我有一个使用Plotly-Dash制作的股票市场数据图表。我画x表示日期,y表示价格。在将x数据绘制为日期时,标签对于数据探索来说总是不可靠和奇怪的。
例如:

如您所见,x轴是一系列年份中的1月和7月。这是自然自动生成的,对于用户体验来说看起来并不好。如果有Q1 2015,Q2 2015,或者2016冬季,2016夏季等会更好……
在Python中,我可以通过编辑数据帧来做到这一点,如下所示:
for column in lst:
column.loc[column["month_int"] == 1, "month"] = "January"
column.loc[column["month_int"] == 2, "month"] = "February"
column.loc[column["month_int"] == 3, "month"] = "March"
column.loc[column["month_int"] == 4, "month"] = "April"
column.loc[column["month_int"] == 5, "month"] = "May"
column.loc[column["month_int"] == 6, "month"] = "June"
column.loc[column["month_int"] == 7, "month"] = "July"
column.loc[column["month_int"] == 8, "month"] = "August"
column.loc[column["month_int"] == 9, "month"] = "September"
column.loc[column["month_int"] == 10, "month"] = "October"
column.loc[column["month_int"] == 11, "month"] = "November"
column.loc[column["month_int"] == 12, "month"] = "December"
# Or like this
for column in lst2:
column.loc[(column['month_int'] > 2) & (column['month_int'] <= 5), 'Season'] = 'Spring'
column.loc[(column['month_int'] > 5) & (column['month_int'] <= 8), 'Season'] = 'Summer'
column.loc[(column['month_int'] > 8) & (column['month_int'] <= 11), 'Season'] = 'Autumn'
column.loc[column['month_int'] <= 2, 'Season'] = 'Winter'
column.loc[column['month_int'] == 12, 'Season'] = 'Winter如果不是Postgres,还有什么等价物呢?我正在尝试学习更多SQL技巧,并替换不必要的python代码。作为参考,下面是我的查询
SELECT symbol, date, adj_close
FROM api.security_price
WHERE security_price.symbol IN %s AND date > (SELECT MAX(date) FROM api.security_price) - interval '5 years'
ORDER by date;发布于 2020-11-13 14:55:29
要获取月份,可以使用postgres中的TO_CHAR函数。
select symbol, date, to_char(date, 'Month') as month, adj_close from api.security_price
WHERE security_price.symbol IN %s AND date > (SELECT MAX(date) FROM api.security_price) - interval '5 years'
ORDER by date;参考:https://www.postgresql.org/docs/current/functions-formatting.html
类似地,您还可以为特殊情况定义自己的sql查询函数。参考:https://www.postgresql.org/docs/current/xfunc-sql.html。
对于季节,另一种简单的方法是让另一列包含month_int到季节的映射,并在输出数据上与该表进行左连接。
https://stackoverflow.com/questions/64816541
复制相似问题