Feat: delete functions on dataframe and move it to functions on rows
This commit is contained in:
parent
2e86b3a0a2
commit
8ec24a24b3
|
|
@ -4,6 +4,81 @@
|
|||
from math import ceil
|
||||
|
||||
|
||||
def is_none_score(x, score_config):
|
||||
"""Is a score correspond to a None numeric_value which
|
||||
|
||||
>>> import pandas as pd
|
||||
>>> d = {"Eleve":["E1"]*7,
|
||||
... "score_rate": [1]*7,
|
||||
... "is_leveled":[0]+[1]*6,
|
||||
... "score":[0.33, "", ".", "a", 1, 2, 3],
|
||||
... }
|
||||
>>> score_config = {
|
||||
... 'BAD': {'value': 0, 'numeric_value': 0},
|
||||
... 'FEW': {'value': 1, 'numeric_value': 1},
|
||||
... 'NEARLY': {'value': 2, 'numeric_value': 2},
|
||||
... 'GOOD': {'value': 3, 'numeric_value': 3},
|
||||
... 'NOTFILLED': {'value': '', 'numeric_value': 'None'},
|
||||
... 'NOANSWER': {'value': '.', 'numeric_value': 0},
|
||||
... 'ABS': {'value': 'a', 'numeric_value': 'None'}
|
||||
... }
|
||||
>>> df = pd.DataFrame(d)
|
||||
>>> df.apply(lambda x:is_none_score(x, score_config), axis=1)
|
||||
0 False
|
||||
1 True
|
||||
2 False
|
||||
3 True
|
||||
4 False
|
||||
5 False
|
||||
6 False
|
||||
dtype: bool
|
||||
|
||||
"""
|
||||
none_values = [
|
||||
v["value"]
|
||||
for v in score_config.values()
|
||||
if str(v["numeric_value"]).lower() == "none"
|
||||
]
|
||||
return x["score"] in none_values
|
||||
|
||||
|
||||
def score_to_numeric_score(x, score_config):
|
||||
"""Convert a score to the corresponding numeric value
|
||||
|
||||
>>> import pandas as pd
|
||||
>>> d = {"Eleve":["E1"]*7,
|
||||
... "score_rate": [1]*7,
|
||||
... "is_leveled":[0]+[1]*6,
|
||||
... "score":[0.33, "", ".", "a", 1, 2, 3],
|
||||
... }
|
||||
>>> score_config = {
|
||||
... 'BAD': {'value': 0, 'numeric_value': 0},
|
||||
... 'FEW': {'value': 1, 'numeric_value': 1},
|
||||
... 'NEARLY': {'value': 2, 'numeric_value': 2},
|
||||
... 'GOOD': {'value': 3, 'numeric_value': 3},
|
||||
... 'NOTFILLED': {'value': '', 'numeric_value': 'None'},
|
||||
... 'NOANSWER': {'value': '.', 'numeric_value': 0},
|
||||
... 'ABS': {'value': 'a', 'numeric_value': 'None'}
|
||||
... }
|
||||
>>> df = pd.DataFrame(d)
|
||||
>>> df.apply(lambda x:score_to_numeric_score(x, score_config), axis=1)
|
||||
0 0.33
|
||||
1 None
|
||||
2 0
|
||||
3 None
|
||||
4 1
|
||||
5 2
|
||||
6 3
|
||||
dtype: object
|
||||
|
||||
"""
|
||||
if x["is_leveled"]:
|
||||
replacements = {v["value"]: v["numeric_value"] for v in score_config.values()}
|
||||
return replacements[x["score"]]
|
||||
|
||||
return x["score"]
|
||||
|
||||
|
||||
def score_to_mark(x, score_max, rounding=lambda x: round(x, 2)):
|
||||
"""Compute the mark from the score
|
||||
|
||||
|
|
@ -125,10 +200,6 @@ def score_to_level(x, level_max=3):
|
|||
return int(ceil(x["score"] / x["score_rate"] * level_max))
|
||||
|
||||
|
||||
def score_to_numeric_score(x, score_config):
|
||||
pass
|
||||
|
||||
|
||||
# -----------------------------
|
||||
# Reglages pour 'vim'
|
||||
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
|
||||
|
|
|
|||
|
|
@ -1,196 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# encoding: utf-8
|
||||
|
||||
from .on_score_column import score_to_mark, score_to_level
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def compute_marks(df, score_max, rounding=lambda x: round(x, 2)):
|
||||
"""Compute the mark for the dataframe
|
||||
|
||||
apply score_to_mark to each row
|
||||
|
||||
:param df: DataFrame with "score" (need to be number), "is_leveled" and "score_rate" columns.
|
||||
|
||||
>>> import pandas as pd
|
||||
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
|
||||
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
|
||||
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
|
||||
... "score":[1, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
|
||||
... }
|
||||
>>> df = pd.DataFrame(d)
|
||||
>>> df
|
||||
Eleve score_rate is_leveled score
|
||||
0 E1 1 0 1.000
|
||||
1 E1 1 0 0.330
|
||||
2 E1 2 0 2.000
|
||||
3 E1 2 0 1.500
|
||||
4 E1 2 1 1.000
|
||||
5 E1 2 1 3.000
|
||||
6 E2 1 0 0.666
|
||||
7 E2 1 0 1.000
|
||||
8 E2 2 0 1.500
|
||||
9 E2 2 0 1.000
|
||||
10 E2 2 1 2.000
|
||||
11 E2 2 1 3.000
|
||||
>>> compute_marks(df, 3)
|
||||
0 1.00
|
||||
1 0.33
|
||||
2 2.00
|
||||
3 1.50
|
||||
4 0.67
|
||||
5 2.00
|
||||
6 0.67
|
||||
7 1.00
|
||||
8 1.50
|
||||
9 1.00
|
||||
10 1.33
|
||||
11 2.00
|
||||
dtype: float64
|
||||
>>> from .on_value import round_half_point
|
||||
>>> compute_marks(df, 3, round_half_point)
|
||||
0 1.0
|
||||
1 0.5
|
||||
2 2.0
|
||||
3 1.5
|
||||
4 0.5
|
||||
5 2.0
|
||||
6 0.5
|
||||
7 1.0
|
||||
8 1.5
|
||||
9 1.0
|
||||
10 1.5
|
||||
11 2.0
|
||||
dtype: float64
|
||||
"""
|
||||
return df[["score", "is_leveled", "score_rate"]].apply(
|
||||
lambda x: score_to_mark(x, score_max, rounding), axis=1
|
||||
)
|
||||
|
||||
|
||||
def compute_level(df, level_max=3):
|
||||
"""Compute level for the dataframe
|
||||
|
||||
Applies score_to_level to each row
|
||||
|
||||
:param df: DataFrame with "score", "is_leveled" and "score_rate" columns.
|
||||
:return: Columns with level
|
||||
|
||||
>>> import pandas as pd
|
||||
>>> import numpy as np
|
||||
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
|
||||
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
|
||||
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
|
||||
... "score":[0, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
|
||||
... }
|
||||
>>> df = pd.DataFrame(d)
|
||||
>>> compute_level(df)
|
||||
0 0
|
||||
1 1
|
||||
2 3
|
||||
3 3
|
||||
4 1
|
||||
5 3
|
||||
6 2
|
||||
7 3
|
||||
8 3
|
||||
9 2
|
||||
10 2
|
||||
11 3
|
||||
dtype: int64
|
||||
"""
|
||||
return df[["score", "is_leveled", "score_rate"]].apply(
|
||||
lambda x: score_to_level(x, level_max), axis=1
|
||||
)
|
||||
|
||||
|
||||
def compute_normalized(df, rounding=lambda x: round(x, 2)):
|
||||
"""Compute the normalized mark (Mark / score_rate)
|
||||
|
||||
:param df: DataFrame with "Mark" and "score_rate" columns
|
||||
:return: column with normalized mark
|
||||
|
||||
>>> import pandas as pd
|
||||
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
|
||||
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
|
||||
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
|
||||
... "score":[0, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
|
||||
... }
|
||||
>>> df = pd.DataFrame(d)
|
||||
>>> df["mark"] = compute_marks(df, 3)
|
||||
>>> compute_normalized(df)
|
||||
0 0.00
|
||||
1 0.33
|
||||
2 1.00
|
||||
3 0.75
|
||||
4 0.34
|
||||
5 1.00
|
||||
6 0.67
|
||||
7 1.00
|
||||
8 0.75
|
||||
9 0.50
|
||||
10 0.66
|
||||
11 1.00
|
||||
dtype: float64
|
||||
"""
|
||||
return rounding(df["mark"] / df["score_rate"])
|
||||
|
||||
|
||||
def filter_none_score(df, score_config):
|
||||
"""Filter rows where scores have None numeric values
|
||||
|
||||
:example:
|
||||
|
||||
>>> import pandas as pd
|
||||
>>> d = {"Eleve":["E1"]*7,
|
||||
... "score_rate": [1]*7,
|
||||
... "is_leveled":[0]+[1]*6,
|
||||
... "score":[0.33, "", ".", "a", 1, 2, 3],
|
||||
... }
|
||||
>>> score_config = {
|
||||
... 'BAD': {'value': 0, 'numeric_value': 0},
|
||||
... 'FEW': {'value': 1, 'numeric_value': 1},
|
||||
... 'NEARLY': {'value': 2, 'numeric_value': 2},
|
||||
... 'GOOD': {'value': 3, 'numeric_value': 3},
|
||||
... 'NOTFILLED': {'value': '', 'numeric_value': 'None'},
|
||||
... 'NOANSWER': {'value': '.', 'numeric_value': 0},
|
||||
... 'ABS': {'value': 'a', 'numeric_value': 'None'}
|
||||
... }
|
||||
>>> df = pd.DataFrame(d)
|
||||
>>> filter_none_score(df, score_config)
|
||||
Eleve score_rate is_leveled score
|
||||
0 E1 1 0 0.33
|
||||
2 E1 1 1 .
|
||||
4 E1 1 1 1
|
||||
5 E1 1 1 2
|
||||
6 E1 1 1 3
|
||||
"""
|
||||
not_leveled_df = df[df["is_leveled"] != 1]
|
||||
leveled_df = df[df["is_leveled"] == 1]
|
||||
|
||||
not_none_values = [
|
||||
v["value"]
|
||||
for v in score_config.values()
|
||||
if str(v["numeric_value"]).lower() != "none"
|
||||
]
|
||||
filtered_leveled_df = leveled_df[leveled_df["score"].isin(not_none_values)]
|
||||
|
||||
return pd.concat([not_leveled_df, filtered_leveled_df])
|
||||
|
||||
|
||||
def score_to_numeric_score(df, score_config):
|
||||
"""Transform a score to the corresponding numeric value
|
||||
|
||||
>>> d = {"Eleve":["E1"]*6 + ["E2"]*6,
|
||||
... "score_rate":[1]*2+[2]*2+[2]*2 + [1]*2+[2]*2+[2]*2,
|
||||
... "is_leveled":[0]*4+[1]*2 + [0]*4+[1]*2,
|
||||
... "score":[0, 0.33, 2, 1.5, 1, 3, 0.666, 1, 1.5, 1, 2, 3],
|
||||
... }
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
# -----------------------------
|
||||
# Reglages pour 'vim'
|
||||
# vim:set autoindent expandtab tabstop=4 shiftwidth=4:
|
||||
# cursor: 16 del
|
||||
Loading…
Reference in New Issue