Investigating variation in proportion of students who finish there bachelor in time by university¶
This isn't actually meant to be read by anybody. I'll fix this once I've figured out a more efficient way to upload my plots.
In [1]:
!which python
/home/tassilo/repos/blog/_notebooks/.venv/bin/python
In [2]:
!which pip
/home/tassilo/repos/blog/_notebooks/.venv/bin/pip
In [3]:
import pip
pip.main(['install','seaborn'])
WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip. Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue. To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.
Defaulting to user installation because normal site-packages is not writeable
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[notice] A new release of pip available: 22.3 -> 23.0.1 [notice] To update, run: python3 -m pip install --upgrade pip
Out[3]:
0
In [4]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [5]:
ba2 = pd.read_csv("BA2.csv")
In [6]:
cs = pd.read_csv("cs.csv")
ba = pd.read_csv("ba.csv")
In [7]:
ba.head()
Out[7]:
| university | students | % in Time MA | % in Time BA | |
|---|---|---|---|---|
| 0 | RWTH Aachen | 2010 | 54.4 | 78.5 |
| 1 | Alanus HS/Alfter (priv.) | 260 | NaN | 82.7 |
| 2 | Uni Augsburg - Wirtschaftswissenschaftliche Fa... | 100 | 100.0 | NaN |
| 3 | Uni Augsburg - Wirtschaftswissenschaftliche Fa... | 2850 | 64.9 | 95.9 |
| 4 | Uni Bamberg | 2230 | 61.2 | 90.2 |
In [8]:
for df in [cs, ba]:
df.rename(columns={'% in Time MA': 'ma_time',
'% in Time BA' : 'ba_time'}, inplace=True, errors='raise')
df.ma_time = df.ma_time
df.ba_time = df.ba_time
In [9]:
cs.head()
Out[9]:
| university | students | ma_time | ba_time | Nc (2019?) | |
|---|---|---|---|---|---|
| 0 | RWTH Aachen | 4420 | 35.5 | 60.3 | 2.6 |
| 1 | Uni Augsburg | 1720 | 49.4 | 64.0 | NaN |
| 2 | Uni Bamberg | 940 | 37.1 | 86.7 | 3.1 |
| 3 | Uni Bayreuth | 750 | NaN | 60.5 | NaN |
| 4 | FU Berlin | 1770 | 22.9 | 33.7 | 1.8 |
In [10]:
ba.head()
Out[10]:
| university | students | ma_time | ba_time | |
|---|---|---|---|---|
| 0 | RWTH Aachen | 2010 | 54.4 | 78.5 |
| 1 | Alanus HS/Alfter (priv.) | 260 | NaN | 82.7 |
| 2 | Uni Augsburg - Wirtschaftswissenschaftliche Fa... | 100 | 100.0 | NaN |
| 3 | Uni Augsburg - Wirtschaftswissenschaftliche Fa... | 2850 | 64.9 | 95.9 |
| 4 | Uni Bamberg | 2230 | 61.2 | 90.2 |
In [11]:
cs = cs.set_index('university')
ba = ba.set_index('university')
In [12]:
cs_sorted = cs[cs.ba_time > 0].sort_values('ba_time', axis=0)
cs_sorted.ba_time.plot.barh(figsize=(12,12))
plt.title("Percent of Computer Science students finished after 8 semesters by university")
plt.xlabel("% finished after (x + 2) semesters")
plt.savefig("cs_ba_time.png")
In [13]:
cs_sorted = cs[cs['students'] > 1000].sort_values('ba_time', axis=0)
cs_sorted.ba_time.plot.barh(figsize=(12,12))
plt.xlabel("% finished after (x + 2) semesters")
plt.savefig("cs_sorted_ba_time.png")
The only thing that strikes me as different from the plot on CS students, is the number of private schools and that they are doing pretty well on this metric. This makes sense to me as the schools as well as students have stronger incentives to make the student graduate early
To get some comparisons I also looked at business administration students as it is the most popular subject in Germany. Thus classes should be big enough that I shouldn't have to worry about statistical significance too much
In [14]:
ba_sorted = ba[ba['students'] > 1000][ba['ba_time']>0].sort_values('ba_time', axis=0)
ba_sorted.ba_time.plot.barh(figsize=(12,12))
plt.xlabel("% finished after (x + 2) semesters")
plt.title("Percent of Business Administration students finished after 8 semesters by university")
plt.savefig("ba_sorted_ba_time.png")
/tmp/ipykernel_1311097/407855156.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
ba_sorted = ba[ba['students'] > 1000][ba['ba_time']>0].sort_values('ba_time', axis=0)
In [15]:
sns.scatterplot(x=cs.students, y=cs.ba_time)
plt.title('CS graduates in "reasonable" time (3+1=4 years)')
plt.savefig("ba_cs_scatter.png")
Not sure how to interpret this. It makes sense that there is more variation for smaller universities and I think it makes it slightly more likely a lot of this can be explained through a combination of selection effects and variation in student ability.
In [16]:
sns.scatterplot(x=ba.students, y=ba.ba_time)
plt.title('BA graduates in "reasonable" time (3+1=4 years)')
plt.savefig("ba_ba_scatter.png")
Looking at relationship between length bachelor and master
In [17]:
sns.scatterplot(x=ba.ba_time, y=ba.ma_time)
Out[17]:
<AxesSubplot:xlabel='ba_time', ylabel='ma_time'>
In [18]:
sns.scatterplot(x=cs.ba_time, y=cs.ma_time)
Out[18]:
<AxesSubplot:xlabel='ba_time', ylabel='ma_time'>
In [19]:
sns.scatterplot(x=cs.ba_time, y=ba.ba_time)
Out[19]:
<AxesSubplot:xlabel='ba_time', ylabel='ba_time'>
So with so few datapoints
In [20]:
ba2.head()
Out[20]:
| university | overall | support 1 | support2 | exams | support3 | |
|---|---|---|---|---|---|---|
| 0 | RWTH Aachen | 1.6 | 0.857143 | 1.9 | 1.8 | 1.8 |
| 1 | Uni Augsburg | NaN | 0.714286 | NaN | NaN | NaN |
| 2 | Uni Bamberg | 1.7 | 0.785714 | 1.9 | 2.0 | 1.8 |
| 3 | Uni Bayreuth | NaN | 0.785714 | NaN | 2.5 | 2.6 |
| 4 | FU Berlin | 2.3 | 0.785714 | 2.4 | 2.2 | 2.1 |
In [21]:
ba2 = ba2.merge(ba, on="university")
In [22]:
sns.pairplot(ba2)
Out[22]:
<seaborn.axisgrid.PairGrid at 0x7f8445bfa530>
In [23]:
ba2.corr(numeric_only=True)
Out[23]:
| overall | support 1 | support2 | exams | support3 | students | ma_time | ba_time | |
|---|---|---|---|---|---|---|---|---|
| overall | 1.000000 | -0.623540 | 0.913608 | 0.688712 | 0.876608 | -0.233796 | 0.090057 | 0.059519 |
| support 1 | -0.623540 | 1.000000 | -0.428131 | -0.005739 | -0.509308 | -0.136898 | -0.227488 | -0.466941 |
| support2 | 0.913608 | -0.428131 | 1.000000 | 0.634814 | 0.723527 | -0.105324 | 0.312464 | 0.545622 |
| exams | 0.688712 | -0.005739 | 0.634814 | 1.000000 | 0.511413 | -0.387469 | 0.025604 | -0.081629 |
| support3 | 0.876608 | -0.509308 | 0.723527 | 0.511413 | 1.000000 | 0.033213 | 0.034096 | 0.001501 |
| students | -0.233796 | -0.136898 | -0.105324 | -0.387469 | 0.033213 | 1.000000 | 0.249460 | 0.326472 |
| ma_time | 0.090057 | -0.227488 | 0.312464 | 0.025604 | 0.034096 | 0.249460 | 1.000000 | 0.650647 |
| ba_time | 0.059519 | -0.466941 | 0.545622 | -0.081629 | 0.001501 | 0.326472 | 0.650647 | 1.000000 |
In [73]:
!pip install causal-learn
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In [29]:
import pip
pip.main(['install','causal-learn'])
WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip. Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue. To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.
Defaulting to user installation because normal site-packages is not writeable
Collecting causal-learn
Using cached causal_learn-0.1.3.3-py3-none-any.whl (172 kB)
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Installing collected packages: tqdm, threadpoolctl, pydot, patsy, joblib, graphviz, scikit-learn, statsmodels, causal-learn
Successfully installed causal-learn-0.1.3.3 graphviz-0.20.1 joblib-1.2.0 patsy-0.5.3 pydot-1.4.2 scikit-learn-1.2.2 statsmodels-0.13.5 threadpoolctl-3.1.0 tqdm-4.65.0
[notice] A new release of pip available: 22.3 -> 23.0.1 [notice] To update, run: python3 -m pip install --upgrade pip
Out[29]:
0
In [27]:
help(pip)
Help on package pip:
NAME
pip
PACKAGE CONTENTS
__main__
__pip-runner__
_internal (package)
_vendor (package)
FUNCTIONS
main(args: Optional[List[str]] = None) -> int
This is an internal API only meant for use by pip's own console scripts.
For additional details, see https://github.com/pypa/pip/issues/7498.
DATA
List = typing.List
A generic version of list.
Optional = typing.Optional
Optional type.
Optional[X] is equivalent to Union[X, None].
VERSION
22.3
FILE
/home/tassilo/.local/lib/python3.10/site-packages/pip/__init__.py
In [ ]:
#to array
In [31]:
data = ba2.values
In [55]:
data = ba2.drop('university', axis = 1).fillna(value=0)
In [56]:
data = data.dropna().to_numpy()
In [57]:
data
Out[57]:
array([[1.60000000e+00, 8.57142857e-01, 1.90000000e+00, 1.80000000e+00,
1.80000000e+00, 2.01000000e+03, 5.44000000e+01, 7.85000000e+01],
[1.70000000e+00, 7.85714286e-01, 1.90000000e+00, 2.00000000e+00,
1.80000000e+00, 2.23000000e+03, 6.12000000e+01, 9.02000000e+01],
[2.30000000e+00, 7.85714286e-01, 2.40000000e+00, 2.20000000e+00,
2.10000000e+00, 1.53000000e+03, 6.75000000e+01, 8.20000000e+01],
[0.00000000e+00, 7.14285714e-01, 2.20000000e+00, 2.10000000e+00,
2.30000000e+00, 1.27000000e+03, 4.60000000e+01, 6.64000000e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 5.20000000e+02, 9.13000000e+01, 8.48000000e+01],
[0.00000000e+00, 7.85714286e-01, 2.60000000e+00, 2.60000000e+00,
2.50000000e+00, 3.50000000e+02, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 8.57142857e-01, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 2.07000000e+03, 5.04000000e+01, 7.40000000e+01],
[0.00000000e+00, 9.28571429e-01, 1.80000000e+00, 2.30000000e+00,
2.10000000e+00, 7.40000000e+02, 3.49000000e+01, 3.82000000e+01],
[0.00000000e+00, 8.57142857e-01, 1.60000000e+00, 1.90000000e+00,
1.90000000e+00, 7.10000000e+02, 4.32000000e+01, 3.94000000e+01],
[2.70000000e+00, 3.57142857e-01, 2.60000000e+00, 2.20000000e+00,
2.60000000e+00, 1.50000000e+03, 5.24000000e+01, 8.95000000e+01],
[2.40000000e+00, 7.85714286e-01, 2.30000000e+00, 2.50000000e+00,
2.30000000e+00, 3.02000000e+03, 7.35000000e+01, 7.69000000e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 3.60000000e+02, 4.19000000e+01, 5.71000000e+01],
[0.00000000e+00, 6.42857143e-01, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 5.30000000e+02, 4.35000000e+01, 8.31000000e+01],
[0.00000000e+00, 7.14285714e-01, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 2.23000000e+03, 6.19000000e+01, 8.09000000e+01],
[0.00000000e+00, 6.42857143e-01, 0.00000000e+00, 2.20000000e+00,
2.70000000e+00, 2.41000000e+03, 6.15000000e+01, 6.24000000e+01],
[2.40000000e+00, 5.71428571e-01, 2.30000000e+00, 2.60000000e+00,
2.20000000e+00, 3.00000000e+01, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 2.32000000e+03, 4.64000000e+01, 8.50000000e+01],
[2.20000000e+00, 7.85714286e-01, 2.10000000e+00, 2.20000000e+00,
2.10000000e+00, 3.00000000e+02, 5.35000000e+01, 4.68000000e+01],
[0.00000000e+00, 5.00000000e-01, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 1.15000000e+03, 4.39000000e+01, 8.56000000e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 1.45000000e+03, 7.48000000e+01, 8.10000000e+01],
[2.10000000e+00, 7.14285714e-01, 2.20000000e+00, 2.20000000e+00,
1.90000000e+00, 1.81000000e+03, 8.11000000e+01, 8.68000000e+01],
[2.20000000e+00, 7.14285714e-01, 2.10000000e+00, 2.00000000e+00,
2.00000000e+00, 1.20000000e+03, 4.46000000e+01, 7.60000000e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 2.54000000e+03, 8.48000000e+01, 9.79000000e+01],
[2.00000000e+00, 5.00000000e-01, 2.10000000e+00, 1.80000000e+00,
2.10000000e+00, 3.41000000e+03, 8.30000000e+01, 9.03000000e+01],
[2.40000000e+00, 6.42857143e-01, 2.30000000e+00, 2.20000000e+00,
2.40000000e+00, 1.37000000e+03, 9.63000000e+01, 9.97000000e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 1.07000000e+03, 6.63000000e+01, 7.09000000e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 1.90000000e+03, 7.28000000e+01, 9.89000000e+01],
[1.90000000e+00, 9.28571429e-01, 2.10000000e+00, 2.10000000e+00,
2.10000000e+00, 1.48000000e+03, 4.45000000e+01, 6.36000000e+01],
[2.10000000e+00, 6.42857143e-01, 2.00000000e+00, 2.10000000e+00,
2.20000000e+00, 3.10000000e+03, 5.24000000e+01, 5.92000000e+01],
[0.00000000e+00, 8.57142857e-01, 2.50000000e+00, 2.40000000e+00,
2.10000000e+00, 1.01000000e+03, 5.88000000e+01, 8.03000000e+01],
[0.00000000e+00, 5.71428571e-01, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 1.17000000e+03, 7.45000000e+01, 7.99000000e+01],
[1.90000000e+00, 7.85714286e-01, 2.00000000e+00, 2.00000000e+00,
1.90000000e+00, 9.80000000e+02, 8.98000000e+01, 9.53000000e+01]])
In [59]:
from causallearn.search.ConstraintBased.PC import pc
cg = pc(data,show_progress=True)
cg.draw_pydot_graph()
from causallearn.utils.GraphUtils import GraphUtils
pyd = GraphUtils.to_pydot(cg.G)
pyd.write_png('simple_test.png')
Depth=1, working on node 7: 100%|████████████████| 8/8 [00:00<00:00, 198.17it/s]
In [67]:
amphi = pd.read_csv("amphibiens.csv",delimiter=";")
In [72]:
amp = amphi.drop(["ID", "Motorway"], axis=1).dropna().to_numpy()
In [75]:
amp
Out[75]:
array([[600, 1, 1, ..., 0, 0, 0],
[700, 1, 5, ..., 0, 1, 0],
[200, 1, 5, ..., 0, 1, 0],
...,
[500, 1, 1, ..., 0, 1, 0],
[300, 1, 12, ..., 0, 0, 0],
[300, 1, 12, ..., 0, 0, 0]])
In [74]:
from causallearn.search.ConstraintBased.PC import pc
cg = pc(amp,0.01)
cg.draw_pydot_graph()
from causallearn.utils.GraphUtils import GraphUtils
pyd = GraphUtils.to_pydot(cg.G)
pyd.write_png('simple_test.png')
Depth=2, working on node 20: 100%|████████████████████████████| 21/21 [00:00<00:00, 824.11it/s]
In [51]:
ba2.head()
Out[51]:
| university | overall | support 1 | support2 | exams | support3 | students | ma_time | ba_time | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | RWTH Aachen | 1.6 | 0.857143 | 1.9 | 1.8 | 1.8 | 2010 | 54.4 | 78.5 |
| 1 | Uni Bamberg | 1.7 | 0.785714 | 1.9 | 2.0 | 1.8 | 2230 | 61.2 | 90.2 |
| 2 | FU Berlin | 2.3 | 0.785714 | 2.4 | 2.2 | 2.1 | 1530 | 67.5 | 82.0 |
| 3 | HU Berlin | NaN | 0.714286 | 2.2 | 2.1 | 2.3 | 1270 | 46.0 | 66.4 |
| 4 | TU Berlin | NaN | NaN | NaN | NaN | NaN | 520 | 91.3 | 84.8 |
In [68]:
import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Input In [68], in <cell line: 1>() ----> 1 import dowhy 2 from dowhy import CausalModel 3 import pandas as pd ModuleNotFoundError: No module named 'dowhy'
In [ ]:
In [66]:
sns.heatmap(ba2.corr(numeric_only=True),annot=True, cmap='coolwarm')
plt.show()
In [ ]: