diff --git a/README.md b/README.md
index fa9e0eeb9635e500c5cf9e2074309d1f78a70da2..c1c32a29163983f68176225377133cb213b7b5f0 100644
--- a/README.md
+++ b/README.md
@@ -1,56 +1,33 @@
# UEQ+ Erweiterung - Dashboard
## Table of Contents
-
+<!-- TOC -->
+* [Table of Contents](#table-of-contents)
* [Description](#description)
* [Project Structure](#project-structure)
* [Roadmap](#roadmap)
-* [New Items for the UEQ+ v2](#new-items-for-the-ueq-v2)
+* [Used scales in the questionnaire](#used-scales-in-the-questionnaire)
+<!-- TOC -->
## Description
-This project is a survey to extend the product classes of the UEQ+
-[UEQ+ v2 Handbook](https://ueqplus.ueq-research.org/Material/UEQ+_Handbook_V2.pdf). Here 16 product categories are
-analyzed and relevant scales are described. To introduce a new product class one can follow
-the procedure of [Winter et al., 2017](https://dx.doi.org/10.18420/muc2017-up-0002). To check replicability we conducted
-a survey regarding two originally known product classes and then added two new ones: *Dashboard* and *VR Applications*.
-We also updated the UEQ+ to UEQ+v2, which means that some items had to be newly created as you can see [here](#new-items-for-the-ueq-v2).
-
-### Research Question and Hypothesis
-
-1. Can the results from the initial study be replicated?
- * H.1: Results for learning management systems (LMS) and Games will be the same as they were in the original study.
- * H.2:
-2. Which scales are relevant for the new category classes Dashboard and VR?
- * H.3: Dashboards and LMS show different results regarding UEQ+ scales.
- * H.4: VR applications and Games show comparable results regarding relevant UEQ+ scales.
-
-
-### Dashboard
+This purpose of this data repository is introduces new product categories, Dashboard and Virtual Reality, to the User
+Experience Questionnaire ([UEQ+](https://ueqplus.ueq-research.org/Material/UEQ+_Handbook_V2.pdf)).
+The UEQ+ is a standardized questionnaire that provides a set of scales considered to be most important for evaluating
+the user experience of a product family.
+We have conducted a quantitative study to determine the most important scales.
+For this purpose, a procedure that had been used in previous studies to determine the most important User Experience
+scales for previously released categories (Games and Learning Platforms) was reproduced.
+These product categories were integrated to ensure that the conditions of our study were similar.
+The results are presented, critically discussed, and compared with previous studies in a scientific publication.
-Dashboard applications could be presented in Info-Web-Sites or Learning Platforms product categories because there are
-some aspects to dashboards that allow users to access information or engage in targeted learning. However, there are
-other use cases that are not covered by these categories, such as the goal of creating understandable data
-visualizations.
-
-In Learning Analytics, dashboards are commonly used to aggregate and display indicators that support stakeholders in the
-learning process
-[Swendimann et al. 2017](https://doi.org/10.1109/TLT.2016.2599522). A quick scan showed that publications about learning
-analytics dashboards with a user experience survey only use the UEQ
-[Publication 1](https://doi.org/10.1145/3303772.3303793),
-[Publication 2](https://www.jstor.org/stable/10.2307/26921130),
-[Publication 3](https://doi.org/10.1145/3375462.337551). This lead to the assumption that there is a need for an own
-category in UEQ+.
-
-### Virtual Reality (VR)
-
-Virtual Reality is a recently emerging technology, which can be used for entertainment activities. There are also
-upcoming simulations used for educational purposes (e.g. [Wiepke et al. 2019](http://dx.doi.org/10.18420/delfi2019_319)),
-which probably share some desired aspects with known entertainment content, but still follow a different goal.
-Since it is hypothesised to rate User Experience of VR applications differently than desktop experiences(see
-[Tcha-Tokey et al. 2018](https://doi.org/10.1155/2018/7827286)) it is analyzed if gaming content evokes other categories
-than VR applications do.
+To reproduce all plots, run:
+````
+pip install -r requirements.txt
+python src/main.py
+````
+The notebook is outdated.
## Project Structure
@@ -59,17 +36,10 @@ The folder structure is based on
```
| -- data # folder for raw (mostly survey) data and questionnaire
-| -- doc # documentation of the computation and analyzation
| -- results # results of the survey and visualizations
| -- src # possible source code to compute the results
```
-## Survey
-
-This survey was conducted using an instance of the online survey tool
-[soSci](https://www.soscisurvey.de/en/index) at the University of Potsdam:
-[Umfragen.UP](https://umfragenup.uni-potsdam.de).
-
## Roadmap
- [x] create questionnaire
@@ -78,33 +48,14 @@ This survey was conducted using an instance of the online survey tool
- [x] start date: 05.07.22
- [x] final date: 30.11.22
- [x] upload data
- - [x] subset 04.08.22
- - [x] final data 25.01.23
-- [ ] analyse data and publish results
-- [ ] produce publication
-
-In dieser Umfragen können Sie an Hand von mehreren Produktkategorien bestimmen, wie wichtig Ihnen einzelne Merkmale der
-Benutzungsoberfläche sind. Die Umfrage wird ca. 5-10 Minuten in Anspruch nehmen.
-
-Ihre Daten werden anonymisiert gespeichert und zu Forschungszwecken weiter verwendet. Das bedeutet, dass die Resultate
-sowohl in Promotionsprojekten als auch für etwaige Publikationen verwendet werden können. Durch die anonymisierte
-Speicherung ist es uns unmöglich, im Nachhinein bereits erhobene, personenspezifische Daten zu ändern oder zu löschen.
-
-Durch das Fortsetzen der Umfrage bestätigen Sie, mit dem Datenschutzbedingungen einverstanden zu sein.
+ - [x] subset 04.08.22
+ - [x] final data 25.01.23
+- [x] analyse data and publish results
+- [x] produce publication - MuC
-Für weitere Fragen und Anmerkungen wenden Sie sich bitte bei an Jan Bernoth (jan.bernoth@uni-potsdam.de).
+## Used scales in the questionnaire
-## New items for the UEQ+ v2
-
-This is just in german, because our questionnaire is in german and the original paper is written in german. Anyway,
-some decisions are made to extend the questionnaire to v2:
-
-* New sentences were formed by the explanatory words for each scale. For example, attractiveness is not included into
- the original questionnaire, then we took the introducing sentence from the scale of the
- [UEQ+ v2 Template](https://ueqplus.ueq-research.org/Material/UEQPlus_Templates.zip) "In my opinion, the product is
- generally" and connect it with some positive attributes from the scale like enjoyable, pleasant, friendly.
-* The last three attributes, which are mainly related to voice assistance, are not considered because the proposed
- product categories are not equipped with this characteristic.
+The list of scales and their corresponding questionnaire sources are presented below.
| Items | In [UEQ+](https://ueqplus.ueq-research.org/Material/UEQ+_Handbook_V2.pdf) | In the [Paper](https://dl.gi.de/bitstream/handle/20.500.12116/5770/2017_UP_002.pdf?sequence=2&isAllowed=y) | used in questionnaire | Erklärender Satz |
|---------------------------------------------------|:-------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|-----------------------|-----------------------------------------------------------------------------------------------------------------|
@@ -132,10 +83,3 @@ some decisions are made to extend the questionnaire to v2:
| Verständnis (Comprehensibility) | ✔ | ⌠| ⌠| -- |
| Vertrauen (Trust) | ✔ | ✔ | ✔ | |
| Wertigkeit (Value) | ✔ | ✔ | ✔ | |
-
-## Notes
-
-* Interview Nr. 92 should be removed
-* Replicability is not completely possible:
- * Structure of the questionnaire is evident in terms of content, but the structuring is not comprehensible
- * maybe too small sample size?
\ No newline at end of file
diff --git a/src/main.py b/src/main.py
index 5f9578f7dacd8e2dd1605ccc4a936cc16bdb1c0e..f2202b0a931453a6f8f4b5aa2419d56fac438037 100644
--- a/src/main.py
+++ b/src/main.py
@@ -8,12 +8,10 @@ from matplotlib import ticker
from scipy.stats import pearsonr
from enum import Enum
-from sklearn.manifold import MDS
-import scipy
from kPOD import k_pod
import time
-
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
class Categories(Enum):
Dashboard = "Dashboard"
LMS = "Learning Plattform"
@@ -30,7 +28,8 @@ class Interviews:
def __init__(self) -> None:
super().__init__()
- self.df = pd.read_csv('../data/data_ueq.csv', delimiter=';', quotechar='"', encoding='utf-16')
+
+ self.df = pd.read_csv(os.path.join(ROOT_DIR, '..', 'data', 'data_ueq.csv'), delimiter=';', quotechar='"', encoding='utf-16')
def __str__(self) -> str:
return f'{self.df.shape[0]} interviews'
@@ -43,8 +42,6 @@ class Interviews:
cat_games = all_categories.get_items(Categories.Games)
cat_lms = all_categories.get_items(Categories.LMS)
# remove interviews which are not complete
- print(cat_games)
- print(self.df)
unwanted_interviews = self.df[self.df[cat_games[0]].isnull() | self.df[cat_lms[0]].isnull()]
print(f'{unwanted_interviews.shape[0]} interviews have not completed the questionnaire.')
df_nonnull_interviews = self.df.drop(unwanted_interviews.index)
@@ -137,7 +134,7 @@ class Variables:
def __init__(self) -> None:
super().__init__()
- self.df = pd.read_csv('../data/variables_ueq.csv', delimiter=';', quotechar='"', encoding='utf-16')
+ self.df = pd.read_csv(os.path.join(ROOT_DIR, '..', 'data', 'variables_ueq.csv'), delimiter=';', quotechar='"', encoding='utf-16')
def get_groups(self):
return self.df[['VAR', 'LABEL']][7:12]
@@ -150,7 +147,7 @@ class Values:
def __init__(self) -> None:
super().__init__()
- self.df = pd.read_csv('../data/values_ueq.csv', delimiter=';', quotechar='"', encoding='utf-16')
+ self.df = pd.read_csv(os.path.join(ROOT_DIR, '..', 'data', 'variables_ueq.csv'), delimiter=';', quotechar='"', encoding='utf-16')
class ProductCategories:
@@ -186,10 +183,12 @@ class ProductCategories:
def save_plot(plot, path):
- dir_path = os.path.abspath('../results/')
+ dir_path = os.path.abspath(os.path.join(ROOT_DIR, '..', 'results'))
if not os.path.exists(dir_path):
os.mkdir(dir_path)
- plot.savefig(fname=os.path.join(dir_path, path + '.png'), dpi='figure', format='png')
+ saved_path = os.path.join(dir_path, path + '.png')
+ print("saved plot to " + saved_path)
+ plot.savefig(fname=saved_path, dpi='figure', format='png')
def replace_cryptic_names(columns):
@@ -304,6 +303,7 @@ class Plotter:
super().__init__()
self.df = df
self.pc = pc
+ self.plotted = 0
def filter_by_gender(self, gender):
if gender == 'male':
@@ -313,15 +313,11 @@ class Plotter:
else:
return self.df
-
def plot_groups(self, variable_labels):
df_groups = self.df[['DE01_01', 'DE01_02', 'DE01_03', 'DE01_04', 'DE01_05']] - 1
- print(variable_labels)
-
df_groups.columns = ['Stu', 'Teach', 'Res', 'Tech', 'Out']
sns.set_theme(style="whitegrid")
df_groups_sum = pd.DataFrame(df_groups.sum(), columns=['Sum']).sort_values(by='Sum', ascending=False)
- print(df_groups_sum)
ax = sns.barplot(data=df_groups_sum, palette="Blues_d", errorbar=None, y="Sum", x=df_groups_sum.index)
ax.set(title='Academic Community (Multiple Choice)')
ax.yaxis.set_major_locator(ticker.MultipleLocator(5))
@@ -330,8 +326,9 @@ class Plotter:
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(15)
sns.despine(left=True)
+ self.plotted = self.plotted + 1
save_plot(plt, 'demographics_communities')
- plt.show()
+ return plt
def plot_gender(self):
df_gender = self.df['DE02'] \
@@ -340,19 +337,18 @@ class Plotter:
.replace(3, 'diverse') \
.replace(-1, 'not mentioned')
# print(df_gender.sum())
- print(pd.DataFrame(df_gender.value_counts(normalize=False)))
df_gender_count = pd.DataFrame(df_gender.value_counts(normalize=False))
- print(df_gender_count)
- print(df_gender_count.index.values)
sns.set_theme(style="whitegrid")
- ax = sns.barplot(data=df_gender_count, palette="Blues_d", errorbar=None, y="count", x=df_gender_count.index.values)
+ ax = sns.barplot(data=df_gender_count, palette="Blues_d", errorbar=None, y="count",
+ x=df_gender_count.index.values)
ax.set(title='Gender')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(15)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(15)
+ self.plotted = self.plotted + 1
save_plot(plt, 'demographics_gender')
- plt.show()
+ return plt
def plot_faculty(self):
df_faculty = self.df['DE03'] \
@@ -369,9 +365,9 @@ class Plotter:
.replace(-9, 'nB')
ax = sns.countplot(x=df_faculty, palette="Blues_d")
ax.set(title='Fakultät')
- print(df_faculty.value_counts(normalize=True))
+ self.plotted = self.plotted + 1
save_plot(plt, 'demographics_disciplines')
- plt.show()
+ return plt
def plot_disciplines(self):
df_faculty = self.df['DE04'] \
@@ -381,12 +377,11 @@ class Plotter:
.replace(4, 'Ingenieurwissenschaften') \
.replace(-1, 'kA') \
.replace(-9, 'nB')
- print(df_faculty)
ax = sns.countplot(x=df_faculty, palette="pastel")
ax.set(title='Disciplines')
- print(df_faculty.value_counts(normalize=False))
+ self.plotted = self.plotted + 1
save_plot(plt, 'demographics_disciplines')
- plt.show()
+ return plt
def plot_item(self, category_title, area, max_count=20, gender='all'):
category_items = self.pc.get_items(category_title)
@@ -406,16 +401,15 @@ class Plotter:
df_category_area = filter_columns_by_value(df_category, df_category.mean() >= 0)
else:
df_category_area = df_category
- print('------------- ' + category_title.value + "-" + area + "-" + gender)
df_category_area.columns = replace_cryptic_names(df_category_area.columns)
df_category_area = df_category_area.reindex(df_category_area.mean().sort_values(ascending=False).index, axis=1)
df_category_area = df_category_area.iloc[:, : max_count]
- print(df_category_area.columns)
ax = sns.barplot(data=df_category_area, palette="Blues_d", orient='h', errorbar="sd")
ax.set(title=category_title.value + ' - ' + area)
sns.despine(left=True)
+ self.plotted = self.plotted + 1
save_plot(plt, 'items_' + category_title.value + '_' + area + '_' + gender)
- plt.show()
+ return plt
def plot_boxes(self, category_title, max_count=5):
df_category_area = self.df[self.pc.get_items(category_title)]
@@ -447,11 +441,11 @@ class Plotter:
labels=translate_scales(df_category_area.columns, True), rotation=0)
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
-
+ self.plotted = self.plotted + 1
save_plot(plt, 'box_' + category_title.value)
- plt.show()
+ return plt
- def m_scatter( self, x, y, ax=None, m=None, **kw):
+ def m_scatter(self, x, y, ax=None, m=None, **kw):
import matplotlib.markers as mmarkers
if not ax: ax = plt.gca()
sc = ax.scatter(x, y, **kw)
@@ -579,25 +573,31 @@ def setup_dataframes():
if __name__ == '__main__':
+ print('---------- Creating plots --------------')
interview, variables, values, product_categories, rankings = setup_dataframes()
print(interview)
df_filtered = interview.get_filtered(product_categories)
- plotter = Plotter(df_filtered, product_categories)
statistics = Statistics(df_filtered, product_categories)
+ plotter = Plotter(df_filtered, product_categories)
+
+ print('average time to complete the questionnaire: ' + time.strftime('%M:%S', time.gmtime(statistics.mean_time())))
+
plotter.plot_groups(variables.get_groups())
+
plotter.plot_gender()
- # plotter.plot_disciplines()
+ plotter.plot_disciplines()
plotter.plot_faculty()
- print(plotter.filter_by_gender('male'))
- # for example in Categories:
- # plotter.plot_item(example, 'top')
- # plotter.plot_item(example, 'top', gender='male')
- # plotter.plot_item(example, 'top', gender='female')
-
- # plotter.plot_item(example, 'mid')
- # plotter.plot_item(example, 'low')
- # plotter.plot_boxes(example, 6)
- # print(statistics.calc_corr())
- # print(statistics.similarity_by_ranked_scales(rankings.get_all_rankings()))
- print(time.strftime('%M:%S', time.gmtime(statistics.mean_time())))
- # print(statistics.kpod_clustering(rankings.get_all_rankings()))
+ for example in Categories:
+ plotter.plot_item(example, 'top')
+ plotter.plot_item(example, 'top', gender='male')
+ plotter.plot_item(example, 'top', gender='female')
+
+ plotter.plot_item(example, 'mid')
+ plotter.plot_item(example, 'low')
+ plotter.plot_boxes(example, 6)
+
+ print(statistics.calc_corr())
+ print(statistics.similarity_by_ranked_scales(rankings.get_all_rankings()))
+ print(statistics.kpod_clustering(rankings.get_all_rankings()))
+
+ print('-------- finished. Plotted: ' + str(plotter.plotted) + ' charts. -----------------')
diff --git a/src/survey_results.ipynb b/src/survey_results.ipynb
index 30737849fa56a06d84db6c0c717b9f909b628839..614de73032345cb459e6c5b7af05e3d76695e216 100644
--- a/src/survey_results.ipynb
+++ b/src/survey_results.ipynb
@@ -2,16 +2,8 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 13,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "69 interviews\n"
- ]
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"import pandas\n",
"from scipy.stats import pearsonr\n",
@@ -25,89 +17,16 @@
"print(interview)"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 14,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['EX05_01', 'EX05_02', 'EX05_03', 'EX05_04', 'EX05_05', 'EX05_06', 'EX05_07', 'EX05_08', 'EX05_09', 'EX05_10', 'EX05_11', 'EX05_12', 'EX05_13', 'EX05_14', 'EX05_15', 'EX05_16', 'EX05_17', 'EX05_18', 'EX05_19', 'EX05_20']\n",
- " CASE SERIAL REF QUESTNNR MODE STARTED DE01 DE01_01 \n",
- "0 43 NaN NaN qnr2 interview 2022-07-05 11:46:33 1 1 \\\n",
- "1 45 NaN NaN qnr2 interview 2022-07-05 11:46:35 1 1 \n",
- "2 47 NaN NaN qnr2 interview 2022-07-05 11:46:41 1 1 \n",
- "3 48 NaN NaN qnr2 interview 2022-07-05 11:46:41 1 1 \n",
- "4 49 NaN NaN qnr2 interview 2022-07-05 11:47:16 1 1 \n",
- ".. ... ... ... ... ... ... ... ... \n",
- "64 193 NaN NaN qnr2 interview 2022-10-24 15:56:28 1 2 \n",
- "65 195 NaN NaN qnr2 interview 2022-10-27 23:55:27 1 2 \n",
- "66 196 NaN NaN qnr2 interview 2022-10-28 00:12:39 1 2 \n",
- "67 200 NaN NaN qnr2 interview 2022-11-18 09:00:27 -1 1 \n",
- "68 201 NaN NaN qnr2 interview 2022-11-18 09:16:54 -1 1 \n",
- "\n",
- " DE01_02 DE01_03 ... MAILSENT LASTDATA FINISHED Q_VIEWER \n",
- "0 1 2 ... NaN 2022-07-05 12:45:14 1 0 \\\n",
- "1 1 2 ... NaN 2022-07-05 11:57:20 1 0 \n",
- "2 1 2 ... NaN 2022-07-05 12:05:35 1 0 \n",
- "3 1 2 ... NaN 2022-07-05 11:49:18 1 0 \n",
- "4 1 1 ... NaN 2022-07-05 12:56:58 1 0 \n",
- ".. ... ... ... ... ... ... ... \n",
- "64 1 1 ... NaN 2022-10-24 15:59:42 0 0 \n",
- "65 1 1 ... NaN 2022-10-27 23:57:45 1 0 \n",
- "66 1 1 ... NaN 2022-10-28 00:41:15 0 0 \n",
- "67 1 1 ... NaN 2022-11-18 09:00:37 0 0 \n",
- "68 1 1 ... NaN 2022-11-18 09:17:23 0 0 \n",
- "\n",
- " LASTPAGE MAXPAGE MISSING MISSREL TIME_RSI DEG_TIME \n",
- "0 5 7 0 0 0.52 0 \n",
- "1 6 7 0 0 0.92 2 \n",
- "2 5 7 0 0 0.67 3 \n",
- "3 5 7 0 0 2.18 95 \n",
- "4 7 7 0 0 0.76 2 \n",
- ".. ... ... ... ... ... ... \n",
- "64 6 8 0 0 2.18 200 \n",
- "65 8 8 0 0 2.11 108 \n",
- "66 2 8 0 0 1.52 125 \n",
- "67 2 8 0 0 3.00 275 \n",
- "68 4 8 47 46 2.50 306 \n",
- "\n",
- "[69 rows x 115 columns]\n",
- "24 interviews have not completed the questionnaire.\n",
- "3 interviews have completed too fast or too weird.\n",
- "42 filtered interviews.\n",
- " VAR LABEL\n",
- "7 DE01_01 Statusgruppe: Studierende\n",
- "8 DE01_02 Statusgruppe: Dozierende\n",
- "9 DE01_03 Statusgruppe: Wissenschaftliches und künsteris...\n",
- "10 DE01_04 Statusgruppe: Personal aus Technik und Verwaltung\n",
- "11 DE01_05 Statusgruppe: Außerhalb der Hochschule\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\multimedia\\Desktop\\Forschung\\Paper\\eigenes\\Jan-UEQ+\\ueq-erweiterung-dashboard\\src\\main.py:313: FutureWarning: \n",
- "\n",
- "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
- "\n",
- " ax = sns.barplot(data=df_groups, palette=\"Blues_d\", estimator=np.count_nonzero, ci=None)\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"df_filtered = interview.get_filtered(product_categories)\n",
"plotter = Plotter(df_filtered, product_categories)\n",
@@ -115,218 +34,95 @@
"statistics = Statistics(df_filtered, product_categories)"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 15,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DE02\n",
- "male 0.547619\n",
- "female 0.404762\n",
- "not mentioned 0.047619\n",
- "Name: proportion, dtype: float64\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"plotter.plot_sex()"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 16,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DE03\n",
- "MathNat 0.641026\n",
- "ZE 0.153846\n",
- "WiSo 0.076923\n",
- "Other 0.051282\n",
- "Jur 0.025641\n",
- "HuWi 0.025641\n",
- "kA 0.025641\n",
- "Name: proportion, dtype: float64\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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AxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGMOlgs2cOXMUFRVl1zZ8+HCFhITY3Zo3b+6kCgEAgCvzcHYBWZYsWaJp06apYcOGdu2HDh3SX//6V3Xr1s3W5u7uXtjlAQCAIsDpwebs2bMaOXKkdu7cqapVq9r1Wa1WHT16VH379lVQUJBzCgQAAEWG009F7du3T56enlqzZo0efPBBu76TJ08qNTVV1atXd1J1AACgKHH6jE3z5s1vuWbm8OHDkqTFixdry5YtcnNz02OPPaaBAwcqICDA4ce0Wq1KTU3N1m6xWOTj4+PwdouStLQ0Wa1WZ5cBAECuWK1WWSyWO45zerC5ncOHD8vNzU3BwcH68MMPdfLkSU2cOFFHjhzRokWL5Obm2IRTRkaGDhw4kK3dx8dHoaGheS27SIiPj1daWpqzywAAINe8vLzuOMalg80rr7yiLl26qFSpUpKk2rVrKygoSJ07d1ZcXFy2U1e55enpqZo1a2Zrz00SNEW1atWYsQEAFBlHjx7N1TiXDjZubm62UJOlVq1akqTExESHg43FYpGvr2+e6yvK7pVTbgAAM+R28sHpi4dvZ/DgwYqOjrZri4uLk6QcZ1wAAMC9zaWDzVNPPaXt27dr5syZOnnypL7//nsNGzZMbdq0UY0aNZxdHgAAcDEufSrqiSee0LRp0zR37lz985//VEBAgNq2basBAwY4uzQAAOCCXCrYTJgwIVtb69at1bp1aydUAwAAihqXPhUFAABwNxwKNrt379aVK1dy7Lt06ZLWrVuXp6IAAAAc4VCw6d69u44dO5Zj3/79+zV06NA8FQUAAOCIXK+xefvtt/Xrr79Kuvm1xqNGjZK/v3+2cSdOnFCZMmXyr0IAAIBcyvWMzVNPPSWr1Wr3bbVZP2fd3NzcFB4ervHjxxdIsQAAALeT6xmbP16sMioqSqNGjeK7ZAAAgEtx6OPeixcvzu86AAAA8syhYHP16lXNnj1b3377rdLS0pSZmWnXb7FYtHnz5nwpEAAAILccCjZjx47VihUr9PDDD6tu3bpyc+PrcAAAgPM5FGw2btyogQMHqm/fvvldDwAAgMMcmmrJyMhQWFhYftcCAACQJw4FmyZNmmjLli35XQsAAECeOHQq6umnn9bIkSOVnJysBx98UD4+PtnGtGvXLq+1AQAA3BWHgs2AAQMkSatXr9bq1auz9VssFoINAAAodA4Fm3/961/5XQcAAECeORRsKlasmN91AAAA5JlDwWbmzJl3HPO3v/3NkU0DAAA4LN+Djb+/v4KDgwk2AACg0DkUbA4ePJitLTU1VbGxsRo1apRGjBiR58IAAADuVr5dC8HX11ePPfaY+vXrp4kTJ+bXZgEAAHIt3y/yVKFCBR07diy/NwsAAHBHDp2KyonValViYqI++ugjPjUFAACcwqFgU6dOHVkslhz7rFYrp6IAAIBTOBRs+vXrl2Ow8ff3V7NmzVS1atW81gUAAHDXHAo2/fv3z+86AAAA8szhNTbJycmaP3++du3apUuXLqlUqVJq2LChoqOjVbp06fysEQAAIFcc+lRUYmKi2rdvr0WLFqlYsWIKDQ2Vh4eHFixYoHbt2uns2bP5XScAAMAdOTRjM2nSJHl4eOjrr79W5cqVbe2nTp1Sz5499d5772nChAn5ViQAAEBuODRjExMTo9dee80u1EhS5cqV1a9fP23ZsiVfigMAALgbDgWbGzduqFSpUjn2BQYG6vLly3kqCgAAwBEOBZuQkBCtXbs2x74vv/xStWvXzlNRAAAAjnBojc2rr76qXr166eLFi3r66acVFBSkc+fOad26dYqJidH06dPzu04AAIA7cijYREZGasKECZo8ebLdepqgoCCNHz9eLVq0yLcCAQAAcsvh77FJSkpSaGio3n77bV28eFEHDx7UjBkzWF8DAACcxqFgM3/+fE2bNk3dunVTjRo1JEnly5fX8ePHNWHCBBUrVkydOnXK10IBAADuxKFgs2zZMg0YMEB9+/a1tZUvX17Dhw9XmTJltHDhQoINAAAodA59Kurs2bOqV69ejn0PPvigTp8+naeiAAAAHOFQsKlYsaK2b9+eY9/u3btVrly5PBUFAADgCIdORXXu3FmTJk1SRkaGnnzySZUuXVrJycn69ttvtWDBAr355pv5XScAAMAdORRsoqOjdfbsWS1evFgLFy60tbu7u+t//ud/1KNHj/yqDwAAINcc/rj322+/rVdffVU//vijUlJSVLx4cYWFhd3yUgsAAAAFzeFgI0kBAQFq2rRpftUCAACQJw4tHgYAAHBFBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGC4VbObMmaOoqCi7tgMHDqhbt24KDw9X8+bN9fHHHzupOgAA4OpcJtgsWbJE06ZNs2u7cOGCevTooSpVqmjlypXq16+fJk+erJUrVzqnSAAA4NI8nF3A2bNnNXLkSO3cuVNVq1a16/vss8/k6empMWPGyMPDQzVq1FBCQoLmzp2rjh07OqdgAADgspwebPbt2ydPT0+tWbNGH3zwgc6cOWPri42N1cMPPywPj/8vs3HjxpozZ45+++03lSlTxqHHtFqtSk1NzdZusVjk4+Pj0DaLmrS0NFmtVmeXAQBArlitVlksljuOc3qwad68uZo3b55jX2JiomrXrm3XFhwcLEn69ddfHQ42GRkZOnDgQLZ2Hx8fhYaGOrTNoiY+Pl5paWnOLgMAgFzz8vK64xinB5vbuXr1aradKFasmCTp2rVrDm/X09NTNWvWzNaemyRoimrVqjFjAwAoMo4ePZqrcS4dbLy9vZWenm7XlhVofH19Hd6uxWLJ0/1NcK+ccgMAmCG3kw8u86monJQrV05JSUl2bVk/ly1b1hklAQAAF+bSwaZRo0b64YcfdOPGDVvbjh07VK1aNZUuXdqJlQEAAFfk0sGmY8eOunz5sv7+97/r6NGjWrVqlRYuXKiXX37Z2aUBAAAX5NLBpnTp0vroo48UHx+v9u3ba+bMmRo8eLDat2/v7NIAAIALcqnFwxMmTMjWFhYWpuXLlzuhGgAAUNS49IwNAADA3SDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjEGwAQAAxiDYINcyrVZnl1CgTN8/ALgXeDi7ABQdbhaLVuw4onOX0pxdSr4LKu6j5xvXcnYZAIA8Itjgrpy7lKZfU1KdXQYAADniVBQAADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDGKxMe9z549q8ceeyxb+/jx49WhQwcnVAQAAFxRkQg2Bw8eVLFixbR582ZZLBZbe0BAgBOrAgAArqZIBJvDhw+ratWqCg4OdnYpAADAhRWJNTaHDh1SjRo1nF0GAABwcUVmxqZUqVLq2rWr4uPjdd999+mVV17Jcd1NblitVqWmZr8sgMVikY+PT17LLRLS0tJkvYuLPt4rx+ZujwsAoHBYrVa75Si34vLB5vr16zp+/Lhq1qypIUOGyN/fX+vWrVPfvn21YMECRURE3PU2MzIydODAgWztPj4+Cg0NzY+yXV58fLzS0nJ/Mct75djc7XEBABQeLy+vO45x+WDj4eGhnTt3yt3dXd7e3pKkBx54QEeOHNG8efMcCjaenp6qWbNmtvbcJEFTVKtW7a5nbO4Fd3tcAACF4+jRo7ka5/LBRpL8/PyytdWqVUsxMTEObc9iscjX1zevZRVp98JpJUdwXADANeX2DbbLLx4+cuSIGjRooJ07d9q1//zzzznOugAAgHuXywebGjVqqHr16hozZoxiY2N17NgxjR8/Xj/++KNeeeUVZ5cHAABciMufinJzc9OHH36oKVOmaMCAAbp06ZJCQ0O1YMEC1a5d29nlAQAAF+LywUaSypQpo/Hjxzu7DAAA4OJc/lQUAABAbhFsAACAMQg2AADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBsgHmZlWZ5dQoEzfPwDm8HB2AYAJ3Nwsmrv5Z/164YqzS8l35Uv5qe+TDzi7DADIFYINkE9+vXBFJ3/73dllAMA9jVNRAADAGAQbAABgDIINAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYA4FJuZGY6u4QCdy/so7NwEUwAgEtxd3PTu7MWK+GXJGeXUiDuqxCsEa9GObsMYxFsAAAuJ+GXJB0+cdrZZaAI4lQUAAAwBsEGAAAYg2ADAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAFQYDIzrc4uocA5uo/3wtWd74V9LGw3bph/TPO6j1wEE0CBcXOzaOLqWJ08/7uzSykQVUoHaHC7hg7d193NTeMWf6OTZ5PzuSrXUKVsoIZFtXJ2GcZxd3fTyH9M04lTZl4gtGrlShr99oA8bYNgA6BAnTz/u44lXnR2GS7p5NlkHT19ztlloIg5ceq0Dh897uwyXBanogAAgDEINgAAwBgEGwAAYAyCDQAAMAbBBgAAGINgAwAAjFEkgk1mZqamT5+upk2bKjw8XH369NGpU6ecXRYAAHAxRSLYzJo1S0uXLtW7776rZcuWKTMzU71791Z6erqzSwMAAC7E5YNNenq65s+fr9dee03NmjVTnTp19N577ykxMVEbN250dnkAAMCFuHywOXjwoK5cuaKIiAhbW/HixRUaGqrdu3c7sTIAAOBqLFar1aWvUrdx40b1799fP/30k7y9vW3tr7/+uq5evao5c+bc1fb27Nkjq9UqT0/PHPstFovS0q8r07UPi8PcLBb5eHnIkf/tFotFV65l6IaBFzZ0d7PIr5inQ8dFunlsfk9L13UDj42Hm0UBPl4OP2cupl7T9RvmHRdJ8nC3qIRvMYePTcrlNF2/caMAKnM+D3d3lfT3cfzYXLps9rEp7u/wsbmQctHoY1OqZIkcj01GRoYsFosaNGhw+20UVHH5JS0tTZLk5eVl116sWDFdvHj315+xWCx2/82Jj5fLH5Y8u93+345fsZwDoSkcPS6SFODjdedBRZijx6aEb7F8rsT1OHpsSvr75HMlrsfhY1PcP58rcT2OHptSJUvkcyWuJ6djY7FYcnXMXP4veNYsTXp6ut2MzbVr1+Tjc/e/FOrXr59vtQEAANfi8mtsypcvL0lKSkqya09KSlLZsmWdURIAAHBRLh9s6tSpI39/f+3cudPWdunSJe3fv1+NGjVyYmUAAMDVuPypKC8vL3Xr1k2TJ09WYGCgKlasqEmTJqlcuXJq2bKls8sDAAAuxOWDjSS99tprun79uoYPH66rV6+qUaNGmjdv3i0/2QQAAO5NLv9xbwAAgNxy+TU2AAAAuUWwAQAAxiDYAAAAYxBsAACAMQg2AADAGAQbAABgDIINAAAwBsEmB82bN1dISIgWLFiQY/8777yjkJAQzZgxI1fbu3Dhgj7//HPbz1FRURoyZMgtx58+fVohISHq37//LevL7WNLUmpqqpYsWZLr8YUhax9vdYuKitKMGTNuO+abb75x9m7ctb/97W/q1KlTtvbOnTsrJCREu3btsmtfs2aN6tSpoz59+igqKsrWnp6erpkzZ6pVq1Z64IEH1KhRI/Xq1Us7duwo8H1wJSEhIVq1apWzyyhwt/udMWTIELvnxq0cPHhQISEh+v777+3at2zZYnvN/VnLli01evRo2+v1j5e2cSU3btzQ0qVL9fzzz6t+/fpq2LChXnzxRa1YsUJ//Kq2I0eO6LvvvrP9fK88f24nt8fgs88+U0hIiMaNG1cIVeUNweYWPD09tWHDhmzt169f18aNG+/qcvMTJ07UmjVr7rqGjRs3at26dXd9vz+bP3++5s2bl+ft5Kfy5csrJiYm223EiBGSpBYtWkiSypUrl+O4mJgYNW/e3Jm74JCIiAgdOHBAV69etbWlpKQoLi5O5cuX19atW+3Gx8bGqk6dOpo6dapdmB0+fLi++uorDRkyRN98840+/vhjVapUST179tT27dsLbX9QdISEhCgwMFB79+61a9+6davKly+vvXv36sqVK7b2pKQkJSQkKDIy0vZ6rV+/fmGXfUcZGRl65ZVXNH36dLVr105ffPGFli9frlatWmnChAnq16+fbty4IUl6+eWXFRcX5+SKi6ZVq1apWrVqWr16ta5du+bscm6LYHMLERER+vHHH5WYmGjXvmPHDvn6+tquOp4bjn65c+XKlTVmzBj99ttvDt0/r49fkNzd3RUUFGR3O3funCZNmqRWrVqpe/futxyXdfPy8nLyXty9xo0bKyMjw+6X63/+8x+VLl1aHTt2zDHYPProowoICFDJkiUlSZcvX9aaNWv05ptvqlmzZqpUqZLq1q2r0aNHq169ei43OwfXYLFY1LhxY+3Zs8euPSYmRj169JCbm5tdKI6NjZWHh4caN25sex264mtuzpw5io2N1dKlS9WtWzdVrVpVNWrUUHR0tD7++GNt2bLF5d7YFTXHjh3T3r17NWjQIF26dEnr1693dkm3RbC5hbCwMFWoUCHb6Y6vv/5arVu3tpux+fzzz9W2bVuFhYUpPDxcXbp0sf3hGjJkiL744gvt2rVLISEhtvtcuXJFQ4cOVcOGDfXQQw9pyJAhSk1NtXusQYMGyd3dXaNGjbptrZs3b1anTp0UHh6uevXqqUOHDrY/kDNmzNDMmTN15swZhYSE6PTp03k5LAXm999/1+uvv67y5ctr7Nixzi6nwNSoUUNly5a1++OydetWNWnSRE2aNNHBgwdtQTY5OVnHjh1TkyZNsp1ucHNzU0xMjK5fv263/enTp9tmvSTp119/1aBBgxQZGanw8HD16tVLBw8eLOC9LHwzZszINoP357aQkBBNnz5df/nLX9SkSROdOHGikKsseDmdVvhjW0REhOLi4mzPmzNnzuj48eN64okn1KhRI8XExNjuFxsbq7CwMPn7+7vsqajMzEwtXrxYHTp0UPXq1bP1h4aG6rnnntPixYvVrFkznTlzRjNnzrR7LcXHxys6Olr16tVT06ZNNWfOHLttfPvtt+rQoYPCwsLUokULTZs2Tenp6bZ+k55X586dU6tWrdSjRw+7WeVVq1apRIkS+stf/qIGDRpo2bJlTqzyzgg2t9G6dWu7YJOenq7NmzfrmWeesbVt2rRJY8aMUe/evbV+/XotXLhQ165d0/DhwyVJf//739W6dWvVr1/f7pfGxo0bFRwcrFWrVmnixIn6+uuv9c9//tPu8UuVKqXRo0dr06ZNWrt2bY41/vzzz+rfv7+eeeYZrV27Vp999pkCAwM1ePBgpaenq2fPnurZs6ftlM7dzDQVpqFDh+rcuXOaMWOG/P39nV1OgYqIiLA7HRATE6PIyEiFhYUpICDA9jz54Ycf5O3trYceesju/v7+/urSpYuWLVumpk2b6s0339SyZct08uRJlS1bVmXLlpV0c2bnpZde0tmzZzV79mwtW7ZM3t7e6tatm86cOVN4O+xCli5dqunTp2vmzJmqWrWqs8spdBEREUpNTbWF25iYGFWtWlWVKlVSZGRktmATGRnprFJzJT4+XikpKWrQoMEtx0RERCgpKUnTp09XuXLl1LNnT7vTup988onatWunr7/+Wi+99JKmTp1qm7nasmWLBgwYoM6dO+urr77SyJEjtX79er311lt2j2HC8yo5OVnR0dGqWLGiPvzwQ3l7e0u6uX7pyy+/1JNPPil3d3c9/fTT2rt3r0u/QSLY3Ebr1q31448/6uzZs5Kkbdu2KTAwUKGhobYxJUuW1NixY/Xcc8+pYsWKCg8P1/PPP6/Dhw9LkgICAuTt7S1PT08FBQXZ7hcWFqaBAweqSpUqeuKJJxQZGamff/45Ww0tWrRQmzZt9L//+786d+5ctn53d3eNGDFC0dHRqly5surWravu3bsrOTlZ58+fl5+fn3x9fW1Tye7u7vl9mPJswYIFtoBYq1Ytu75ffvlF9evXz3YriutrsmQFG6vVqoMHD+rcuXOKjIyUu7u7IiIibLNtu3fvVsOGDVWsWLFs2xg+fLimTJmiOnXqaOPGjRo5cqRatGihXr162Z6va9as0YULF/T+++8rLCxMderU0ZQpU+Tt7X3Pnq567rnnVK9ePYWHhzu7lLu2du3aHF8Lt3rTk5PKlSurUqVKthnDrNlCSWrSpIlOnTqlEydO6NKlSzpy5IjLB5uLFy9Kuvkm8Fay+m7cuCF3d3f5+vraTutKUpcuXdSuXTtVrlxZr776qgICAmy/iz/88EN17txZL774oqpUqaImTZpo9OjR+uabb+xmv4vy80q6uc4vOjpaFSpU0OzZs+1+52zZskXnzp2zvaFv1aqV3N3dtXz5cmeVe0cezi7AlT3wwAOqXLmyNmzYoO7du+vrr7+2m62RpEaNGunYsWP64IMPdPz4cSUkJOjQoUPKzMy87bb/nOpLlChxy3fRw4cPV5s2bTRy5EjNmjXLrq9u3boqUaKE5s6da3v8rCSdtWDOle3du1dTpkxRly5d9Oyzz2brDw4O1uLFi7O1u7kV3UweERGhlJQUHT9+XDExMQoNDVVgYKAkKTIyUjNnzpR08x3zn59vf9SmTRu1adNGV69e1d69e7Vp0yZ99tln6t+/vz777DMdPnxYVatWtW1bkry9vRUWFmYL3vea++67z9klOKx58+YaNGhQtvbJkycrJSUl19uJiIjQnj171KVLF23fvl2TJk2SJNWuXVvBwcHauXOngoKC5Ofnp7CwsPwqv0BkhZbff//9lmOyws8fXwd/9OffxcWLF7ctjt2/f7/++9//asWKFbb+rDWLx44dU6VKlSQV7eeVJL333nvKyMjQAw88kG0d1cqVK1W6dGk1btxYklSmTBk1btxYa9as0VtvvSVfX19nlHxbBJs7yDod9cILL+hf//qX3ce2pZvvooYMGaK2bduqQYMGevHFF3X48GGNGTPmttu9m5mTrFNS/fr105dffmnXt2vXLvXq1UvNmjXTQw89pLZt2yotLU39+vXL/U46SXJysgYMGKA6depo6NChOY7x8PAo8r80/qxs2bKqVq2a9u7dq23bttneMUs33zW/88472rdvnw4ePJjjRyt37typf//737Zj5u3trYiICEVERKhGjRoaM2aMkpOTb7loPDMzUx4eRe+l/9tvv+n8+fO2tWpZ+3er19Kf1x9Jsk2vF0V+fn45vhb8/PxuGWxyOgYRERGaNGmSfvrpJ127dk2PPPKIra9JkybavXu3goODbYuGXVmVKlUUFBSk3bt3q2XLljmO2bVrl4KCgmwh5M9y2ses51ZmZqZ69+6t9u3bZxvzxxn4ovy8kqRHH31UHTt2VP/+/fX000/bficlJyfru+++U0ZGhurVq2cbn5mZKavVqq+++kqdO3d2Vtm3VHTf9haS1q1ba8+ePVq5cqUqV66sGjVq2PXPnTtXzz//vCZMmKCuXbuqUaNGOnXqlKT/f3HczUfDb+XJJ59U27ZtNXbsWF2+fNnWPn/+fD3yyCOaMWOGoqOjFRkZqV9//TXfHz+/Wa1WvfXWW7p69aqmT5/ukp+2KEiPPvqo9uzZo71799pN91esWFFVq1bVkiVLFBgYaLfgPMvly5e1cOFC/fTTT9n6sk59+vv7KyQkRCdOnND58+dt/deuXdPPP/+smjVrFsyOFaB58+bpjTfesP38x3finp6edh9VlqSEhIRCrc8VeHp62v1+yOkYREREKDExUevXr1f9+vXl5+dn64uMjNT+/fu1Z88ePfroo4VSc164u7srOjpaK1as0LFjx7L1HzlyRKtXr1a3bt0cCmm1atVSfHy87rvvPtstMTFREydOzPZ8K8qeeuoptWzZUk8//bRGjBhhew6tWbNGGRkZ+uCDD7R69Wq7W2BgoMsuIibY3EHdunV13333acqUKTmeFihfvrz27Nmjffv26eTJk1q4cKE++eQTSbKtnPf19VVSUpIt8Dhq+PDh8vLysv1Cz3r8Q4cOKTY2VqdPn9bKlSv1/vvvZ3v8ixcvKj4+XhkZGXmqIb/MmTNH27Zt07Bhw+Tp6alz587Z3ZKTkyXdPJ32576s2x9/gRc1ERERWr9+vSwWS7aFj02bNtX69esVERGRYyj9y1/+oocfflivvPKKPv30U8XHx+vo0aP64osvNHHiRPXp00deXl5q27atSpYsqQEDBui///2vDh48qEGDBik1NVUvvPBCYe1qvnn00Udt+3nq1ClNnz5dxYsXV/369RUeHq6UlBTNmzdPp0+f1rJly7RlyxZnl1zowsPD9fnnn+vAgQPav3+/Ro0ale1NQ2BgoGrXrq2VK1fazRZKN4/xyZMntW/fvmx9rqpnz5567LHH1LVrVy1ZskQJCQlKSEjQkiVL1K1bNzVu3Fh9+vSRdHN268SJE7n+Co0+ffpow4YNmjlzpuLj47V9+3YNHTpUv//+u92MjSn+/ve/68qVK5o4caKkm6eh6tevryeffFK1a9e23erUqaMuXbpo3759Lvm9QASbXGjdurUuX76sp59+OlvfiBEjVKZMGXXr1k2dOnXSt99+a3tSZP0Pb9eundLS0tSmTRvbwk5HlCxZUqNHj7Zre+211xQeHq6//vWvateunT7//HONGzdO3t7etsdv2bKlgoKC9Oyzz2r//v0OP35+2rZtm6xWqwYPHmz7qPMfb88//7wkKTExMcf+Jk2aaOrUqU7eC8c98sgjunr1qh555BF5enra9TVp0kSpqam3XLjp5uamuXPnqkuXLlq6dKk6dOigjh07avHixXr99ddtpyEDAgL0ySefqHjx4oqOjlaXLl109epVffrpp6pcuXKB72N+a9q0qYYMGaIZM2bomWee0X//+1/Nnj1b/v7+aty4sfr376/58+frmWee0bZt2/Taa685u+RCN2rUKJUoUUKdO3dW//791alTJ5UrVy7buKxPR/35OZY1SxgcHKwqVaoUVtl54ubmpvfff19DhgzRV199pY4dO6pDhw5au3atBg0apNmzZ9tma6KiovTdd9+pZ8+eudp2q1at9N5772nz5s1q27at3nrrLTVp0sS2Ds40ZcqU0eDBg7V8+XLNnz9fhw8fVteuXXMc26VLFxUrVswlZ20sVlf89jYAAAAHMGMDAACMQbABAADGINgAAABjEGwAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABij6F0JD4BRoqKitGvXLtvPFotFPj4+qlatmtq1a6cuXbrYLto5ZMgQffHFF7fcVpkyZbRt2zbbz3FxcZo4caJ+/vln+fn5qUOHDvrb3/5md5mBvXv3aurUqbYxrVq10oABA+Tv718AewugoBFsADhdaGioRo4cKenm9cEuXryoLVu2aPz48YqNjdW0adPk5nZzgjkoKOiWX2n/x8tTnDp1Sj169FB4eLimTZumY8eO6b333lNKSorGjBkjSTp48KCio6MVERGhGTNmKCkpSVOmTFF8fLzmzZtXwHsNoCAQbAA4nb+/v8LDw+3amjdvrurVq2vs2LH66quv9Oyzz0qSvLy8so3NyT//+U/5+flp1qxZ8vLy0uOPPy5vb2+9++67+utf/6oKFSpo0aJFKlGiRLYrzA8dOlTHjx9X9erV83M3ARQC1tgAcFndunVT2bJlHbrQXkxMjB5//HG7wNKqVStlZmYqJiZGkjRgwADNnTvXbkzWrE96enoeqwfgDMzYAHBZbm5uioiI0Lp163T9+nVb+x///Ufu7u6yWCy6evWqzpw5o2rVqtn1BwYGyt/fX/Hx8ZKksmXLqmzZspKk1NRU/fjjj3rvvffUoEED1alTp4D2CkBBItgAcGllypRRRkaGUlJSJElnzpzR/fffn+PYwYMHq1evXvr9998lKccFwH5+frp8+bJdm9VqVePGjXXt2jWVLFlSI0aMyN+dAFBoCDYAXJrVapV089NS0s3Fw7Nnz85xbPny5SVJmZmZt91m1rayXL9+XbNnz9a1a9c0d+5cde3aVZ9++imzNkARRLAB4NLOnj0rb29vlSxZUtLNxcP16tW77X2yZmquXLmSre/y5csKCAiwa/P09FRkZKQkqVGjRmrevLkWLVqk8ePH58MeAChMLB4G4LKuX7+unTt3qkGDBnJ3d8/1/fz8/FS2bFklJCTYtZ8/f15XrlxRjRo1JEn//ve/tXv3brsxAQEBqly5spKSkvK+AwAKHcEGgMtavny5zp07p5deeumu7xsZGanvvvvO7tNNGzZskLu7uxo3bixJWrhwoUaNGqUbN27YxiQmJurYsWMKCQnJ+w4AKHScigLgdJcvX9aPP/4o6eb6mAsXLigmJkbLly/Xs88+q5YtW9rGpqen28bmJCQkRD4+Purdu7fWrVun3r17q0ePHjpx4oSmTp2qzp07q0KFCpKkV199VT179tTAgQPVuXNnJScna9asWSpevLh69uxZkLsMoIBYrFkr8wDACXK6pIKfn59q166t9u3bq1OnTrbFvne6pIIkrV69WnXr1pUkxcbGauLEiTpw4IBKlSql5557Tq+99prdNxTv2LFD06dP18GDB+Xh4aGmTZtq0KBBtoXIAIoWgg0AADAGa2wAAIAxCDYAAMAYBBsAAGAMgg0AADAGwQYAABiDYAMAAIxBsAEAAMYg2AAAAGMQbAAAgDEINgAAwBgEGwAAYIz/AygZhz+UW8u4AAAAAElFTkSuQmCC"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"plotter.plot_faculty()"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 17,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "------------- all\n",
- "Index(['Stimulation', 'Immersion', 'Vertrauen', 'Steuerbarkeit',\n",
- " 'Durchschaubarkeit', 'Attraktivität', 'Intuitive Bedienung',\n",
- " 'Originalität', 'visuelle Ästhetik', 'Effizienz', 'Übersichtlichkeit',\n",
- " 'Wertigkeit', 'Anpassbarkeit', 'Inhaltsseriosität', 'Akustik',\n",
- " 'Inhaltsqualität', 'Verbundenheit', 'Haptik', 'Identität',\n",
- " 'Nützlichkeit'],\n",
- " dtype='object')\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "------------- all\n",
- "Index(['Inhaltsqualität', 'Inhaltsseriosität', 'Vertrauen', 'Effizienz',\n",
- " 'Nützlichkeit', 'Durchschaubarkeit', 'Steuerbarkeit',\n",
- " 'Ãœbersichtlichkeit', 'Intuitive Bedienung', 'Anpassbarkeit',\n",
- " 'Wertigkeit', 'Attraktivität', 'visuelle Ästhetik', 'Stimulation',\n",
- " 'Akustik', 'Identität', 'Verbundenheit', 'Originalität', 'Haptik',\n",
- " 'Immersion'],\n",
- " dtype='object')\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"plotter.plot_item(Categories.Games, 'all')\n",
"plotter.plot_item(Categories.LMS, 'all')"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 18,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "------------- top\n",
- "Index(['Vertrauen', 'Steuerbarkeit', 'Durchschaubarkeit', 'Inhaltsseriosität',\n",
- " 'Effizienz', 'Stimulation', 'Ãœbersichtlichkeit', 'Intuitive Bedienung',\n",
- " 'Akustik', 'Immersion', 'Wertigkeit', 'Inhaltsqualität',\n",
- " 'Attraktivität', 'visuelle Ästhetik', 'Haptik'],\n",
- " dtype='object')\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"plotter.plot_item(Categories.VR, 'top')"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 19,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "------------- mid\n",
- "Index(['Anpassbarkeit', 'Originalität', 'Nützlichkeit', 'Verbundenheit'], dtype='object')\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"plotter.plot_item(Categories.VR, 'mid')"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 20,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "------------- low\n",
- "Index(['Identität'], dtype='object')\n"
- ]
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"plotter.plot_item(Categories.VR, 'low')"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -337,52 +133,25 @@
"Boxplot with quantile whiskers, mean and SD. No median or beveled boxes."
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 21,
- "outputs": [
- {
- "data": {
- "text/plain": "<Figure size 640x600 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x600 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x600 with 1 Axes>",
- "image/png": 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- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x600 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"for c in Categories:\n",
" plotter.plot_boxes(c, 5)"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -393,16 +162,22 @@
"Showing \"drastic difference\" by a step size from one mean to the next bigger one, when it exceeds threshold p."
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"outputs": [],
"source": [],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -412,29 +187,25 @@
"Correlating means of scales of each category. Significance (p<= 0.05) is displayed with *"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 22,
- "outputs": [
- {
- "data": {
- "text/plain": " lms dashboard games vr\nlms 1.0*** 0.98*** 0.05 0.53*\ndashboard 0.98*** 1.0*** 0.08 0.59**\ngames 0.05 0.08 1.0*** 0.64**\nvr 0.53* 0.59** 0.64** 1.0***",
- "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>lms</th>\n <th>dashboard</th>\n <th>games</th>\n <th>vr</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>lms</th>\n <td>1.0***</td>\n <td>0.98***</td>\n <td>0.05</td>\n <td>0.53*</td>\n </tr>\n <tr>\n <th>dashboard</th>\n <td>0.98***</td>\n <td>1.0***</td>\n <td>0.08</td>\n <td>0.59**</td>\n </tr>\n <tr>\n <th>games</th>\n <td>0.05</td>\n <td>0.08</td>\n <td>1.0***</td>\n <td>0.64**</td>\n </tr>\n <tr>\n <th>vr</th>\n <td>0.53*</td>\n <td>0.59**</td>\n <td>0.64**</td>\n <td>1.0***</td>\n </tr>\n </tbody>\n</table>\n</div>"
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"statistics = Statistics(df_filtered, product_categories)\n",
"statistics.calc_corr()"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -445,145 +216,16 @@
"(doesn't need to be programmed I guess)"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 23,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " rate distance similarity \n",
- "0 0.571429 4.0 7 \\\n",
- "0 0.571429 4.0 7 \n",
- "0 0.846154 11.0 13 \n",
- "0 0.846154 11.0 13 \n",
- "0 1.000000 4.0 4 \n",
- "0 1.000000 7.0 7 \n",
- "0 1.000000 7.0 7 \n",
- "0 1.000000 4.0 4 \n",
- "0 1.750000 21.0 12 \n",
- "0 1.750000 21.0 12 \n",
- "0 2.000000 14.0 7 \n",
- "0 2.000000 14.0 7 \n",
- "0 2.333333 7.0 3 \n",
- "0 2.333333 7.0 3 \n",
- "0 2.375000 38.0 16 \n",
- "0 2.375000 38.0 16 \n",
- "0 2.461538 32.0 13 \n",
- "0 2.461538 32.0 13 \n",
- "0 2.500000 10.0 4 \n",
- "0 2.500000 10.0 4 \n",
- "0 2.750000 33.0 12 \n",
- "0 2.750000 33.0 12 \n",
- "0 2.937500 47.0 16 \n",
- "0 2.937500 47.0 16 \n",
- "0 4.583333 55.0 12 \n",
- "0 4.583333 55.0 12 \n",
- "0 5.000000 30.0 6 \n",
- "0 5.000000 30.0 6 \n",
- "0 5.083333 61.0 12 \n",
- "0 5.083333 61.0 12 \n",
- "0 5.800000 116.0 20 \n",
- "0 5.800000 116.0 20 \n",
- "0 5.812500 93.0 16 \n",
- "0 5.812500 93.0 16 \n",
- "0 5.846154 76.0 13 \n",
- "0 5.846154 76.0 13 \n",
- "0 5.846154 76.0 13 \n",
- "0 5.846154 76.0 13 \n",
- "0 6.166667 74.0 12 \n",
- "0 6.166667 74.0 12 \n",
- "0 6.250000 100.0 16 \n",
- "0 6.250000 100.0 16 \n",
- "0 6.571429 46.0 7 \n",
- "0 6.571429 46.0 7 \n",
- "0 7.000000 21.0 3 \n",
- "0 7.000000 21.0 3 \n",
- "0 7.125000 114.0 16 \n",
- "0 7.125000 114.0 16 \n",
- "0 7.285714 51.0 7 \n",
- "0 7.285714 51.0 7 \n",
- "0 8.000000 32.0 4 \n",
- "0 8.000000 32.0 4 \n",
- "0 12.750000 51.0 4 \n",
- "0 12.750000 51.0 4 \n",
- "0 NaN NaN NaN \n",
- "0 NaN 0.0 0 \n",
- "0 NaN 0.0 0 \n",
- "\n",
- " comparison \n",
- "0 LMS from paperLMS from handbook \n",
- "0 LMS from handbookLMS from paper \n",
- "0 LMS from paperLMS from latest paper \n",
- "0 LMS from latest paperLMS from paper \n",
- "0 games from papergames from handbook \n",
- "0 LMS from latest paperLMS from handbook \n",
- "0 LMS from handbookLMS from latest paper \n",
- "0 games from handbookgames from paper \n",
- "0 games from latest papergames from paper \n",
- "0 games from papergames from latest paper \n",
- "0 LMS from handbookLMS from our study \n",
- "0 LMS from our studyLMS from handbook \n",
- "0 games from handbookgames from latest paper \n",
- "0 games from latest papergames from handbook \n",
- "0 LMS from paperLMS from our study \n",
- "0 LMS from our studyLMS from paper \n",
- "0 LMS from latest paperLMS from our study \n",
- "0 LMS from our studyLMS from latest paper \n",
- "0 games from our studygames from handbook \n",
- "0 games from handbookgames from our study \n",
- "0 games from latest papergames from our study \n",
- "0 games from our studygames from latest paper \n",
- "0 games from our studygames from paper \n",
- "0 games from papergames from our study \n",
- "0 LMS from latest papergames from latest paper \n",
- "0 games from latest paperLMS from latest paper \n",
- "0 games from latest paperLMS from handbook \n",
- "0 LMS from handbookgames from latest paper \n",
- "0 games from latest paperLMS from paper \n",
- "0 LMS from papergames from latest paper \n",
- "0 games from our studyLMS from our study \n",
- "0 LMS from our studygames from our study \n",
- "0 LMS from papergames from our study \n",
- "0 games from our studyLMS from paper \n",
- "0 games from paperLMS from latest paper \n",
- "0 LMS from latest papergames from our study \n",
- "0 LMS from latest papergames from paper \n",
- "0 games from our studyLMS from latest paper \n",
- "0 LMS from our studygames from latest paper \n",
- "0 games from latest paperLMS from our study \n",
- "0 games from paperLMS from paper \n",
- "0 LMS from papergames from paper \n",
- "0 games from paperLMS from handbook \n",
- "0 LMS from handbookgames from paper \n",
- "0 LMS from latest papergames from handbook \n",
- "0 games from handbookLMS from latest paper \n",
- "0 LMS from our studygames from paper \n",
- "0 games from paperLMS from our study \n",
- "0 LMS from handbookgames from our study \n",
- "0 games from our studyLMS from handbook \n",
- "0 LMS from papergames from handbook \n",
- "0 games from handbookLMS from paper \n",
- "0 games from handbookLMS from our study \n",
- "0 LMS from our studygames from handbook \n",
- "0 NaN \n",
- "0 LMS from handbookgames from handbook \n",
- "0 games from handbookLMS from handbook \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\multimedia\\Desktop\\Forschung\\Paper\\eigenes\\Jan-UEQ+\\ueq-erweiterung-dashboard\\src\\main.py:467: RuntimeWarning: invalid value encountered in scalar divide\n",
- " [distance / similarity_count,\n"
- ]
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"paper_LMS = ['Inhaltsqualität', 'Nützlichkeit', 'Durchschaubarkeit', 'Übersichtlichkeit', 'Effizienz',\n",
" 'Intuitive Bedienung', 'Vertrauen', 'Steuerbarkeit', 'Stimulation', 'Anpassbarkeit', 'Wertigkeit', 'visuelle Ästhetik', 'Immersion',\n",
@@ -616,22 +258,16 @@
"print(statistics.similarity_by_ranked_scales(all_rankings))"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
"cell_type": "code",
- "execution_count": 28,
- "outputs": [
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "outputs": [],
"source": [
"df_groups = statistics.kpod_clustering(all_rankings)\n",
"\n",
@@ -647,7 +283,60 @@
"plt.show()"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "x = []\n",
+ "y = []\n",
+ "\n",
+ "symbol = \"AAPL\"\n",
+ "\n",
+ "x = range(5)\n",
+ "y = [5,10,12,15,11]\n",
+ "\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "scatter = plotter.m_scatter(x,y,c=[0,0,0,0,0], s=[25,25,25,25,25], m=['a','b','c','d','e'], ax=ax)\n",
+ "\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "df_groups = statistics.kpod_clustering(all_rankings)\n",
+ "val = 0\n",
+ "#plt.plot(\n",
+ "# df_groups['group'],\n",
+ "# np.zeros_like(df_groups['group']) + val,\n",
+ "# marker=r\"$ {} $\".format(df_groups['group'].index[2], markersize=25)\n",
+ "#)\n",
+ "#plt.show()\n",
+ "\n",
+ "df_groups['group'].index[2]"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -691,7 +380,10 @@
"plt.show()"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -711,7 +403,10 @@
"df_groups['group'].index[2]"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -723,7 +418,10 @@
"Disconfirming the corresponding claims would increase the quality of the findings above."
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%% md\n"
+ }
}
},
{
@@ -753,7 +451,10 @@
"# check for common variance"
],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
},
{
@@ -762,7 +463,10 @@
"outputs": [],
"source": [],
"metadata": {
- "collapsed": false
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
}
}
],
@@ -787,4 +491,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
+}
\ No newline at end of file