我在这里苦苦挣扎，我希望获取以下数据，按日期分组，然后检查组内的行以确定该组是否具有与之关联的任何位置数据，如果是，则将其提取.
我的数据样本:
id,dates,text,place
1,2017-01-26 01:06:47,text,"Place(country_code='US', full_name='Manhattan, NY', place_type='city', name='Manhattan', contained_within=[], _api=<tweepy.api.API object at 0x10336f320>, attributes={}, country='United States', bounding_box=BoundingBox(type='Polygon', coordinates=[[[-74, 40], [-73, 40], [-73, 40], [-74, 40]]], _api=<tweepy.api.API object at 0x10336f320>))"
2,2017-01-26 01:05:51,text,"Place(country_code='US', full_name='Manhattan, NY', place_type='city', name='Manhattan', contained_within=[], _api=<tweepy.api.API object at 0x10336f320>, attributes={}, country='United States', bounding_box=BoundingBox(type='Polygon', coordinates=[[[-74, 40], [-73, 40], [-73, 40], [-74, 40]]], _api=<tweepy.api.API object at 0x10336f320>))"
4,2017-01-23 01:38:29,text,
5,2017-01-23 01:36:53,text,
我首先加载 csv 并对日期进行分组
import pandas as pd
import matplotlib.pyplot as plt
import datetime
fig = plt.figure(figsize=(5,5))
df1 = pd.read_csv('data.csv')
df = df1[['dates','place']]
df['dates']=pd.to_datetime(df['dates'],format='%Y-%m-%d')
df.index=df['dates']
grp = pd.groupby(df,by=[df.index.year,df.index.month,df.index.day])
for date,group in grp:
print(date)
print(group)
这将产生一个如下所示的数据框:
(2017, 1, 26)
dates \
dates
2017-01-26 01:06:47 2017-01-26 01:06:47
2017-01-26 01:05:51 2017-01-26 01:05:51
place
dates
2017-01-26 01:06:47 Place(country_code='US', full_name='Manhattan,...
2017-01-26 01:05:51 NaN
这是我遇到过滤/条件问题的地方，我的目标是拥有一个数据框，我可以将其保存到如下所示的 csv 中:
date, item_count, has_location, location
2017-01-26, 2, yes, Manhattan
2017-01-23, 2, no, na
最好的方法是什么？谢谢
请您参考如下方法:
我认为你可以使用:
extract name 列 place 首先，然后 groupby by dt.date (如果 dates 列的 dtype 是 datetime，to_datetime 可以删除)并按 size 聚合一些列例如id 并按 first 列 place。最后 insert numpy.where 创建的新列:
print (df)
id dates text \
0 1 2017-01-26 01:06:47 text
1 2 2017-01-26 01:05:51 text
2 4 2017-01-23 01:38:29 text
3 5 2017-01-23 01:36:53 text
place
0 Place(country_code='US', full_name='Manhattan,...
1 Place(country_code='US', full_name='Manhattan,...
2 NaN
3 NaN
df.place = df.place.str.extract(", name='(.*)', contained_within", expand=True)
print (df)
id dates text place
0 1 2017-01-26 01:06:47 text Manhattan
1 2 2017-01-26 01:05:51 text Manhattan
2 4 2017-01-23 01:38:29 text NaN
3 5 2017-01-23 01:36:53 text NaN
df1 = df.groupby(pd.to_datetime(df.dates).dt.date).agg({'id':'size', 'place':'first'})
df1.columns = ['item_count','location']
df1.insert(1, 'has_location', np.where(df1.location.isnull(), 'no', 'yes'))
print (df1)
item_count has_location location
dates
2017-01-23 2 no NaN
2017-01-26 2 yes Manhattan
