Step 1: Context Based Data Manipulation and Analysis Process - Part 1¶

According to (Weintrop et al., 2015) "Data manipulation includes sorting, filtering, cleaning, normalizing, and joining disparate datasets. There are many strategies that can be employed when analyzing data for use in a scientific or mathematical context, including looking for patterns or anomalies, defining rules to categorize data, and identifying trends and correlations." Below are the steps performed for data manipulation and analysis process using Python programming language on the Certificates of Freedom dataset. The modules are split into two parts - Part 1 and Part 2.

Acquiring or Accessing the Data¶

The data for this project was originally crawled from the Maryland State Archives Legacy of Data collections. The data source is included in this module as a comma-separated values file. The link below will take you to a view the data file:

LoS_CoF.csv

The dataset has 23,655 rows of data.

To process a csv file in Python, one of the first steps is to import a Python library called as 'pandas' which would help the program convert the csv file into a dataframe format or commonly called as a table format. We import the library into the program as below:

In [1]:

# Importing libraries - pandas used for data science/data analysis and machine learning tasks and numpy - which provides support for multi-dimensional arrays
import pandas as pd
import numpy as np

Using the pandas library, we created a new dataframe in the name 'df' using read_csv function as shown below: After creating the dataframe, the print() function is used to display the top 10 rows loaded in the dataframe.

In [2]:

# creating a data frame which is a table-like data structure that could read csv files, flat files, and other delimited data.
# Converting input data into a data frame is a key starting point with Python programming language for big data analytics
# Below command reads in the Certificates of Freedom dataset which should already be loaded in a folder called 'Datasets' as LoS_CoF.csv
df = pd.read_csv("Datasets/LoS_CoF.csv") 
# Below command prints the first 10 records after the data is copied from the csv file
df.head(10)

Out[2]:

	DataID	DataItem	County	Owner_FirstName	Owner_LastName	Witness	Date	Freed_FirstName	Freed_LastName	Alias	...	Folder	Document	Page	Entry	DatasetName	Notes	ChangeDate	CreateDate
0	AR7-46	1	AA	Ann	Ailsworth	NaN	NaN	Keziah	Cromwell	NaN	...	NaN	NaN	42686.0	12.0	FF	NaN	39:20.3	39:20.3
1	AR7-46	2	AA	Ann	Ailsworth	Zachariah Duvall	1811-06-24	Resiah	Cromwell	NaN	...	NaN	NaN	24.0	3.0	FF	NaN	39:20.3	39:20.3
2	AR7-46	3	AA	Ann	Ailsworth	Jenifer Duvall	1811-06-24	Kesiah	Cromwell	NaN	...	55.0	NaN	NaN	NaN	FF	Freed by will of Mrs. Ann Ailsworth.	39:20.3	39:20.3
3	AR7-46	4	AA	William	Alexander	NaN	1815-03-28	Handy	McCeomey	NaN	...	NaN	NaN	50.0	2.0	FF	Freed by manumission, dated 27 March 1815. Rai...	39:20.3	39:20.3
4	AR7-46	5	AA	Thomas	Allen	NaN	1837-07-10	Nancy	Ennis	NaN	...	NaN	NaN	257.0	1.0	FF	Freed by petition to Anne Arundel County Court...	39:20.3	39:20.3
5	AR7-46	6	AA	Thomas	Allen	NaN	1837-08-03	Jim	Sharpe	NaN	...	NaN	NaN	257.0	2.0	FF	Freed by petition to Anne Arundel County Court...	39:20.3	39:20.3
6	AR7-46	7	AA	James	Alleson	NaN	1826-10-28	Belly	NaN	NaN	...	NaN	NaN	242.0	1.0	FF	Freed by manumission, dated 28 Oct 1826. Raise...	39:20.3	39:20.3
7	AR7-46	8	AA	Mary	Alwell	NaN	1844-11-08	Howard	Davis	NaN	...	NaN	NaN	372.0	1.0	FF	son of Nelly. Freed by manumission, dated 12 A...	39:20.3	39:20.3
8	AR7-46	9	AA	Mary	Armiger	NaN	1819-01-27	Abigail	NaN	NaN	...	NaN	NaN	126.0	2.0	FF	along with Richard G. Stetton. Freed by manumi...	39:20.3	39:20.3
9	AR7-46	10	AA	Mary	Atcock	Jacob Franklin, Jr.	1812-12-30	Ned	NaN	NaN	...	NaN	NaN	31.0	3.0	FF	NaN	39:20.3	39:20.3

10 rows × 28 columns

Of these features, the below ones were chosen to be cleaned and manipulated for use in the following steps for simplicity purposes.

Date -- This indicates the date of issue of Certificate of Freedom
Prior Status -- Prior status of the Enslaved person before issue of the CoF document
Height -- Height of the Enslaved person
Age -- Age at the time of document issue

We anticipated errors and misinterpretation of names, numbers, etc. since this database was mostly transcribed manually by hand from the physical or scanned copies of the Certificates of Freedom. Our approach was to individually explore and clean the aforementioned columns utilizing the text and numerical operation functions in Python programming language for this purpose mostly. We looked at the dataset holistically at first, identifying features that allowed us to generate meaningful stories or visualizations. Upon confirmation of the features list, we analyzed each of them in detail to document bad data and eliminate them if possible, modify data types, exclude them from the final visualizations if found to be invalid, etc

This project involved team members from a diverse group of technology, historical, and archivist background. There were opportunities to work individually or to work in groups, but we decided to do a hybrid setup of analyzing alone and reporting the results back to the group for discussion. With respect to the analysis performed on the dataset, decisions were data-driven or historical facts driven.

Through researching the literature, conversations with historians and experts in the field, discussions with archivists from the Maryland State Archives, the team members followed a set of steps where certain unique characteristics of a particular feature for instance were identified and shared with the entire group for their inputs before finalizing the results

Date Feature (Issue of CoF)¶

Through healthy discussions on what-if scenarios as most of the data were historical and we were bringing each of our expertise into the conversations, several insights were gleaned for specific columns which were vital to this Project. Also there were discussions on how data should be presented, collected, and analyzed without impacting the sensitivity of the people involved, especially since this set of collection was unique.

One of them is the date, there were different formats of date captured in the transcribed collection. This field is to indicate the date when the certificate of freedom was prepared and signed. There were a number of issues with this date field in the original dataset. Different date formats -- There were around 600 records with NULL value, a bunch of them with just YYYYMM format, most of them in the format YYYY-MM-DD and YYYYMMDD format.

In [3]:

# Below command prints out the descriptive details of the column 'Date'
df["Date"].describe()

Out[3]:

count          23057
unique          9956
top       1832-05-28
freq             296
Name: Date, dtype: object

In [4]:

# Below command list the number of null or na values in the 'Date' column of the data frame
df["Date"].isna().sum()

Out[4]:

In [5]:

# Below command displays an array of unique date values in the 'Date' column
df["Date"].unique()

Out[5]:

array([nan, '1811-06-24', '1815-03-28', ..., '18430912', '18430913',
       '18430916'], dtype=object)

As could be seen above, there are different formats for the date column, some with missing month etc, some of these were manually verified for accuracy by checking the scanned documents from the MSA database as shown below:

In [6]:

# Below command replaces all Null or nan values to the literal 'None' for ease of manipulation later in the process
df["Date"]=df["Date"].fillna('None')
df["Date"].unique()

Out[6]:

array(['None', '1811-06-24', '1815-03-28', ..., '18430912', '18430913',
       '18430916'], dtype=object)

Below is a key function that identifies the bad date records and places a 'NaT' (Not a good date) value¶

In [7]:

# Below command creates a new column 'DateFormatted' on-the-fly (one of the cool things I like about python) and is copied with the results from the 'Date' column using a 
# transformation function called 'to_datetime()' by passing in the parameter 'error=coerce' which converts all erroneous date values into a string called 'NaT'
df['DateFormatted'] = pd.to_datetime(df["Date"], errors="coerce")

In [8]:

# Below command prints the unique converted date values from the newly created column and also displays 'NaT' for errorneous date values.
df["DateFormatted"].unique()

Out[8]:

array([                          'NaT', '1811-06-24T00:00:00.000000000',
       '1815-03-28T00:00:00.000000000', ...,
       '1843-08-22T00:00:00.000000000', '1843-08-24T00:00:00.000000000',
       '1843-09-12T00:00:00.000000000'], dtype='datetime64[ns]')

In [9]:

# Below command prints a sample of the output for the new columns 'Date' and 'DateFormatted' side-by-side to show how the original field values were transformed to a proper date
# format and the bad values are given a 'NaT'
df[['Date','DateFormatted']]

Out[9]:

	Date	DateFormatted
0	None	NaT
1	1811-06-24	1811-06-24
2	1811-06-24	1811-06-24
3	1815-03-28	1815-03-28
4	1837-07-10	1837-07-10
...	...	...
23650	18430826	1843-08-26
23651	18430905	1843-09-05
23652	18430912	1843-09-12
23653	18430913	1843-09-13
23654	18430916	1843-09-16

23655 rows × 2 columns

In [10]:

x = 0
bad_date=[]
# Below function is a loop function which processes each value of the new column 'DateFormatted' to check for invalid value marker 'NaT' and if found, it picks up the original 
# value from the 'Date' column and appends to a list. Once all the records are checked, it prints the unique values of this list using the 'set' function and the total number of 
# bad ones
for i in range(len(df['DateFormatted'])):
    if pd.isna(df['DateFormatted'][i]):
        bad_date.append(df['Date'][i])
        x += 1
print(set(bad_date))
print("Number of Bad date records", x)
print("Number of unique items in the Bad date", len(set(bad_date)))

{'1845-19-05', '1856-01-00', '1836-09-31', '1821-00-00', '1855-01-00', '1829-04-31', '1819-04-00', '1819-12-00', '184004', '1819-00-00', '184006', '1823-00-00', 'Kamer', '1822-00-00', '1854-00-00', '1831-04-31', '1816-19-06', '1853-00-00', '1858-09-31', '1816-17-23', '1840516', '1859-00-00', '1815-16-20', '189390417', '1802-30-30', '1830-04-31', '1846-05-00', '1819-07-00', '1842-00-00', '1819-03-00', '1855-00-00', 'None', '1858-12-00', '1819-15-04', '1856-02-30', '18404024', '1847-00-00', '1858-08-00', '1854-08-00', '1860-09-00', '1807-07-33', '1819-15-10'}
Number of Bad date records 657
Number of unique items in the Bad date 42

In [11]:

# Below command displays the specific records that was identified as erroneously entered. The inner command 'df[]' first converts the 'Date' feature to a 'String' data type, and then uses another
# in-built function to filter the records that match with the supplied criteria and the outer 'df[]' displays the results of that filtered records from the inner dataframe.
df[df['Date'].astype(str).str.strip()=="184006"]

Out[11]:

	DataID	DataItem	County	Owner_FirstName	Owner_LastName	Witness	Date	Freed_FirstName	Freed_LastName	Alias	...	Document	Page	Entry	DatasetName	Notes	isWorking	isError	ChangeDate	CreateDate	DateFormatted
23307	AR7-46	23310	BA	Geo	Gillingham	NaN	184006	Jeremiah W.	Brown	Jerry	...	NaN	224.0	5.0	FF	Freed by manumission, dated 15 June 1824, reco...	0	0	37:45.8	03:44.1	NaT
23308	AR7-46	23311	BA	NaN	NaN	NaN	184006	Rachael	Brown	NaN	...	NaN	224.0	6.0	FF	NaN	0	0	37:45.8	05:54.2	NaT

2 rows × 29 columns

In two of the instances, as seen below, the day of issue has not been found to be legible or visible, hence the MSA transcriber may have not been able recorded the date. There was no date but only month and year captured on the original CoF itself for c290 page 224 - Jeremiah Brown

DateIssue1 DateIssue2

Another instance of data entry error was for c290 page 185 Charles W Jones as shown below with the date captured as 1840516 instead of 18400516

DateIssue3

All these 657 bad date records were identified and marked with 'NaT' and would not be used for further processing. However, the entire record was not removed as there could be other useful features that could provide us with some good insights. These 'NaT' date records would be shared with the MSA, to fix their Source of Record.

Prior Status¶

This feature indicates the previous status of the enslaved person while the CoF document was being issued. This holds historical importance and needed carefule inputs from the historians. To address the issues with this feature in CoF dataset - Prior Status Column: Research was conducted to determine the prior status of those who were categorized as a “Descendant of a white female woman” as shown below from the set of unique categories. Source: Wikipedia - History of slavery in Maryland. This research was beneficial in identifying what group certain observations belong to.

PriorStatus

In [12]:

# df is the data frame variable which stores the entire dataset in a table form. Below command converts the specific column or feature 'PriorStatus' as Categorical type instead of String for manipulation
df["PriorStatus"]=df["PriorStatus"].astype('category')

Below list shows the different formats of the Prior Status as transcribed and it shows that these entries have to be grouped together by customized cleaning process.

In [13]:

# After conversion, let's print the number of categories available for that particular feature from the dataset
print(set(df["PriorStatus"]))

{nan, 'slave', 'Born Free', 'Free born', 'free born', 'Free Born', 'Descendant of a white female woman', 'S;ave', 'Slave; Slave', 'Enslaved', 'Free', '?', 'born free', 'Unknown; Free Born', 'Unknown', 'Freeborn', 'Slave', 'Slave ', 'Free; Slave', 'John', 'Unknown; Slave', 'Born free', 'Free  ', 'Free born ', 'BornFree'}

Below is a key IF function that tries to find for a good prior status value in each column and converts the value to a standard value. Others are given a 'Unknown'¶

In [14]:

# As could be seen above, there are various types of Prior Status that are similar in nature. the value 'nan' in Python means it has no values.
# Below set of commands form a component in Python called as a function. Functions are a block of commands which could be used to perform the same action every time they are called.
# The below function converts the input parameter to the right Prior Status category based on some conditional statements.
def fix_prior_status(status):
    # initiate variables to hold the literal value
    free = "free"
    born = "born"
    enslaved = "slave"
    descend = "Descend"
    # conditional statements to use in-built 'find' function to check if the prior status passed has the value of the literal checked, and if so the status would be modified as mentioned
    # in the 'return' statement
    if status.find(born) != -1:
        # it should also be noted that indentation is a key requirement with Python, not where the return statement starts after the 'if'
        return "Born Free"
    else:
        # nested if's are possible in Python to conditionally control the else logic
        if status.find(enslaved) != -1:
            return "Slave"
        else:
            if status.find(descend) != -1:
                return "Born Free"
            else:
                if status.find(free) != -1:
                    return "Free"
                else:
                    return "Unknown"
# Below command starts with the beginning indentation indicating a new set of commands outside of the function, even if its in the same cell block like shown here.
# The 'apply' function applies the function definted above to the data frame's each records' Prior Status field avlue. 
df["PriorStatusFormatted"] = df["PriorStatus"].apply(fix_prior_status)
# The 'unique' in-built function prints out the distinct values of the transformed or modified prior status of the data frame
print(df["PriorStatusFormatted"].unique())

['Unknown' 'Slave' 'Born Free' nan 'Free']

As with the date field, those values that did not fall into one of these good categories they were assigned a 'Unknown' value.

In [15]:

# Below commands help us to save the modified dataframe into a new output csv file which could be used in further steps of processing in the next notebook modules.
dfo = pd.DataFrame(df)
dfo.to_csv('Datasets/LoS_Clean_Output_Mod1.csv', index=False)

Notebooks¶

The below module is organized into a sequential set of Python Notebooks that allows to interact with the Legacy of Slavery's Certificates of Freedom collection by exploring, cleaning, preparing, visualizing and analysing it from historical context perspective.

Certificates Of Freedom: Context Based Data Manipulation and Analysis -Part 2
Certificates Of Freedom: Context Based Data Visualization(If you get a 500 Internal Server Error, please refresh the browser to render this module again)

Click here to go the Next Module

In [ ]: