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Added non-normalized with 0 NaN cleaned wellness csv

master
Ryan Missel 5 years ago
parent
commit
0199dcf57f
5 changed files with 5057 additions and 15060 deletions
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      data_preparation/cleaned/dirty_wellness.csv
  2. +0
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      data_preparation/cleaned/dirty_wellness_na.csv
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      data_preparation/cleaned/notnormalized_with_0NaN_wellness.csv
  4. +0
    -5018
      data_preparation/dirty_wellness_na.csv
  5. +45
    -6
      data_preparation/vectorization_ex.py

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data_preparation/cleaned/dirty_wellness.csv
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data_preparation/cleaned/dirty_wellness_na.csv
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data_preparation/cleaned/notnormalized_with_0NaN_wellness.csv
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data_preparation/dirty_wellness_na.csv
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data_preparation/vectorization_ex.py View File

@ -1,12 +1,51 @@
import pandas as pd
# read in CSV
df = pd.read_csv('cleaned/dirty_wellness_na.csv')
def vectorize_mult(column, dictionary, file=None):
def vectorize_mult(df, column, dictionary):
"""
Changes all the categorical values into its respective
number in the dictionary and then saves it in the DF
:param df: dataframe
:param column: column name
:param dictionary: alterations to make
"""
newCol = column + "Num"
df[newCol] = df[column].map(dictionary)
if file is not None:
df.to_csv('cleaned/{}.csv'.format(file))
class WellnessCSV:
def __init__(self):
self.file = "data/wellness.csv"
self.end = "cleaned/notnormalized_with_0NaN_wellness.csv"
def vectorize(self):
df = pd.read_csv(self.file)
# Vectorizing appropriate data
vectorize_mult(df, "Pain", {"No": 0, "Yes": 1})
vectorize_mult(df, "Illness", {"No": 0, "Slightly Off": 0.5, "Yes": 1})
vectorize_mult(df, "Menstruation", {"No": 0, "Yes": 1})
vectorize_mult(df, "Nutrition", {"Poor": 0, "Okay": 0.5, "Excellent": 1})
vectorize_mult(df, "NutritionAdjustment", {"No": 0, "Yes": 1})
vectorize_mult(df, "USGMeasurement", {"No": 0, "Yes": 1})
readiness = []
for i, value in df["TrainingReadiness"].iteritems():
value = value.split("%")[0]
value = int(value) * (1/100)
readiness.append(value)
df["TrainingReadinessNum"] = readiness
# Filling in NaNs for appropriate layers where they won't make a statistical difference
df["MenstruationNum"] = df["MenstruationNum"].fillna(0)
df["USGMeasurementNum"] = df["USGMeasurementNum"].fillna(0)
df["NutritionNum"] = df["MenstruationNum"].fillna(0)
df["NutritionAdjustmentNum"] = df["NutritionAdjustmentNum"].fillna(0)
# Saving the df to the "cleaned" CSV
df.to_csv(self.end)
cls = WellnessCSV()
cls.vectorize()

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