文章目录
数据分析 第七讲 pandas练习 数据的合并和分组聚合
; 一、pandas-DataFrame
练习1
对于这一组电影数据，如果我们想runtime(电影时长)的分布情况，应该如何呈现数据？
数据分析
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib
font = {
'family':'SimHei',
'weight':'bold',
'size':12
}
matplotlib.rc("font", **font)
file_path = './IMDB-Movie-Data.csv'
df = pd.read_csv(file_path)
'''
RangeIndex: 1000 entries, 0 to 999
Data columns (total 12 columns):
# Column Non-Null Count Dtype
0 Brand 25600 non-null object
1 Store Number 25600 non-null object
2 Store Name 25600 non-null object
3 Ownership Type 25600 non-null object
4 Street Address 25598 non-null object
5 City 25585 non-null object
6 State/Province 25600 non-null object
7 Country 25600 non-null object
8 Postcode 24078 non-null object
9 Phone Number 18739 non-null object
10 Timezone 25600 non-null object
11 Longitude 25599 non-null float64
12 Latitude 25599 non-null float64
dtypes: float64(2), object(11)
memory usage: 2.5+ MB
None'''
print(df.head())
'''
Brand Store Number ... Longitude Latitude
0 Starbucks 47370-257954 ... 1.53 42.51
1 Starbucks 22331-212325 ... 55.47 25.42
2 Starbucks 47089-256771 ... 55.47 25.39
3 Starbucks 22126-218024 ... 54.38 24.48
4 Starbucks 17127-178586 ... 54.54 24.51
'''
pd.set_option('display.max_rows', 10)
pd.set_option('display.max_columns', 13)
print(df)
'''
City State/Province Country Postcode Phone Number \
0 Andorra la Vella 7 AD AD500 376818720
1 Ajman AJ AE NaN NaN
2 Ajman AJ AE NaN NaN
3 Abu Dhabi AZ AE NaN NaN
4 Abu Dhabi AZ AE NaN NaN
... ... ... ... ... ...
25595 Thành Phố Hồ Chí Minh SG VN 70000 08 3824 4668
25596 Thành Phố Hồ Chí Minh SG VN 70000 08 5413 8292
25597 Johannesburg GT ZA 2194 27873500159
25598 Menlyn GT ZA 181 NaN
25599 Midrand GT ZA 1682 27873500215
Timezone Longitude Latitude
0 GMT+1:00 Europe/Andorra 1.53 42.51
1 GMT+04:00 Asia/Dubai 55.47 25.42
2 GMT+04:00 Asia/Dubai 55.47 25.39
3 GMT+04:00 Asia/Dubai 54.38 24.48
4 GMT+04:00 Asia/Dubai 54.54 24.51
... ... ... ...
25595 GMT+000000 Asia/Saigon 106.70 10.78
25596 GMT+000000 Asia/Saigon 106.71 10.72
25597 GMT+000000 Africa/Johannesburg 28.04 -26.15
25598 GMT+000000 Africa/Johannesburg 28.28 -25.79
25599 GMT+000000 Africa/Johannesburg 28.11 -26.02
[25600 rows x 13 columns]
'''
grouped = df.groupby(by='Country').count()['Brand']
print(type(grouped))
print(grouped)
'''
AD 1
AE 144
AR 108
AT 18
AU 22
...
TT 3
TW 394
US 13608
VN 25
ZA 3
Name: Brand, Length: 73, dtype: int64'''
us_count = grouped['US']
print(us_count)
cn_count = grouped['CN']
print(cn_count)
print("美国的星巴克总数位%s,中国的星巴克为%s" % (us_count, cn_count))
'''美国的星巴克总数位13608,中国的星巴克为2734'''
china_data = df[df['Country'] == 'CN']
print(china_data)
'''
Brand Store Number Store Name Ownership Type \
2091 Starbucks 22901-225145 北京西站第一咖啡店 Company Owned
2092 Starbucks 32320-116537 北京华宇时尚店 Company Owned
2093 Starbucks 32447-132306 北京蓝色港湾圣拉娜店 Company Owned
2094 Starbucks 17477-161286 北京太阳宫凯德嘉茂店 Company Owned
2095 Starbucks 24520-237564 北京东三环北店 Company Owned
... ... ... ... ...
4820 Starbucks 17872-186929 Sands Licensed
4821 Starbucks 24126-235784 Wynn II Licensed
4822 Starbucks 28490-242269 Wynn Palace BOH Licensed
4823 Starbucks 22210-218665 Sands Cotai Central Licensed
4824 Starbucks 17108-179449 One Central Licensed
Street Address City State/Province \
2091 丰台区, 北京西站通廊7-1号, 中关村南大街2号 北京市 11
2092 海淀区, 数码大厦B座华宇时尚购物中心内, 蓝色港湾国际商区1座C1-3单元首层、 北京市 11
2093 朝阳区朝阳公园路6号, 二层C1-3单元及二层阳台, 太阳宫中路12号 北京市 11
2094 朝阳区, 太阳宫凯德嘉茂一层01-44/45号, 东三环北路27号 北京市 11
2095 朝阳区, 嘉铭中心大厦A座B1层024商铺, 金融大街7号 北京市 11
... ... ... ...
4820 Portion of Shop 04, Ground Floor, Sands, Largo... Macau 92
4821 Wynn Macau, Rua Cidada de Sintra, NAPE Macau 92
4822 Employee Entrance Outlet, Wynn Cotai, Resort Macau 92
4823 Shop K201 & K202, Level 02, Parcela 5&6, Estra... Macau 92
4824 Promenade Rd, Open Area, 2/F Macau 92
Country Postcode Phone Number Timezone Longitude \
2091 CN 100073 NaN GMT+08:00 Asia/Beijing 116.32
2092 CN 100086 010-51626616 GMT+08:00 Asia/Beijing 116.32
2093 CN 100020 010-59056343 GMT+08:00 Asia/Beijing 116.47
2094 CN 100028 010-84150945 GMT+08:00 Asia/Beijing 116.45
2095 CN NaN NaN GMT+08:00 Asia/Beijing 116.46
... ... ... ... ... ...
4820 CN NaN (853)28782773 GMT+08:00 Asia/Beijing 113.55
4821 CN NaN 85328723516 GMT+08:00 Asia/Beijing 113.55
4822 CN NaN NaN GMT+08:00 Asia/Beijing 113.54
4823 CN NaN 85328853439 GMT+08:00 Asia/Beijing 113.56
4824 CN NaN NaN GMT+08:00 Asia/Beijing 113.55
Latitude
2091 39.90
2092 39.97
2093 39.95
2094 39.97
2095 39.93
... ...
4820 22.19
4821 22.19
4822 22.20
4823 22.15
4824 22.19
[2734 rows x 13 columns]
'''
china_province_data = china_data.groupby(by='State/Province').count()
print(china_province_data['Brand'])
'''
State/Province
11 236
12 58
13 24
14 8
15 8
...
62 3
63 3
64 2
91 162
92 13
Name: Brand, Length: 31, dtype: int64'''
- DataFrameGroupBy对象方法
方法 说明
count 分组中非NA值的数量
sum 非NA值的和
mean 非NA值的平均值
min,max 非NA值的最小值和最大值
4.索引和复合索引
- 简单的索引操作: t.index
- 指定index: t.index = [‘a’,’b’,’c’]
- 重新设置index : t.reindex([“a”,”e”])
- 指定某一列作为index : t.set_index(“M”,drop=False)
- 返回index的唯一值: t.set_index(“M”).index.unique()
-
设置两个索引的时候会是什么样子呢?
-
示例
import numpy as np
import pandas as pd
t1 = pd.DataFrame(np.arange(12).reshape(3, 4), index=list('ABC'), columns=list('WXYZ'))
print(t1)
'''
W X Y Z
A 0 1 2 3
B 4 5 6 7
C 8 9 10 11'''
print(t1.index)
print(t1.reindex(['a','e']))
'''
W X Y Z
a NaN NaN NaN NaN
e NaN NaN NaN NaN'''
print(t1.reindex(['A','e']))
'''
W X Y Z
A 0.0 1.0 2.0 3.0
e NaN NaN NaN NaN'''
print(t1.set_index('W'))
'''
X Y Z
W
0 1 2 3
4 5 6 7
8 9 10 11'''
print(t1.set_index('W',drop=False))
'''
W X Y Z
W
0 0 1 2 3
4 4 5 6 7
8 8 9 10 11'''
print(t1.set_index('W').index)
'''Int64Index([0, 4, 8], dtype='int64', name='W')'''
print(t1.set_index('W').index.unique())
'''Int64Index([0, 4, 8], dtype='int64', name='W')'''
t1.loc['B','W'] = 8
print(t1)
'''
W X Y Z
A 0 1 2 3
B 8 5 6 7
C 8 9 10 11'''
print(t1.set_index('W').index.unique())
'''Int64Index([0, 8], dtype='int64', name='W')'''
t2 = pd.DataFrame(np.arange(12).reshape(3, 4), index=list('ABC'), columns=list('WXYZ'))
print(t2)
'''
W X Y Z
A 0 1 2 3
B 4 5 6 7
C 8 9 10 11'''
print(t2.set_index(['W','X']))
'''
Y Z
W X
0 1 2 3
4 5 6 7
8 9 10 11'''
print(type(t2.set_index(['W','X'])))
Series复合索引
a = pd.DataFrame({‘a’: range(7),’b’: range(7, 0, -1),’c’: [‘one’,’one’,’one’,’two’,’two’,’two’, ‘two’],’d’:
list(“hjklmno”)})
设置c,d为索引
import numpy as np
import pandas as pd
a = pd.DataFrame({'a': range(7), 'b': range(7, 0, -1), 'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'], 'd':
list("hjklmno")})
print(type(a))
print(a)
'''
a b c d
0 0 7 one h
1 1 6 one j
2 2 5 one k
3 3 4 two l
4 4 3 two m
5 5 2 two n
6 6 1 two o'''
b = a.set_index(['c','d'])
print(b)
'''
a b
c d
one h 0 7
j 1 6
k 2 5
two l 3 4
m 4 3
n 5 2
o 6 1
'''
print(b.loc['one'])
'''
a b
d
h 0 7
j 1 6
k 2 5'''
print(b.loc['one'].loc['j'])
'''
a 1
b 6
Name: j, dtype: int64'''
print(b.loc['one'].loc['j']['a'])
c = b['a']
print(type(c))
print(c)
'''
c d
one h 0
j 1
k 2
two l 3
m 4
n 5
o 6
Name: a, dtype: int64'''
print(c['one']['j'])
print(c['one','j'])
设置d,c为索引
import numpy as np
import pandas as pd
a = pd.DataFrame({'a': range(7), 'b': range(7, 0, -1), 'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'], 'd':
list("hjklmno")})
print(type(a))
print(a)
'''
a b c d
0 0 7 one h
1 1 6 one j
2 2 5 one k
3 3 4 two l
4 4 3 two m
5 5 2 two n
6 6 1 two o'''
b = a.set_index(['d','c'])
print(b)
'''
a b
d c
h one 0 7
j one 1 6
k one 2 5
l two 3 4
m two 4 3
n two 5 2
o two 6 1
'''
print(b.loc['j'])
'''
a b
c
one 1 6'''
print(b.loc['j'].loc['one'])
'''
a 1
b 6
Name: one, dtype: int64'''
print(b.loc['j'].loc['one']['a'])
print(b.loc['j'].loc['one','a'])
print(b.swaplevel())
'''
a b
c d
one h 0 7
j 1 6
k 2 5
two l 3 4
m 4 3
n 5 2
o 6 1'''
DateFrame复合索引
import numpy as np
import pandas as pd
a = pd.DataFrame({'a': range(7), 'b': range(7, 0, -1), 'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'], 'd':
list("hjklmno")})
print(type(a))
print(a)
'''
a b c d
0 0 7 one h
1 1 6 one j
2 2 5 one k
3 3 4 two l
4 4 3 two m
5 5 2 two n
6 6 1 two o'''
b = a.set_index(['c','d'])
print(b)
'''
a b
c d
one h 0 7
j 1 6
k 2 5
two l 3 4
m 4 3
n 5 2
o 6 1
'''
print(b.loc['one'].loc['j','b'])
print(b.swaplevel().loc['j'].loc['one','b'])
5.练习
1.使用matplotlib呈现出店铺总数排名前10的国家
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
file_path = './starbucks_store_worldwide.csv'
df = pd.read_csv(file_path)
print(df.info())
'''
RangeIndex: 25600 entries, 0 to 25599
Data columns (total 13 columns):
# Column Non-Null Count Dtype
0 Brand 25600 non-null object
1 Store Number 25600 non-null object
2 Store Name 25600 non-null object
3 Ownership Type 25600 non-null object
4 Street Address 25598 non-null object
5 City 25585 non-null object
6 State/Province 25600 non-null object
7 Country 25600 non-null object
8 Postcode 24078 non-null object
9 Phone Number 18739 non-null object
10 Timezone 25600 non-null object
11 Longitude 25599 non-null float64
12 Latitude 25599 non-null float64
dtypes: float64(2), object(11)
memory usage: 2.5+ MB
None'''
df = df[df['Country'] == 'CN']
'''
Brand Store Number ... Longitude Latitude
2091 Starbucks 22901-225145 ... 116.32 39.90
2092 Starbucks 32320-116537 ... 116.32 39.97
2093 Starbucks 32447-132306 ... 116.47 39.95
[3 rows x 13 columns]'''
data = df.groupby(by='City').count()['Brand'].sort_values(ascending=False)[0:30]
x = data.index
y = data.values
plt.figure(figsize=(15, 8), dpi=80)
plt.bar(x, y)
plt.xticks(rotation=45)
plt.show()
三、pandas中的时间序列
1.时间范围
时间范围
pd.date_range(start=None, end=None, periods=None, freq=’D’)
periods 时间范围的个数
freq 频率,以天为单位还是以月为单位
关于频率的更多缩写
- 示例
import pandas as pd
d = pd.date_range(start='20210101', end='20210201')
print(d)
'''DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',
'2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',
'2021-01-09', '2021-01-10', '2021-01-11', '2021-01-12',
'2021-01-13', '2021-01-14', '2021-01-15', '2021-01-16',
'2021-01-17', '2021-01-18', '2021-01-19', '2021-01-20',
'2021-01-21', '2021-01-22', '2021-01-23', '2021-01-24',
'2021-01-25', '2021-01-26', '2021-01-27', '2021-01-28',
'2021-01-29', '2021-01-30', '2021-01-31', '2021-02-01'],
dtype='datetime64[ns]', freq='D')'''
m = pd.date_range(start='20210101', end='20211231',freq='M')
print(m)
'''
DatetimeIndex(['2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30',
'2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31',
'2021-09-30', '2021-10-31', '2021-11-30', '2021-12-31'],
dtype='datetime64[ns]', freq='M')'''
d10 = pd.date_range(start='20210101', end='20210201',freq='10D')
print(d10)
'''
DatetimeIndex(['2021-01-01', '2021-01-11', '2021-01-21', '2021-01-31'], dtype='datetime64[ns]', freq='10D')'''
d3 = pd.date_range(start='20210101', end='20210201',freq='3D')
print(d3)
'''
DatetimeIndex(['2021-01-01', '2021-01-04', '2021-01-07', '2021-01-10',
'2021-01-13', '2021-01-16', '2021-01-19', '2021-01-22',
'2021-01-25', '2021-01-28', '2021-01-31'],
dtype='datetime64[ns]', freq='3D')'''
d3 = pd.date_range(start='20210101', periods=5, freq='3D')
print(d3)
'''
DatetimeIndex(['2021-01-01', '2021-01-04', '2021-01-07', '2021-01-10',
'2021-01-13'],
dtype='datetime64[ns]', freq='3D')'''
2.DataFrame中使用时间序列
index=pd.date_range(“20190101”,periods=10)
df = pd.DataFrame(np.arange(10),index=index) 作为行索引
df = pd.DataFrame(np.arange(10).reshape(1,10),columns=index) 作为列索引
- 示例
import pandas as pd
import numpy as np
dindex = pd.date_range(start='20210101', periods=10)
df = pd.DataFrame(np.arange(10),index=dindex)
print(df)
'''
0
2021-01-01 0
2021-01-02 1
2021-01-03 2
2021-01-04 3
2021-01-05 4
2021-01-06 5
2021-01-07 6
2021-01-08 7
2021-01-09 8
2021-01-10 9
'''
d_index = pd.date_range(start='20210101', periods=10)
df1 = pd.DataFrame(np.arange(10).reshape(1,10),columns=d_index)
print(df1)
'''
2021-01-01 2021-01-02 2021-01-03 ... 2021-01-08 2021-01-09 2021-01-10
0 0 1 2 ... 7 8 9
[1 rows x 10 columns]'''
3.pandas重采样
- 重采样：指的是将时间序列从一个频率转化为另一个频率进行处理的过程，将高频率数据转化为低频率数据为降采样，低频率转化为高频率为升采样
- pandas提供了一个resample的方法来帮助我们实现频率转化
- 示例
import pandas as pd
import numpy as np
dindex = pd.date_range(start='2021-01-01',end='2021-02-24')
t = pd.DataFrame(np.arange(55).reshape(55,1),index=dindex)
print(t)
'''
0
2021-01-01 0
2021-01-02 1
2021-01-03 2
2021-01-04 3
2021-01-05 4
2021-01-06 5
2021-01-07 6
2021-01-08 7
2021-01-09 8
2021-01-10 9
2021-01-11 10
2021-01-12 11
2021-01-13 12
2021-01-14 13
2021-01-15 14
2021-01-16 15
2021-01-17 16
2021-01-18 17
2021-01-19 18
2021-01-20 19
2021-01-21 20
2021-01-22 21
2021-01-23 22
2021-01-24 23
2021-01-25 24
2021-01-26 25
2021-01-27 26
2021-01-28 27
2021-01-29 28
2021-01-30 29
2021-01-31 30
2021-02-01 31
2021-02-02 32
2021-02-03 33
2021-02-04 34
2021-02-05 35
2021-02-06 36
2021-02-07 37
2021-02-08 38
2021-02-09 39
2021-02-10 40
2021-02-11 41
2021-02-12 42
2021-02-13 43
2021-02-14 44
2021-02-15 45
2021-02-16 46
2021-02-17 47
2021-02-18 48
2021-02-19 49
2021-02-20 50
2021-02-21 51
2021-02-22 52
2021-02-23 53
2021-02-24 54
'''
print(t.resample('M').mean())
'''
0
2021-01-31 15.0
2021-02-28 42.5'''
print(t.resample('10D').mean())
'''
0
2021-01-01 4.5
2021-01-11 14.5
2021-01-21 24.5
2021-01-31 34.5
2021-02-10 44.5
2021-02-20 52.0'''
4.练习
练习1：统计出911数据中不同月份的电话次数
import pandas as pd
from matplotlib import pyplot as plt
file_path = './911.csv'
df = pd.read_csv(file_path)
pd.set_option('display.max_rows', 30)
pd.set_option('display.max_columns', 9)
'''
RangeIndex: 249737 entries, 0 to 249736
Data columns (total 9 columns):
# Column Non-Null Count Dtype
0 No 52584 non-null int64
1 year 52584 non-null int64
2 month 52584 non-null int64
3 day 52584 non-null int64
4 hour 52584 non-null int64
5 season 52584 non-null int64
6 PM_Dongsi 25052 non-null float64
7 PM_Dongsihuan 20508 non-null float64
8 PM_Nongzhanguan 24931 non-null float64
9 PM_US Post 50387 non-null float64
10 DEWP 52579 non-null float64
11 HUMI 52245 non-null float64
12 PRES 52245 non-null float64
13 TEMP 52579 non-null float64
14 cbwd 52579 non-null object
15 Iws 52579 non-null float64
16 precipitation 52100 non-null float64
17 Iprec 52100 non-null float64
dtypes: float64(11), int64(6), object(1)
memory usage: 7.2+ MB
None'''
pd.set_option('display.max_rows', 30)
pd.set_option('display.max_columns', 18)
'''
No year month day hour season PM_Dongsi PM_Dongsihuan \
0 1 2010 1 1 0 4 NaN NaN
1 2 2010 1 1 1 4 NaN NaN
2 3 2010 1 1 2 4 NaN NaN
3 4 2010 1 1 3 4 NaN NaN
4 5 2010 1 1 4 4 NaN NaN
PM_Nongzhanguan PM_US Post DEWP HUMI PRES TEMP cbwd Iws \
0 NaN NaN -21.0 43.0 1021.0 -11.0 NW 1.79
1 NaN NaN -21.0 47.0 1020.0 -12.0 NW 4.92
2 NaN NaN -21.0 43.0 1019.0 -11.0 NW 6.71
3 NaN NaN -21.0 55.0 1019.0 -14.0 NW 9.84
4 NaN NaN -20.0 51.0 1018.0 -12.0 NW 12.97
precipitation Iprec
0 0.0 0.0
1 0.0 0.0
2 0.0 0.0
3 0.0 0.0
4 0.0 0.0
'''
periods = pd.PeriodIndex(year=df["year"], month=df["month"], day=df["day"], hour=df["hour"], freq="H")
'''
PeriodIndex(['2010-01-01 00:00', '2010-01-01 01:00', '2010-01-01 02:00',
'2010-01-01 03:00', '2010-01-01 04:00', '2010-01-01 05:00',
'2010-01-01 06:00', '2010-01-01 07:00', '2010-01-01 08:00',
'2010-01-01 09:00',
...
'2015-12-31 14:00', '2015-12-31 15:00', '2015-12-31 16:00',
'2015-12-31 17:00', '2015-12-31 18:00', '2015-12-31 19:00',
'2015-12-31 20:00', '2015-12-31 21:00', '2015-12-31 22:00',
'2015-12-31 23:00'],
dtype='period[H]', length=52584, freq='H')
'''
df['datetime'] = periods
df.set_index('datetime',inplace=True)
'''
No year month day hour season PM_Dongsi \
datetime
2010-01-01 00:00 1 2010 1 1 0 4 NaN
2010-01-01 01:00 2 2010 1 1 1 4 NaN
2010-01-01 02:00 3 2010 1 1 2 4 NaN
2010-01-01 03:00 4 2010 1 1 3 4 NaN
2010-01-01 04:00 5 2010 1 1 4 4 NaN
PM_Dongsihuan PM_Nongzhanguan PM_US Post DEWP HUMI \
datetime
2010-01-01 00:00 NaN NaN NaN -21.0 43.0
2010-01-01 01:00 NaN NaN NaN -21.0 47.0
2010-01-01 02:00 NaN NaN NaN -21.0 43.0
2010-01-01 03:00 NaN NaN NaN -21.0 55.0
2010-01-01 04:00 NaN NaN NaN -20.0 51.0
PRES TEMP cbwd Iws precipitation Iprec
datetime
2010-01-01 00:00 1021.0 -11.0 NW 1.79 0.0 0.0
2010-01-01 01:00 1020.0 -12.0 NW 4.92 0.0 0.0
2010-01-01 02:00 1019.0 -11.0 NW 6.71 0.0 0.0
2010-01-01 03:00 1019.0 -14.0 NW 9.84 0.0 0.0
2010-01-01 04:00 1018.0 -12.0 NW 12.97 0.0 0.0
'''
df = df.resample('10D').mean()
data = df['PM_US Post'].dropna()
'''
datetime
2010-01-01 23:00 129.0
2010-01-02 00:00 148.0
2010-01-02 01:00 159.0
2010-01-02 02:00 181.0
2010-01-02 03:00 138.0
...
2015-12-31 19:00 133.0
2015-12-31 20:00 169.0
2015-12-31 21:00 203.0
2015-12-31 22:00 212.0
2015-12-31 23:00 235.0
Freq: H, Name: PM_US Post, Length: 50387, dtype: float64
'''
x = data.index
y = data.values
plt.figure(figsize=(15,8),dpi=80)
plt.plot(range(len(x)),y)
plt.xticks(range(0,len(x),10),list(x)[::10],rotation=45)
plt.show()
四、pandas画图
1.折线图
from matplotlib import pyplot as plt
import pandas as pd
iris_data = pd.read_csv(‘iris.csv’)
”’
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
# Column Non-Null Count Dtype
Original: https://blog.csdn.net/yangyusir/article/details/115145492
Author: yangyusir
Title: 数据分析 第七讲 pandas练习 数据的合并、分组聚合、时间序列、pandas绘图
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