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02_data_clean.py
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02_data_clean.py
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import pandas as pd
import seaborn as sns
import numpy as np
df_join = pd.read_csv("../data/Austin_Animal_Center_Joined.csv")
df_clean = df_join[(~df_join.sex_upon_intake.isnull()) | (~df_join.intake_datetime.isnull())]
df_clean['intake_datetime'] = pd.to_datetime(df_clean['intake_datetime'])
df_clean['outcome_datetime'] = pd.to_datetime(df_clean['outcome_datetime'])
df_clean['date_of_birth'] = pd.to_datetime(df_clean['date_of_birth'])
df_clean.loc[df_clean['name'].isnull(), "has_name"] = 0
df_clean.loc[df_clean['name'] == df_clean['animal_id'], "has_name"] = 0
df_clean.loc[~df_clean['name'].isnull(), "has_name"] = 1
def getBirthSex(sex_upon_intake):
sex_upon_intake = str(sex_upon_intake)
if "Male" in sex_upon_intake:
return "Male"
elif "Female" in sex_upon_intake:
return "Female"
return np.nan
def isPureColor(color):
color = str(color)
if color != "nan":
color_list = color.split("/")
if "Tricolor" in color_list:
return 0
if len(color_list) > 1:
return 0
else:
return 1
else:
return np.nan
def sex_changed(sex_upon_outcome):
sex = str(sex_upon_outcome).lower()
if ("splayed" in sex) or ("neutered" in sex):
return 1
elif ("unknown" in sex) or ("nan" in sex):
return np.nan
return 0
df_clean['age_upon_intake_day'] = (df_clean['intake_datetime'] - df_clean['date_of_birth']).dt.days
df_clean['age_upon_outcome_day'] = (df_clean['outcome_datetime'] - df_clean['date_of_birth']).dt.days
# 168 rows having negatives
df_clean = df_clean[(df_clean.age_upon_intake_day >=0) | (df_clean.age_upon_intake_day.isnull()) ]
df_clean = df_clean[(df_clean.age_upon_outcome_day >=0) | (df_clean.age_upon_outcome_day.isnull()) ]
df_clean['birth_sex'] = df_clean.sex_upon_intake.apply(getBirthSex)
df_clean['pure_color'] = df_clean.color.apply(isPureColor)
df_clean['sex_changed'] = df_clean.sex_upon_outcome.apply(sex_changed)
df_clean.to_csv("../data/Austin_Animal_Center_Cleaned.csv", index=False)