0 --- 前情提要（pandas）

【问题1】分组聚合-----非时间类型

import pandas as pd
from matplotlib import pyplot as pltdf = pd.read_csv('./books-Copy1.csv')# step1：去掉"年份"中的缺失值
df1 = df[ pd.notnull(df['original_publication_year']) ]# step2：按照”年份“分组，再求评分的平均值
'''
注意：下面这3种方式是一样的。推荐第二种
（1）应该先groupby完后，再选“rating”列，最后求均值mean（）。这个顺序更好 
'''
data1 = df1.groupby( by=df['original_publication_year'] )['average_rating'].mean()data1 = df1.groupby( by=df['original_publication_year'] ).mean()['average_rating']data1 = df1['average_rating'].groupby( by=df['original_publication_year'] ).mean()print(data1)

original_publication_year
-1750.0    3.630000
-762.0     4.030000
-750.0     4.005000
-720.0     3.730000
-560.0     4.050000...   2013.0    4.0122972014.0    3.9853782015.0    3.9546412016.0    4.0275762017.0    4.100909
Name: average_rating, Length: 293, dtype: float64

【问题2】 分组聚合------时间类型步骤：
（1）先将时间字符串转化为时间类型                       df['timeStamp'] = pd.to_datetime（df['timeStamp']）（2）再将该列设置为索引                                 df.set_index（'timeStamp'， inplace=True）  # 默认删除原列。inplace=True，对df原地修改注意：只有设置为索引后，才能对时间类型进行分组聚合  （3）对时间序列进行分组聚合例如：按照“月”计数。取“title”列                count_by_month = df.resample（'M'）.count()['title']     #   时间类型的分组聚合例如：按照“cate”列计数。取“title”列            grouped = df.groupby(by='cate').count()['title']         # 非时间类型的分组聚合

# 统计不同月份的电话次数的变化情况
import pandas as pd
from matplotlib import pyplot as pltdf = pd.read_csv('./911-Copy1.csv')# step1
df['timeStamp'] = pd.to_datetime(df['timeStamp'])       # （1）将时间字符串转化为时间类型------- pd.to_datetime（）
df.set_index('timeStamp', inplace=True)                # （2）将该列设置为索引--------df.set_index ，默认删除原列     
count_by_month = df.resample('M').count()['title']      # （3）时间类型的分组聚合------取“title”列          “title”-----表示不同类型的事件print(df.head())
print(count_by_month)# step2
_x = count_by_month.index
_y = count_by_month.values_x = [i.strftime('%Y%m%d') for i in _x]                # 将_x格式化：只有“xx年xx月xx日”plt.figure(figsize=(20,8), dpi=80)
plt.plot(range(len(_x)), _y, label='title')
plt.xticks(range(len(_x)), _x, rotation=45)
plt.legend(loc='best')                                 # plt.legend（）-----设置图例。与参数label=“title”相对应
plt.show()

                           lat        lng  \
timeStamp                                   
2015-12-10 17:10:52  40.297876 -75.581294   
2015-12-10 17:29:21  40.258061 -75.264680   
2015-12-10 14:39:21  40.121182 -75.351975   
2015-12-10 16:47:36  40.116153 -75.343513   
2015-12-10 16:56:52  40.251492 -75.603350   desc  \
timeStamp                                                                
2015-12-10 17:10:52  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   
2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   
2015-12-10 14:39:21  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   
2015-12-10 16:47:36  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   
2015-12-10 16:56:52  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   zip                    title                twp  \
timeStamp                                                                  
2015-12-10 17:10:52  19525.0   EMS: BACK PAINS/INJURY        NEW HANOVER   
2015-12-10 17:29:21  19446.0  EMS: DIABETIC EMERGENCY  HATFIELD TOWNSHIP   
2015-12-10 14:39:21  19401.0      Fire: GAS-ODOR/LEAK         NORRISTOWN   
2015-12-10 16:47:36  19401.0   EMS: CARDIAC EMERGENCY         NORRISTOWN   
2015-12-10 16:56:52      NaN           EMS: DIZZINESS   LOWER POTTSGROVE   addr  e  
timeStamp                                           
2015-12-10 17:10:52      REINDEER CT & DEAD END  1  
2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN  1  
2015-12-10 14:39:21                    HAWS AVE  1  
2015-12-10 16:47:36          AIRY ST & SWEDE ST  1  
2015-12-10 16:56:52    CHERRYWOOD CT & DEAD END  1  
timeStamp
2015-12-31     7916
2016-01-31    13096
2016-02-29    11396
2016-03-31    11059
2016-04-30    11287
2016-05-31    11374
2016-06-30    11732
2016-07-31    12088
2016-08-31    11904
2016-09-30    11669
2016-10-31    12502
2016-11-30    12091
2016-12-31    12162
2017-01-31    11605
2017-02-28    10267
2017-03-31    11684
2017-04-30    11056
2017-05-31    11719
2017-06-30    12333
2017-07-31    11768
2017-08-31    11753
2017-09-30    11332
2017-10-31    12337
2017-11-30    11548
2017-12-31    12941
2018-01-31    13123
2018-02-28    11165
2018-03-31    14923
2018-04-30    11240
2018-05-31    12551
2018-06-30    12106
2018-07-31    12549
2018-08-31    12315
2018-09-30    12338
2018-10-31    12976
2018-11-30    14097
2018-12-31    12144
2019-01-31    12304
2019-02-28    11556
2019-03-31    12441
2019-04-30    11845
2019-05-31    12823
2019-06-30    12322
2019-07-31    13166
2019-08-31    12387
2019-09-30    11874
2019-10-31    13425
2019-11-30    12446
2019-12-31    12529
2020-01-31    12208
2020-02-29    11043
2020-03-31     9920
2020-04-30     8243
2020-05-31     7220
Freq: M, Name: title, dtype: int64

【问题3】关于索引

若“one”为列  ：则a['one']
若“one”为索引：则a.loc['one']