在Pandas数据框架中制作迭代日期

Question

我有一个问题陈述如下：

在每个考试中心，考试将分两班进行；批次II(报告时间上午9:00和下午2点(。考试可以在2020年12月1日至30日期间的任何一天在一个地区进行，具体取决于该地区的考生人数。请注意，每个地区只能有一个考试中心，一个班次最多可以有20名学生参加。根据上述信息，通过分配完成考试数据库：

• Rollno：候选人的卷号将从NL2000001开始(例如：NL2000001、NL2000002、NL2000003……(
• cent_allot：通过输入考试城市代码来分配中心
• cent_add：放置NL"；地区名称"；作为每个位置的中心地址(例如，如果地区名称为ADI，则中心地址为NL ADI(
• 考试日期：分配2020年12月12020日至12月30日之间的任何考试日期，保持最低考试天数，且不违反上述任何条件
• 批次：分配批次I或II，确保满足上述所有条件
• rep_time：第一批报告时间为上午9点，第二批报告时间是下午2点

根据以上描述，我需要制作一个满足上述条件的表。我已经制作了Rollno、cent_allot和cent_add列，但我正在努力制作examDate栏，因为每40个地区值都应该有相同的日期。

以下是地区及其发生频率的列表：

Dist    Count
WGL     299
MAHB    289
KUN     249
GUN     198
KARN    196
KRS     171
CTT     169
VIZ     150
PRA     145
NALG    130
MED     128
ADI     123
KPM     119
TRI     107
ANA     107
KHAM    85
NEL     85
VIZI    84
EGOD    84
SOA     84
SIR     80
NIZA    73
PUD     70
KRK     69
WGOD    56

以下是数据帧的前25行：

Rollno     cent_allot   cent_add    examDate    batch   rep_time
NL2000001   WGL          NL WGL       NaN        NaN    NaN
NL2000002   WGL          NL WGL       NaN        NaN    NaN
NL2000003   WGL          NL WGL       NaN        NaN    NaN
NL2000004   KUN          NL KUN       NaN        NaN    NaN
NL2000005   KUN          NL KUN       NaN        NaN    NaN
NL2000006   KUN          NL KUN       NaN        NaN    NaN
NL2000007   GUN          NL GUN       NaN        NaN    NaN
NL2000008   GUN          NL GUN       NaN        NaN    NaN
NL2000009   GUN          NL GUN       NaN        NaN    NaN
NL2000010   GUN          NL GUN       NaN        NaN    NaN
NL2000011   VIZ          NL VIZ       NaN        NaN    NaN
NL2000012   VIZ          NL VIZ       NaN        NaN    NaN
NL2000013   VIZ          NL VIZ       NaN        NaN    NaN
NL2000014   VIZ          NL VIZ       NaN        NaN    NaN
NL2000015   MAHB         NL MAHB      NaN        NaN    NaN
NL2000016   MAHB         NL MAHB      NaN        NaN    NaN
NL2000017   MAHB         NL MAHB      NaN        NaN    NaN
NL2000018   WGOD         NL WGOD      NaN        NaN    NaN
NL2000019   WGOD         NL WGOD      NaN        NaN    NaN
NL2000020   WGOD         NL WGOD      NaN        NaN    NaN
NL2000021   WGOD         NL WGOD      NaN        NaN    NaN
NL2000022   EGOD         NL EGOD      NaN        NaN    NaN
NL2000023   EGOD         NL EGOD      NaN        NaN    NaN
NL2000024   EGOD         NL EGOD      NaN        NaN    NaN
NL2000025   EGOD         NL EGOD      NaN        NaN    NaN

最后3列都是NaN，因为这三列尚未制作。

让我们以WGL为例。根据以上描述，每个地区每班最多允许20名候选人，这意味着每个地区将分配40次相同的日期，每个地区需要分配20次相同的批次和相同的报告时间。

有人知道怎么做吗？

Answer 1

关键是使用.groupby().cumcount()首先获取运行数。CCD_ 3和CCD_。

数据

使用每个CCD_ 5的给定总计数来生成随机行。

import numpy as np
import pandas as pd
import io
import datetime
df_count = pd.read_csv(io.StringIO("""
Dist    Count
WGL     299
MAHB    289
KUN     249
GUN     198
KARN    196
KRS     171
CTT     169
VIZ     150
PRA     145
NALG    130
MED     128
ADI     123
KPM     119
TRI     107
ANA     107
KHAM    85
NEL     85
VIZI    84
EGOD    84
SOA     84
SIR     80
NIZA    73
PUD     70
KRK     69
WGOD    56
"""), sep=r"s{2,}", engine="python")
# generate random cent_allot
df = df_count.loc[np.repeat(df_count.index.values, df_count["Count"]), "Dist"]
.sample(frac=1)
.reset_index(drop=True)
.to_frame()
.rename(columns={"Dist": "cent_allot"})
df["Rollno"] = df.index.map(lambda s: f"NL2{s+1:06}")
df["cent_add"] = df["cent_allot"].map(lambda s: f"NL {s}")

df到现在应该和你的一样。

代码

# Assign the first examDate
first_day = datetime.date(2020, 12, 1)
# running no. grouped by "cent_allot" (i.e. "Dist")
df["gp_no"] = df.groupby("cent_allot").cumcount()
# increase one day for every 40 records
df["examDate"] = df["gp_no"].apply(lambda x: first_day + datetime.timedelta(days=int(x / 40)))
# batch - can be determined by the even-ness of int(no. / 20)
df["batch"] = df["gp_no"].apply(lambda x: 1 + int(x / 20) % 2)
# map batch to time (or "9 AM" / "2 PM" as you'd like)
df["rep_time"] = df["batch"].apply(lambda x: datetime.time(9, 0) if x == 1 else datetime.time(14, 0))

输出

print(df[["Rollno", "cent_allot", "cent_add", "examDate", "batch", "rep_time"]])
Rollno cent_allot cent_add    examDate  batch  rep_time
0     NL2000001        CTT   NL CTT  2020-12-01      1  09:00:00
1     NL2000002       MAHB  NL MAHB  2020-12-01      1  09:00:00
2     NL2000003        CTT   NL CTT  2020-12-01      1  09:00:00
3     NL2000004        SOA   NL SOA  2020-12-01      1  09:00:00
4     NL2000005        PUD   NL PUD  2020-12-01      1  09:00:00
...        ...      ...         ...    ...       ...
3345  NL2003346       KHAM  NL KHAM  2020-12-03      1  09:00:00
3346  NL2003347        ADI   NL ADI  2020-12-04      1  09:00:00
3347  NL2003348       KARN  NL KARN  2020-12-05      2  14:00:00
3348  NL2003349        SIR   NL SIR  2020-12-02      2  14:00:00
3349  NL2003350        ADI   NL ADI  2020-12-04      1  09:00:00
[3350 rows x 6 columns]

Answer 2

我为获得解决方案而苦苦挣扎，但最终在那天结束时，当我问这个问题时，我找到了一个解决方案：

# examDate column
n_stud = 20   # mention the number of students per batch here
n_batch = 2   # mention the number of batches per day here
temp = data['TH_CENT_CH'].value_counts().sort_index().reset_index()  # storing centers and their counts in a temp variable
cent = temp['index'].to_list()      # storing centers in a list
cnt = temp['TH_CENT_CH'].to_list()  # storing counts in a list
cent1 = []
cnt1 = []
j = 0
# for loops to repeat each center by count times
for c in cent:
for i in range(1, cnt[j] + 1):
cent1.append(c)
cnt1.append(i)
j += 1
df1 = pd.DataFrame(list(zip(cent1, cnt1)), columns = ['cent','cnt'])  # dataframe to store the centers and new count list
counts = df1['cnt'].to_list() # storing the new counts in a list
helper = {}  # helper dictionary
max_no = max(cnt)
# for-while loops to map helper number to each counts number
for i in counts:
j = 0
while(j < (round(max_no / (n_stud * n_batch)) + 1)):
if((i > (n_stud * n_batch * j)) & (i < (n_stud * n_batch * (i + 1)))):
helper[i] = j
j += 1
# mapping the helper with counts
counts = pd.Series(counts)
helper = pd.Series(helper)
hel = counts.map(helper).to_list()
df1['helper'] = hel
examDate = {}  # dictionary to store exam dates
# for loop to map dates to each helper number
for i in hel:
examDate[i] = pd.to_datetime(date(2020, 12, 1) + timedelta(days = (2 * i)))
# mapping the dates with helpers
hel = pd.Series(hel)
examDate = pd.Series(examDate)
exam = hel.map(examDate).to_list()
df1['examDate'] = exam

# adding the dates to the original dataframe
examDate = df1['examDate'].to_list()
data['examDate'] = examDate
data['examDate']

这里TH_CENT_CH是指原始数据帧中的区域列。当我运行data.head()时，我得到了我需要的输出，即40名学生的一次约会。我对剩下的两个专栏做了类似的事情，我需要为20名学生提供相同的批次。所以我得到的输出是：

Rollno  cent_allot  cent_add  examDate   batch  rep_time
0     NL2000001        ADI   NL ADI  2020-12-01      1  09:00:00
1     NL2000002        ADI   NL ADI  2020-12-01      1  09:00:00
2     NL2000003        ADI   NL ADI  2020-12-01      1  09:00:00
3     NL2000004        ADI   NL ADI  2020-12-01      1  09:00:00
4     NL2000005        ADI   NL ADI  2020-12-01      1  09:00:00
...        ...      ...         ...    ...       ...
3345  NL2003346        WGOD  NL WGOD 2020-12-03      1  09:00:00
3346  NL2003347        WGOD  NL WGOD 2020-12-04      1  09:00:00
3347  NL2003348         KRS  NL KRS  2020-12-05      1  09:00:00
3348  NL2003349        WGOD  NL WGOD 2020-12-02      1  09:00:00
3349  NL2003350        WGOD  NL WGOD 2020-12-04      1  09:00:00

请找到其余两列的代码：

# batch column
counts = df1['cnt'].to_list()  # storing the new counts in a list
helper2 = {}  # helper dictionary
# for-while loops to map helper number to each counts number
for i in counts:
j = 0
while(j < (round(max_no / (n_stud)) + 1)):
if((i > (n_stud * j)) & (i < (n_stud * (i + 1)))):
helper2[i] = j
j += 1
# mapping the helper with counts
counts = pd.Series(counts)
helper2 = pd.Series(helper2)
hel2 = counts.map(helper2).to_list()
df1['helper2'] = hel2
batch = {}   # dictionary to store batch numbers
# for loop to map batch numbers to each helper number
for i in hel2:
if(i % 2 == 0):
batch[i] = 1
else:
batch[i] = 2

# mapping the batches with helpers
hel2 = pd.Series(hel2)
batch = pd.Series(batch)
bat = hel2.map(batch).to_list()
df1['batch'] = bat
# adding the batches to the original dataframe
batch = df1['batch'].to_list()
data['batch'] = batch
data['batch'].unique()
# rep_time column
data.loc[data['batch'] == 1, 'rep_time'] = '9:00 AM'
data.loc[data['batch'] == 2, 'rep_time'] = '2:00 PM'
data['rep_time'].unique()