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