[英]Pandas TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'
I've got some order data that I want to analyse. 我有一些要分析的订单数据。 Currently of interest is: How often has which SKU been bought in which month?
当前感兴趣的是:在哪个月份多久购买一次SKU?
Here a small example: 这里有个小例子:
import datetime
import pandas as pd
import numpy as np
d = {'sku': ['RT-17']}
df_skus = pd.DataFrame(data=d)
print(df_skus)
d = {'date': ['2017/02/17', '2017/03/17', '2017/04/17', '2017/04/18', '2017/05/02'], 'item_sku': ['HT25', 'RT-17', 'HH30', 'RT-17', 'RT-19']}
df_orders = pd.DataFrame(data=d)
print(df_orders)
for i in df_orders.index:
print("\n toll")
df_orders.loc[i,'date']=pd.to_datetime(df_orders.loc[i, 'date'])
df_orders = df_orders[df_orders["item_sku"].isin(df_skus["sku"])]
monthly_sales = df_orders.groupby(["item_sku", pd.Grouper(key="date",freq="M")]).size()
monthly_sales = monthly_sales.unstack(0)
print(monthly_sales)
That works fine, but if I use my real order data (from CSV) I get after some minutes: 效果很好,但是如果我使用真实订单数据(来自CSV),则几分钟后会得到:
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'
TypeError:仅对DatetimeIndex,TimedeltaIndex或PeriodIndex有效,但具有“ Int64Index”的实例
That problem comes from the line: 这个问题来自于线:
monthly_sales = df_orders.groupby(["item_sku", pd.Grouper(key="date",freq="M")]).size()
month_sales = df_orders.groupby([[“ item_sku”,pd.Grouper(key =“ date”,freq =“ M”)])。size()
Is it possible to skip over the error? 是否可以跳过该错误? I tried a try except block:
我尝试了一下,除了块:
try:
monthly_sales = df_orders.groupby(["item_sku", pd.Grouper(key="date",freq="M")]).size()
monthly_sales = monthly_sales.unstack(0)
except:
print "\n Here seems to be one issue"
Then I get for the print(monthly_sales) 然后我得到打印(monthly_sales)
Empty DataFrame
空数据框
Columns: [txn_id, date, item_sku, quantity]列:[txn_id,日期,item_sku,数量]
Index: []索引:[]
So something in my data empties or brakes the grouping it seems like? 因此,我的数据中的某些内容可能会清空或阻止分组吗? How can I 'clean' my data?
如何“清理”我的数据?
Or I'd be even fine with loosing the data of a sale here and there if I can just 'skip' over the error, is this possible? 或者,我什至可以在这里和那里丢失销售数据,如果我可以“跳过”错误,这可能吗?
When reading your CSV, use the parse_dates
argument - 读取CSV时,请使用
parse_dates
参数-
df_order = pd.read_csv('file.csv', parse_dates=['date'])
Which automatically converts date
to datetime. 自动将
date
转换为日期时间。 If that doesn't work, then you'll need to load it in as a string, and then use the errors='coerce'
argument with pd.to_datetime
- 如果这不起作用,则需要将其作为字符串加载,然后在
pd.to_datetime
使用errors='coerce'
参数-
df_order['date'] = pd.to_datetime(df_order['date'], errors='coerce')
Note that you can pass series objects (amongst other things) to pd.to_datetime`. 请注意,您可以将系列对象(除其他外)传递给pd.to_datetime`。
Next, filter and group as you've been doing, and it should work. 接下来,按照您的操作进行过滤和分组,它应该可以工作。
df_orders[df_orders["item_sku"].isin(df_skus["sku"])]\
.groupby(['item_sku', pd.Grouper(key='date', freq='M')]).size()
item_sku date
RT-17 2017-03-31 1
2017-04-30 1
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