In Python, If there is a duplicate, use the date column to choose the what duplicate to use

Question

I have code that runs 16 test cases against a CSV, checking for anomalies from poor data entry. A new column, 'Test case failed,' is created. A number corresponding to which test it failed is added to this column when a row fails a test. These failed rows are separated from the passed rows; then, they are sent back to be corrected before they are uploaded into a database.

There are duplicates in my data, and I would like to add code to check for duplicates, then decide what field to use based on the date, selecting the most updated fields.

Here is my data with two duplicate IDs, with the first row having the most recent Address while the second row has the most recent name.

ID	MnLast	MnFist	MnDead?	MnInactive?	SpLast	SpFirst	SPInactive?	SpDead	Addee	Sal	Address	NameChanged	AddrChange
123	Doe	John	No	No	Doe	Jane	No	No	Mr. John Doe	Mr. John	123 place	05/01/2022	11/22/2022
123	Doe	Dan	No	No	Doe	Jane	No	No	Mr. John Doe	Mr. John	789 road	11/01/2022	05/06/2022

Here is a snippet of my code showing the 5th testcase, which checks for the following: Record has Name information, Spouse has name information, no one is marked deceased, but Addressee or salutation doesn't have "&" or "AND." Addressee or salutation needs to be corrected; this record is married.

import pandas as pd 
import numpy as np

data = pd.read_csv("C:/Users/file.csv", encoding='latin-1' )

# Create array to store which test number the row failed
data['Test Case Failed']= ''
data = data.replace(np.nan,'',regex=True)
data.insert(0, 'ID', range(0, len(data)))

# There are several test cases, but they function primarily the same
# Testcase 1
# Testcase 2
# Testcase 3
# Testcase 4

# Testcase 5 - comparing strings in columns
df = data[((data['FirstName']!='') & (data['LastName']!='')) & 
              ((data['SRFirstName']!='') & (data['SRLastName']!='') &
              (data['SRDeceased'].str.contains('Yes')==False) & (data['Deceased'].str.contains('Yes')==False) 
              )]
df1 = df[df['PrimAddText'].str.contains("AND|&")==False] 
data_5 = df1[df1['PrimSalText'].str.contains("AND|&")==False] 
ids = data_5.index.tolist()

# Assign 5 for each failed
for i in ids:
  data.at[i,'Test Case Failed']+=', 5'


# Failed if column 'Test Case Failed' is not empty, Passed if empty
failed = data[(data['Test Case Failed'] != '')]
passed = data[(data['Test Case Failed'] == '')]


failed['Test Case Failed'] =failed['Test Case Failed'].str[1:]
failed = failed[(failed['Test Case Failed'] != '')]

# Clean up
del failed["ID"]
del passed["ID"]

failed['Test Case Failed'].value_counts()

# Print to console 
print("There was a total of",data.shape[0], "rows.", "There was" ,data.shape[0] - failed.shape[0], "rows passed and" ,failed.shape[0], "rows failed at least one test case")

# output two files
failed.to_csv("C:/Users/Failed.csv", index = False)
passed.to_csv("C:/Users/Passed.csv", index = False)

What is the best approach to check for duplicates, choose the most updated fields, drop the outdated fields/row, and perform my test?

Answer 1

First, try to set a mapping that associates update date columns to their corresponding value columns.

date2val = {"AddrChange": ["Address"], "NameChanged": ["MnFist", "MnLast"], ...}

Then, transform date columns into datetime format to be able to compare them (using argmax later).

for key in date2val.keys():
   failed[key] = pd.to_datetime(failed[key])

Then, group by ID the duplicates (since ID is the value that decides whether it is a duplicate), and for each date column get the maximum value in the group (which refers to the most recent update) and retrieve the columns to update from the initial mapping. I'll update the last row and set it as the final updated result (by putting it in corrected list).

corrected = list()
for _, grp in failed.groupby("ID"):
   for key in date2val.keys():
      recent = grp[key].argmax()
      for col in date2val[key]:
         grp.iloc[-1][col] = grp.iloc[recent][col]      
   corrected.append(grp.iloc[-1])
corrected = pd.DataFrame(corrected)

Answer 2

Preparing data:

import pandas as pd
c = 'ID MnLast  MnFist  MnDead? MnInactive? SpLast  SpFirst SPInactive? SpDead  Addee   Sal Address NameChanged AddrChange'.split()
data1 = '123    Doe John    No  No  Doe Jane    No  No  Mr.JohnDoe  Mr.John 123place    05/01/2022  11/22/2022'.split()
data2 = '123    Doe Dan No  No  Doe Jane    No  No  Mr.JohnDoe  Mr.John 789road 11/01/2022  05/06/2022'.split()
data3 = '8888 Brown Peter No No Brwon Peter No No Mr.PeterBrown M.Peter 666Avenue 01/01/2011 01/01/2011'.split()
df = pd.DataFrame(columns = c, data = [data1, data2, data3])
df.AddrChange.astype('datetime64')
df.NameChanged.astype('datetime64')
df

DataFrame is like the example:

Then you pick a piece of the dataframe avoiding changes in original. Adjacent rows have the same ID and the first one has the apropriate name:

df1 = df[['ID', 'MnFist', 'NameChanged']].sort_values(by=['ID', 'NameChanged'], ascending = False)
df1

Then you build a dictionary putting key as df.ID and the appropriate name for its value. You intend to build all the column MnFist:

d = {}
for id in set(df.ID.values):
    df_mask = df1.ID == id # filter only rows with same id
    filtered_df = df1[df_mask]
    if len(filtered_df) <= 1:
        d[id] = filtered_df.iat[0, 1] # id has only one row, so no changes
        continue
    for name in filtered_df.MnFist:
        if name in ['unknown', '', ' '] or name is None: # name discards
            continue
        else:
            d[id] = name # found a servible name
    if id not in d.keys():
        d[id] = filtered_df.iat[0, 1] # no servible name, so picked the first
print(d)

The partial output of the dictionary is: {'8888': 'Peter', '123': 'Dan'}

Then you build all the column:

df.MnFist = [d[id] for id in df.ID]
df

The partial output is:

Then the same procedure to the other column:

df1 = df[['ID', 'Address', 'AddrChange']].sort_values(by=['ID', 'AddrChange'], ascending = False)
df1
d = { id: df1.loc[df1.ID == id, 'Address'].values[0] for id in set(df.ID.values) }
d
df.Address = [d[id] for id in df.ID]
df

The final output is:

Edited after author comented possibility of unknow inservible data.

Answer 3

Let me restate what I understood from the question:

1. You have a dataset on which you are doing several sanity checks. (Looks like you already have everything in place for this step)
2. In next step you are finding duplicates row with different columns updated at different dates. (I assume that you already have this)
3. Now, you are looking for a new dataset that has non-duplicated rows with updated fields using the latest date entries.

First, define different dates and their related columns in a form of dictionary:

date_to_cols = {"AddrChange": "Address", "NameChanged": ["MnLast", "MnFirst"]}

Next, apply group by using "ID" and then get the index for maximum value of different dates. Once we have the index, we can pull the related fields for that date from the data.

data[list(date_to_cols.keys())] =data[list(date_to_cols.keys())].astype('datetime64')
latest_data = df.groupby('ID')[list(date_to_cols.keys())].idxmax().reset_index()

for date_field, cols_to_update in date_to_cols.items():
    latest_data[cols_to_update] = latest_data[date_field].apply(lambda x: data.iloc[x][cols_to_update])
    latest_data[date_field] = latest_data[date_field].apply(lambda x: data.iloc[x][date_field])

Next, you can merge these latest_data with the original data (after removing old columns):

cols_to_drop = list(latest_data.columns)
cols_to_drop.remove("ID")
data.drop(columns= cols_to_drop, inplace=True)
latest_data_all_fields = data.merge(latest_data, on="ID", how="left")
latest_data_all_fields.drop_duplicates(inplace=True)