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Big5PersonalityTest.ipynb
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Big 5 personality test: openpsychometrics"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Trait definition taken from: https://en.wikipedia.org/wiki/Big_Five_personality_traits\n#### Test and data can be found: https://openpsychometrics.org/tests/IPIP-BFFM/"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport numpy as np\nfrom matplotlib import colors\nimport numpy as np",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df = pd.read_csv('data-final.csv', sep='\\t')",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df",
"execution_count": 3,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>EXT1</th>\n <th>EXT2</th>\n <th>EXT3</th>\n <th>EXT4</th>\n <th>EXT5</th>\n <th>EXT6</th>\n <th>EXT7</th>\n <th>EXT8</th>\n <th>EXT9</th>\n <th>EXT10</th>\n <th>...</th>\n <th>dateload</th>\n <th>screenw</th>\n <th>screenh</th>\n <th>introelapse</th>\n <th>testelapse</th>\n <th>endelapse</th>\n <th>IPC</th>\n <th>country</th>\n <th>lat_appx_lots_of_err</th>\n <th>long_appx_lots_of_err</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>4.0</td>\n <td>1.0</td>\n <td>5.0</td>\n <td>2.0</td>\n <td>5.0</td>\n <td>1.0</td>\n <td>5.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>2016-03-03 02:01:01</td>\n <td>768.0</td>\n <td>1024.0</td>\n <td>9.0</td>\n <td>234.0</td>\n <td>6</td>\n <td>1</td>\n <td>GB</td>\n <td>51.5448</td>\n <td>0.1991</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3.0</td>\n <td>5.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>2.0</td>\n <td>5.0</td>\n <td>1.0</td>\n <td>5.0</td>\n <td>...</td>\n <td>2016-03-03 02:01:20</td>\n <td>1360.0</td>\n <td>768.0</td>\n <td>12.0</td>\n <td>179.0</td>\n <td>11</td>\n <td>1</td>\n <td>MY</td>\n <td>3.1698</td>\n <td>101.706</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>2.0</td>\n <td>1.0</td>\n <td>3.0</td>\n <td>2.0</td>\n <td>5.0</td>\n <td>...</td>\n <td>2016-03-03 02:01:56</td>\n <td>1366.0</td>\n <td>768.0</td>\n <td>3.0</td>\n <td>186.0</td>\n <td>7</td>\n <td>1</td>\n <td>GB</td>\n <td>54.9119</td>\n <td>-1.3833</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>1.0</td>\n <td>4.0</td>\n <td>...</td>\n <td>2016-03-03 02:02:02</td>\n <td>1920.0</td>\n <td>1200.0</td>\n <td>186.0</td>\n <td>219.0</td>\n <td>7</td>\n <td>1</td>\n <td>GB</td>\n <td>51.75</td>\n <td>-1.25</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>5.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>5.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>...</td>\n <td>2016-03-03 02:02:57</td>\n <td>1366.0</td>\n <td>768.0</td>\n <td>8.0</td>\n <td>315.0</td>\n <td>17</td>\n <td>2</td>\n <td>KE</td>\n <td>1.0</td>\n <td>38.0</td>\n </tr>\n <tr>\n <th>...</th>\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>...</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 </tr>\n <tr>\n <th>1015336</th>\n <td>4.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>...</td>\n <td>2018-11-08 12:04:58</td>\n <td>1920.0</td>\n <td>1080.0</td>\n <td>3.0</td>\n <td>160.0</td>\n <td>10</td>\n <td>2</td>\n <td>US</td>\n <td>39.9883</td>\n <td>-75.2208</td>\n </tr>\n <tr>\n <th>1015337</th>\n <td>4.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>...</td>\n <td>2018-11-08 12:07:18</td>\n <td>1920.0</td>\n <td>1080.0</td>\n <td>3.0</td>\n <td>122.0</td>\n <td>7</td>\n <td>1</td>\n <td>US</td>\n <td>38.0</td>\n <td>-97.0</td>\n </tr>\n <tr>\n <th>1015338</th>\n <td>4.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>5.0</td>\n <td>1.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>...</td>\n <td>2018-11-08 12:07:49</td>\n <td>1920.0</td>\n <td>1080.0</td>\n <td>2.0</td>\n <td>135.0</td>\n <td>12</td>\n <td>6</td>\n <td>US</td>\n <td>36.1473</td>\n <td>-86.777</td>\n </tr>\n <tr>\n <th>1015339</th>\n <td>2.0</td>\n <td>4.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>1.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>...</td>\n <td>2018-11-08 12:08:34</td>\n <td>1920.0</td>\n <td>1080.0</td>\n <td>6.0</td>\n <td>212.0</td>\n <td>8</td>\n <td>1</td>\n <td>US</td>\n <td>34.1067</td>\n <td>-117.8067</td>\n </tr>\n <tr>\n <th>1015340</th>\n <td>4.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>1.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>...</td>\n <td>2018-11-08 12:08:45</td>\n <td>1920.0</td>\n <td>1080.0</td>\n <td>3.0</td>\n <td>176.0</td>\n <td>9</td>\n <td>1</td>\n <td>US</td>\n <td>38.0</td>\n <td>-97.0</td>\n </tr>\n </tbody>\n</table>\n<p>1015341 rows × 110 columns</p>\n</div>",
"text/plain": " EXT1 EXT2 EXT3 EXT4 EXT5 EXT6 EXT7 EXT8 EXT9 EXT10 ... \\\n0 4.0 1.0 5.0 2.0 5.0 1.0 5.0 2.0 4.0 1.0 ... \n1 3.0 5.0 3.0 4.0 3.0 3.0 2.0 5.0 1.0 5.0 ... \n2 2.0 3.0 4.0 4.0 3.0 2.0 1.0 3.0 2.0 5.0 ... \n3 2.0 2.0 2.0 3.0 4.0 2.0 2.0 4.0 1.0 4.0 ... \n4 3.0 3.0 3.0 3.0 5.0 3.0 3.0 5.0 3.0 4.0 ... \n... ... ... ... ... ... ... ... ... ... ... ... \n1015336 4.0 2.0 4.0 3.0 4.0 3.0 3.0 3.0 3.0 3.0 ... \n1015337 4.0 3.0 4.0 3.0 3.0 3.0 4.0 4.0 3.0 3.0 ... \n1015338 4.0 2.0 4.0 3.0 5.0 1.0 4.0 2.0 4.0 4.0 ... \n1015339 2.0 4.0 3.0 4.0 2.0 2.0 1.0 4.0 2.0 4.0 ... \n1015340 4.0 2.0 4.0 2.0 4.0 1.0 4.0 2.0 4.0 4.0 ... \n\n dateload screenw screenh introelapse testelapse \\\n0 2016-03-03 02:01:01 768.0 1024.0 9.0 234.0 \n1 2016-03-03 02:01:20 1360.0 768.0 12.0 179.0 \n2 2016-03-03 02:01:56 1366.0 768.0 3.0 186.0 \n3 2016-03-03 02:02:02 1920.0 1200.0 186.0 219.0 \n4 2016-03-03 02:02:57 1366.0 768.0 8.0 315.0 \n... ... ... ... ... ... \n1015336 2018-11-08 12:04:58 1920.0 1080.0 3.0 160.0 \n1015337 2018-11-08 12:07:18 1920.0 1080.0 3.0 122.0 \n1015338 2018-11-08 12:07:49 1920.0 1080.0 2.0 135.0 \n1015339 2018-11-08 12:08:34 1920.0 1080.0 6.0 212.0 \n1015340 2018-11-08 12:08:45 1920.0 1080.0 3.0 176.0 \n\n endelapse IPC country lat_appx_lots_of_err long_appx_lots_of_err \n0 6 1 GB 51.5448 0.1991 \n1 11 1 MY 3.1698 101.706 \n2 7 1 GB 54.9119 -1.3833 \n3 7 1 GB 51.75 -1.25 \n4 17 2 KE 1.0 38.0 \n... ... ... ... ... ... \n1015336 10 2 US 39.9883 -75.2208 \n1015337 7 1 US 38.0 -97.0 \n1015338 12 6 US 36.1473 -86.777 \n1015339 8 1 US 34.1067 -117.8067 \n1015340 9 1 US 38.0 -97.0 \n\n[1015341 rows x 110 columns]"
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#separate dataframes for each Big5 trait\nEXT = df[['EXT1', 'EXT2', 'EXT3', 'EXT4', 'EXT5', 'EXT6', 'EXT7', 'EXT8', 'EXT9', 'EXT10']]\nEST = df[['EST1', 'EST2', 'EST3', 'EST4', 'EST5', 'EST6', 'EST7', 'EST8', 'EST9', 'EST10']]\nAGR = df[['AGR1', 'AGR2', 'AGR3', 'AGR4', 'AGR5', 'AGR6', 'AGR7', 'AGR8', 'AGR9', 'AGR10']]\nCSN = df[['CSN1', 'CSN2', 'CSN3', 'CSN4', 'CSN5', 'CSN6', 'CSN7', 'CSN8', 'CSN9', 'CSN10']]\nOPN = df[['OPN1', 'OPN2', 'OPN3', 'OPN4', 'OPN5', 'OPN6', 'OPN7', 'OPN8', 'OPN9', 'OPN10']]",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#dictionary to reverse score 'negative' statements\nrev = {1:5, 2:4, 4:2, 5:1}",
"execution_count": 5,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# EXTRAVERSION"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### \"Extroversion is characterized by breadth of activities (as opposed to depth), surgency from external activity/situations, and energy creation from external means\""
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "ext_n = [\"EXT2\", \"EXT4\", \"EXT6\", \"EXT8\", \"EXT10\"]\nfor i in ext_n:\n EXT.replace({i: rev}, inplace = True)",
"execution_count": 6,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/home/neetje/.local/lib/python3.7/site-packages/pandas/core/generic.py:6666: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n regex=regex,\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average of all EXT scores per participant\nEXT['EXT_SUM'] = EXT.sum(axis=1)\nEXT['EXT_SUM'] = EXT['EXT_SUM'] / 10",
"execution_count": 7,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n This is separate from the ipykernel package so we can avoid doing imports until\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average Extraversion\nx = EXT['EXT_SUM']\nnum_bins = 5\nEXT_MEAN = EXT[\"EXT_SUM\"].mean()\nEXT_STD = EXT[\"EXT_SUM\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\nn, bins, patches = plt.hist(x, num_bins, facecolor='purple', alpha=0.5, edgecolor = 'black')\nplt.xlabel('Extraversion', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on EXTRAVERSION: 1 = low extraversion, 5 = high extraversion', size = 15)\nplt.show()",
"execution_count": 8,
"outputs": [
{
"data": {
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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graphs shows the frequency of participants by average extraversion score. This graph closely represents a bell curve, though postively skewed towards scoring higher on extraversion."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#EXT5: I start conversations.\ny = EXT['EXT5']\nnum_bins = 5\nEXT_MEAN = EXT[\"EXT5\"].mean()\nEXT_STD = EXT[\"EXT5\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\n\nn, bins, patches = plt.hist(y, num_bins, facecolor='purple', alpha=0.5, edgecolor = 'black')\nplt.xlabel('\"I start conversations\"', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on Question \"I start conversations\": 1 = disagree, 5 = agree', size = 15)\nplt.show()",
"execution_count": 9,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/home/neetje/.local/lib/python3.7/site-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n keep = (tmp_a >= first_edge)\n/home/neetje/.local/lib/python3.7/site-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n keep &= (tmp_a <= last_edge)\n"
},
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graph shows how the participants scored on one of the extraversion-related questions. This sample appears to be skewed towards scoring higher on the question \"I start conversations\"."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Neuroticism"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Neuroticism is the tendency to experience negative emotions, such as anger, anxiety, or depression"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "est_n = [\"EST2\", \"EST4\"]\nfor i in est_n:\n EST.replace({i: rev}, inplace = True)",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average of all EST scores per participant\nEST['EST_SUM'] = EST.sum(axis=1)\nEST['EST_SUM'] = EST['EST_SUM'] / 10",
"execution_count": 11,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n This is separate from the ipykernel package so we can avoid doing imports until\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average neuroticism\nx = EST['EST_SUM']\nnum_bins = 5\nEST_MEAN = EST[\"EST_SUM\"].mean()\nEST_STD = EST[\"EST_SUM\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\nn, bins, patches = plt.hist(x, num_bins, facecolor='green', alpha=0.5, edgecolor = 'black')\nplt.xlabel('Neuroticism', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on NEUROTICISM: 1 = low neuroticism, 5 = high neuroticism', size = 15)\nplt.show()",
"execution_count": 12,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graphs shows the frequency of participants by average neuroticism score. This graph also closely resembles a bell curve, with the mean being 3.04. "
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#EST1: I get stressed out easily.\ny = EST['EST1']\nnum_bins = 5\nEXT_MEAN = EST[\"EST1\"].mean()\nEXT_STD = EST[\"EST1\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\n\nn, bins, patches = plt.hist(y, num_bins, facecolor='green', alpha=0.5, edgecolor = 'black')\nplt.xlabel('\"I get stressed out easily\"', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on Question \"I get stressed out easily\": 1 = disagree, 5 = agree', size = 15)\nplt.show()",
"execution_count": 13,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graphs shows how participants scored on the question \"I get stressed out easily\". The average for this question is a 3.02, which corresponds with 'neutral' on the questionnaire scale."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Agreeableness"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### \"The agreeableness trait reflects individual differences in general concern for social harmony. Agreeable individuals value getting along with others\""
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "agr_n = [\"AGR1\", \"AGR3\", \"AGR5\", \"AGR7\"]\nfor i in agr_n:\n AGR.replace({i: rev}, inplace = True)",
"execution_count": 14,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average of all AGR scores per participant\nAGR['AGR_SUM'] = AGR.sum(axis=1)\nAGR['AGR_SUM'] = AGR['AGR_SUM'] / 10",
"execution_count": 15,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n This is separate from the ipykernel package so we can avoid doing imports until\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average Agreeableness\nx = AGR['AGR_SUM']\nnum_bins = 5\nAGR_MEAN = AGR[\"AGR_SUM\"].mean()\nAGR_STD = AGR[\"AGR_SUM\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\nn, bins, patches = plt.hist(x, num_bins, facecolor='yellow', alpha=0.5, edgecolor = 'black')\nplt.xlabel('Agreeableness', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on AGREEABLENESS: 1 = low agreeableness, 5 = high agreeableness', size = 15)\nplt.show()",
"execution_count": 16,
"outputs": [
{
"data": {
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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graph shows that most participants score high on agreeableness. "
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#AGR9: I feel others' emotions.\ny = AGR['AGR9']\nnum_bins = 5\nEXT_MEAN = AGR[\"AGR9\"].mean()\nEXT_STD = AGR[\"AGR9\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\n\nn, bins, patches = plt.hist(y, num_bins, facecolor='yellow', alpha=0.5, edgecolor = 'black')\nplt.xlabel('\"I feel others emotions\"', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on Question: \"I feel others emotions\": 1 = disagree, 5 = agree', size = 15)\nplt.show()",
"execution_count": 17,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graph shows how the sample scored on the question \"I geel others' emotions\". Most participants in the sample agree with this statement. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Conscientiousness "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### \"Conscientiousness is a tendency to display self-discipline, act dutifully, and strive for achievement against measures or outside expectations\""
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "ext_n = [\"CSN2\", \"CSN4\", \"CSN6\", \"CSN8\"]\nfor i in ext_n:\n EXT.replace({i: rev}, inplace = True)",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average of all CSN scores per participant\nCSN['CSN_SUM'] = CSN.sum(axis=1)\nCSN['CSN_SUM'] = CSN['CSN_SUM'] / 10",
"execution_count": 19,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n This is separate from the ipykernel package so we can avoid doing imports until\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average conscientiousness\nx = CSN['CSN_SUM']\nnum_bins = 5\nCSN_MEAN = CSN[\"CSN_SUM\"].mean()\nCSN_STD = CSN[\"CSN_SUM\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\nn, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5, edgecolor = 'black')\nplt.xlabel('Conscientiousness', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on CONSCIENTIOUSNESS: 1 = low conscientiousness, 5 = high conscientiousness', size = 15)\nplt.show()",
"execution_count": 20,
"outputs": [
{
"data": {
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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graph shows the average score of conscientiousness in the sample. The average is 3.12. Most participants score a 'somewhat agree' on conscientiousness."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#CSN9: I get chores done right away\ny = CSN['CSN5']\nnum_bins = 5\nEXT_MEAN = CSN[\"CSN5\"].mean()\nEXT_STD = CSN[\"CSN5\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\n\nn, bins, patches = plt.hist(y, num_bins, facecolor='blue', alpha=0.5, edgecolor = 'black')\nplt.xlabel('\"I get chores done right away\"', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on Question: \"I get chores done right away\": 1 = disagree, 5 = agree', size = 15)\nplt.show()",
"execution_count": 21,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graphs shows the average scores in the sample on the question \"I get chores done right away\". There is no consensus on this question, the participants are almost even distributed between 'somewhat disagree' and 'agree'. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Openness"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### \"Openness is a general appreciation for art, emotion, adventure, unusual ideas, imagination, curiosity, and variety of experience\""
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "ext_n = [\"OPN2\", \"OPN4\", \"OPN6\"]\nfor i in ext_n:\n EXT.replace({i: rev}, inplace = True)",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average of all OPN scores per participant\nOPN['OPN_SUM'] = OPN.sum(axis=1)\nOPN['OPN_SUM'] = OPN['OPN_SUM'] / 10",
"execution_count": 23,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \n/home/neetje/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n This is separate from the ipykernel package so we can avoid doing imports until\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Average openness\nx = OPN['OPN_SUM']\nnum_bins = 5\nOPN_MEAN = OPN[\"OPN_SUM\"].mean()\nOPN_STD = OPN[\"OPN_SUM\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\nn, bins, patches = plt.hist(x, num_bins, facecolor='orange', alpha=0.5, edgecolor = 'black')\nplt.xlabel('Openness', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on OPENNESS: 1 = low openness, 5 = high openness', size = 15)\nplt.show()",
"execution_count": 24,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graph shows how participants in the sample score on openness. Most participants fall in the category of \"somewhat agree\" when it comes to openness."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#OPN3: I have a vivid imagination\ny = OPN['OPN3']\nnum_bins = 5\nEXT_MEAN = OPN[\"OPN3\"].mean()\nEXT_STD = OPN[\"OPN3\"].std()\n\nfig, ax = plt.subplots(figsize=(10, 8))\n\n\nn, bins, patches = plt.hist(y, num_bins, facecolor='orange', alpha=0.5, edgecolor = 'black')\nplt.xlabel('\"I have a vivid imagination\"', size = 19)\nplt.ylabel('Frequency participants', size = 19)\nplt.title('Average scores on Question: \"I have a vivid imagination: 1 = disagree, 5 = agree', size = 15)\nplt.show()",
"execution_count": 25,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x576 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### This graph shows the average scores on the question \"I have a vivid imagination\". Most of the participants agreed with this statement. "
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# All big 5 traits"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "num_bins = 5\nO = OPN['OPN_SUM']\nOPN_MEAN = OPN[\"OPN_SUM\"].mean()\nOPN_STD = OPN[\"OPN_SUM\"].std()\nC = CSN['CSN_SUM']\nCSN_MEAN = CSN[\"CSN_SUM\"].mean()\nCSN_STD = CSN[\"CSN_SUM\"].std()\nA = AGR['AGR_SUM']\nAGR_MEAN = AGR[\"AGR_SUM\"].mean()\nAGR_STD = AGR[\"AGR_SUM\"].std()\nN = EST['EST_SUM']\nEST_MEAN = EST[\"EST_SUM\"].mean()\nEST_STD = EST[\"EST_SUM\"].std()\nE = EXT['EXT_SUM']\nEXT_MEAN = EXT[\"EXT_SUM\"].mean()\nEXT_STD = EXT[\"EXT_SUM\"].std()\n\nfig, axs = plt.subplots(1, 5, sharey=True, tight_layout=True, figsize=(18, 3))\n\nfig.suptitle('Average scores on Big 5 traits', size = 22, x=0.5, y=1.1)\n\naxs[0].hist(O, bins=num_bins,facecolor='orange', alpha=0.5, edgecolor = 'black')\naxs[0].set_title('Openness', size = 20)\naxs[1].hist(C, bins=num_bins,facecolor='blue', alpha=0.5, edgecolor = 'black')\naxs[1].set_title('Conscientiousness', size = 20)\naxs[2].hist(E, bins=num_bins, facecolor='purple', alpha=0.5, edgecolor = 'black')\naxs[2].set_title('Extraversion', size = 20) \naxs[3].hist(A, bins=num_bins,facecolor='yellow', alpha=0.5, edgecolor = 'black')\naxs[3].set_title('Agreeableness', size = 20) \naxs[4].hist(N, bins=num_bins, facecolor='green', alpha=0.5, edgecolor = 'black')\naxs[4].set_title('Neuroticism', size = 20) ",
"execution_count": 74,
"outputs": [
{
"data": {
"text/plain": "Text(0.5, 1.0, 'Neuroticism')"
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": "<Figure size 1296x216 with 5 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.7.6",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"gist": {
"id": "",
"data": {
"description": "Big5PersonalityTest.ipynb",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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