11、pandas的分组操作
'''示例数组'''
df = pd.DataFrame({'Team': ['Riders', 'Riders', 'Devils',],
'Rank': [1, 2, 2,],
'Year': [2014,2015,2014],
'Points':[876,789,863]})
---> Points Rank Team Year
0 876 1 Riders 2014
1 789 2 Riders 2015
2 863 2 Devils 2014
'''按 指定列 分组'''
df.groupby('Team') #返回的是地址
---><pandas.core.groupby.DataFrameGroupBy object at 0x000000000705FC18>
----------------------------------------------------------------------------------
'''打印 分组'''
df.groupby(['Team','Year']).groups #可以指定多个列分组
--->{('Devils', 2014): Int64Index([2], dtype='int64'), ('Riders', 2014): Int64Index([0], dtype='int64'), ('Riders', 2015): Int64Index([1], dtype='int64')}
----------------------------------------------------------------------------------
'''美美地打印 分组'''
for name,group in df.groupby(['Team','Year']): #类似字典
print(name)
print(group)
--->('Devils', 2014)
Points Rank Team Year
2 863 2 Devils 2014
('Riders', 2014)
Points Rank Team Year
0 876 1 Riders 2014
('Riders', 2015)
Points Rank Team Year
1 789 2 Riders 2015
'''获得分组内 某个具体的组别'''
df.groupby('Team').get_group('Riders')
---> Points Rank Team Year
0 876 1 Riders 2014
1 789 2 Riders 2015
'''组内的统计函数，'''
#求组内平均数
grouped.mean()
---> Points Rank
Team Year
Devils 2014 863 2
Riders 2014 876 1
2015 789 2
#在分组结果中加入前缀
grouped.mean().add_prefix('mean_')
--->
mean_Points mean_Rank
Team
Devils 863.0 2.0
Riders 832.5 1.5
#将上面的一维表转换成二维表
grouped.mean().unstack()
---> Points Rank
Year 2014 2015 2014 2015
Team
Devils 863.0 NaN 2.0 NaN
Riders 876.0 789.0 1.0 2.0
-----------------------------------------------------------------------------------
#可以针对其他未分组列进行统计,如求Point列的平均值、和、个数
df.groupby('Team')['Points'].agg([np.mean,np.sum,np.size])
---> mean sum size #统计分数列的指标
Team
Devils 863.0 863 1
Riders 832.5 1665 2
'''分组后的函数运用'''
apply函数
#定义函数top
def top(df,n = 3, column = 'Year'):
return df.sort_values(by=column, ascending=False)[:n]
#在组内调用函数
df.groupby('Team').apply(top)
---> Points Rank Team Year
Team
Devils 2 863 2 Devils 2014 #对每个Team按Year排倒序，并返回前3行
Riders 1 789 2 Riders 2015
0 876 1 Riders 2014
#可以直接在apply内对调用的函数传递参数
df.groupby('Team').apply(top,n = 1,column = 'Rank')
---> Points Rank Team Year
Team
Devils 2 863 2 Devils 2014
Riders 1 789 2 Riders 2015
--------------------------------------------------------------------------------------------------------
transform函数
score = lambda x: (x - x.mean()) / x.std()*10
df.groupby('Team').transform(score) #运用自定义公式
---> Points Rank Year
0 7.071068 -7.071068 -7.071068
1 -7.071068 7.071068 7.071068
2 NaN NaN NaN
----------------------------------------------------------------------------------
filter函数
filter = df.groupby('Team').filter(lambda x: len(x) >= 2) #过滤行数
---> Points Rank Team Year
0 876 1 Riders 2014
1 789 2 Riders 2015
12、pandas的连接操作
'''两个数组的左右合并-merge函数'''
#示例数组1
left = pd.DataFrame({
'id':[1,2,3,4,5],
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame(
{'id':[1,2,3,4,5],
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5']})
---> Name id subject_id
0 Alex 1 sub1
1 Amy 2 sub2
2 Allen 3 sub4
3 Alice 4 sub6
4 Ayoung 5 sub5
========================================
Name id subject_id
0 Billy 1 sub2
1 Brian 2 sub4
2 Bran 3 sub3
3 Bryce 4 sub6
4 Betty 5 sub5
#根据1个或多个条件列合并
rs = pd.merge(left,right,on=['id','subject_id']) #合并条件=id&subject_id
---> Name_x id subject_id Name_y
0 Alice 4 sub6 Bryce
1 Ayoung 5 sub5 Betty
----------------------------------------------------------------------------------
#左/右连接
rs = pd.merge(left, right, on='subject_id', how='left') #same as how = 'right'
---> Name_x id_x subject_id Name_y id_y
0 Alex 1 sub1 NaN NaN
1 Amy 2 sub2 Billy 1.0
2 Allen 3 sub4 Brian 2.0
3 Alice 4 sub6 Bryce 4.0
4 Ayoung 5 sub5 Betty 5.0
----------------------------------------------------------------------------------
#merge默认内连接，通过how参数修改为外连接
rs = pd.merge(left, right, how='outer', on='subject_id')
---> Name_x id_x subject_id Name_y id_y
0 Alex 1.0 sub1 NaN NaN
1 Amy 2.0 sub2 Billy 1.0
2 Allen 3.0 sub4 Brian 2.0
3 Alice 4.0 sub6 Bryce 4.0
4 Ayoung 5.0 sub5 Betty 5.0
5 NaN NaN sub3 Bran 3.0
----------------------------------------------------------------------------------
#！内连接效果，同上区别
rs = pd.merge(left, right, on='subject_id')
---> Name_x id_x subject_id Name_y id_y
0 Amy 2 sub2 Billy 1
1 Allen 3 sub4 Brian 2
2 Alice 4 sub6 Bryce 4
3 Ayoung 5 sub5 Betty 5
----------------------------------------------------------------------------------
#改变连接关键列的名称
left = left.rename(columns = {'id':'left_id'})
right = right.rename(columns = {'id':'left_id'})
#连接函数有变化
pd.merge(left,right,left_on = 'left_id',right_on = 'right_id')
--->略
'''两个数组的 “上下”/“左右” 合并-concat函数'''
#示例数组2
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'],index=[0,1,2])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['e','b','c','d'],index=[1,2,3])
---> a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
========================================
---> e b c d
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
----------------------------------------------------------------------------------
#1、默认上下合并,可修改 axis=1 为左右合并
#2、默认外连接，可修改 join='inner' 为内连接,内连接去除带NaN的列
#3、可通过 ignore_index=True 参数对行索引重新命名
rs = pd.concat([df1,df2])
---> a b c d e
0 0.0 0.0 0.0 0.0 NaN
1 0.0 0.0 0.0 0.0 NaN
2 0.0 0.0 0.0 0.0 NaN
1 NaN 1.0 1.0 1.0 1.0
2 NaN 1.0 1.0 1.0 1.0
3 NaN 1.0 1.0 1.0 1.0
----------------------------------------------------------------------------------
'''dataframe和series合并'''
#创建系列s
s = pd.Series(np.random.randn(5))
--->
0 -0.280951
1 -2.324420
2 -0.605283
3 0.347614
4 1.091512
dtype: float64
#创建数据帧df
df = pd.DataFrame(np.arange(9).reshape(3,3),columns = ['col1','col2','col3'])
--->
col1 col2 col3
0 0 1 2
1 3 4 5
2 6 7 8
#df和s延横轴合并
df_concat = pd.concat([df,s],axis = 1)
--->
col1 col2 col3 0
0 0.0 1.0 2.0 -0.280951
1 3.0 4.0 5.0 -2.324420
2 6.0 7.0 8.0 -0.605283
3 NaN NaN NaN 0.347614
4 NaN NaN NaN 1.091512
#df和s延纵轴合并
df_concat = pd.concat([df,s],axis = 0)
--->
col1 col2 col3 0
0 0.0 1.0 2.0 NaN
1 3.0 4.0 5.0 NaN
2 6.0 7.0 8.0 NaN
0 NaN NaN NaN -0.280951
1 NaN NaN NaN -2.324420
2 NaN NaN NaN -0.605283
3 NaN NaN NaN 0.347614
4 NaN NaN NaN 1.091512
'''两个数组的上下合并-append函数'''
#append只有纵向合并，没有横向合并
rs = df1.append([df1,df2],ignore_index=True)
---> a b c d e
0 0.0 0.0 0.0 0.0 NaN
1 0.0 0.0 0.0 0.0 NaN
2 0.0 0.0 0.0 0.0 NaN
3 NaN 1.0 1.0 1.0 1.0
4 NaN 1.0 1.0 1.0 1.0
5 NaN 1.0 1.0 1.0 1.0
6 0.0 0.0 0.0 0.0 NaN
7 0.0 0.0 0.0 0.0 NaN
8 0.0 0.0 0.0 0.0 NaN
----------------------------------------------------------------------------------
#也可以与Series系列合并，同concat合并区别！
s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
rs = df1.append(s1,ignore_index=True)
---> a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 2.0 3.0 4.0
'''部分重合 的数据合并-combine_first'''
s1 = pd.Series(np.arange(5))
--->
0 0
1 1
2 2
3 3
4 4
dtype: int32
s2 = pd.Series(np.arange(2,7),index = [1,2,3,4,5])
--->
1 2
2 3
3 4
4 5
5 6
dtype: int32
#以s1的数值优先如果没有则用s2的数字补充
s1.combine_first(s2)
--->
0 0.0
1 1.0
2 2.0
3 3.0
4 4.0
5 6.0
dtype: float64
'''近似匹配'''
left = pd.DataFrame({'a': [1, 5, 10], 'left_val': ['a', 'b', 'c']})
--->
a left_val
0 1 a
1 5 b
2 10 c
right = pd.DataFrame({'a': [1, 2, 3, 6, 7],'right_val': [1, 2, 3, 6, 7]})
--->
a right_val
0 1 1
1 2 2
2 3 3
3 6 6
4 7 7
#只有左连接，根据left的'a'列，在right的'a'列查找比其小且最接近的值，同excel的vlookup模糊查找
pd.merge_asof(left, right, on='a') #可选参数direction : ‘backward’ (default),‘forward’, or ‘nearest’
--->
a left_val right_val
0 1 a 1
1 5 b 3
2 10 c 7
----------------------------------------------------------------------------------
>>> quotes
time ticker bid ask
0 2016-05-25 13:30:00.023 GOOG 720.50 720.93
1 2016-05-25 13:30:00.023 MSFT 51.95 51.96
2 2016-05-25 13:30:00.030 MSFT 51.97 51.98
3 2016-05-25 13:30:00.041 MSFT 51.99 52.00
4 2016-05-25 13:30:00.048 GOOG 720.50 720.93
5 2016-05-25 13:30:00.049 AAPL 97.99 98.01
6 2016-05-25 13:30:00.072 GOOG 720.50 720.88
7 2016-05-25 13:30:00.075 MSFT 52.01 52.03
>>> trades
time ticker price quantity
0 2016-05-25 13:30:00.023 MSFT 51.95 75
1 2016-05-25 13:30:00.038 MSFT 51.95 155
2 2016-05-25 13:30:00.048 GOOG 720.77 100
3 2016-05-25 13:30:00.048 GOOG 720.92 100
4 2016-05-25 13:30:00.048 AAPL 98.00 100
#在相同'ticker'条件下查找time最接近的值
pd.merge_asof(trades, quotes,
... on='time',
... by='ticker',
... tolerance=pd.Timedelta('2ms'))
--->
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
1 2016-05-25 13:30:00.038 MSFT 51.95 155 NaN NaN
2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93
3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
13、pandas的时间/日期操作
'''对时间操作'''
#获取当前时间
pd.datetime.now() --->2018-04-17 18:28:29.033226
#创建一个时间戳
pd.Timestamp('2018-11-01') --->2018-11-01 00:00:00 #1
pd.Timestamp(0,unit='s') --->1970-01-01 00:00:00 #2
#改变时间显示格式
#原格式
dtnow = pd.to_datetime('today') --->Timestamp('2018-08-07 00:00:00')
#修改后格式
dtnow.strftime('%y-%m-%d') --->'18-08-07'
#把不同格式时间转换为一样的时间戳
#！同时将格式转换为datetime64[ns]
pd.to_datetime(pd.Series(['Jul 31, 2009','2019-10-10', None]))
--->0 2009-07-31
1 2019-10-10
2 NaT
dtype: datetime64[ns]
#创建一个时间范围
#！通过修改 freq='H' 参数可按小时累加
pd.date_range("12:00", "13:59", freq="30min")
--->DatetimeIndex(['2018-04-17 12:00:00', '2018-04-17 12:30:00','2018-04-17 13:00:00', '2018-04-17 13:30:00'],dtype='datetime64[ns]', freq='30T')
'''对日期操作'''
#创建日期范围
#！通过修改 freq='M' 参数可按月或按年累加
pd.date_range('2011/11/03', periods=5)
--->DatetimeIndex(['2011-11-03', '2011-11-04', '2011-11-05', '2011-11-06','2011-11-07'],dtype='datetime64[ns]', freq='D')
#通过指定偏移量
sum_offset = pd.tseries.offsets.Week(2) #偏移量为2周
pd.date_range('1/1/2018', periods=10, freq=sum_offset)
--->DatetimeIndex(['2018-01-08', '2018-01-22', '2018-02-05', '2018-02-19',
'2018-03-05'],dtype='datetime64[ns]', freq='2W')
#！同上区别，创建<工作日>日期范围
pd.bdate_range('2011/11/03', periods=5) #pd.bdate_range / 没了11/5和11/6
--->DatetimeIndex(['2011-11-03', '2011-11-04', '2011-11-07', '2011-11-08','2011-11-09'],dtype='datetime64[ns]', freq='B')
#给定开始和结束日期，创建一个日期范围
start = pd.datetime(2017, 11, 1)
end = pd.datetime(2017, 11, 5)
dates = pd.date_range(start, end)
--->DatetimeIndex(['2017-11-01', '2017-11-02', '2017-11-03', '2017-11-04','2017-11-05'],dtype='datetime64[ns]', freq='D')
#示例日期
rng = pd.date_range('1/1/2018', periods=10, freq='W-SAT') #日期以周为频率，从指定日期“1/1/2018”之后的第一个周六开始，见结果
ts = pd.Series(np.arange(10), index=rng)
--->2018-01-06 0
2018-01-13 1
2018-01-20 2
2018-01-27 3
2018-02-03 4
2018-02-10 5
2018-02-17 6
2018-02-24 7
2018-03-03 8
2018-03-10 9
Freq: W-SAT, dtype: int32
'''索引日期'''
#当日期列为行索引时,对日期进行索引
ts[ts.index[2]] #等同于ts['2018/01/13']、ts['20180113']
--->2
#以2为步长进行索引
ts[::2] #从前往后
--->2018-01-06 0
2018-01-20 2
2018-02-03 4
2018-02-17 6
2018-03-03 8
Freq: 2W-SAT, dtype: int32
#索引指定“年月”
ts['2018-2']
--->2018-02-03 4
2018-02-10 5
2018-02-17 6
2018-02-24 7
Freq: W-SAT, dtype: int32
#索引指定日期之后的
#！注意before、after
ts.truncate(before='2018-3-1')
--->2018-03-03 8
2018-03-10 9
Freq: W-SAT, dtype: int32
#同上相反，索引指定日期之前的
ts.truncate(after='2018-3-1')
--->2018-01-06 0
2018-01-13 1
2018-01-20 2
2018-01-27 3
2018-02-03 4
2018-02-10 5
2018-02-17 6
2018-02-24 7
Freq: W-SAT, dtype: int32
-----------------------------------------------------------------------------------------
'''移动数据'''
#日期索引不变，数据值往后移动
ts.shift(1)
--->2018-01-06 NaN #第一个数值变为NaN
2018-01-13 0.0
2018-01-20 1.0
2018-01-27 2.0
2018-02-03 3.0
2018-02-10 4.0
2018-02-17 5.0
2018-02-24 6.0
2018-03-03 7.0
2018-03-10 8.0
Freq: W-SAT, dtype: float64
#日期索引不变，数据值往前移动
ts.shift(-1)
2018-01-06 1.0
2018-01-13 2.0
2018-01-20 3.0
2018-01-27 4.0
2018-02-03 5.0
2018-02-10 6.0
2018-02-17 7.0
2018-02-24 8.0
2018-03-03 9.0
2018-03-10 NaN #最后一个数值变为NaN
Freq: W-SAT, dtype: float64
-----------------------------------------------------------------------------------------
#检查日期索引是否有重复
ts.index.is_unique
--->True #True=无重复
#对日期索引进行分组
grouped = ts.groupby(level=0) # level=0
'''时间差操作'''
#创建一个时间差
timediff = pd.Timedelta(6,unit='s') #unit可以是'd','h','m','s'
timediff = pd.Timedelta(seconds = 6) #写法2
--->0 days 00:00:06
#时间差的加减
s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
td = pd.Series([ pd.Timedelta(days=i) for i in range(3) ])
df = pd.DataFrame(dict(A = s, B = td))
---> A B
0 2012-01-01 0 days
1 2012-01-02 1 days
2 2012-01-03 2 days
df['C']=df['A']+df['B'] #也可以作减法
---> A B C
0 2018-01-01 0 days 2018-01-01
1 2018-01-02 1 days 2018-01-03
2 2018-01-03 2 days 2018-01-05
# 计算时间间隔
x = pd.Series(pd.date_range('2013-1-1', periods=3, freq='D'))
--->
0 2013-01-01
1 2013-01-02
2 2013-01-03
dtype: datetime64[ns]
# 直接相减得到时间差格式
x-s
--->
0 366 days
1 366 days
2 366 days
dtype: timedelta64[ns]
# 以下得到时间差整数
(x-s).dt.days
--->
0 366
1 366
2 366
dtype: int64
'''时间数据重采样'''
'''降采样'''
#示例数组1，注意行索引和数据列都包含日期
df = pd.DataFrame({'date': pd.date_range('2015-01-01', freq='W',periods=5),'a': np.arange(5)},
index=pd.MultiIndex.from_arrays([[1,2,3,4,5],pd.date_range('2015-01-01', freq='W',periods=5)],names=['v','d']))
---> a date
v d
1 2015-01-04 0 2015-01-04
2 2015-01-11 1 2015-01-11
3 2015-01-18 2 2015-01-18
4 2015-01-25 3 2015-01-25
5 2015-02-01 4 2015-02-01
#按date列的日期以<月>为单位求a列的和
df.resample('M', on='date').sum()
---> a
date
2015-01-31 6
2015-02-28 4
#按索引d列的日期以<月>为单位求a列的和
#！同上区别
df.resample('M', level='d').sum()
---> a
d
2015-01-31 6
2015-02-28 4
--------------------------------------------------------------------------------------
#示例数组2
df = pd.DataFrame({'a': [1]*100},
index=pd.date_range('2018-01-01', periods = 100))
---> a
2018-01-01 1
2018-01-02 1
2018-01-03 1
2018-01-04 1
2018-01-05 1
#按5天采样，并计算5天中的开盘、收盘、最高、最低值
df.resample('5D').ohlc()
---> a
open high low close
2018-01-01 1 1 1 1
2018-01-06 1 1 1 1
......
2018-04-01 1 1 1 1
2018-04-06 1 1 1 1
#按月份重采样，注意用的是groupby()
df.groupby(lambda x: x.month).sum()
---> a
1 31
2 28
3 31
4 10
'''升采样'''
#示例数组，按周一为间隔单位
df = pd.DataFrame(np.random.randn(2, 3),
index=pd.date_range('20170101', periods=2, freq='W-MON'),
columns=['S1', 'S2', 'S3'])
---> S1 S2 S3
2017-01-02 -1.204836 0.538345 -0.933471
2017-01-09 2.307653 -0.112200 0.942536
# 直接升采样会产生空值
df.resample('D').asfreq()
---> S1 S2 S3
2017-01-02 -1.204836 0.538345 -0.933471
2017-01-03 NaN NaN NaN
2017-01-04 NaN NaN NaN
2017-01-05 NaN NaN NaN
2017-01-06 NaN NaN NaN
2017-01-07 NaN NaN NaN
2017-01-08 NaN NaN NaN
2017-01-09 2.307653 -0.112200 0.942536
#ffill用NaN的前一个值填充，bfill用NaN的后一个值填充，可以指定填充个数
df.resample('D').ffill(2)
---> S1 S2 S3
2017-01-02 -1.204836 0.538345 -0.933471
2017-01-03 -1.204836 0.538345 -0.933471
2017-01-04 -1.204836 0.538345 -0.933471
2017-01-05 NaN NaN NaN
2017-01-06 NaN NaN NaN
2017-01-07 NaN NaN NaN
2017-01-08 NaN NaN NaN
2017-01-09 2.307653 -0.112200 0.942536
#采用线性填充
df.resample('D').interpolate('linear')
---> S1 S2 S3
2017-01-02 -1.204836 0.538345 -0.933471
2017-01-03 -0.703052 0.445410 -0.665470
2017-01-04 -0.201268 0.352475 -0.397469
2017-01-05 0.300517 0.259540 -0.129468
2017-01-06 0.802301 0.166605 0.138533
2017-01-07 1.304085 0.073670 0.406534
2017-01-08 1.805869 -0.019265 0.674535
2017-01-09 2.307653 -0.112200 0.942536
日期和时间处理
频率freq的参数设置1
频率freq的参数设置2
14、pandas的IO操作
#原始数据
S.No Name Age City Salary
0 1 Tom 28 Toronto 20000
1 2 Lee 32 HongKong 3000
2 3 Steven 43 Bay Area 8300
3 4 Ram 38 Hyderabad 3900
'''csv文件'''
#读取
df = pd.read_csv('01.csv')
#指定索引列
df=pd.read_csv("01.csv",index_col=['S.No'])
---> Name Age City Salary
S.No
1 Tom 28 Toronto 20000
2 Lee 32 HongKong 3000
3 Steven 43 Bay Area 8300
4 Ram 38 Hyderabad 3900
#修改指定列的数据类型
df=pd.read_csv("01.csv",index_col=['S.No'],dtype = {'Salary':np.float64})
df2.dtypes
--->Name object
Age int64
City object
Salary float64 #由导入默认的int64 ---> float64
dtype: object
#重新标注并取代默认导入的列名称
df=pd.read_csv("01.csv",names=['a','b','c','d','e'],header=0)
---> a b c d e #列名称变了，如果没有参数 header=0，保留原列名称行
0 1 Tom 28 Toronto 20000
1 2 Lee 32 HongKong 3000
2 3 Steven 43 Bay Area 8300
3 4 Ram 38 Hyderabad 3900
#跳过指定行数
df=pd.read_csv("01.csv", skiprows=2)
---> 2 Lee 32 HongKong 3000 #！列名称
0 3 Steven 43 Bay Area 8300
1 4 Ram 38 Hyderabad 3900
'''excel文件'''
#读取
with pd.ExcelFile('01.xlsx') as xlsx:
df1 = pd.read_excel(xlsx,'Sheet1')
df2 = pd.read_excel(xlsx,'Sheet2')
#查看表格统计信息
df1.info()
---><class 'pandas.core.frame.DataFrame'>
RangeIndex: 233614 entries, 0 to 233613
Data columns (total 3 columns):
用户名 233614 non-null object
真实姓名 141860 non-null object
注册时间 233614 non-null object
dtypes: object(3)
memory usage: 14.3+ MB
None
#写入
df.to_excel('001.xlsx') #参数 index = False 可以不输出行索引
IO文件类型
15、pandas的可视化操作
'''示例数据组1'''
df = pd.DataFrame(np.random.rand(10,4),columns=list('ABCD'))
#折线图
df.plot()
#条形图
df.plot.bar()
#堆积条形图
df.plot.bar()
#水平条形图
df.plot.barh()
#水平堆积条形图
df.plot.barh(stacked=True)
'''示例数据组2'''
df = pd.DataFrame({'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':
np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
#直方图，按列分图
df.plot.hist(bins=50) #列合并在一张图，参数bins表示宽度
df.hist(bins=20) #几列就几张图
#箱形图
df.plot.box()
#散点图
df.plot.scatter(x='a', y='b') #x，y代表横纵坐标轴
#饼图
df.plot.pie(subplots=True)
16、Pandas的类SQL用法
'''Pandas的类SQL用法'''
#示例数组
total_bill tip sex smoker day time size
59 48.27 6.73 Male No Sat Dinner 4
125 29.80 4.20 Female No Thur Lunch 6
141 34.30 6.70 Male No Thur Lunch 6
142 41.19 5.00 Male No Thur Lunch 5
143 27.05 5.00 Female No Thur Lunch 6
155 29.85 5.14 Female No Sun Dinner 5
156 48.17 5.00 Male No Sun Dinner 6
170 50.81 10.00 Male Yes Sat Dinner 3
182 45.35 3.50 Male Yes Sun Dinner 3
185 20.69 5.00 Male No Sun Dinner 5
187 30.46 2.00 Male Yes Sun Dinner 5
212 48.33 9.00 Male No Sat Dinner 4
216 28.15 3.00 Male Yes Sat Dinner 5
#SELECT用法
tips[['total_bill', 'tip', 'smoker', 'time']].head(5) #查询某几列，.head(n)返回前n行
---> total_bill tip smoker time
0 16.99 1.01 No Dinner
1 10.34 1.66 No Dinner
2 21.01 3.50 No Dinner
3 23.68 3.31 No Dinner
4 24.59 3.61 No Dinner
-----------------------------------------------------------------------------------------
#WHERE用法
tips[tips['time'] == 'Dinner'].head(5) #附加查询条件
---> total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
-----------------------------------------------------------------------------------------
#GroupBy用法
tips.groupby('sex').size() #计算性别人数
--->sex
Female 2
Male 3
dtype: int64
tips.groupby('sex')['total_bill'].count() #计算total_bill列下的性别人数
--->sex
Female 2
Male 3
Name: total_bill, dtype: int64
tips.groupby('day').agg({'tip': np.mean, 'day': np.size}) #求均值
---> tip day
day
Fri 2.734737 19
Sat 2.993103 87
tips.groupby(['smoker', 'day']).agg({'tip': [np.size, np.mean]})
--->tip
size mean
smoker day
No Fri 4.0 2.812500
Sat 45.0 3.102889
Yes Fri 15.0 2.714000
Sat 42.0 2.875476
17、pandas的透视表操作
#示例数组
df = pd.DataFrame(data={'Province' : ['ON','QC','BC','AL','AL','MN','ON'],
'City' : ['Toronto','Montreal','Vancouver','Calgary','Edmonton','Winnipeg','Windsor'],
'Sales' : [13,6,16,8,4,3,1]})
---> City Province Sales
0 Toronto ON 13
1 Montreal QC 6
2 Vancouver BC 16
3 Calgary AL 8
4 Edmonton AL 4
5 Winnipeg MN 3
6 Windsor ON 1
#创建透视表
table = pd.pivot_table(df,values=['City'],index=['Province'],columns=['City'],aggfunc=np.size)
---> Sales
City Calgary Edmonton Montreal Toronto Vancouver Windsor Winnipeg
Province
AL 1.0 1.0 NaN NaN NaN NaN NaN
BC NaN NaN NaN NaN 1.0 NaN NaN
MN NaN NaN NaN NaN NaN NaN 1.0
ON NaN NaN NaN 1.0 NaN 1.0 NaN
QC NaN NaN 1.0 NaN NaN NaN NaN
#把二维透视表转成一维，同上区别
table.stack('City')
---> Sales
Province City
AL Calgary 1.0
Edmonton 1.0
BC Vancouver 1.0
MN Winnipeg 1.0
ON Toronto 1.0
Windsor 1.0
QC Montreal 1.0
18、其他
#判断单个值是否存在
s = pd.Series(range(5))
print (s==4)
--->0 False
1 False
2 False
3 False
4 True
dtype: bool
-------------------------------------------------------------------------
#判断多个值是否存在
s = pd.Series(list('abc'))
x = s.isin(['a', 'c', 'e']) #返回的是系列的bool值
print (x)
--->0 True
1 False
2 True
dtype: bool
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