pandas :从数据框获取json [英] Pandas: get json from data frame
本文介绍了 pandas :从数据框获取json的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有数据框
member_id,2015-05-01,2015-05-02,2015-05-03,2015-05-04,2015-05-05,2015-05-06,2015-05-07,2015-05-08,2015-05-09,2015-05-10,2015-05-11,2015-05-12,2015-05-13,2015-05-14,2015-05-15,2015-05-16,2015-05-17,2015-05-18,2015-05-19,2015-05-20,2015-05-21,2015-05-22,2015-05-23,2015-05-24,2015-05-25,2015-05-26,2015-05-27,2015-05-28,2015-05-29,2015-05-30,2015-05-31,2015-06-01,2015-06-02,2015-06-03,2015-06-04,2015-06-05,2015-06-06,2015-06-07,2015-06-08,2015-06-09,2015-06-10,2015-06-11,2015-06-12,2015-06-13,2015-06-14,2015-06-15,2015-06-16,2015-06-17,2015-06-18,2015-06-19,2015-06-20,2015-06-21,2015-06-22,2015-06-23,2015-06-24,2015-06-25,2015-06-26,2015-06-27,2015-06-28,2015-06-29,2015-06-30,2015-07-01,2015-07-02,2015-07-03,2015-07-04,2015-07-05,2015-07-06,2015-07-07,2015-07-08,2015-07-09,2015-07-10,2015-07-11,2015-07-12,2015-07-13,2015-07-14,2015-07-15,2015-07-16,2015-07-17,2015-07-18,2015-07-19,2015-07-20,2015-07-21,2015-07-22,2015-07-23,2015-07-24,2015-07-25,2015-07-26,2015-07-27,2015-07-28,2015-07-29,2015-07-30,2015-07-31,2015-08-01,2015-08-02,2015-08-03,2015-08-04,2015-08-05,2015-08-06,2015-08-07,2015-08-08,2015-08-09,2015-08-10,2015-08-11,2015-08-12,2015-08-13,2015-08-14,2015-08-15,2015-08-16,2015-08-17,2015-08-18,2015-08-19,2015-08-20,2015-08-21,2015-08-22,2015-08-23,2015-08-24,2015-08-25,2015-08-26,2015-08-27,2015-08-28,2015-08-29,2015-08-30,2015-08-31,2015-09-01,2015-09-02,2015-09-03,2015-09-04,2015-09-05,2015-09-06,2015-09-07,2015-09-08,2015-09-09,2015-09-10,2015-09-11,2015-09-12,2015-09-13,2015-09-14,2015-09-15,2015-09-16,2015-09-17,2015-09-18,2015-09-19,2015-09-20,2015-09-21,2015-09-22,2015-09-23,2015-09-24,2015-09-25,2015-09-26,2015-09-27,2015-09-28,2015-09-29,2015-09-30,2015-10-01,2015-10-02,2015-10-03,2015-10-04,2015-10-05,2015-10-06,2015-10-07,2015-10-08,2015-10-09,2015-10-10,2015-10-11,2015-10-12,2015-10-13,2015-10-14,2015-10-15,2015-10-16,2015-10-17,2015-10-18,2015-10-19,2015-10-20,2015-10-21,2015-10-22,2015-10-23,2015-10-24,2015-10-25,2015-10-26,2015-10-27,2015-10-28,2015-10-29,2015-10-30,2015-10-31,2015-11-01,2015-11-02,2015-11-03,2015-11-04,2015-11-05,2015-11-06,2015-11-07,2015-11-08,2015-11-09,2015-11-10,2015-11-11,2015-11-12,2015-11-13,2015-11-14,2015-11-15,2015-11-16,2015-11-17,2015-11-18,2015-11-19,2015-11-20,2015-11-21,2015-11-22,2015-11-23,2015-11-24,2015-11-25,2015-11-26,2015-11-27,2015-11-28,2015-11-29,2015-11-30,2015-12-01,2015-12-02,2015-12-03,2015-12-04,2015-12-05,2015-12-06,2015-12-07,2015-12-08,2015-12-09,2015-12-10,2015-12-11,2015-12-12,2015-12-13,2015-12-14,2015-12-15,2015-12-16,2015-12-17,2015-12-18,2015-12-19,2015-12-20,2015-12-21,2015-12-22,2015-12-23,2015-12-24,2015-12-25,2015-12-26,2015-12-27,2015-12-28,2015-12-29,2015-12-30,2015-12-31,2016-01-01,2016-01-02,2016-01-03,2016-01-04,2016-01-05,2016-01-06,2016-01-07,2016-01-08,2016-01-09,2016-01-10,2016-01-11,2016-01-12,2016-01-13,2016-01-14,2016-01-15,2016-01-16,2016-01-17,2016-01-18,2016-01-19,2016-01-20,2016-01-21,2016-01-22,2016-01-23,2016-01-24,2016-01-25,2016-01-26,2016-01-27,2016-01-28,2016-01-29,2016-01-30,2016-01-31,2016-02-01,2016-02-02,2016-02-03,2016-02-04,2016-02-05,2016-02-06,2016-02-07,2016-02-08,2016-02-09,2016-02-10,2016-02-11,2016-02-12,2016-02-13,2016-02-14,2016-02-15,2016-02-16,2016-02-17,2016-02-18,2016-02-19,2016-02-20,2016-02-21,2016-02-22,2016-02-23,2016-02-24,2016-02-25,2016-02-26,2016-02-27,2016-02-28,2016-02-29,2016-03-01,2016-03-02,2016-03-03,2016-03-04,2016-03-05,2016-03-06,2016-03-07,2016-03-08,2016-03-09,2016-03-10,2016-03-11,2016-03-12,2016-03-13,2016-03-14,2016-03-15,2016-03-16,2016-03-17,2016-03-18,2016-03-19,2016-03-20,2016-03-21,2016-03-22,2016-03-23,2016-03-24,2016-03-25,2016-03-26,2016-03-27,2016-03-28,2016-03-29,2016-03-30,2016-03-31,2016-04-01,2016-04-02,2016-04-03,2016-04-04,2016-04-05,2016-04-06,2016-04-07,2016-04-08,2016-04-09,2016-04-10,2016-04-11,2016-04-12,2016-04-13,2016-04-14,2016-04-15,2016-04-16,2016-04-17,2016-04-18,2016-04-19,2016-04-20,2016-04-21,2016-04-22,2016-04-23,2016-04-24,2016-04-25,2016-04-26,2016-04-27,2016-04-28,2016-04-29,2016-04-30,2016-05-01,2016-05-02,2016-05-03,2016-05-04,2016-05-05,2016-05-06,2016-05-07,2016-05-08,2016-05-09,2016-05-10,2016-05-11,2016-05-12,2016-05-13,2016-05-14,2016-05-15,2016-05-16,2016-05-17,2016-05-18,2016-05-19,2016-05-20,2016-05-21,2016-05-22,2016-05-23,2016-05-24,2016-05-25,2016-05-26,2016-05-27,2016-05-28,2016-05-29,2016-05-30,2016-05-31,2016-06-01,2016-06-02,2016-06-03,2016-06-04,2016-06-05,2016-06-06,2016-06-07,2016-06-08,2016-06-09,2016-06-10,2016-06-11,2016-06-12,2016-06-13,2016-06-14,2016-06-15,2016-06-16,2016-06-17,2016-06-18,2016-06-19,2016-06-20,2016-06-21,2016-06-22,2016-06-23,2016-06-24,2016-06-25,2016-06-26,2016-06-27,2016-06-28,2016-06-29,2016-06-30,2016-07-01,2016-07-02,2016-07-03,2016-07-04,2016-07-05,2016-07-06,2016-07-07,2016-07-08,2016-07-09,2016-07-10,2016-07-11,2016-07-12,2016-07-13,2016-07-14,2016-07-15,2016-07-16,2016-07-17,2016-07-18,2016-07-19,2016-07-20,2016-07-21,2016-07-22,2016-07-23,2016-07-24,2016-07-25,2016-07-26,2016-07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19205,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
19276,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
19404,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
如果列！= 0中的值，我需要将其替换为1.(将3替换为1，将7替换为1，依此类推.)我需要以这种格式获取json. (在df中有整天，但我需要几个月才能完成)
I need if value in column != 0, replace it to 1. (3 replace to 1, 7 replace to 1 etc.)I need to get json like this format. (In df there are all days, but I need do it to months)
{
"19205": {
"2015-05": 0,
"2015-06": 0,
"2015-07": 1,
"2015-08": 0,
"2015-09": 0,
"2015-10": 0,
"2015-11": 0,
"2015-12": 0,
"2016-01": 0,
"2016-02": 0,
"2016-03": 0,
"2016-04": 0,
"2016-05": 1,
"2016-06": 0
},
"19276": { ...
}
}
推荐答案
您可以使用:
#convert int xolum to string
df['member_id'] = df.member_id.astype(str)
#reshaping and convert to months period
df.set_index('member_id', inplace=True)
df = df.unstack().reset_index(name='val').rename(columns={'level_0':'date'})
df['date'] = pd.to_datetime(df.date).dt.to_period('m').dt.strftime('%Y-%m')
#groupby by date and member_id and aggregate sum
df = df.groupby(['date','member_id'])['val'].sum()
#convert all values !=0 to 1
df = (df != 0).astype(int).reset_index()
#working in pandas 0.18.1
d = df.groupby('member_id')['date', 'val'].apply(lambda x: pd.Series(x.set_index('date')['val'].to_dict())).to_json(orient='index')
print (d)
{'19404': {'2016-07': 1, '2015-12': 0, '2015-09': 0, '2015-08': 0, '2015-11': 0, '2015-10': 0, '2015-05': 0, '2016-06': 1, '2015-06': 0, '2016-04': 0, '2016-05': 0, '2015-07': 0, '2016-03': 0, '2016-01': 0, '2016-08': 0, '2016-02': 0},
'19276': {'2016-07': 0, '2015-12': 0, '2015-09': 0, '2015-08': 0, '2015-11': 0, '2015-10': 0, '2015-05': 0, '2016-06': 0, '2015-06': 0, '2016-04': 0, '2016-05': 0, '2015-07': 0, '2016-03': 0, '2016-01': 1, '2016-08': 0, '2016-02': 0},
'19205': {'2016-07': 0, '2015-12': 0, '2015-09': 0, '2015-08': 0, '2015-11': 0, '2015-10': 0, '2015-05': 0, '2016-06': 0, '2015-06': 0, '2016-04': 0, '2016-05': 0, '2015-07': 0, '2016-03': 1, '2016-01': 0, '2016-08': 0, '2016-02': 0}}
这篇关于 pandas :从数据框获取json的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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