JAWWII_Incarceration_Module1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "skilled-leather",
   "metadata": {},
   "source": [
    "## Step 1: A Framework for Unlocking and Linking WWII Japanese American Incarceration Biographical Data - Context Based Data Manipuation and Analysis Process- Part 1 \n",
    "\n",
    "Data manipuation is the process of fine tuning data to organize and prepare the data for research purposes. Data manipulation can include sorting, filtering, cleaning, normalizing, and joining separate datasets. Once the data has been prepared, data analysis can be perfomed. According to the Office of Research Integrity, \"Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.\" Below are the steps performed for data manipulation and analysis process using Python programming language on the Japanese American World War II datasets. The modules are split into two parts - Part 1 and Part 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "duplicate-formula",
   "metadata": {},
   "source": [
    "### Acquiring or Accessing the Data\n",
    "\n",
    "The data for this project comes from three disparate collections housed at the National Archives and Records Administration (NARA) this includes the War Relocation Authority (WRA), Final Accountability Roster (FAR), and Internal Security Case Reports, also referred to as Index Card or Incident Cards. The data sources are included in this module for the WRA and FAR as comma separated values (csv) files. The link below will direct you to those data files: \n",
    "\n",
    "WRA [link]\n",
    "FAR [link]\n",
    "Incident Card upon request\n",
    "\n",
    "The WRA dataset has X rows of data, the FAR has X rows, and the Incident Card has X rows.\n",
    "\n",
    "To work with a csv file in Python, one of the first steps is to import a Python library called as 'pandas' which will 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 shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "spiritual-importance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "# Pandas is used for data science/data analysis and machine learning tasks \n",
    "import pandas as pd\n",
    "# Numpy provides support for multi-dimensional arrays\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "painted-auckland",
   "metadata": {},
   "source": [
    "Using the pandas library, new dataframes can be created from the datasets imported into the notebook. Once the dataframes have been created from the imported files, the print() function is used to display the top first 5 rows in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "parliamentary-powell",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'far_cleanup-09-26-2021.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [2], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Creating a dataframe is essentially creating a table-like data structure that can read csv files, flat files, and other delimited data.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39m# Converting input data into a dataframe is the starting point with Python programming language for big data analytics\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39m# Below command reads in the WRA, FAR, and Incident card datasets. The WRA and FAR should already be loaded in a folder called 'Datasets' wra_cleanup-09-26-2021.csv and Copy-far_cleanup-09-26-2021.csv\u001b[39;00m\n\u001b[1;32m      4\u001b[0m wradf \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mwra_cleanup-09-26-2021.csv\u001b[39m\u001b[39m'\u001b[39m,dtype\u001b[39m=\u001b[39m\u001b[39mobject\u001b[39m,na_values\u001b[39m=\u001b[39m[],keep_default_na\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m----> 5\u001b[0m fardf \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_csv(\u001b[39m'\u001b[39;49m\u001b[39mfar_cleanup-09-26-2021.csv\u001b[39;49m\u001b[39m'\u001b[39;49m,dtype\u001b[39m=\u001b[39;49m\u001b[39mobject\u001b[39;49m,na_values\u001b[39m=\u001b[39;49m[],keep_default_na\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m      6\u001b[0m incidentdf \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mincidentdfv1.csv\u001b[39m\u001b[39m'\u001b[39m,dtype\u001b[39m=\u001b[39m\u001b[39mobject\u001b[39m,na_values\u001b[39m=\u001b[39m[],keep_default_na\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m      8\u001b[0m \u001b[39m# Below command prints the first 5 records of the Incident Cards after the data is copied from the csv file\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:948\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    944\u001b[0m     dtype_backend\u001b[39m=\u001b[39mdtype_backend,\n\u001b[1;32m    945\u001b[0m )\n\u001b[1;32m    946\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 948\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    608\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[1;32m    610\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 611\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    613\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1448\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m   1447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1448\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1705\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1703\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[1;32m   1704\u001b[0m         mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 1705\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[1;32m   1706\u001b[0m     f,\n\u001b[1;32m   1707\u001b[0m     mode,\n\u001b[1;32m   1708\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1709\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1710\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[1;32m   1711\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[1;32m   1712\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m   1713\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1714\u001b[0m )\n\u001b[1;32m   1715\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1716\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/common.py:863\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[1;32m    859\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    860\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    861\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[1;32m    862\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[0;32m--> 863\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[1;32m    864\u001b[0m             handle,\n\u001b[1;32m    865\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[1;32m    866\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[1;32m    867\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m    868\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    869\u001b[0m         )\n\u001b[1;32m    870\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    871\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[1;32m    872\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'far_cleanup-09-26-2021.csv'"
     ]
    }
   ],
   "source": [
    "# Creating a dataframe is essentially creating a table-like data structure that can read csv files, flat files, and other delimited data.\n",
    "# Converting input data into a dataframe is the starting point with Python programming language for big data analytics\n",
    "# Below command reads in the WRA, FAR, and Incident card datasets. The WRA and FAR should already be loaded in a folder called 'Datasets' wra_cleanup-09-26-2021.csv and Copy-far_cleanup-09-26-2021.csv\n",
    "wradf = pd.read_csv('wra_cleanup-09-26-2021.csv',dtype=object,na_values=[],keep_default_na=False)\n",
    "fardf = pd.read_csv('far_cleanup-09-26-2021.csv',dtype=object,na_values=[],keep_default_na=False)\n",
    "incidentdf = pd.read_csv('incidentdfv1.csv',dtype=object,na_values=[],keep_default_na=False)\n",
    "\n",
    "# Below command prints the first 5 records of the Incident Cards after the data is copied from the csv file\n",
    "incidentdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "metric-helen",
   "metadata": {},
   "source": [
    "Below are the variables in the Incident Cards that were chosen to be cleaned and manipulated for use in the following steps.\n",
    "\n",
    "Offense -- This indicates the offenses assigned to Japanese Americans by government officials\n",
    "Case # -- A case number was assigned according to the type of offense\n",
    "Date -- This indicates the date the offense took place \n",
    "Residence -- The location of where that specific individual resided \n",
    "Other -- This indicates additional notes that were recorded by officials on the Index, \"Incident Cards\"\n",
    "First Name -- The first name of the incarcerated Japanese American\n",
    "Last Name -- The last name of the incarcerated Japanese American\n",
    "\n",
    "\n",
    "Below are the variables in the WRA and FAR that were chosen to be cleaned and manipulated for use in the following steps.\n",
    "First Name -- The first name of the incarcerated Japanese American\n",
    "Last Name -- The last name of the incarcerated Japanese American\n",
    "Family Number -- A number assigned to families upon entry into the assembly centers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cubic-scope",
   "metadata": {},
   "source": [
    "We anticipated errors and misinterpretation of names, numbers, etc. in the data since these databases were either transcribed manually or physical copies digitized using OCR techniques. We approached this by individually exploring and cleaning the selected columns using the text and numerical operation functions in Python. To begin, we looked at the datasets holistically to identify variables that would allow for us to generate meaningful stories and visualizations. After a list of variables were confirmed, 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.\n",
    "\n",
    "This project involved team members with diverse backgrounds and knowledge in the technical, historical, and archival domains. The project presented opportunties for collaboration and individual work. We decided it was more productive to have a hybrid approach where we analyzed the data on our own and created sub-projects and reported our results and progress back to the group for discussion. With respect to the analysis performed on the dataset, decisions were data-driven or historical facts driven.\n",
    "\n",
    "Through researching the literature, conversations with community partner Geoff Froh, from Densho, and experts in the field, the team members were able to follow steps where specific variables were identified and shared among the entire group for their inputs before finalizing the results. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pediatric-retail",
   "metadata": {},
   "source": [
    "## Case Number (Issue of Incident Card)\n",
    "Through many conversations and pulling in our differing expertise we were able to develop ideas that let us integrate a range of methods and techniques to exhibit multifaceted perspectives of incarcerated Japanese Americans during World War II. Out of these discussions one of the many sub projects that developed was Spatial Analysis of incarcerated Japanese Americans recorded as being invovled in the uprising ('riot') at Tule Lake on November 4, 1943. \n",
    "\n",
    "Additionally, much of our discussion was also centered on how the data should be analyzed, presented, and shared without impacting the sensitivity of the people involved, especially since this set of collection is unique.\n",
    "\n",
    "One of the variables conisdered for this project was case number. This field indicates the case number assigned to offenses that occured at Tule Lake. Due to the case number being entered on the index cards by different government personnel the formatting was inconsistent. There were a number of issues with the case number field in the original dataset. Examples of the variations of case number formats includes 'A7-'; ‘A-7 ’; A-7- also have 'A-7-P' also have 'A-7-p'; 'A-7p' Also have 'A-7P'; ‘A-7,’; 'A7,'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dated-acoustic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     15748\n",
       "unique     4475\n",
       "top            \n",
       "freq        680\n",
       "Name: casenumber, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The below command prints out the descriptive details of the column 'casenumber'\n",
    "incidentdf[\"casenumber\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "attached-columbia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A-607', 'A7-P138', 'A-7 Page 1465', ..., '#477', 'A-7 Page 1393',\n",
       "       'A-7-P180'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The below command displays an array of unique date values in the 'casenumber' column\n",
    "incidentdf[\"casenumber\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "human-arbitration",
   "metadata": {},
   "source": [
    "### Indentifying Cases of 'Riot'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adopted-mattress",
   "metadata": {},
   "source": [
    "As can be seen from above, there are varying formats for case number, some have dashes, others have a lowercase or a capital 'P', and some are missing the letter but have the word 'Page', etc. To identify and capture all of the variations of case number we can use regular expressions within the contains() function as shown below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "intimate-thesis",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/pandas/core/strings/accessor.py:101: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  return func(self, *args, **kwargs)\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# Apply regex will help us find all those variations\n",
    "caseno = incidentdf[incidentdf['casenumber'].str.contains(\"A[-\\s]?7([p,?-p\\s-].*)?$\", na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "confidential-bikini",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 1              A7-P138\n",
       "2        A-7 Page 1465\n",
       "14            A-7-P284\n",
       "20        A-7 Page 543\n",
       "26            A-7-P284\n",
       "             ...      \n",
       "15742    A-7 Page 1380\n",
       "15743    A-7 Page 1376\n",
       "15744    A-7 Page 1377\n",
       "15745         A-7-P180\n",
       "15746          A-7-P95\n",
       "Name: casenumber, Length: 3850, dtype: object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The below command prints out the number of times these variations appear within the dataframe. \n",
    "caseno['casenumber'].unique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "authorized-noise",
   "metadata": {},
   "source": [
    "As can be seen above there are, give or take, about 3,850 cases associated with 'riot' that meet the regular expression criteria used to capture several variations. \n",
    "\n",
    "**Note, this number does not necesarily mean we captured all of the case numbers linked to 'riot',. Since the data was manually entered on the index cards by government personnel it is likely there are errors and some cards tied to 'riot' are not captured in this variation. \n",
    "\n",
    "Another alternative for looking for cards linked to 'riot' is to search through the offense column. As shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "final-french",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Image#                    3872\n",
       "newdate                   3872\n",
       "casenumber                3872\n",
       "offense                   3872\n",
       "newoffense                3872\n",
       "DEATH                     3872\n",
       "name                      3872\n",
       "lastname                  3872\n",
       "firstname                 3872\n",
       "Other Names (known as)    3872\n",
       "Job/Role/Title            3872\n",
       "Residence                 3872\n",
       "NEW-residence             3872\n",
       "X                         3872\n",
       "Y                         3872\n",
       "otherpeople               3872\n",
       "Age                       3872\n",
       "Family / Alien No.        3872\n",
       "Other                     3872\n",
       "Unnamed: 19               3872\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# Search through the 'newoffense' column using contains function\n",
    "noffriot = incidentdf[incidentdf['newoffense'].str.contains('riot')]\n",
    "noffriot.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collectible-search",
   "metadata": {},
   "source": [
    "After performing the filter, the output of cards linked to 'riot' is higher than that of the case number column. Let's apply the unique function to view what unique values the contains function captured when 'riot' was searched for in the new offense column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aggregate-antarctica",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['riot'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# the unique function will return unique values in a series aka column. \n",
    "noffriot['newoffense'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "owned-influence",
   "metadata": {},
   "source": [
    "As shown above other values that contain the word 'riot' like 'disorderly conduct false riot call' and 'assualt attempted riot' are also returned. Though these cases may be associated with the 'riot' that occured on 11/4/1944 we want to focus on cases that only contain 'riot'. \n",
    "\n",
    "Regular expressions can also be applied here to filter and only return values that contain 'riot'. Regular expressions are a sequence of characters, that can sometimes comprise of several characters, used to search through strings for patterns. Though regular expressions are helpful and a powerful tool, they can also be challenging as some characters have to be in a specific order. Here are a few helpful tools to introduce regular expressions: [Regular Expressions HOWTO](https://docs.python.org/3/howto/regex.html), [pythex.org](https://pythex.org), [How to use Regex in Pandas](https://kanoki.org/2019/11/12/how-to-use-regex-in-pandas/), [AutoRegex](https://www.autoregex.xyz).  \n",
    "\n",
    "Below we use the ^ regular expression to return values where 'riot' is at the start of the string. By simply using this character we have reduced the number of values returned which more closely aligns with the number of cases linked to 'riot'.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "appropriate-parish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Image#                    3860\n",
       "newdate                   3860\n",
       "casenumber                3860\n",
       "offense                   3860\n",
       "newoffense                3860\n",
       "DEATH                     3860\n",
       "name                      3860\n",
       "lastname                  3860\n",
       "firstname                 3860\n",
       "Other Names (known as)    3860\n",
       "Job/Role/Title            3860\n",
       "Residence                 3860\n",
       "NEW-residence             3860\n",
       "X                         3860\n",
       "Y                         3860\n",
       "otherpeople               3860\n",
       "Age                       3860\n",
       "Family / Alien No.        3860\n",
       "Other                     3860\n",
       "Unnamed: 19               3860\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The ^ is used to search for values at the start of a string. \n",
    "noffriot = incidentdf[incidentdf['newoffense'].str.contains('^riot')]\n",
    "noffriot.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifty-editor",
   "metadata": {},
   "source": [
    "To further explore the differences in the results of 'riot' in the new offense and the case number column we can use OpenRefine. OpenRefine will allow us to quickly view, filter, cluster and confirm the accuracy of case numbers associated with 'riot'. \n",
    "\n",
    "In the instance seen below, the difference is a result of the case number being inncorrectly entered or designated at the initial time of entry. **NOTE: It is unclear whether this error is an outcome of the initial data entry or a result of the data being manually entered from the index cards to a spreadsheet.  \n",
    "\n",
    "<center><img src=\"OpenRefine-newoffense-data-1.png\" width=\"800\" alt=\"Locations\"></center>\n",
    "<center><img src=\"OpenRefine-newoffense-data-2.png\" width=\"800\" alt=\"Locations\"></center>\n",
    "\n",
    "More time could be spent refining the regular expression string used to search through the case numbers, but this is a good opportunity to practice using regular expressions on your own."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "circular-wireless",
   "metadata": {},
   "source": [
    "## Discovering Influential Figures in the 'Riot'\n",
    "\n",
    "Once the incident card data has been fine tuned and reflects cases only related to 'riot' we can then begin exploring the dataset to discover individuals who's names appears the most among the 'riot' cards. The decision was made to begin this project by focusing on the key figures invovled in the 'riot' with the possiblility of expanding and applying these methods to a larger dataset. \n",
    "\n",
    "The case number series that was filtered using the contains function earlier in the process will be used to identify the individuals recorded as being part of the 'riot'. As a reminder, when the data was filtered, caseno was assigned to that new output so we know that we are working specifically with data associated with case numbers linked to 'riot'. By applying values count to the caseno series we can identify these key or influential figures. \n",
    "\n",
    "The values count function will return objects, in descending order, that contain counts of unique values. So the first element will always be the most frequently occuring element, as shown below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "excellent-embassy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kuratomi                 58\n",
       "kobayashi                40\n",
       "kai                      36\n",
       "tom yoshio kobayashi     23\n",
       "\"b\" area                 22\n",
       "tsuda                    20\n",
       "kobayashi, tom yoshio    20\n",
       "rev. kai                 19\n",
       "yamane, tokio            18\n",
       "tom kobayashi            17\n",
       "tokio yamane             16\n",
       "mori                     16\n",
       "george kuratomi          15\n",
       "\"b\" area report          15\n",
       "tokunaga                 15\n",
       "kazama                   14\n",
       "kuratomi, george         14\n",
       "terada, singer           14\n",
       "oshita, tomoji           14\n",
       "kazama, tomio            14\n",
       "shigenobu murakimi       13\n",
       "yoshiyama, tom           13\n",
       "isamu uchida             13\n",
       "oki, kakuma              12\n",
       "yamauchi, dr.            11\n",
       "saito, shu               11\n",
       "oscar itani              11\n",
       "abe, tetsuo              11\n",
       "tetsuo abe               11\n",
       "nakao, masaru            11\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The below command returns count values of the names in the caseno series\n",
    "riotcount = caseno['name'].value_counts()\n",
    "riotcount.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "timely-agent",
   "metadata": {},
   "source": [
    "As shown above, the name Kuratomi appears the most frequently among the 'riot' incident cards. Following, Kuratomi is Kobayashi, Kai, Tom Yoshio Kobayashi, Tsuda, Reverend Kai, and Tokio Yamane, to name a few. \n",
    "\n",
    "Also recorded in this count is \"b\" area or \"b\" area report. Area B is a significant location in Tule Lake. After the 'riot' of November 4, suspected \"troublemakers\" were picked up--it is recorded around 209 in November and 107 in December--and placed in \"Area B\" also known as the \"Surveillance Area\" and later designated as the \"Stockade\". The barracks underwent 24 hour surveillance by armed soldiers from watchtowers, were searched almost daily for \"contraband\", visitors were not permitted except by permission from the Tule Lake Director, and curfew was set from 7:00 P.M. to 6:00 A.M. The stockade was situated northwest of the hospital for the entire period of its existence, November 5, 1943, to August 24, 1944, and enclosed by a high wire fence. \n",
    "\n",
    "<center><img src=\"https://www.nps.gov/tule/getinvolved/images/Jail-being-built.jpg?maxwidth=1200&maxheight=1200&autorotate=false\" width=\"400\" alt=\"Locations\"></center>\n",
    "<center>*Note.* Photograph is courtesy from Densho Digital Archive, photograph, accessed from the National Park Service, Tule Lake website. [1]</center>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uniform-milan",
   "metadata": {},
   "source": [
    "From the output above, we'll take 25 of the individuals listed and explore their geographical spaces in more detail; one of those being, Kuratomi.\n",
    "\n",
    "Now that we've organized our data and identified key figures, we are prepared to begin gathering geographic information. All three datasets--the WRA, FAR, and Incident Cards-- will be used to identify different geographical spaces, specifically the WRA will be helpful for pinpointing places of origin before being forced to an assembly center as well as the location of the first assembly center, the FAR will be useful for identifying forced migration to internal and external camp locations, and the Incident Cards will be helpful for locating where individuals lived during incarceration. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dutch-collapse",
   "metadata": {},
   "source": [
    "The “Japanese-American Internee Data File, 1942 – 1946”, with camp intake records of evacuated Japanese-Americans, also known as WRA Form 26 is one of the major federal records associated with the War Relocation Authority (WRA), the agency established to handle the forced relocation and detention of Japanese-Americans during World War II. At the time, clerks recorded people’s information on the WRA Form 26 when brought into assembly centers or camps, afterwards, 34 fields were encoded onto punch cards. Later on the National Archives and Records Administration (NARA) digitized these punch cards and permenantly houses these records. The WRA data files are publically available.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aboriginal-jonathan",
   "metadata": {},
   "source": [
    "<center><img src=\"WRA-NARA-Technical-Punchcard-Instructions.png\" width=\"400\" alt=\"Locations\"></center>\n",
    "\n",
    "<center><img src=\"WRA-NARA-Technical-Punchcard-Instructions-2.png\" width=\"400\" alt=\"Locations\"></center>\n",
    "\n",
    "<center><img src=\"WRA-NARA-Technical-Punchcard-Instructions-3.png\" width=\"400\" alt=\"Locations\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strange-origin",
   "metadata": {},
   "source": [
    "The “Final Accountability Rosters (FAR) of Evacuees at Relocation Centers, 1944-1946, records the camp outtake of evacuees at the time of their final release or transfer. During this time, clerks recorded outtake information on individuals as they exited camps. It's important to note that some individuals exited multiple camps due to forced transfers and have more than one FAR record. The FAR records were later transcribed by Densho, Ancestry.com, among others.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lonely-belize",
   "metadata": {},
   "source": [
    "<center><img src=\"FAR-TL-March1946-Vol1-2.png\" width=\"900\" alt=\"Locations\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suffering-scholarship",
   "metadata": {},
   "source": [
    "The Index Cards, also called, Incident Cards are Internal Security Case Reports” index cards, from the War Relocation Authority (Record Group 210 from 1941-47) records series. The Incident Cards reference narrative reports prepared by camp investigators, police officers, and directors of internal security, relating cases of alleged “disorderly conduct, rioting, seditious behavior,” etc. at each of the 10 camps, with detailed information on the names and addresses in the camps of the persons involved, the time and place where the alleged incident occurred, an account of what happened, and a statement of action taken by the investigating officer. There are 25,045 index cards, 63% of which (15,648) come from the Tule Lake concentration camp.  \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "burning-bleeding",
   "metadata": {},
   "source": [
    "<center><img src=\"IC-Kuratomi-Box12-Image840.png\" width=\"500\" alt=\"Locations\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceramic-particular",
   "metadata": {},
   "source": [
    "## Finding Names in the Incident Cards, WRA, & FAR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sufficient-short",
   "metadata": {},
   "source": [
    "Though this data is rich with information the WRA, FAR, and Incident Cards present unique challenges for us. For example, at times it was difficult to determine the order of a persons first and last name as well as many of the names are similiar to one another with one or two characters being different. Keeping this in mind, we have to be conscientious we are looking at the correct individual(s). A method for doing this is comparing information across the three separate data files like age, residence, family, individual or alien registration number, or using details found in the Other column. At other times we found that information was incomplete, for example, the WRA has truncated last names, leaving us to deduce that we were looking at the correct person after comparing the previously mentioned fields. \n",
    "\n",
    "As shown previously, we will follow a similiar process by using the contains function to locate data on Kuratomi. Because I am familiar with the data, I know Kuratomi's first name is George--a good practice to get in the habit of is to become familiar with the data you are working with before manipulating and analyzing it.  \n",
    "\n",
    "By performing a simple contains search, as shown below, on the name column we are supplied with Kuratomi's residence, including other valueable information like his age, other individuals linked to Kuratomi, case numbers, as well as notes that are recorded on that specific index card which can be helpful for identifying this is correct person. \n",
    "\n",
    "By looking at the table below, under New-residence we find the the location of George's barrack, 6605-E. The address can be interpreted in the following manner, 66 as the block, 05 as the ward and E as the assigned barrack. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adequate-population",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2366</th>\n",
       "      <td>Box10-0245.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P139</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30</td>\n",
       "      <td></td>\n",
       "      <td>Chairman, Age 30 years, Came from Jerome. List...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2368</th>\n",
       "      <td>Box10-0247.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P92</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6605-E</td>\n",
       "      <td>66-05</td>\n",
       "      <td>635285.2772</td>\n",
       "      <td>4638923.446</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Youth leader from Jerome - given as possible l...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2369</th>\n",
       "      <td>Box10-0248.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7P89</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>One of names listed on \"Approved Segregation C...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5358</th>\n",
       "      <td>Box12-0840.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P139</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi -1</td>\n",
       "      <td>kuratomi</td>\n",
       "      <td>george</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>reverend kai -2, \"slim\" tsuda - 3, hayashi -4,...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Kazama gave as his group of representatives, a...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6326</th>\n",
       "      <td>Box13-0614.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P404</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>YOSHITOMO HONDA (Alien) 3714-D 21896\\n\\nSent f...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6328</th>\n",
       "      <td>Box13-0616.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P402</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Chairman of meeting at Mess Hall #15 on 11/4/4...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6334</th>\n",
       "      <td>Box13-0622.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P373</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>TAKAHASHI\\n\\nMentioned during questioning of G...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6335</th>\n",
       "      <td>Box13-0623.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P377</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>UCHIDA\\n\\nName brought up during questioning o...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6336</th>\n",
       "      <td>Box13-0624.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P373</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>TOM KOBAYASHI\\n\\nMentioned during questioning ...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>Box13-0625.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td></td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>MITS\\n\\nName brought up during questioning of ...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>Box13-0626.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P375</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>SUGIMOTO\\n\\nName brought up during questioning...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>Box13-0627.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P390</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>MR. KOMIYA\\n\\nMr. Komiya is the Japanese secre...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6376</th>\n",
       "      <td>Box13-0664.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P89</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>One of names listed on \"Approved Segregation C...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6408</th>\n",
       "      <td>Box13-0696.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P417</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>GEORGE\\n\\nNote smuggled out on one of prisoner...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6417</th>\n",
       "      <td>Box13-0705.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P139</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30</td>\n",
       "      <td></td>\n",
       "      <td>Listed as first in importance by KAZAMA in con...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6418</th>\n",
       "      <td>Box13-0706.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P92</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6605-E</td>\n",
       "      <td>66-05</td>\n",
       "      <td>635285.2772</td>\n",
       "      <td>4638923.446</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yout leader from Jerome - given as possible le...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6421</th>\n",
       "      <td>Box13-0709.jpg</td>\n",
       "      <td>43-11-04</td>\n",
       "      <td>A-7-P286</td>\n",
       "      <td>riot</td>\n",
       "      <td>riot</td>\n",
       "      <td></td>\n",
       "      <td>george kuratomi, chairman</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>f.d. amat, consult of the spanish embassy / h....</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>The above were present at meeting at Dining Ha...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Image#   newdate casenumber offense newoffense DEATH  \\\n",
       "2338  Box10-0217.jpg  43-11-04   A-7-P417    riot       riot         \n",
       "2366  Box10-0245.jpg  43-11-04   A-7-P139    riot       riot         \n",
       "2368  Box10-0247.jpg  43-11-04    A-7-P92    riot       riot         \n",
       "2369  Box10-0248.jpg  43-11-04     A-7P89    riot       riot         \n",
       "5358  Box12-0840.jpg  43-11-04   A-7-P139    riot       riot         \n",
       "6326  Box13-0614.jpg  43-11-04   A-7-P404    riot       riot         \n",
       "6328  Box13-0616.jpg  43-11-04   A-7-P402    riot       riot         \n",
       "6334  Box13-0622.jpg  43-11-04   A-7-P373    riot       riot         \n",
       "6335  Box13-0623.jpg  43-11-04   A-7-P377    riot       riot         \n",
       "6336  Box13-0624.jpg  43-11-04   A-7-P373    riot       riot         \n",
       "6337  Box13-0625.jpg  43-11-04               riot       riot         \n",
       "6338  Box13-0626.jpg  43-11-04   A-7-P375    riot       riot         \n",
       "6339  Box13-0627.jpg  43-11-04   A-7-P390    riot       riot         \n",
       "6376  Box13-0664.jpg  43-11-04    A-7-P89    riot       riot         \n",
       "6408  Box13-0696.jpg  43-11-04   A-7-P417    riot       riot         \n",
       "6417  Box13-0705.jpg  43-11-04   A-7-P139    riot       riot         \n",
       "6418  Box13-0706.jpg  43-11-04    A-7-P92    riot       riot         \n",
       "6421  Box13-0709.jpg  43-11-04   A-7-P286    riot       riot         \n",
       "\n",
       "                           name  lastname firstname Other Names (known as)  \\\n",
       "2338            george kuratomi                                              \n",
       "2366            george kuratomi                                              \n",
       "2368            george kuratomi                                              \n",
       "2369            george kuratomi                                              \n",
       "5358         george kuratomi -1  kuratomi    george                          \n",
       "6326            george kuratomi                                              \n",
       "6328            george kuratomi                                              \n",
       "6334            george kuratomi                                              \n",
       "6335            george kuratomi                                              \n",
       "6336            george kuratomi                                              \n",
       "6337            george kuratomi                                              \n",
       "6338            george kuratomi                                              \n",
       "6339            george kuratomi                                              \n",
       "6376            george kuratomi                                              \n",
       "6408            george kuratomi                                              \n",
       "6417            george kuratomi                                              \n",
       "6418            george kuratomi                                              \n",
       "6421  george kuratomi, chairman                                              \n",
       "\n",
       "     Job/Role/Title Residence NEW-residence            X            Y  \\\n",
       "2338                                                                    \n",
       "2366                                                                    \n",
       "2368                   6605-E         66-05  635285.2772  4638923.446   \n",
       "2369                                                                    \n",
       "5358                                                                    \n",
       "6326                                                                    \n",
       "6328                                                                    \n",
       "6334                                                                    \n",
       "6335                                                                    \n",
       "6336                                                                    \n",
       "6337                                                                    \n",
       "6338                                                                    \n",
       "6339                                                                    \n",
       "6376                                                                    \n",
       "6408                                                                    \n",
       "6417                                                                    \n",
       "6418                   6605-E         66-05  635285.2772  4638923.446   \n",
       "6421                                                                    \n",
       "\n",
       "                                            otherpeople Age  \\\n",
       "2338                                                          \n",
       "2366                                                     30   \n",
       "2368                                                          \n",
       "2369                                                          \n",
       "5358  reverend kai -2, \"slim\" tsuda - 3, hayashi -4,...       \n",
       "6326                                                          \n",
       "6328                                                          \n",
       "6334                                                          \n",
       "6335                                                          \n",
       "6336                                                          \n",
       "6337                                                          \n",
       "6338                                                          \n",
       "6339                                                          \n",
       "6376                                                          \n",
       "6408                                                          \n",
       "6417                                                     30   \n",
       "6418                                                          \n",
       "6421  f.d. amat, consult of the spanish embassy / h....       \n",
       "\n",
       "     Family / Alien No.                                              Other  \\\n",
       "2338                     Note smuggled out on one of prisoners being re...   \n",
       "2366                     Chairman, Age 30 years, Came from Jerome. List...   \n",
       "2368                     Youth leader from Jerome - given as possible l...   \n",
       "2369                     One of names listed on \"Approved Segregation C...   \n",
       "5358                     Kazama gave as his group of representatives, a...   \n",
       "6326                     YOSHITOMO HONDA (Alien) 3714-D 21896\\n\\nSent f...   \n",
       "6328                     Chairman of meeting at Mess Hall #15 on 11/4/4...   \n",
       "6334                     TAKAHASHI\\n\\nMentioned during questioning of G...   \n",
       "6335                     UCHIDA\\n\\nName brought up during questioning o...   \n",
       "6336                     TOM KOBAYASHI\\n\\nMentioned during questioning ...   \n",
       "6337                     MITS\\n\\nName brought up during questioning of ...   \n",
       "6338                     SUGIMOTO\\n\\nName brought up during questioning...   \n",
       "6339                     MR. KOMIYA\\n\\nMr. Komiya is the Japanese secre...   \n",
       "6376                     One of names listed on \"Approved Segregation C...   \n",
       "6408                     GEORGE\\n\\nNote smuggled out on one of prisoner...   \n",
       "6417                     Listed as first in importance by KAZAMA in con...   \n",
       "6418                     Yout leader from Jerome - given as possible le...   \n",
       "6421                     The above were present at meeting at Dining Ha...   \n",
       "\n",
       "     Unnamed: 19  \n",
       "2338              \n",
       "2366              \n",
       "2368              \n",
       "2369              \n",
       "5358              \n",
       "6326              \n",
       "6328              \n",
       "6334              \n",
       "6335              \n",
       "6336              \n",
       "6337              \n",
       "6338              \n",
       "6339              \n",
       "6376              \n",
       "6408              \n",
       "6417              \n",
       "6418              \n",
       "6421              "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The contains function will return values with the element 'George Kuratomi'\n",
    "incidentdf[incidentdf['name'].str.contains(\"george kuratomi\", na=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conservative-approval",
   "metadata": {},
   "source": [
    "The .iloc is used to view the details of the notes in the Other column. The .iloc function is an integer position based function, meaning you can use indexing for selection of a particular position or cell and row within a table (dataset or also commonly called an array). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "knowing-pledge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"TAKAHASHI\\n\\nMentioned during questioning of Geroge Kuratomi on 12/3/43 in connection with Kashima's funeral.\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
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     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The below function returns information the cell with the index of 6334, 18\n",
    "incidentdf.iloc[5358,18]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "auburn-leeds",
   "metadata": {},
   "source": [
    "As with the Incident Card data, performing the contains function allows us to extract values containing Kuratomi. \n",
    "\n",
    "Demonstrated below, George Kuratomi, can be found in the second row of the WRA datafile. Similiar to the Incident Cards, the WRA provides personal information that can be used to confirm we are looking at the correct person. To view additional details of a row, the .iloc function can be applied here as well. In the table below we can extract the following geographic information for Kuratomi: original state, assembly center, and first camp. This data will be helpful later to find the latitute and longitude coordinates for these separate locations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moved-bumper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m_dataset</th>\n",
       "      <th>m_pseudoid</th>\n",
       "      <th>m_camp</th>\n",
       "      <th>m_lastname</th>\n",
       "      <th>NEW-m_lastname</th>\n",
       "      <th>m_firstname</th>\n",
       "      <th>NEW-m_firstname</th>\n",
       "      <th>m_birthyear</th>\n",
       "      <th>NEW-m_birthyear</th>\n",
       "      <th>m_gender</th>\n",
       "      <th>...</th>\n",
       "      <th>w_citizenshipstatus</th>\n",
       "      <th>w_highestgrade</th>\n",
       "      <th>w_language</th>\n",
       "      <th>w_religion</th>\n",
       "      <th>w_occupqual1</th>\n",
       "      <th>w_occupqual2</th>\n",
       "      <th>w_occupqual3</th>\n",
       "      <th>w_occuppotn1</th>\n",
       "      <th>w_occuppotn2</th>\n",
       "      <th>w_filenumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40132</th>\n",
       "      <td>wra-master</td>\n",
       "      <td>2-poston_kuratomi_1934_akiko</td>\n",
       "      <td>2-poston</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>AKIKO</td>\n",
       "      <td>AKIKO</td>\n",
       "      <td>1934</td>\n",
       "      <td>1934</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Did not attend Japanese Lang. School: Does not...</td>\n",
       "      <td>No schooling or kindergarten [J]</td>\n",
       "      <td>Not applicable (11 years old and under)</td>\n",
       "      <td>Buddhist [1]</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>200060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40133</th>\n",
       "      <td>wra-master</td>\n",
       "      <td>6-jerome_kuratomi_1915_george</td>\n",
       "      <td>6-jerome</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>GEORGE</td>\n",
       "      <td>GEORGE</td>\n",
       "      <td>1915</td>\n",
       "      <td>1915</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>Did not attend Japanese Lang. School: Has SS #...</td>\n",
       "      <td>In U.S.: High School 4 [I]</td>\n",
       "      <td>Japanese speak, read (&amp; write), English speak,...</td>\n",
       "      <td>Buddhist [1]</td>\n",
       "      <td>71</td>\n",
       "      <td>170</td>\n",
       "      <td></td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>956857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40134</th>\n",
       "      <td>wra-master</td>\n",
       "      <td>6-jerome_kuratomi_1897_hide</td>\n",
       "      <td>6-jerome</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>HIDE</td>\n",
       "      <td>HIDE</td>\n",
       "      <td>1897</td>\n",
       "      <td>1897</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Did not attend Japanese Lang. School: Has AR #...</td>\n",
       "      <td>In Japan: High School 4 [D]</td>\n",
       "      <td>Japanese speak, read (&amp; write), English speak</td>\n",
       "      <td>Buddhist [1]</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>625</td>\n",
       "      <td>614</td>\n",
       "      <td>952839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40135</th>\n",
       "      <td>wra-master</td>\n",
       "      <td>6-jerome_kuratomi_1923_ikuko</td>\n",
       "      <td>6-jerome</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>IKUKO</td>\n",
       "      <td>IKUKO</td>\n",
       "      <td>1923</td>\n",
       "      <td>1923</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>Did not attend Japanese Lang. School: Has neit...</td>\n",
       "      <td>In U.S.: College 1 [5]</td>\n",
       "      <td>Japanese speak and English speak, read (&amp; write)</td>\n",
       "      <td>No religion, undecided, none, atheist, agnosti...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>952840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40136</th>\n",
       "      <td>wra-master</td>\n",
       "      <td>6-jerome_kuratomi_1884_iwami</td>\n",
       "      <td>6-jerome</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>KURATOMI</td>\n",
       "      <td>IWAMI</td>\n",
       "      <td>IWAMI</td>\n",
       "      <td>1884</td>\n",
       "      <td>1884</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>Did not attend Japanese Lang. School: Has both...</td>\n",
       "      <td>In Japan: High School 2 [B]</td>\n",
       "      <td>Japanese speak, read (&amp; write), English speak,...</td>\n",
       "      <td>Baptist [A]</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>303</td>\n",
       "      <td>433</td>\n",
       "      <td></td>\n",
       "      <td>953282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        m_dataset                     m_pseudoid    m_camp m_lastname  \\\n",
       "40132  wra-master   2-poston_kuratomi_1934_akiko  2-poston   KURATOMI   \n",
       "40133  wra-master  6-jerome_kuratomi_1915_george  6-jerome   KURATOMI   \n",
       "40134  wra-master    6-jerome_kuratomi_1897_hide  6-jerome   KURATOMI   \n",
       "40135  wra-master   6-jerome_kuratomi_1923_ikuko  6-jerome   KURATOMI   \n",
       "40136  wra-master   6-jerome_kuratomi_1884_iwami  6-jerome   KURATOMI   \n",
       "\n",
       "      NEW-m_lastname m_firstname NEW-m_firstname m_birthyear NEW-m_birthyear  \\\n",
       "40132       KURATOMI       AKIKO           AKIKO        1934            1934   \n",
       "40133       KURATOMI      GEORGE          GEORGE        1915            1915   \n",
       "40134       KURATOMI        HIDE            HIDE        1897            1897   \n",
       "40135       KURATOMI       IKUKO           IKUKO        1923            1923   \n",
       "40136       KURATOMI       IWAMI           IWAMI        1884            1884   \n",
       "\n",
       "      m_gender  ...                                w_citizenshipstatus  \\\n",
       "40132        F  ...  Did not attend Japanese Lang. School: Does not...   \n",
       "40133        M  ...  Did not attend Japanese Lang. School: Has SS #...   \n",
       "40134        F  ...  Did not attend Japanese Lang. School: Has AR #...   \n",
       "40135        F  ...  Did not attend Japanese Lang. School: Has neit...   \n",
       "40136        M  ...  Did not attend Japanese Lang. School: Has both...   \n",
       "\n",
       "                         w_highestgrade  \\\n",
       "40132  No schooling or kindergarten [J]   \n",
       "40133        In U.S.: High School 4 [I]   \n",
       "40134       In Japan: High School 4 [D]   \n",
       "40135            In U.S.: College 1 [5]   \n",
       "40136       In Japan: High School 2 [B]   \n",
       "\n",
       "                                              w_language  \\\n",
       "40132            Not applicable (11 years old and under)   \n",
       "40133  Japanese speak, read (& write), English speak,...   \n",
       "40134      Japanese speak, read (& write), English speak   \n",
       "40135   Japanese speak and English speak, read (& write)   \n",
       "40136  Japanese speak, read (& write), English speak,...   \n",
       "\n",
       "                                              w_religion w_occupqual1  \\\n",
       "40132                                       Buddhist [1]                \n",
       "40133                                       Buddhist [1]           71   \n",
       "40134                                       Buddhist [1]                \n",
       "40135  No religion, undecided, none, atheist, agnosti...                \n",
       "40136                                        Baptist [A]           72   \n",
       "\n",
       "      w_occupqual2 w_occupqual3 w_occuppotn1 w_occuppotn2 w_filenumber  \n",
       "40132                                                           200060  \n",
       "40133          170                        27            6       956857  \n",
       "40134                                    625          614       952839  \n",
       "40135                                    125            4       952840  \n",
       "40136           73          303          433                    953282  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
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      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
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      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The contains function will return results with Kuratomi.\n",
    "wradf[wradf['NEW-m_lastname'].str.contains(\"KURATOMI\", na=False)].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fancy-juice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "m_dataset                                                       wra-master\n",
       "m_pseudoid                                   6-jerome_kuratomi_1915_george\n",
       "m_camp                                                            6-jerome\n",
       "m_lastname                                                        KURATOMI\n",
       "NEW-m_lastname                                                    KURATOMI\n",
       "m_firstname                                                         GEORGE\n",
       "NEW-m_firstname                                                     GEORGE\n",
       "m_birthyear                                                           1915\n",
       "NEW-m_birthyear                                                       1915\n",
       "m_gender                                                                 M\n",
       "m_originalstate                                                         CA\n",
       "m_familyno                                                            4119\n",
       "NEW-m_familyno                                                        4119\n",
       "m_familyno_normal                                                     4119\n",
       "NEW-m_familyno_normal                                                 4119\n",
       "m_individualno                                                      04119A\n",
       "m_altfamilyid                                                             \n",
       "m_altindividualid                                                         \n",
       "m_ddrreference                                                            \n",
       "m_notes                                                                   \n",
       "w_assemblycenter                                               Santa Anita\n",
       "w_originaladdress                            San Diego, California [13&14]\n",
       "w_birthcountry                              Father-Japan, Mother-Japan [1]\n",
       "w_fatheroccupus                       Unskilled laborers (except farm) [9]\n",
       "w_fatheroccupabr                                               Unknown [&]\n",
       "w_yearsschooljapan                                                       7\n",
       "w_gradejapan                                 1 - 8 yrs. of school only [1]\n",
       "w_schooldegree                                                    None [0]\n",
       "w_yearofusarrival                                                         \n",
       "w_timeinjapan                    5 yrs. but less than 10 yrs. in Japan [4]\n",
       "w_notimesinjapan                                                          \n",
       "w_ageinjapan             Between ages 0-9 and also 10-19 and also 20 an...\n",
       "w_militaryservice        No Military or Naval Service, No Public Assist...\n",
       "w_maritalstatus                                                     Single\n",
       "w_ethnicity                                        Japanese, No spouse [4]\n",
       "w_birthplace                                                    California\n",
       "w_citizenshipstatus      Did not attend Japanese Lang. School: Has SS #...\n",
       "w_highestgrade                                  In U.S.: High School 4 [I]\n",
       "w_language               Japanese speak, read (& write), English speak,...\n",
       "w_religion                                                    Buddhist [1]\n",
       "w_occupqual1                                                            71\n",
       "w_occupqual2                                                           170\n",
       "w_occupqual3                                                              \n",
       "w_occuppotn1                                                            27\n",
       "w_occuppotn2                                                             6\n",
       "w_filenumber                                                        956857\n",
       "Name: 40133, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
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      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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    {
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     "evalue": "",
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      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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    }
   ],
   "source": [
    "# Apply the .iloc to a row index to view all information within that given row\n",
    "wradf.iloc[40133]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-bronze",
   "metadata": {},
   "source": [
    "Lastly, we will look through the FAR to locate exit locations for George Kuratomi. Exit locations include forced movements from one camp to another throughout incarceration. \n",
    "\n",
    "A quick scan of the table below, we find that George Kuratomi doesn't appear among the listed names, this is because 'George' is an evangelical name used by Toshio Kuratomi. This was a phenomena discovered by simply becoming familiar with the data. In fact, George was not the only individual to use an evangelical name during incarceration. Now that we are equipped with this information, this will require us to do further investigation so we are careful not to rule out a person because their doesnt immediately appear in a dataset. It may end up being the case here where an alternative name is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "offshore-tackle",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Kuratomi</td>\n",
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       "      <td>Kuratomi</td>\n",
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       "      <td>Ind-Invit</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>16104</th>\n",
       "      <td>granada1</td>\n",
       "      <td>3819</td>\n",
       "      <td>3712</td>\n",
       "      <td>4091</td>\n",
       "      <td>4091</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Masako</td>\n",
       "      <td>Masako</td>\n",
       "      <td>...</td>\n",
       "      <td>Ind-Invit</td>\n",
       "      <td>Ind-Invit</td>\n",
       "      <td>7/26/44 0:00</td>\n",
       "      <td>CO</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16105</th>\n",
       "      <td>jerome1</td>\n",
       "      <td>3855</td>\n",
       "      <td>3810</td>\n",
       "      <td>4091</td>\n",
       "      <td>4091</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Masako</td>\n",
       "      <td>Masako</td>\n",
       "      <td>...</td>\n",
       "      <td>T-T</td>\n",
       "      <td>T</td>\n",
       "      <td>6/19/44 0:00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16106</th>\n",
       "      <td>granada1</td>\n",
       "      <td>3818</td>\n",
       "      <td>3711</td>\n",
       "      <td>4091</td>\n",
       "      <td>4091</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Rintaro</td>\n",
       "      <td>Rintaro</td>\n",
       "      <td>...</td>\n",
       "      <td>Ind-Invit</td>\n",
       "      <td>Ind-Invit</td>\n",
       "      <td>7/26/44 0:00</td>\n",
       "      <td>CO</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16107</th>\n",
       "      <td>jerome1</td>\n",
       "      <td>3858</td>\n",
       "      <td>3813</td>\n",
       "      <td>4091</td>\n",
       "      <td>4091</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Rintaro</td>\n",
       "      <td>Rintaro</td>\n",
       "      <td>...</td>\n",
       "      <td>T-T</td>\n",
       "      <td>T</td>\n",
       "      <td>6/19/44 0:00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>jerome1</td>\n",
       "      <td>3859</td>\n",
       "      <td>3814</td>\n",
       "      <td>4119</td>\n",
       "      <td>4119</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Toshio</td>\n",
       "      <td>Toshio</td>\n",
       "      <td>...</td>\n",
       "      <td>T-S</td>\n",
       "      <td>T-S</td>\n",
       "      <td>9/26/43 0:00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>tulelake1</td>\n",
       "      <td>10726</td>\n",
       "      <td>10251</td>\n",
       "      <td>4119</td>\n",
       "      <td>4119</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Utako</td>\n",
       "      <td>Utako</td>\n",
       "      <td>...</td>\n",
       "      <td>Term-with Grant</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>1/10/46 0:00</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>(Nee Terada - 1685)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16257</th>\n",
       "      <td>tulelake1</td>\n",
       "      <td>10727</td>\n",
       "      <td>10252</td>\n",
       "      <td>4119</td>\n",
       "      <td>4119</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Yuri</td>\n",
       "      <td>Yuri</td>\n",
       "      <td>...</td>\n",
       "      <td>Term-with Grant</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>1/10/46 0:00</td>\n",
       "      <td>PA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38724</th>\n",
       "      <td>poston1</td>\n",
       "      <td>7304</td>\n",
       "      <td>41</td>\n",
       "      <td>10033</td>\n",
       "      <td>10033</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Akiko</td>\n",
       "      <td>Akiko</td>\n",
       "      <td>...</td>\n",
       "      <td>Term W-G</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>1/16/45 0:00</td>\n",
       "      <td>CO</td>\n",
       "      <td></td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38725</th>\n",
       "      <td>poston1</td>\n",
       "      <td>7303</td>\n",
       "      <td>40</td>\n",
       "      <td>10033</td>\n",
       "      <td>10033</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>...</td>\n",
       "      <td>Term W-G</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>1/16/45 0:00</td>\n",
       "      <td>CO</td>\n",
       "      <td></td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38726</th>\n",
       "      <td>poston1</td>\n",
       "      <td>7302</td>\n",
       "      <td>39</td>\n",
       "      <td>10033</td>\n",
       "      <td>10033</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Masato</td>\n",
       "      <td>Masato</td>\n",
       "      <td>...</td>\n",
       "      <td>Ind-Empl</td>\n",
       "      <td>Ind-Empl</td>\n",
       "      <td>12/6/44 0:00</td>\n",
       "      <td>CO</td>\n",
       "      <td></td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38727</th>\n",
       "      <td>poston1</td>\n",
       "      <td>7305</td>\n",
       "      <td>42</td>\n",
       "      <td>10033</td>\n",
       "      <td>10033</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Tatsuto</td>\n",
       "      <td>Tatsuto</td>\n",
       "      <td>...</td>\n",
       "      <td>Term W-G</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>1/16/45 0:00</td>\n",
       "      <td>CO</td>\n",
       "      <td></td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142361</th>\n",
       "      <td>rohwer1</td>\n",
       "      <td>4581</td>\n",
       "      <td>4477</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Iwami</td>\n",
       "      <td>Iwami</td>\n",
       "      <td>...</td>\n",
       "      <td>Term W-G</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>8/31/45 0:00</td>\n",
       "      <td>NY</td>\n",
       "      <td>6-8-C</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>C</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142362</th>\n",
       "      <td>jerome1</td>\n",
       "      <td>3851</td>\n",
       "      <td>3806</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Iwami</td>\n",
       "      <td>Iwami</td>\n",
       "      <td>...</td>\n",
       "      <td>T-T</td>\n",
       "      <td>T</td>\n",
       "      <td>6/30/44 0:00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142363</th>\n",
       "      <td>rohwer1</td>\n",
       "      <td>4582</td>\n",
       "      <td>4478</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Ray</td>\n",
       "      <td>Ray</td>\n",
       "      <td>...</td>\n",
       "      <td>Ind-Empl</td>\n",
       "      <td>Ind-Empl</td>\n",
       "      <td>9/5/44 0:00</td>\n",
       "      <td>IL</td>\n",
       "      <td>6-8-C</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>C</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142364</th>\n",
       "      <td>jerome1</td>\n",
       "      <td>3853</td>\n",
       "      <td>3808</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Ray</td>\n",
       "      <td>Ray</td>\n",
       "      <td>...</td>\n",
       "      <td>T-T</td>\n",
       "      <td>T</td>\n",
       "      <td>6/30/44 0:00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142365</th>\n",
       "      <td>rohwer1</td>\n",
       "      <td>4583</td>\n",
       "      <td>4479</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Sadaco</td>\n",
       "      <td>Sadaco</td>\n",
       "      <td>...</td>\n",
       "      <td>Term W-G</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>8/31/45 0:00</td>\n",
       "      <td>NY</td>\n",
       "      <td>6-8-C</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>C</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142366</th>\n",
       "      <td>jerome1</td>\n",
       "      <td>3854</td>\n",
       "      <td>3809</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Sadako</td>\n",
       "      <td>Sadako</td>\n",
       "      <td>...</td>\n",
       "      <td>T-T</td>\n",
       "      <td>T</td>\n",
       "      <td>6/10/44 0:00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142367</th>\n",
       "      <td>rohwer1</td>\n",
       "      <td>4584</td>\n",
       "      <td>4480</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Tsuyoka</td>\n",
       "      <td>Tsuyoka</td>\n",
       "      <td>...</td>\n",
       "      <td>Term W-G</td>\n",
       "      <td>Term-With Grant</td>\n",
       "      <td>8/31/45 0:00</td>\n",
       "      <td>NY</td>\n",
       "      <td>6-8-C</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>C</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142368</th>\n",
       "      <td>jerome1</td>\n",
       "      <td>3852</td>\n",
       "      <td>3807</td>\n",
       "      <td>11-178</td>\n",
       "      <td>11-178</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Kuratomi</td>\n",
       "      <td>Tsuyoka</td>\n",
       "      <td>Tsuyoka</td>\n",
       "      <td>...</td>\n",
       "      <td>T-T</td>\n",
       "      <td>T</td>\n",
       "      <td>6/10/44 0:00</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FAR Exit Dataset original_order far_line_id family_number  \\\n",
       "16101          granada1           3820        3713          4091   \n",
       "16102           jerome1           3856        3811          4091   \n",
       "16103           jerome1           3857        3812          4091   \n",
       "16104          granada1           3819        3712          4091   \n",
       "16105           jerome1           3855        3810          4091   \n",
       "16106          granada1           3818        3711          4091   \n",
       "16107           jerome1           3858        3813          4091   \n",
       "16255           jerome1           3859        3814          4119   \n",
       "16256         tulelake1          10726       10251          4119   \n",
       "16257         tulelake1          10727       10252          4119   \n",
       "38724           poston1           7304          41         10033   \n",
       "38725           poston1           7303          40         10033   \n",
       "38726           poston1           7302          39         10033   \n",
       "38727           poston1           7305          42         10033   \n",
       "142361          rohwer1           4581        4477        11-178   \n",
       "142362          jerome1           3851        3806        11-178   \n",
       "142363          rohwer1           4582        4478        11-178   \n",
       "142364          jerome1           3853        3808        11-178   \n",
       "142365          rohwer1           4583        4479        11-178   \n",
       "142366          jerome1           3854        3809        11-178   \n",
       "142367          rohwer1           4584        4480        11-178   \n",
       "142368          jerome1           3852        3807        11-178   \n",
       "\n",
       "       NEW-family_number last_name_corrected NEW-last_name_corrected  \\\n",
       "16101               4091            Kuratomi                Kuratomi   \n",
       "16102               4091            Kuratomi                Kuratomi   \n",
       "16103               4091            Kuratomi                Kuratomi   \n",
       "16104               4091            Kuratomi                Kuratomi   \n",
       "16105               4091            Kuratomi                Kuratomi   \n",
       "16106               4091            Kuratomi                Kuratomi   \n",
       "16107               4091            Kuratomi                Kuratomi   \n",
       "16255               4119            Kuratomi                Kuratomi   \n",
       "16256               4119            Kuratomi                Kuratomi   \n",
       "16257               4119            Kuratomi                Kuratomi   \n",
       "38724              10033            Kuratomi                Kuratomi   \n",
       "38725              10033            Kuratomi                Kuratomi   \n",
       "38726              10033            Kuratomi                Kuratomi   \n",
       "38727              10033            Kuratomi                Kuratomi   \n",
       "142361            11-178            Kuratomi                Kuratomi   \n",
       "142362            11-178            Kuratomi                Kuratomi   \n",
       "142363            11-178            Kuratomi                Kuratomi   \n",
       "142364            11-178            Kuratomi                Kuratomi   \n",
       "142365            11-178            Kuratomi                Kuratomi   \n",
       "142366            11-178            Kuratomi                Kuratomi   \n",
       "142367            11-178            Kuratomi                Kuratomi   \n",
       "142368            11-178            Kuratomi                Kuratomi   \n",
       "\n",
       "       last_name_original first_name_corrected first_name_original  ...  \\\n",
       "16101            Kuratomi                 Hide                Hide  ...   \n",
       "16102            Kuratomi                 Hide                Hide  ...   \n",
       "16103            Kuratomi                Ikuko               Ikuko  ...   \n",
       "16104            Kuratomi               Masako              Masako  ...   \n",
       "16105            Kuratomi               Masako              Masako  ...   \n",
       "16106            Kuratomi              Rintaro             Rintaro  ...   \n",
       "16107            Kuratomi              Rintaro             Rintaro  ...   \n",
       "16255            Kuratomi               Toshio              Toshio  ...   \n",
       "16256            Kuratomi                Utako               Utako  ...   \n",
       "16257            Kuratomi                 Yuri                Yuri  ...   \n",
       "38724            Kuratomi                Akiko               Akiko  ...   \n",
       "38725            Kuratomi            Charlotte           Charlotte  ...   \n",
       "38726            Kuratomi               Masato              Masato  ...   \n",
       "38727            Kuratomi              Tatsuto             Tatsuto  ...   \n",
       "142361           Kuratomi                Iwami               Iwami  ...   \n",
       "142362           Kuratomi                Iwami               Iwami  ...   \n",
       "142363           Kuratomi                  Ray                 Ray  ...   \n",
       "142364           Kuratomi                  Ray                 Ray  ...   \n",
       "142365           Kuratomi               Sadaco              Sadaco  ...   \n",
       "142366           Kuratomi               Sadako              Sadako  ...   \n",
       "142367           Kuratomi              Tsuyoka             Tsuyoka  ...   \n",
       "142368           Kuratomi              Tsuyoka             Tsuyoka  ...   \n",
       "\n",
       "       type_of_final_departure NEW-type_of_final_departure  \\\n",
       "16101                Ind-Invit                   Ind-Invit   \n",
       "16102                      T-T                           T   \n",
       "16103                Ind-Invit                   Ind-Invit   \n",
       "16104                Ind-Invit                   Ind-Invit   \n",
       "16105                      T-T                           T   \n",
       "16106                Ind-Invit                   Ind-Invit   \n",
       "16107                      T-T                           T   \n",
       "16255                      T-S                         T-S   \n",
       "16256          Term-with Grant             Term-With Grant   \n",
       "16257          Term-with Grant             Term-With Grant   \n",
       "38724                 Term W-G             Term-With Grant   \n",
       "38725                 Term W-G             Term-With Grant   \n",
       "38726                 Ind-Empl                    Ind-Empl   \n",
       "38727                 Term W-G             Term-With Grant   \n",
       "142361                Term W-G             Term-With Grant   \n",
       "142362                     T-T                           T   \n",
       "142363                Ind-Empl                    Ind-Empl   \n",
       "142364                     T-T                           T   \n",
       "142365                Term W-G             Term-With Grant   \n",
       "142366                     T-T                           T   \n",
       "142367                Term W-G             Term-With Grant   \n",
       "142368                     T-T                           T   \n",
       "\n",
       "       date_of_final_departure final_departure_state camp_address_original  \\\n",
       "16101             7/26/44 0:00                    CO                         \n",
       "16102             6/19/44 0:00                                               \n",
       "16103              6/1/44 0:00                    IL                         \n",
       "16104             7/26/44 0:00                    CO                         \n",
       "16105             6/19/44 0:00                                               \n",
       "16106             7/26/44 0:00                    CO                         \n",
       "16107             6/19/44 0:00                                               \n",
       "16255             9/26/43 0:00                                               \n",
       "16256             1/10/46 0:00                    PA                         \n",
       "16257             1/10/46 0:00                    PA                         \n",
       "38724             1/16/45 0:00                    CO                         \n",
       "38725             1/16/45 0:00                    CO                         \n",
       "38726             12/6/44 0:00                    CO                         \n",
       "38727             1/16/45 0:00                    CO                         \n",
       "142361            8/31/45 0:00                    NY                 6-8-C   \n",
       "142362            6/30/44 0:00                                               \n",
       "142363             9/5/44 0:00                    IL                 6-8-C   \n",
       "142364            6/30/44 0:00                                               \n",
       "142365            8/31/45 0:00                    NY                 6-8-C   \n",
       "142366            6/10/44 0:00                                               \n",
       "142367            8/31/45 0:00                    NY                 6-8-C   \n",
       "142368            6/10/44 0:00                                               \n",
       "\n",
       "       camp_address_block camp_address_barracks camp_address_room reference  \\\n",
       "16101                                                                         \n",
       "16102                                                                         \n",
       "16103                                                                         \n",
       "16104                                                                         \n",
       "16105                                                                         \n",
       "16106                                                                         \n",
       "16107                                                                         \n",
       "16255                                                                         \n",
       "16256                                                                         \n",
       "16257                                                                         \n",
       "38724                  28                    11                 A             \n",
       "38725                  28                    11                 A             \n",
       "38726                  28                    11                 A             \n",
       "38727                  28                    11                 A             \n",
       "142361                  6                     8                 C             \n",
       "142362                                                                        \n",
       "142363                  6                     8                 C             \n",
       "142364                                                                        \n",
       "142365                  6                     8                 C             \n",
       "142366                                                                        \n",
       "142367                  6                     8                 C             \n",
       "142368                                                                        \n",
       "\n",
       "                      notes  \n",
       "16101                        \n",
       "16102                        \n",
       "16103                        \n",
       "16104                        \n",
       "16105                        \n",
       "16106                        \n",
       "16107                        \n",
       "16255                        \n",
       "16256   (Nee Terada - 1685)  \n",
       "16257                        \n",
       "38724                        \n",
       "38725                        \n",
       "38726                        \n",
       "38727                        \n",
       "142361                       \n",
       "142362                       \n",
       "142363                       \n",
       "142364                       \n",
       "142365                       \n",
       "142366                       \n",
       "142367                       \n",
       "142368                       \n",
       "\n",
       "[22 rows x 36 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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     "evalue": "",
     "output_type": "error",
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      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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    {
     "ename": "",
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      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# Apply the contains function to return results for Kuratomi\n",
    "fardf[fardf['NEW-last_name_corrected'].str.contains(\"Kuratomi\", na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "peripheral-encyclopedia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FAR Exit Dataset                     jerome1\n",
       "original_order                          3859\n",
       "far_line_id                             3814\n",
       "family_number                           4119\n",
       "NEW-family_number                       4119\n",
       "last_name_corrected                 Kuratomi\n",
       "NEW-last_name_corrected             Kuratomi\n",
       "last_name_original                  Kuratomi\n",
       "first_name_corrected                  Toshio\n",
       "first_name_original                   Toshio\n",
       "NEW-first_name_corrected              Toshio\n",
       "other_names                           George\n",
       "NEW-other_names                       George\n",
       "date_of_birth                               \n",
       "NEW-date_of_birth                           \n",
       "year_of_birth                               \n",
       "NEW-year_of_birth                           \n",
       "sex                                        M\n",
       "NEW_marital_status                         S\n",
       "citizenship                                C\n",
       "alien_registration_no.                      \n",
       "type_of_original_entry                  SAAC\n",
       "NEW-type_of_original_entry              SaAC\n",
       "NEW-pre-evacuation_address         San Diego\n",
       "pre-evacuation_state                      CA\n",
       "date_of_original_entry         10/30/42 0:00\n",
       "type_of_final_departure                  T-S\n",
       "NEW-type_of_final_departure              T-S\n",
       "date_of_final_departure         9/26/43 0:00\n",
       "final_departure_state                       \n",
       "camp_address_original                       \n",
       "camp_address_block                          \n",
       "camp_address_barracks                       \n",
       "camp_address_room                           \n",
       "reference                                   \n",
       "notes                                       \n",
       "Name: 16255, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "ename": "",
     "evalue": "",
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      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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    {
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      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# Apply the .iloc function to view additional information in index (row) below\n",
    "fardf.iloc[16255]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supreme-benefit",
   "metadata": {},
   "source": [
    "In this first module, I have shown how to use Pythons contains function to manipulate and analyze specific data within datasets. We harnessed the power of regular expressions to return different variations of values that were inconsisently entered, inlcuding limiting the contains function to return particular values. We identified key figures involved in the 'riot' at Tule Lake by using the counts value function. Futhermore, we located the place of origin, assembly center, first camp location, and place of residence at Tule Lake for one of the many persons invovled in the 'riot' that took place at Tule Lake on November 4, 1944. \n",
    "\n",
    "In the following module, we will focus on how to use the geographical data collected here to construct datasets that will allow us model and visualize the movements of 25 selected individuals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "different-laser",
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
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      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# The below command let's us save the modified dataframes into a new output csv file. \n",
    "# This can be useful when using these files for further steps of processing.\n",
    "caseno.to_csv('casenodf.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sharing-blade",
   "metadata": {},
   "source": [
    "### References\n",
    "[1] Photograph; Tule Lake, California. Tule Lake Historic Jail Rehabilitation Project Public Review. . .; Densho Digital Archive; Found on the National Park Service, Tule Lake. [https://www.nps.gov/tule/getinvolved/tule-lake-historic-jail-rehabilitation-project-public-review.htm, May 31, 2018]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charged-contents",
   "metadata": {},
   "source": [
    "## Notebooks\n",
    "\n",
    "The below module is organized into a sequential set of Python Notebooks that allows us to interact with the collections related to the Framework for Unlocking and Linking WWII Japanese American Incarceration Biographical Data to explore, clean, prepare, visualize and analyze it from historical context perspective.\n",
    "\n",
    "1. A Framework for Unlocking and Linking WWII Japanese American Incarceration Biographical Data - Context Based Data Manipulation and Analysis - Part 2\n",
    "2. A Framework for Unlocking and Linking WWII Japanese American Incarceration Biographical Data - Data Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dated-placement",
   "metadata": {},
   "outputs": [
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     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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    {
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      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  }
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