Pandas TypeError：仅对 DatetimeIndex、TimedeltaIndex 或 PeriodIndex 有效，但得到了“Int64Index”的实例
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Pandas TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'
提问by Chris
I've got some order data that I want to analyse. Currently of interest is: How often has which SKU been bought in which month?
我有一些要分析的订单数据。目前感兴趣的是：哪个SKU在哪个月被购买的频率？
Here a small example:
这里有一个小例子：
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:
这工作正常，但如果我使用我的真实订单数据（来自 CSV），几分钟后我会得到：
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'
类型错误：仅对 DatetimeIndex、TimedeltaIndex 或 PeriodIndex 有效，但有一个“Int64Index”实例
That problem comes from the line:
该问题来自以下行：
monthly_sales = df_orders.groupby(["item_sku", pd.Grouper(key="date",freq="M")]).size()
monthly_sales = df_orders.groupby(["item_sku", pd.Grouper(key="date",freq="M")]).size()
Is it possible to skip over the error? I tried a try except block:
是否可以跳过错误？我尝试了一个除了块之外的尝试：
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)
然后我得到印刷品（monthly_sales）
Empty DataFrame
Columns: [txn_id, date, item_sku, quantity]
Index: []
空数据帧
列：[txn_id，日期，item_sku，数量]
索引：[]
So something in my data empties or brakes the grouping it seems like?
How can I 'clean' my data?
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?
那么我的数据中的某些内容会清空或破坏分组吗？如何“清理”我的数据？
或者，如果我可以“跳过”错误，我什至可以在这里和那里丢失销售数据，这可能吗？
回答by cs95
When reading your CSV, use the parse_datesargument -
阅读 CSV 时，请使用parse_dates参数 -
df_order = pd.read_csv('file.csv', parse_dates=['date'])
Which automatically converts dateto datetime. 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-
它会自动转换date为日期时间。如果这不起作用，那么您需要将其作为字符串加载，然后将errors='coerce'参数与pd.to_datetime-
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，像你一直在做的那样过滤和分组，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
