python - PANDAS 从开始位置连续计算连续日期

Question

这是我正在使用的数据框示例:

 d = {'item_number':['bdsm1000', 'bdsm1000', 'bdsm1000', 'ZZRWB18','ZZRWB18', 'ZZRWB18', 'ZZRWB18', 'ZZHP1427BLK', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1414', 'ZZHP1414', 'ZZHP1414', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE'], 'Comp_ID':, [2454, 2454, 2454, 1395, 1395, 1395, 1395, 3378, 1266941, 660867, 43978, 1266941, 660867, 43978, 1266941, 660867, 43978, 1266941, 660867, 43978, 43978, 43978, 43978, 1197347907, 70745, 4737, 1197347907, 4737, 1197347907, 70745, 4737, 1197347907, 70745, 4737, 1197347907, 4737, 1197487704, 1197347907, 70745, 23872, 4737, 1197347907, 4737, 1197487704, 1197347907, 23872, 4737, 1197487704, 1197347907, 70745]
'date': ['2016-11-22', '2016-11-20', '2016-11-19', '2016-11-22', '2016-11-20', '2016-11-19', '2016-11-18', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-19', '2016-11-19', '2016-11-19', '2016-11-18', '2016-11-18', '2016-11-18', '2016-11-22', '2016-11-20', '2016-11-19', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-21', '2016-11-21', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-19', '2016-11-19', '2016-11-19', '2016-11-18', '2016-11-18', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-21', '2016-11-21', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-19', '2016-11-19', '2016-11-19']}

df = pd.DataFrame(data=d)

我想计算从 2016 年 11 月 22 日开始的连续观察，这些观察按 Comp_ID 和 item_number 分组。

本质上，我想要做的是计算每个 Comp_ID 和 item_number 从今天开始倒计时的观测值连续多少天。 (这个例子是在 11 月 22 日放在一起的)今天之前几周/几天观察到的连续观察是不相关的。只有像今天……昨天……前天……这样的序列是相关的。

我让它在较小的样本上工作，但它似乎在更大的数据集上被绊倒了。

d = {'item_number':['KIN005','KIN005','KIN005','KIN005','KIN005','A789B','A789B','A789B','G123    H','G123H','G123H'],
'Comp_ID':['1395','1395','1395','1395','1395','7787','7787','7787','1395','1395','1395'],
'date':['2016-11-22','2016-11-21','2016-11-20','2016-11-14','2016-11-13','2016-11-22','2016-11-21','2016-11-12','2016-11-22','2016-11-21','2016-11-08']}

df = pd.DataFrame(data=d)
df.date = pd.to_datetime(df.date)
d = pd.Timedelta(1, 'D')

df = df.sort_values(['item_number','date','Comp_ID'],ascending=False)

g = df.groupby(['Comp_ID','item_number'])
sequence = g['date'].apply(lambda x: x.diff().fillna(0).abs().le(d)).reset_index()
sequence.set_index('index',inplace=True)
test = df.join(sequence)
test.columns = ['Comp_ID','date','item_number','consecutive']
g = test.groupby(['Comp_ID','item_number'])
g['consecutive'].apply(lambda x: x.idxmin() - x.idxmax() )

这得到了这个数据集的期望结果:

Comp_ID  item_number
1395     G123H          2
         KIN005         3
7787     KIN005         2
Name: consecutive, dtype: int64

Answer 1

设置
修复了你的数据

d = {'item_number':['KIN005','KIN005','KIN005','KIN005','KIN005'],
 'Comp_ID':['1395','1395','1395','1395','1395'],
 'date':['2016-11-22','2016-11-21','2016-11-20','2016-11-14','2016-11-13']}

df = pd.DataFrame(data=d)

df.date = pd.to_datetime(df.date)

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解决方案

d = pd.Timedelta(1, 'D')
df = df.sort_values('date', ascending=False)
consecutive = df.date.diff().fillna(0).abs().le(d)
idx_loc = df.index.get_loc(consecutive.idxmin())
df.iloc[:idx_loc]

---

你最近的连续次数是

idx_loc - 1

2