showusthedata-eda-visualization-utils.ipynb

{
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    "tags": []
   },
   "source": [
    "## Show Us The Data\n",
    "\n",
    "![](https://storage.googleapis.com/kaggle-competitions/kaggle/25925/logos/header.png)\n",
    "\n",
    "This notebook shares several utilities to make it easy to navigate through your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    "execution": {
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   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "\n",
    "# Default Setting \n",
    "mpl.rcParams['figure.dpi'] = 150\n",
    "mpl.rcParams['axes.spines.top'] = False\n",
    "mpl.rcParams['axes.spines.right'] = False\n",
    "\n",
    "train = pd.read_csv('../input/coleridgeinitiative-show-us-the-data/train.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "start_time": "2021-03-24T05:39:46.305656",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Label Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2250x1050 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_label = train['dataset_label'].value_counts()[:20]\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "\n",
    "gs = fig.add_gridspec(5, 6)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0,:])\n",
    "dataset_label = train['dataset_label'].value_counts()\n",
    "ax1.bar(dataset_label.index, dataset_label,\n",
    "       color=['#244747']*20 + ['#d4dddd']*(len(dataset_label) - 20)\n",
    "       )\n",
    "ax1.set_xticks([])\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(gs[1:,:])\n",
    "dataset_label = train['dataset_label'].value_counts()[:20]\n",
    "ax2.bar(dataset_label.index, dataset_label,\n",
    "        color='#244747'\n",
    "       )\n",
    "ax2.set_xticks(range(len(dataset_label.index)))\n",
    "ax2.set_xticklabels(dataset_label.index, rotation = 90)\n",
    "\n",
    "\n",
    "fig.text(0.125,0.9,'Dataset Label Distribution', fontsize=20, fontweight='bold', fontfamily='serif')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.009841,
     "end_time": "2021-03-24T05:39:47.097680",
     "exception": false,
     "start_time": "2021-03-24T05:39:47.087839",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Single Data Loading Utils\n",
    "\n",
    "**Feel Free To Use**\n",
    "\n",
    "> Please be sure to leave a reference when using code or taking ideas in my visualizations.\n",
    "\n",
    "- Convert Json to DataFrame\n",
    "- Interactive Table (Comportable to Read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-24T05:39:47.126366Z",
     "iopub.status.busy": "2021-03-24T05:39:47.125554Z",
     "iopub.status.idle": "2021-03-24T05:39:47.165020Z",
     "shell.execute_reply": "2021-03-24T05:39:47.165531Z"
    },
    "papermill": {
     "duration": 0.057992,
     "end_time": "2021-03-24T05:39:47.165698",
     "exception": false,
     "start_time": "2021-03-24T05:39:47.107706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from fastcore.all import *\n",
    "\n",
    "def read_single_file(df, traintest='train', idx=0):\n",
    "    dataset_path = Path('../input/coleridgeinitiative-show-us-the-data')\n",
    "    data = pd.DataFrame((dataset_path/traintest/(df['Id'][idx]+'.json')).read_json())\n",
    "    fig = go.Figure(data=[go.Table(\n",
    "        columnwidth = [100,400],\n",
    "        header=dict(values=['Title', 'Article'],\n",
    "                    line_color='darkslategray',\n",
    "                    fill_color='darkblue',\n",
    "                    align='left',  \n",
    "                    font=dict(color='white', size=12)\n",
    "                   ),\n",
    "        cells=dict(values=data.T, \n",
    "                   line_color='darkslategray',\n",
    "                   fill_color=['lightgray','white'],\n",
    "                   align='left'))\n",
    "    ])\n",
    "\n",
    "    dstitle, dslabel = train['dataset_title'][idx], train['dataset_label'][idx]\n",
    "    fig.update_layout(title_text=f'{traintest}[{idx}] |{dstitle} | {dslabel} ')\n",
    "    fig.update_layout(width=800, height=1000)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-24T05:39:47.191138Z",
     "iopub.status.busy": "2021-03-24T05:39:47.190429Z",
     "iopub.status.idle": "2021-03-24T05:39:47.481905Z",
     "shell.execute_reply": "2021-03-24T05:39:47.482347Z"
    },
    "papermill": {
     "duration": 0.306421,
     "end_time": "2021-03-24T05:39:47.482518",
     "exception": false,
     "start_time": "2021-03-24T05:39:47.176097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<div>                            <div id=\"5f8bdad7-d8d8-4bf3-8048-a52fd0c0e499\" class=\"plotly-graph-div\" style=\"height:1000px; width:800px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"5f8bdad7-d8d8-4bf3-8048-a52fd0c0e499\")) {                    Plotly.newPlot(                        \"5f8bdad7-d8d8-4bf3-8048-a52fd0c0e499\",                        [{\"cells\": {\"align\": \"left\", \"fill\": {\"color\": [\"lightgray\", \"white\"]}, \"line\": {\"color\": \"darkslategray\"}, \"values\": [[\"What is this study about?\", \"Features of Dual Enrollment Programs\", \"WWC Single Study Review\", \"WWC Rating\", \"Intervention group\", \"Comparison group\", \"Outcomes and measurement\", \"Support for implementation\", \"Reason for review\", \"Degree attainment\", \"Bachelor's degree attainment\", \"Study Notes:\", \"Glossary of Terms\", \"Multiple comparison adjustment\", \"Quasi-experimental design (QED)\", \"Randomized controlled trial (RCT)\", \"Single-case design (SCD)\", \"Standard deviation\"], [\"This study used data from the National Education Longitudinal Study (NELS:88) to examine the effects of dual enrollment programs for high school students on college degree attainment. The study also reported whether the impacts of dual enrollment programs were different for first generation college students versus students whose parents had attended at least some college. In addition, a supplemental analysis reports on the impact of different amounts of dual enrollment course-taking and college degree attainment.\\nDual enrollment programs offer college-level learning experiences for high school students. The programs offer college courses and/or the opportunity to earn college credits for students while still in high school.\\nThe intervention group in the study was comprised of NELS participants who attended a postsecondary school and who participated in a dual enrollment program while in high school (n = 880). The study author used propensity score matching methods to create a comparison group of NELS participants who also attended a postsecondary school but who did not participate in a dual enrollment program in high school (n = 7,920).\", \"Dual enrollment programs allow high school students to take college courses and earn college credits while still in high school. These programs are intended to improve college attainment, especially among low-income students, by helping students prepare academically for the rigors of college coursework and enabling students to accumulate college credits toward a degree.\\nThe study reported program impacts on two outcomes: attainment of any college degree and attainment of a Bachelor's degree. These impacts were examined for various subgroups of students, which are described below.\", \"What did the study find?\\nThe study reported, and the WWC confirmed, that dual enrollment programs significantly increased the likelihood of attaining (a) any college degree and (b) a bachelor's degree.\\nThe study reported on the impact of dual enrollment programs for first generation college students, students whose parents had some college, students whose parents had a Bachelor's degree, and students with post-Bachelor's degree parents. Although point estimates differed for the subgroups of students with different parental educational backgrounds, the subgroups were not significantly different from each other. The overall impact of dual enrollment programs is, therefore, the WWC's best estimate of effectiveness for these subgroups (see Appendix D for more information).\\nThe study author also examined whether students who earned more dual enrollment credits prior to attending college achieved greater benefits from the dual enrollment programs. The impact of dual enrollment was significantly different for students earning three dual enrollment credits versus those earning six or more credits. Students who earned three credits (i.e., had one dual enrollment course) were not more likely to attain a college degree than comparison group students. However, students who earned six credits (i.e., two courses) and students who earned seven or more credits were significantly more likely to attain any college degree or a bachelor's degree than comparison students (see Appendix D for more information).\\nA sample of these respondents was then resurveyed through four follow-ups in 1990, 1992, 1994, and 2000 . The fourth follow-up (2000) was used for this study.\\nAppendix B: Outcome measures for the degree attainment domain\\n\\nStudy Notes: A correction for multiple comparisons was needed but did not affect significance levels. The p-values presented here were reported in the original study. The mean differences reported in the table are covariate-adjusted mean differences and were taken from \", \"The research described in this report meets WWC evidence standards with reservations Cautions: Although the study matched students who participated in dual enrollment programs to those who did not, students who self-selected to participate in dual enrollment programs may have been different from students in general high school programs in ways that were unobserved in the study data. Study sample A nationally representative sample of eighth graders was first surveyed in the spring of 1988.\", \"The intervention group was comprised of those individuals in the NELS sample who attended college and participated in dual enrollment programs in high school. No other information about the nature or characteristics of the dual enrollment programs was provided.\", \"The comparison group was comprised of NELS participants who attended college but did not participate in a dual enrollment program in high school. The comparison students participated in other high school programs (e.g., traditional high school programs or Advanced Placement programs).\", \"There were two outcomes examined in the study: any college degree attainment and bachelor's degree attainment. Both outcomes were derived from the fourth follow-up of the NELS collected in 2000. For a more detailed description of these outcome measures, see Appendix B.\", \"The study was based on secondary data from a nationally representative survey of students. No information about the nature of the training for dual enrollment providers or implemenation was provided.\", \"This study was identified for review by receiving media attention.\", \"Any college degree attainment The study author collected information on college degree attainment from the fourth follow-up of the National Education Longitudinal Study collected in 2000. Only students who attended college were included in the study.\", \"The study author collected information on bachelor's degree attainment from the fourth follow-up of the National Education Longitudinal Study collected in 2000. Only students who attended college were included in the study. The effect size is a standardized measure of the effect of an intervention on student outcomes, representing the change (measured in standard deviations) in an average student's outcome that can be expected if the student is given the intervention. The improvement index is an alternate presentation of the effect size, reflecting the change in an average student's percentile rank that can be expected if the student is given the intervention. The WWC-computed average effect size is a simple average rounded to two decimal places; the average improvement index is calculated from the average effect size. The statistical significance of the study's domain average was determined by the WWC; the study is characterized as having a statistically significant positive effect because univariate statistical tests are reported for each outcome measure and both effects are positive and statistically significant, accounting for multiple comparisons. nr = not reported.\", \"A correction for multiple comparisons was needed, but did not affect the statistical significance of the outcomes. The p-values presented here were reported in the original study. Effect sizes were computed using the covariate-adjusted mean difference and standard errors reported in Table 1 of the manuscript. This quasi-experimental study used propensity score matching to create a comparison group; therefore, the WWC required that baseline equivalence be established to determine whether the study met standards with reservations. Because the two outcomes reported above did not have pretests, two key covariates were selected by the WWC from among those used by the study author in the propensity score matching: family income and prior achievement. In response to a request by the WWC, the author provided baseline means and standard deviations for the analytic sample on these two covariates. The baseline effect sizes for family income and prior achievement were 0.02 and 0.00, respectively. The study therefore meets WWC standards with reservations because baseline equivalence was met on these two covariates. Table Notes : Positive results for mean difference, effect size, and improvement index favor the intervention group; negative results favor the comparison group. The effect size is a standardized measure of the effect of an intervention on student outcomes, representing the change (measured in standard deviations) in an average student's outcome that can be expected if the student is given the intervention. The improvement index is an alternate presentation of the effect size, reflecting the change in an average student's percentile rank that can be expected if the student is given the intervention. nr = not reported.\", \"Attrition Attrition occurs when an outcome variable is not available for all participants initially assigned to the intervention and comparison groups. The WWC considers the total attrition rate and the difference in attrition rates across groups within a study.\\nClustering adjustment If intervention assignment is made at a cluster level and the analysis is conducted at the student level, the WWC will adjust the statistical significance to account for this mismatch, if necessary.\\nConfounding factor A confounding factor is a component of a study that is completely aligned with one of the study conditions, making it impossible to separate how much of the observed effect was due to the intervention and how much was due to the factor.\\nDesign The design of a study is the method by which intervention and comparison groups were assigned.\\nDomain A domain is a group of closely related outcomes.\\nEffect size The effect size is a measure of the magnitude of an effect. The WWC uses a standardized measure to facilitate comparisons across studies and outcomes.\\nEligibility A study is eligible for review if it falls within the scope of the review protocol and uses either an experimental or matched comparison group design.\\nEquivalence A demonstration that the analysis sample groups are similar on observed characteristics defined in the review area protocol.\\nImprovement index Along a percentile distribution of students, the improvement index represents the gain or loss of the average student due to the intervention. As the average student starts at the 50th percentile, the measure ranges from -50 to +50.\", \"When a study includes multiple outcomes or comparison groups, the WWC will adjust the statistical significance to account for the multiple comparisons, if necessary.\", \"A quasi-experimental design (QED) is a research design in which subjects are assigned to intervention and comparison groups through a process that is not random.\", \"A randomized controlled trial (RCT) is an experiment in which investigators randomly assign eligible participants into intervention and comparison groups.\", \"A research approach in which an outcome variable is measured repeatedly within and across different conditions that are defined by the presence or absence of an intervention.\", \"The standard deviation of a measure shows how much variation exists across observations in the sample. A low standard deviation indicates that the observations in the sample tend to be very close to the mean; a high standard deviation indicates that the observations in the sample are spread out over a large range of values.\\nStatistical significance Statistical significance is the probability that the difference between groups is a result of chance rather than a real difference between the groups. 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       "<div>                            <div id=\"ec271ede-3ff8-4d33-8415-3e2bef1d874f\" class=\"plotly-graph-div\" style=\"height:1000px; width:800px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"ec271ede-3ff8-4d33-8415-3e2bef1d874f\")) {                    Plotly.newPlot(                        \"ec271ede-3ff8-4d33-8415-3e2bef1d874f\",                        [{\"cells\": {\"align\": \"left\", \"fill\": {\"color\": [\"lightgray\", \"white\"]}, \"line\": {\"color\": \"darkslategray\"}, \"values\": [[\"Differences in Outcomes for Female and Male Students in Special Education\", \"P olicy reform initiatives, such as the No Child Left Behind (NCLB) Act of 2001 and the President's Commission on Excellence in Special Education\", \"METHOD\", \"Sample\", \"Outcome Variables\", \"Data Analysis\", \"RESULTS\", \"Employment\", \"Postschool Employment Satisfaction\", \"Postschool Education and Aspirations\", \"Postschool Demographic Outcomes\", \"Postschool Attitudes\", \"Community Engagement and Crime Involvement Outcomes\", \"DISCUSSION\", \"Limitations\", \"Implications for Secondary Education and Transition Practices\", \"CONCLUSION\", \"ABOUT THE AUTHORS\", \"AUTHORS' NOTE\"], [\"\", \", stress satisfactory outcomes for all youth, including those who have traditionally been underserved and those who do not readily achieve adult milestones of independence and productivity. Despite increasing evidence that all students have access to comparable experiences and services during school, studies have reported that girls who received special education have less favorable outcomes compared to their male counterparts (Benz, Doren, & Yovanoff, 1998; Doren & Benz, 2001; Fulton & Sabornie, 1994; Hasazi, Gordon, & Roe, 1985; Lindstrom, Benz, & Doren, 2004; Oswald, Coutinho, Best, & Nagle, 2002; Sitlington & Frank, 1985; Wagner, Cameto, & Newman, 2003) . These studies have documented lower earnings and restricted occupational options for women with disabilities.\\nEducators need information about school completion rates, postsecondary aspirations, employment, and other longer-term outcomes to respond to the accountability mandate of NCLB, improve secondary school curricula, and implement more effective transition activities. Accessible, nationally representative informa-MARTHA J. COUTINHO DONALD P. OSWALD AL M. BEST Interest in differences in outcomes for male and female students in special education has increased in recent years. Using the nationally representative, longitudinal National Education Longitudinal Study of 1988 (NELS-88) data set, a logistic regression model was used to examine the extent to which outcome variables were differentially associated with gender for students participating in special education. Evidence was obtained for several differential effects, with most results favoring males. Among other findings, females as compared to males with disabilities were less likely to obtain a high school diploma, were less likely to be employed, earned less, and were more likely to be a biological parent. Recommendations are provided for improved transition services, the implementation of promising practices related to self-determination, and renewed emphasis on comprehensive and flexible life-career preparation to foster better outcomes among females with disabilities.\\ntion is limited because the U.S. Department of Education (DOE) collects information by disability, age, and race/ethnicity, but not by gender. For example, the DOE's child count data indicate that the high school graduation rate for students with disabilities increased from 52.6% in 1995-1996 to 56.2% in 1999-2000 , but the data do not indicate whether this increase was the same for both boys and girls (U.S. Department of Education, 2002) . Educators also need information about the outcomes of youth with disabilities as compared to those who are typically achieving, at risk, or gifted. The reporting provided by school systems offers results of standardized testing broken down by these various student groups but, again, does not include gender. Educators have little information about the differences between boys and girls with regard to employment, postsecondary education and aspirations, postschool demographic outcomes, and community engagement. Women make up an increasing share of the workforce, but important gender differences persist, including lower earnings and more restricted occupational choices (Stephenson & Burge, 1997 ; U.S. Department of Labor, 2004) .\\nThe purpose of the present study was to determine the extent to which there were gender-based differences in socially significant outcomes for special education students and whether similar differences were observed in other groups of students (e.g., typically achieving, at risk, gifted). Evidence of gender differences in outcomes signals the need to improve education and transition planning and services at the secondary level.\", \"\", \"We analyzed data collected in 1994, which are the most recent data that allow direct comparisons of students with and without disabilities. Students in the sample were receiving services during high school after requirements for transition services were strengthened by the Individuals with Disabilities Education Act (IDEA) of 1990.\\nThe National Education Longitudinal Study (NELS) program was instituted by the National Center for Education Statistics (NCES) with the aim of studying \\\"the educational, vocational, and personal development of students at various grade levels, and the personal, familial, social, institutional, and cultural factors that may affect that development\\\" (NCES, 1994, p. 2) . The program began with a 12th-grade cohort in the National Longitudinal Study of the high school class of 1972 and was continued in the 1980s with a study of high school and beyond, which included a cohort of 10th-and 12th-graders. NELS-88, the data set analyzed in this study, involved a sample of students who were in the eighth grade in 1988. The study included follow-ups in 1990, 1992, and 1994 . The focus of the present study was on the transition into postschool functioning; as a result, we elected to draw from the 1994 follow-up. Although data from a fourth follow-up, conducted in 2000, were released when the present analyses were in the final stages, consideration of those data would have altered the research questions substantially by addressing longerterm outcomes.\\nThe base year (1988) sample was recruited using a two-stage stratified probability design to create a nationally representative sample of eighth-grade schools and students. The first stage yielded 1,052 participating schools: 815 public schools and 237 private schools. The second sampling stage (student sampling) produced a random selection of 26,432 students from the selected schools, of whom 24,599 participated in the 1988 data collection (NCES, 1994) .\\nThree waves of follow-up data collection occurred at 2-year intervals, so that the final follow-up data were gathered in 1994 when most of the students were 2 years past high school graduation. Data-gathering methods in the base year included written surveys and tests of students, as well as surveys of parents, school administrators, and teachers; follow-up data were collected by means of a student survey. All outcome variables included in the present study were drawn from the 1994 data set.\\nThe NELS sample analyzed in the present study included those students for whom both base year data and 1994 data were present: a total of 13,391 participants with usable data, consisting of 6,526 males and 6,865 females. The race/ethnicity distribution of the sample was as follows: 7.0% Asian/Pacific Islander (AS; n = 939), 10.8% Black (BK; n = 1,452), 13.5% Hispanic (HI; n = 1,813), and 68.6% White (WH; n = 9,187). Native American students were excluded from the analyses as their numbers were low and they made up such a small portion of the total sample (n = 286).\\nOn the basis of the data from the base year, participants in the NELS sample were divided into four status groups: special education (SE), low achieving (LA), typically achieving (TA), and gifted and talented (GT). SE students (n = 1,931) were extracted from the pool first and were defined as those students whose parents answered affirmatively to the question, \\\"Has your eighth grader ever received special services for any or all of the following: visual handicap (not correctable by glasses), hearing problem, deafness, speech problem, orthopedic problem (e.g., club foot, absence of arm or leg, cerebral palsy, amputation, polio), other physical disability, specific learning problem (e.g., dyslexia or other reading, spelling, writing, or math disability), emotional problem, other health problem?\\\" Many students had received more than one type of special service. The SE sample included 832 female students (43.1%) and 1,099 male students (56.9%).\\nGT students (n = 1,503) were extracted from the sample next and were defined as participants whose parents answered affirmatively to the question, \\\"Is your eighth grader currently enrolled in a gifted or talented program?\\\" Forty-six percent of the GT students were male. Low achieving students were defined as those who, among the remaining students, fell into the lowest quartile of standardized achievement testing scores for either reading or math (n = 3,015); 51% were male. Typical students constituted the remainder of the sample (n = 6,942); 46% were male. U.S. census data for the year 2000 indicated that the general population in the country was 49.1% male (Smith & Spraggins, 2001 ).\", \"Twenty-four variables were selected from the 1994 followup data set to represent a range of socially significant outcomes. Selection was based on a conceptual framework that includes individual student, family, and school components, with particular emphasis on variables for which there were indications in the literature of differential effects for male and female students (see Tables  1-6 ; Lee, Chen, & Smerdon, 1996; Oswald, Coutinho, Best, & Nagle, 2002) . Outcome variables were analyzed separately, but for ease of interpretation, they were grouped in the following categories: Employment (Table 1), Postschool Employment Satisfaction (Table 2) , Postschool Education and Aspirations (Table 3) , Postschool Demographic Outcomes (Table 4) , Postschool Attitudes (Table 5) , and Community Engagement and Crime Involvement Outcomes (Table 6 ).\", \"Some of the outcome variables were continuous (e.g., average hours worked per week), and some were nominal (e.g., voting in local/state selections in the past 12 months); a general linear model or a multinomial logistic regression model, respectively, was used to analyze the data. A two-step process was used to isolate differential gender effects. In the first step, each dependent (outcome) variable was used in a regression model with the following predictors: gender, group (SE, LA, TA, GT), race (White, Black, Asian, Hispanic), and gender-bygroup interaction. If the interaction effect was nonsignificant in this first-step model, the variable was dropped from further consideration. A nonsignificant interaction may be interpreted as supporting the conclusion that gender effects were statistically indistinguishable across the four groups; that is, any gender-related differences in the outcome were similar for special education and other students. If the gender-by-group interaction was significant (using a screening \\u03b1 = 0.20), analysis of the outcome variable was continued in the second step.\\nThe second step used an adaptation of a logistic regression model created by the investigators in an earlier stage of the project. The model included 22 school, family, and individual predictors: percentage minority in school, percentage of students in special education, percentage of students in gifted programs, urbanicity, family socioeconomic status (SES), number of risk factors for dropping out of school, how often a parent talks to the child about school, how often a parent helps with homework, whether anyone is home when the child returns home, student grades, standardized test scores, locus of control, self concept, likelihood of post-high school education, higher education plans, retention, disruptiveness, absenteeism, counseling about career, counseling about courses, employment, and hours spent on homework. This model was found to predict whether a student was likely to be in the SE, LA, TA, or GT group (Oswald, Coutinho, & Best, under review) . In the present study, the same model was used with the addition of effects for group, race-by-group, and gender-bygroup. The purpose of this complex model was to test for gender-by-group differences in outcomes, taking into account a range of individual, family, and community characteristics that are likely to be associated with student outcomes.\\nA significant gender-by-group interaction (p < .05) in the second step suggested that a differential gender effect existed. A differential gender effect indicated that gender was a significantly more influential variable for one group than for other groups. We then tested for a gender effect within the student groups to clarify where the male-female differences were found. In the absence of a significant gender-by-group interaction in the second step, we briefly discuss significant group or gender main effects.\", \"Generally, the study's results examine gender differences for students in special education, compared to gender differences for all other students. In some cases, the pattern of differences was similar for the SE and LA groups and for the TA and GT groups; in these instances, SE and LA were collapsed into one group and TA and GT collapsed into a second group for ease of description.\", \"Three employment-related outcomes were considered: job/occupation longest held, proportion of months employed between June 1992 and August 1994, and average hours worked per week (see Table 1 ). Although the screening analysis revealed a significant gender effect for job/occupation longest held, \\u03c7 2 (5, 13,391) = 512, p < .05, there was no evidence of a differential gender effect across the four groups, \\u03c7 2 (15, 13,391) = 18, p > .05. For example, although more women than men were employed in clerical positions, this difference was observed in both special education and nonspecial education groups. Similarly, substantially more men than women were reported to be in skilled or technical positions across all groups.\\nFor the proportion of months employed, the screening analysis indicated a differential gender effect across the four groups, F(3, 13,294) = 19, p < .05. In the full model, this differential gender effect remained significant (p < .05). After the effects of all other factors have been covaried out, among SE and LA students, men were employed during more of the months than were women (95% adjusted CI men = [62.5%-68.2%]; women = [54.8%-60.7%], p < .05), but among TA and GT students, men and women were employed the same proportion of months (95% adjusted CI both = [60%-64%], p > .05).\\nThe screening analysis also indicated a gender effect for average hours of work per week (p < .05) and a differential gender effect across the groups (p < .05). In the full model (i.e., after the other factors were covaried out), however, the gender effect remained (p < .05), but the analysis showed no evidence for a differential gender effect (p > .05). After adjusting for the other covariates, men were employed an average of approximately 33 hours per week (95% adjusted CI = [31.9-33.9]), whereas women were employed an average of 27 hours per week (95% adjusted CI = [26.6-28.6]); these adjusted averages did not vary significantly among the four groups.\", \"Three postschool employment satisfaction outcomes were considered: satisfied with opportunity to use past training or education, employer provided medical benefits, and job satisfaction (see Table 2 ). In the case of satisfaction with opportunities to use past training and education, there was no evidence for a gender difference (p > .05) or for a differential gender effect (p > .05). That is, satisfaction ratings are constant across the four student groups (SE male, SE female, non-SE male, non-SE female).\\nAverage job satisfaction differed depending on gender (p < .05), but there was no evidence for a differential gender effect across groups (p > .05). In all groups, males had higher job satisfaction than females. Similarly, with regard to employer-provided medical benefits, there was evidence for a gender difference (p < .05) but no evidence for a differential effect (p > .05). Significantly more males (adjusted 95% CI = [48.0- Note. F = female; M = male; SE = special education group; LA = low-achieving group; TA = typically achieving group; GT = gifted group. Groups were derived from the National Education Longitudinal Study data (National Center for Education Statistics, 1994). \", \"Five postschool education and aspiration variables were considered: high school diploma/general equivalency diploma (GED) present, ever dropped out, highest postschool education (PSE) level attained, highest level of education expected, and job expected at age 30 (see Table 3 ). With regard to having a high school diploma or GED, the screening analysis indicated no evidence for a gender effect (p > .05), but a differential gender effect was observed (p < .05). In the full model, a differential gender effect remained after the other factors were covaried out (p < .05). Among typically developing students, there was no difference in the adjusted proportion of men and women who had a high school diploma (93%), whereas in the SE and LA groups, a higher adjusted proportion of men (92.6%) than women (91.7%) had a diploma. These adjusted rates for students in the SE and LA groups are substantially higher than actual rates in the data set; adjusted rates are affected by the distribution of other predictors in the model.\\nIn the case of dropout status, the screening analysis yielded no evidence for a gender effect (p > .05) or a differential gender effect (p > .05). There, however, was a group difference in the dropout rate; 26% of SE students dropped out, whereas 16% of non-SE students dropped out (p < .05).\\nThe analysis did find evidence for a gender effect in the case of PSE level attained (p < .05) but no evidence for a differential gender effect (p > .05). More men than women reported no postschool education (53% of men; 47% of women), and more women than men reported being enrolled in a PSE program (46% of men; 54% of women). Similarly, in the case of highest level of education expected, there was some evidence for a gender effect (p = .05) but not for a differential gender effect (p > .05). The gender effect suggested that somewhat more female students than male students expected to attend college and get a bachelor's or graduate degree (52% of women; 47% for men) and that there are more men than women who (a) do not expect to finish high school, (b) expect only to earn a high school equivalency degree or a vocational/trade certificate, or (c) attend college but do not earn a degree (54% men; 46% women).\\nWith regard to job expected, the analysis found a gender effect (p < .05) and evidence for a differential gender effect (p < .05). The differential gender effect persisted in the full model (p < .05). In all groups, a higher percentage of women than men expected to be in the following occupations: clerical, professional, and sales and service. In all of the groups except GT, a higher proportion of men expected to be in manager positions, and more men expected to be in skilled technical, owner, and other positions in all groups.\", \"Postschool demographic outcomes consist of marital status, health problems that limit work, number of biological children, and total earnings from jobs (see Table 4 ). Marital status showed a gender effect in the screening analysis (p < .05), but there was no evidence for a differential gender effect (p > .05). For example, 27% of women in the SE group were married (compared to 14% of men in the same group), and 19% of women from the other groups were married (compared to 10% of men from those groups). There was no indication of a gender effect in health problems that limit work (p > .05) and no evidence for a differential gender effect (p > .05). The analysis, however, did find a significant group effect (p < .05): The GT group had the lowest number of students with health problems (1.7%), followed by TA (2%), LA (5%), and SE (6.9%).\\nThere was a clear gender difference in the number of biological children reported (p < .05), and the screening analysis also indicated differential gender disproportionality (p < .05), which persisted in the full model (p < .05). In both the SE and LA groups, men reported very few children; approximately 1 out of 22 men in the SE group reported having a child, and even fewer men in the LA group did so. Among men in the GT and TA groups, only about 1 in 10 reported having a child. In contrast, one fourth of the women in the SE group and almost one third of those in the LA group had a child; among women in the GT and TA groups, approximately one fifth had a child.\\nWith regard to total earnings, the screening analysis indicated evidence for a gender effect (p < .05), with weak evidence for a differential gender effect (p = .19). In the full model, after adjusting for other factors, the gender effect remained (p < .05), and there was evidence for a differential gender effect (p < .05). In all groups, men earned more than women, but the magnitude of the difference varied. In the SE and LA groups, the average man earned $8,471 (95% CI = [$7,629-$9,315]), whereas the average woman earned only $5,480 ($4,598-$6,362). Among the TA group, the average man earned $8,115 ($7,582-$8,647) , and the average woman earned $6,518 ($6,022-$7,014). In the GT group, men were only marginally more highly compensated (p < .05) than women, with men earning $8,754 ($7,920-$9,588) and the women earning $7,713 ($6,907-$8,520).\", \"Five postschool attitude variables were considered: importance of success in work, importance of having lots of money, importance of strong friendship, importance of being able to find steady work, and values-work orientation scale (see Table 5 ). With regard to the importance of success in work, the analysis did not show evidence for a gender difference (p > .05) or a differential gender effect (p > .05). The importance of having lots of money showed a gender difference (p < .05) but not a differential gender effect (p > .05). More men than women tended to say that having lots of money was very important (39.9% vs. 31.1%, respectively). The importance of strong friendship had a clear gender difference (p < .05) and evidence for a differential gender effect (p < .05) in the screening model, confirmed by the full model. The differential gender effect was evident in the SE group: a higher proportion of men considered a strong friendship \\\"very important\\\" (94%) than did women (87%). This is in contrast to the other groups, in which approximately equal proportions of men and women (about 88%) considered strong friendships very important.\\nThe importance of finding steady work showed a gender difference (p < .05) but no evidence for a differential gender effect (p > .05). In all groups, approximately 1% of both genders viewed steady work as \\\"not important\\\" and about 90% viewed it as \\\"very important.\\\"What varies is that 9.9% of men viewed steady work as \\\"somewhat important,\\\" whereas 8.2% of women did.\\nThe values-work orientation scale also showed a gender difference (p < .05) but not a differential gender effect (p > .05). Men scored slightly higher than women on work orientation across groups.\", \"Finally, the analysis looked at two community engagement outcomes and two crime involvement outcomes (see Table 6 ). There was no evidence of a gender difference in voting (p > .05), but the screening model showed some evidence for a differential gender effect (p = .06), Note. F = female; M = male; LA = low-achieving group. Group was derived from the National Education Longitudinal Study data (National Center for Education Statistics, 1994). a\\nSignificant effects in the model (p < .05) are listed and direction of effect is stated. and the full model confirmed a differential gender effect (p < .05). The gender difference occurred in the LA group, in which a higher percentage of males voted than did females (33% and 28%, respectively). The number of volunteer hours worked per week did not differ by gender (p > .05), nor was there a differential gender effect (p > .05). Overall, students reported volunteering about 6 hours per week.\\nThe proportion of students who had been arrested or had a close friend who had been arrested showed a gender difference (p < .05) but not a differential gender effect (p > .05). About 30% of male students reported that they or a close friend had been arrested, compared to only about 15% of female students. Crime victimization (\\\"You or a family member were a victim of a serious crime\\\") did not differ by gender (p > .05) nor was there a differential gender effect (p > .05).\", \"The results obtained in this study underscore the importance of collecting and analyzing outcome data by gender. Out of 24 outcome variables, a significant main effect for gender was observed for 12 variables. For most of those variables, the differences favored men; for example, men generally earned more, worked a greater number of hours, and more frequently received job benefits. Men also reported beliefs or aspirations commonly thought of as more favorable, such as satisfaction with their jobs and work orientation. Women were more likely to be married and to report more biological children, but these are not necessarily favorable events for young adults who were approximately 20 years old (Levine & Edgar, 1994) . For a few variables, findings for men and women did not differ, such as school dropout rate, health problems, volunteer work, crime victimization, the opportunity to use past training or education, and the importance of success. For two variables, related to postschool education and educational aspirations, the findings favored women: they were more likely to report being enrolled in a postsecondary education program and to report an aspiration to attend college to get a bachelor's or graduate degree. For one variable, the outcome for women was clearly better: Women were less likely to be arrested. More men than women endorsed \\\"the importance of having lots of money,\\\" but it is unclear whether this was a favorable outcome.\\nEvidence was obtained for several differential effects by gender for students who had received special education, and these results also favored men. With respect to employment, men who had disabilities or were low achieving were likely to be employed for a greater number of months than their female counterparts. As compared to women, men in the SE and LA groups had significantly higher total earnings. With respect to postschool education and demographic outcomes, men in the SE or LA groups were more likely than their female counterparts to receive a high school diploma and were much less likely to report biological children. With respect to postschool attitudes, men in the SE group rated the importance of strong friendship more highly than women in the SE group did.\\nSome of the findings in this study are similar to those obtained in the National Longitudinal Transition Study (NLTS), in which investigators conducted a longitudinal study of two nationally representative cohorts of youth in 1987 and 2001 (Wagner et al., 1991 (Wagner et al., , 2003 . Wagner et al. (2003) reported increases in paid employment during the past year for both men and women in 2001 as compared to 1987, but the increases for women were larger, eliminating the statistically significant difference observed in 1987. This suggests some outcomes may be improving. Unfortunately, Wagner et al. (2003) also found that both men and women experienced reductions in hours worked, increases in pay, and lower rates of working at a given point in time, suggesting employment in 2001 was more sporadic than in 1987. Overall, men were still more likely than women to be paid at or above the minimum wage. Future research is needed to determine whether the gender gaps in earnings or hourly wages have narrowed over time and whether employment for men and women is becoming more sporadic despite the better pay and benefits observed for the second cohort of the NLTS study. Both the study reported here and the NLTS study (Wagner et al., 2003) found that women are less likely than men to experience negative consequences for certain behaviors, such as being arrested. Further research is needed to investigate trends in the rates at which men and women experienced negative outcomes.\", \"The data analyzed in the study reported here do not reflect possible changes in transition service delivery over the past several years. The work of Wagner and colleagues (2003) , however, suggests that transition-related issues for men and women with disabilities have not changed substantially over approximately the same period, and in some cases, earlier concerns have been exacerbated.\\nGeneralization of these findings is limited by the fact that the sample of students in special education is unlikely to include adequate representation of students with moderate to severe disabilities. The sample is limited to students able to participate in the self-report survey, so students with more severe impairments were not included. Furthermore, the SE group is defined by parent report rather than by a school record review, so a small number of students may have been misclassified due to faulty parent reports. Also, the survey methodology did not allow for verification of outcome data, and deliberate or inadvertent misrepresentation cannot be ruled out. Finally, due to the number of logistic regression models included in the study, the experiment-wise probability of Type I error is somewhat elevated.\", \"The findings obtained in this study point directly to the need for differentiated and improved transition services for women with disabilities. In this nationally representative sample, women with disabilities were less likely than men with disabilities to obtain a high school diploma, worked fewer hours, earned less, and were more likely to be a biological parent. Also, evidence of another important gender difference exists: Wagner et al. (1991) and Wehmeyer and Schwartz (2001b) have reported that girls identified as eligible for special education under IDEA may be more impaired (e.g., have lower mean IQs) than boys who are identified. These differential gender effects indicate an urgent need for timely, gendersensitive, expanded career development opportunities and improved self-determination skills.\\nTo respond to the differential gender differences observed in this study, these opportunities and skills must be addressed through the individualized transition planning process ) to prevent important differential outcomes for women (e.g., the lower likelihood of finishing high school, the greater chance of having biological children). Given that occupational aspirations for youth are relatively stable as early as eighth grade (Rojewski & Yang, 1997) , individualized transition services, and not simply planning, should begin for girls at the beginning of the secondary school years so that an integrated and intensive plan to address academic and career goals can be implemented.\\nExpanded career development opportunities should be implemented in line with the recommendations of Lindstrom et al. (2004) : Provide early workplace experiences and paid employment to teach basic employability skills and develop the expectation of employment in young women with disabilities. A wider breadth and larger number of occupational and vocational coursesas well as community-based work experiences-are needed, but to be effective, the opportunities must address such gender-related issues as biased teacher expectations, stereotypes that only place young women in jobs traditionally filled by women, and environmental barriers on job sites, such as the need for childcare arrangements (Lindstrom et al., 2004; Stephenson & Burge, 1997; Wehmeyer & Schwartz, 2001a) .\\nFor girls with disabilities, increases in self-determination may also influence the decision to remain in school, graduate, and delay having children-areas in which differential gender effects were observed. Therefore, transition services that teach girls self-advocacy and selfdetermined behavior may help young women with disabilities use the experiences and outcomes of ongoing, gender-appropriate course offerings and work experiences to select and refine actual career goals and to pursue postsecondary education and training options that are based on personal strengths and interests. Interventions focused on self-advocacy also build on one of the few findings obtained in this study that favor women, including those with disabilities: More women than men plan to graduate and aspire to go further in postsecondary school. Evidence exists that self-determination skills act as an \\\"entry point\\\" to and support progress in the general curriculum (Palmer, Wehmeyer, Gipson, & Agran, 2004) . Methods of curricular modification are available that both support self-determination and foster increased access to the general curriculum (Wehmeyer, Field, Doren, Jones, & Mason, 2004) . Girls may be particularly receptive to self-determination training related to high school completion and continuing education needs because these are areas in which their initial aspirations, but not subsequent experiences, exceed their male counterparts.\\nMany of the significant differential effects for gender among students with disabilities were also true for women who were low achieving. These included their earnings, types of jobs held, total months employed, likelihood of receiving a high school diploma, and likelihood of having biological children. These findings have important implications for those seeking to build support for and implement somewhat differentiated transition services for girls with disabilities. Most students with disabilities spend most of the school day in general education settings that include low-achieving students, and low-achieving students and students with mild disabilities follow similar course-taking patterns (U.S. Department of Education, 2002) . A substantial proportion of a school's female population demonstrates a need for enhanced skills in personal self-advocacy and participation in gender-responsive career preparation options. Interventions can be implemented more easily and in a more cost-effective manner because of the common course-patterns of girls who are disabled and those who are low achieving. Moreover, schools are more likely to commit resources when more students are likely to benefit.\", \"Transition planning and services for young women with disabilities must begin long before they approach the age of majority. The findings from this study underscore the importance of a comprehensive, life-career development approach (Sitlington, Clark, & Kolstoe, 2000) that addresses gender-related differences and issues. This approach may be especially difficult to implement at a time when the emphasis is on increasing the access of youth with disabilities to traditional academic course offerings and when progress tends to be interpreted narrowly, often in terms of a score on a district or statewide standardized competency test. The evidence of differential gender effects obtained in this study, however, support the need for differentiated transition services that offer young women with disabilities early, ongoing, and carefully selected and implemented work experiences; occupational and vocational course offerings that address gender related issues; and access to genderresponsive self-determination interventions. Public policy also must continue to support ongoing, methodologically sound research to determine how best to address differences in the progress and outcomes of boys and girls with disabilities to ensure changes in policy and practice are evidence based and cost effective during an era of competing public interests and accountability.\", \"Martha J. Coutinho, PhD, is a special education professor in the Department of Human Development and Learning at East Tennessee State University. Her research interests include education services for students with Asperger syndrome and high-functioning autism, early literacy interventions, and the human-animal bond. \", \"Support for this study was provided in part by a grant from the U.S. Office of Special Education Programs, U.S. Department of Education (H324D000040).\"]]}, \"columnwidth\": [100, 400], \"header\": {\"align\": \"left\", \"fill\": {\"color\": \"darkblue\"}, \"font\": {\"color\": \"white\", \"size\": 12}, \"line\": {\"color\": \"darkslategray\"}, \"values\": [\"Title\", \"Article\"]}, \"type\": \"table\"}],                        {\"height\": 1000, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", 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       "                            \n",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "read_single_file(train, 'train', 2)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012822,
     "end_time": "2021-03-24T05:39:47.559049",
     "exception": false,
     "start_time": "2021-03-24T05:39:47.546227",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Title > Dataset Label (Treemap)"
   ]
  },
  {
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   "execution_count": 6,
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    "_kg_hide-input": true,
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     "iopub.execute_input": "2021-03-24T05:39:47.589372Z",
     "iopub.status.busy": "2021-03-24T05:39:47.588423Z",
     "iopub.status.idle": "2021-03-24T05:39:48.869627Z",
     "shell.execute_reply": "2021-03-24T05:39:48.870056Z"
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     "start_time": "2021-03-24T05:39:47.572547",
     "status": "completed"
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    "tags": []
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   "outputs": [
    {
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