早上好，我的代码的目标是比较多个数据框中的记录。如果记录ID在另一个数据框中，则输出记录存在的月份。例如，如果在3月，4月的月份中出现ID 1，则输出3月4月。但是，当我在测试样本上运行此代码时，它可以完美运行。当我在原始数据集上运行时，所需的输出是全部或全部，而不是单个月。
下面的代码与所需的输出作为测试样本完美配合。
代码
#Import of Libraries
import pandas as pd
import numpy as np
import xlsxwriter
import nltk
from itertools import chain
data1 = {'aa',1,3,2,12,3,4,5,'bb',6,7,8,9,100,65,56,'cc',70}
data2 = {'aa',11,12,3,4,5,123,12,14,8,'cc',100,56}
data3 = {'aa',12,111,33,13,5,6,4,555,'bb',3333,65,634,7,8,8888,100}
data4 = {'aa',44,33,5,6,7,8,999,'bb',4,2,66,3,70,1,1,2}
df1 = pd.DataFrame(data1,columns=['RPN'])
df2 = pd.DataFrame(data2,columns=['RPN'])
df3 = pd.DataFrame(data3,columns=['RPN'])
df4 = pd.DataFrame(data4,columns=['RPN'])
df1 = df1.astype(str)
df2 = df2.astype(str)
df3 = df3.astype(str)
df4 = df4.astype(str)
#Creates list of Source and RPN to compare data
march = df4['RPN'].values.tolist()
april = df3['RPN'].values.tolist()
may = df2['RPN'].values.tolist()
june = df1['RPN'].values.tolist()
#turns list of each month into sets.
june = set(june)
may = set(may)
april = set(april)
march = set(march)
#creates list of every record in all months
setlist = [june,may,april,march]
#creats an interestion of all like values in the list of months
setall = set.intersection(*setlist)
setall
#Checks to see if current dataframe RPN and Source is in the previous audit report data
compare = []
for index,x in df1.iterrows():
RPN = x['RPN']
if RPN in setall:
compare.append('All Months')
elif RPN not in chain(setall, april, may) and RPN in march:
compare.append('March')
elif RPN not in chain(setall, march, may) and RPN in april:
compare.append('April')
elif RPN not in chain(setall, march, april) and RPN in may:
compare.append('May')
elif RPN not in chain(setall,march) and RPN in may and april:
compare.append('April and May')
elif RPN not in chain(setall,april) and RPN in may and march:
compare.append('March and May')
elif RPN not in chain(setall,may) and RPN in april and march:
compare.append('March and April')
else:
compare.append('New Record')
df1['Aging'] = compare
df1
正确的输出
RPN Aging
0 1 March
1 2 March
2 3 March and May
3 4 All Months
4 5 All Months
5 bb March and April
6 6 March and April
7 7 March and April
8 8 All Months
9 9 New Record
10 100 April and May
11 12 April and May
12 65 April
13 cc May
14 70 March
15 aa All Months
16 56 May
我遇到的问题是，当我向原始数据集引入完全相同的代码和格式时，结果为ALL或NOTHING，而不是显示每个记录之间的差异。
#Import of Libraries
import pandas as pd
import numpy as np
import xlsxwriter
import nltk
from itertools import chain
#Creates dataframes
#Current Month
bucket='sagemaker-bucket-826404949026/Provider Data/Audit Comparison'
data_key = 'AuditJune2019.xlsx'
data_location = 's3://{}/{}'.format(bucket, data_key)
df = pd.read_excel(data_location)
df.info()
#Previous Month
bucket2 ='sagemaker-bucket-826404949026/Provider Data/Audit Comparison'
data_key2 = 'AuditMay2019.xlsx'
data_location2 = 's3://{}/{}'.format(bucket2, data_key2)
dfprev2 = pd.read_excel(data_location2)
dfprev2.info()
#April Month
bucket3 ='sagemaker-bucket-826404949026/Provider Data/Audit Comparison'
data_key3 = 'AuditApril2019.xlsx'
data_location3 = 's3://{}/{}'.format(bucket3, data_key2)
dfprev3 = pd.read_excel(data_location3)
dfprev3.info()
#March Month
bucket4 ='sagemaker-bucket-826404949026/Provider Data/Audit Comparison'
data_key4 = 'AuditMarch2019.xlsx'
data_location4 = 's3://{}/{}'.format(bucket4, data_key2)
dfprev4 = pd.read_excel(data_location4)
dfprev4.info()
#Creates list of Source and RPN to compare data
dfprev4 = dfprev4.fillna('0')
dfprev3 = dfprev3.fillna('0')
dfprev2 = dfprev2.fillna('0')
df = df.fillna('0')
df = df.astype(str)
dfprev2 = dfprev2.astype(str)
dfprev3 = dfprev3.astype(str)
dfprev4 = dfprev4.astype(str)
dfprev4['RPN'] = dfprev4['RPN'] + dfprev4['SOURCE']
dfprev3['RPN'] = dfprev3['RPN'] + dfprev3['SOURCE']
dfprev2['RPN'] = dfprev2['RPN'] + dfprev2['SOURCE']
df['RPN'] = df['RPN'] + df['SOURCE']
#Creates list of Source and RPN to compare data
march = dfprev4['RPN'].values.tolist()
april = dfprev3['RPN'].values.tolist()
may = dfprev2['RPN'].values.tolist()
june = df['RPN'].values.tolist()
#turns list of each month into sets.
june = set(june)
may = set(may)
april = set(april)
march = set(march)
#creates list of every record in all months
setlist = [june,may,april,march]
#creats an interestion of all like values in the list of months
setall = set.intersection(*setlist)
setall
#creates a dataframe of just RPN
df1 = pd.DataFrame(df['RPN'],columns = ['RPN'])
#Checks to see if current dataframe RPN and Source is in the previous audit report data
compare = []
for index,x in df1.iterrows():
RPN = x['RPN']
if RPN in setall:
compare.append('All Months')
elif RPN not in chain(setall, april, may) and RPN in march:
compare.append('March')
elif RPN not in chain(setall, march, may) and RPN in april:
compare.append('April')
elif RPN not in chain(setall, march, april) and RPN in may:
compare.append('May')
elif RPN not in chain(setall,march) and RPN in may and april:
compare.append('April and May')
elif RPN not in chain(setall,april) and RPN in may and march:
compare.append('March and May')
elif RPN not in chain(setall,may) and RPN in april and march:
compare.append('March and April')
else:
compare.append('New Record')
df1['Aging'] = compare
df1
不正确的输出
RPN Aging
0 testPORTICO New Record
1 test123PORTICO New Record
2 AG50001PORTICO New Record
3 AG50001FACETS New Record
4 0370001PORTICO New Record
5 0370001FACETS New Record
6 JY00001PORTICO New Record
7 JY00001FACETS New Record
8 JQ00001PORTICO New Record
9 JQ00001FACETS New Record
10 DH70001PORTICO All Months
11 DH70001FACETS All Months
12 8120001PORTICO All Months
13 8120001FACETS All Months
14 J760001PORTICO All Months
15 J760001FACETS All Months
16 MS200012PORTICO All Months
17 MS200012FACETS All Months
18 MS200012FACETS All Months
19 BZ400013PORTICO All Months
20 BZ400013FACETS All Months
我认为这可能与导入数据框有关吗？即时通讯不确定，请帮助！
答案 0 :(得分：0)
请考虑对代码进行以下修改。四个print可让您直观地（以表格形式）检查数据，因此相信会对您有所帮助。
import pandas as pd
# dictionary of sets
data = {
'June':
{'aa', 1, 3, 2, 12, 3, 4, 5, 'bb', 6, 7, 8, 9, 100, 65, 56, 'cc', 70},
'May':
{'aa', 11, 12, 3, 4, 5, 123, 12, 14, 8, 'cc', 100, 56},
'April':
{'aa', 12, 111, 33, 13, 5, 6, 4, 555, 'bb', 3333, 65, 634, 7, 8, 8888, 100},
'March':
{'aa', 44, 33, 5, 6, 7, 8, 999, 'bb', 4, 2, 66, 3, 70, 1, 1, 2}}
# print(*(f'{key:>5}: {val}' for key, val in data.items()), sep='\n')
# dictionary with months only
months = {month: 0 for month in data}
# transform dictionary of sets into dictionary of dictionaries
new_data = dict()
for month in data:
for value in data[month]:
new_data.setdefault(str(value), months.copy())[month] = 1
# print(*(f'{key:>5}: {val}' for key, val in new_data.items()), sep='\n')
# create dataframe
df = pd.DataFrame.from_dict(new_data, orient='index')
df.index.name = 'RPN'
# print(df)
def compare(srs):
if srs.sum() == len(months):
return 'All months'
else:
return ', '.join(month
for month, check
in zip(srs.index, srs)
if check)
# add aging string
df['Aging'] = df.apply(compare, axis=1)
# print(df)
编辑
假设您的数据是从数据格式为panda的文件中加载的，则可以这样创建data：
# kinda loaded data
a = pd.DataFrame({'RPN': ['aa', 1, 3, 2, 12, 3, 4, 5, 'bb', 6, 7, 8, 9, 100, 65, 56, 'cc', 70]})
b = pd.DataFrame({'RPN': ['aa', 11, 12, 3, 4, 5, 123, 12, 14, 8, 'cc', 100, 56]})
c = pd.DataFrame({'RPN': ['aa', 12, 111, 33, 13, 5, 6, 4, 555, 'bb', 3333, 65, 634, 7, 8, 8888, 100]})
d = pd.DataFrame({'RPN': ['aa', 44, 33, 5, 6, 7, 8, 999, 'bb', 4, 2, 66, 3, 70, 1, 1, 2]})
# dictionary of sets
data = {'June': a['RPN'], 'May': b['RPN'],
'April': c['RPN'], 'March': d['RPN']}