 The Hague
1 3 Amsterdam
2 9 Rotterdam
tips:
EmpNr Amount
0 5 10.0
1 9 5.0
2 7 2.5
pandas支持所有的这些连接类型，这里仅介绍内部连接与完全外部连接。
- 用merge函数按照员工编号进行连接处理，代码如下：
print "Merge() on key:\n",pd.merge(dests,tips,on='EmpNr')Merge() on key:
EmpNr Dest Amount
0 5 The Hague 10.0
1 9 Rotterdam 5.0
- 使用join方法执行连接操作，需要使用后缀来指示左操作对象和右操作对象：
print "Dest join() tips:\n",dests.join(tips,lsuffix='Dest',rsuffix='Tips')Dest join() tips:
EmpNrDest Dest EmpNrTips Amount
0 5 The Hague 5 10.0
1 3 Amsterdam 9 5.0
2 9 Rotterdam 7 2.5- 用merge()执行内部连接和外部连接时，更显示的方法如下所示：
代码：
print "Inner join with merge():\n",pd.merge(dests,tips,how='inner') #内连接
print "Outer join with merge():\n",pd.merge(dests,tips,how='outer') #完全外部连接Inner join with merge():
EmpNr Dest Amount
0 5 The Hague 10.0
1 9 Rotterdam 5.0
Outer join with merge():
EmpNr Dest Amount
0 5 The Hague 10.0
1 3 Amsterdam NaN
2 9 Rotterdam 5.0
3 7 NaN 2.58、处理缺失数据
a)：读取数据。
代码：
df=pd.read_csv("H:\Python\data\WHO.csv")
#print df.head()
df=df[['Country',df.columns[6]]][:2] #将原df的Country列和第6列组成新DataFrame，并取前两行
print "New df:\n",dfNew df:
Country Net primary school enrolment ratio female (%)
0 Afghanistan NaN
1 Albania 93.0
b)：pandas会把缺失的数值标记为NaN，表示None。pandas的isnull()函数可以帮我们检查缺失的数据。
代码：
print "Null Values:\n",pd.isnull(df) #检查每行缺失的数
print "Not Null Values:\n",pd.notnull(df) #检查非缺失的数
print "Last Column Doubled:\n",2*df[df.columns[-1]] #NAN值乘以一个数后还是NAN
print "Last Column plus NaN:\n",df[df.columns[-1]]+np.nan #非NAN值加上NAN后变为了NAN
print "Zero filled:\n",df.fillna(0) #使用0替换NAN
Null Values:
Country Net primary school enrolment ratio female (%)
0 False True
1 False False
Not Null Values:
Country Net primary school enrolment ratio female (%)
0 True False
1 True True
Last Column Doubled:
0 NaN
1 186.0
Name: Net primary school enrolment ratio female (%), dtype: float64
Last Column plus NaN:
0 NaN
1 NaN
Name: Net primary school enrolment ratio female (%), dtype: float64
Zero filled:
Country Net primary school enrolment ratio female (%)
0 Afghanistan 0.0
1 Albania 93.09、处理日期数据
a)：设定从1900年1月1日开始为期42天的时间范围。
代码：
print "Date range:\n",pd.date_range('1/1/1900',periods=42,freq='D') #42表示天数，D表示使用日频率。如果periods='W',表示42周Date range:
DatetimeIndex(['1900-01-07', '1900-01-14', '1900-01-21', '1900-01-28',
'1900-02-04', '1900-02-11', '1900-02-18', '1900-02-25',
'1900-03-04', '1900-03-11', '1900-03-18', '1900-03-25',
'1900-04-01', '1900-04-08', '1900-04-15', '1900-04-22',
'1900-04-29', '1900-05-06', '1900-05-13', '1900-05-20',
'1900-05-27', '1900-06-03', '1900-06-10', '1900-06-17',
'1900-06-24', '1900-07-01', '1900-07-08', '1900-07-15',
'1900-07-22', '1900-07-29', '1900-08-05', '1900-08-12',
'1900-08-19', '1900-08-26', '1900-09-02', '1900-09-09',
'1900-09-16', '1900-09-23', '1900-09-30', '1900-10-07',
'1900-10-14', '1900-10-21'],
dtype='datetime64[ns]', freq='W-SUN')
b)：在pandas中，日期区间是有限制的。pandas的时间戳基于numpy datetime64类型，以纳秒为单位，并且用一个64位整数来表示具体数值。因此，日期有效的时间戳介于1677年至2262年。当然，这些年份也不是所有日期都是有效的。这个时间范围的精确中点是1970年1月1日。这样，1677年1月1日就无法用pandas时间戳定义，而1677年9月30日就可以，下面用代码说明：
代码：
import pandas as pd
import sys
try:
print "Date range:\n",pd.date_range('1/1/1677',periods=4,frep='D')
except:
etype,value,_=sys.exc_info() #获得错误类型，错误值
print "Error encountered:\n",etype,value #打印Date range:
Error encountered:
Out of bounds nanosecond timestamp: 1677-01-01 00:00:00
b）：使用pandas的Dateoffset函数计算允许的日期范围：
代码：
offset=pd.DateOffset(seconds=2**63/10**9)
mid=pd.to_datetime('1/1/1970')
print "Start valid range:\n",mid-offset
print "End valid range:\n",mid+offsetStart valid range:
1677-09-21 00:12:44
End valid range:
2262-04-11 23:47:16
代码：
print "With format:\n",pd.to_datetime(['1901113','19031230'],format='%Y%m%d')With format:
DatetimeIndex(['1901-11-03', '1903-12-30'], dtype='datetime64[ns]', freq=None)
d)：如果一个字符串明显不是日期，无法转化。可以使用参数coerce设置为True强制转化：
代码：
print "Illegal date:\n",pd.to_datetime(['1901-11-13','not a date']) #第二个字符串无法转换，运行报错
print "Illegal date:\n",pd.to_datetime(['1901-11-13','not a date'],coerce=True) #强制转化，得到非时间数NATIllegal date:
DatetimeIndex(['1901-11-13', 'NaT'], dtype='datetime64[ns]', freq=None)
10、数据透析表
a）：数据透析表可以从一个平面文件中指定的行和列中聚合数据，这种聚合操作可以是求和、求平均值，求标准差等运算。import pandas as pd
from numpy.random import seed
from numpy.random import rand
from numpy.random import randint
import numpy as np
seed(42)
N=7
df=pd.DataFrame({'Weather':['cold','hot','cold','hot','cold','hot','cold'],'Food':['soup','soup','icecream','chocolate','icecream','icecream','soup'],
'Price':10*rand(7),'Number':randint(1,9,size=(7,))})
print "DataFrame:\n",df
print pd.pivot_table(df,index='Food',aggfunc=np.sum) #计算各类型Food的统计值DataFrame:
Food Number Price Weather
0 soup 8 3.745401 cold
1 soup 5 9.507143 hot
2 icecream 4 7.319939 cold
3 chocolate 8 5.986585 hot
4 icecream 8 1.560186 cold
5 icecream 3 1.559945 hot
6 soup 6 0.580836 cold
Number Price
Food
chocolate 8 5.986585
icecream 15 10.440071
soup 19 13.833380