lafrite/recopytex
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Feat: Minor thing

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This commit is contained in:
Bertrand Benjamin 2019-08-06 07:02:07 +02:00
parent e9a8310f15
commit 1fc7270bed
5 changed files with 33 additions and 90 deletions
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15
recopytex/csv_extraction.py
View File

@ -8,6 +8,12 @@ from .config import NO_ST_COLUMNS, COLUMNS, VALIDSCORE
pd.set_option("Precision", 2)
def try_replace(x, old, new):
try:
return str(x).replace(old, new)
except ValueError:
return x
def extract_students(df, no_student_columns=NO_ST_COLUMNS.values()):
""" Extract the list of students from df
@ -44,9 +50,9 @@ def flat_df_students(df, no_student_columns=NO_ST_COLUMNS.values()):
value_vars=st,
var_name=COLUMNS["student"],
value_name=COLUMNS["score"],
).dropna(subset=[COLUMNS["score"]])
)
)
return pd.concat(scores).dropna(subset=[COLUMNS["score"]])
return pd.concat(scores)
def flat_clear_csv(csv_df, no_student_columns=NO_ST_COLUMNS.values()):
@ -67,11 +73,12 @@ def flat_clear_csv(csv_df, no_student_columns=NO_ST_COLUMNS.values()):
df[COLUMNS["score"]] = pd.to_numeric(
df[COLUMNS["score"]]
.replace(VALIDSCORE["NOANSWER"], -1)
.apply(lambda x: str(x).replace(",", "."))
.apply(lambda x: try_replace(x, ",", "."))
)
df[COLUMNS["score_rate"]] = pd.to_numeric(
df[COLUMNS["score_rate"]]
.apply(lambda x: str(x).replace(",", "."))
.apply(lambda x: try_replace(x, ",", ".")),
errors="coerce"
)
return df

8
recopytex/df_marks_manip.py
View File

@ -77,13 +77,13 @@ def score_to_level(x):
>>> score_to_level(df.loc[10])
2
"""
# -1 is no answer
if x[COLUMNS["score"]] == -1:
return x[COLUMNS["score"]]
# negatives are no answer or negatives points
if x[COLUMNS["score"]] <= -1:
return np.nan
if x[COLUMNS["is_leveled"]]:
return int(x[COLUMNS["score"]])
else:
return int(ceil(x[COLUMNS["score"]] / x[COLUMNS["score_rate"]] * 3))

66
requirement.txt
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@ -1,66 +0,0 @@
ansiwrap==0.8.4
attrs==19.1.0
backcall==0.1.0
bleach==3.1.0
certifi==2019.6.16
chardet==3.0.4
Click==7.0
colorama==0.4.1
cycler==0.10.0
decorator==4.4.0
defusedxml==0.6.0
entrypoints==0.3
future==0.17.1
idna==2.8
importlib-resources==1.0.2
ipykernel==5.1.1
ipython==7.7.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
jedi==0.14.1
Jinja2==2.10.1
jsonschema==3.0.2
jupyter==1.0.0
jupyter-client==5.3.1
jupyter-console==6.0.0
jupyter-core==4.5.0
jupytex==0.0.3
kiwisolver==1.1.0
MarkupSafe==1.1.1
matplotlib==3.1.1
mistune==0.8.4
nbconvert==5.5.0
nbformat==4.4.0
notebook==6.0.0
numpy==1.17.0
pandas==0.25.0
pandocfilters==1.4.2
papermill==1.0.1
parso==0.5.1
pexpect==4.7.0
pickleshare==0.7.5
prometheus-client==0.7.1
prompt-toolkit==2.0.9
ptyprocess==0.6.0
Pygments==2.4.2
pyparsing==2.4.2
pyrsistent==0.15.4
python-dateutil==2.8.0
pytz==2019.2
PyYAML==5.1.2
pyzmq==18.0.2
qtconsole==4.5.2
requests==2.22.0
Send2Trash==1.5.0
six==1.12.0
tenacity==5.0.4
terminado==0.8.2
testpath==0.4.2
textwrap3==0.9.2
tornado==6.0.3
tqdm==4.32.2
traitlets==4.3.2
urllib3==1.25.3
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.5.1

1
requirements.txt
View File

@ -50,6 +50,7 @@ pytz==2019.2
PyYAML==5.1.2
pyzmq==18.0.2
qtconsole==4.5.2
-e git+git_opytex:/lafrite/recopytex.git@e9a8310f151ead60434ae944d726a2fd22b23d06#egg=Recopytex
requests==2.22.0
scipy==1.3.0
seaborn==0.9.0

31
templates/tpl_evaluation.ipynb
View File

@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@ -12,12 +12,13 @@
"from pathlib import Path\n",
"from datetime import datetime\n",
"from recopytex import flat_clear_csv, pp_q_scores\n",
"#import prettytable as pt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 2,
"metadata": {
"tags": [
"parameters"
@ -33,7 +34,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 3,
"metadata": {},
"outputs": [
{
@ -58,18 +59,18 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"stack_scores = pd.read_csv(csv_file)\n",
"stack_scores = pd.read_csv(csv_file, encoding=\"latin_1\")\n",
"scores = flat_clear_csv(stack_scores).dropna(subset=[\"Score\"])\n",
"scores = pp_q_scores(scores)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 12,
"metadata": {},
"outputs": [
{
@ -145,7 +146,7 @@
" ALI SAID Anchourati 0.00 6.0"
]
},
"execution_count": 24,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@ -157,7 +158,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 15,
"metadata": {},
"outputs": [
{
@ -338,7 +339,7 @@
"SAIDALI Irichad 5.00 12.0"
]
},
"execution_count": 25,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@ -350,7 +351,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 7,
"metadata": {},
"outputs": [
{
@ -367,7 +368,7 @@
"Name: Note, dtype: float64"
]
},
"execution_count": 26,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@ -378,16 +379,16 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3cd95c1990>"
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0ae61e5cf8>"
]
},
"execution_count": 32,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
@ -434,7 +435,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.3"
}
},
"nbformat": 4,