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DataPrep.py
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DataPrep.py
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# TIME SERIES REGRESSION PART 1 - KAGGLE STORE SALES COMPETITION
# DATA HANDLING SCRIPT
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Set printing options
np.set_printoptions(suppress=True, precision=4)
pd.options.display.float_format = '{:.4f}'.format
# Load original datasets
df_train = pd.read_csv("./OriginalData/train.csv", encoding="utf-8")
df_test = pd.read_csv("./OriginalData/test.csv", encoding="utf-8")
df_stores = pd.read_csv("./OriginalData/stores.csv", encoding="utf-8")
df_oil = pd.read_csv("./OriginalData/oil.csv", encoding="utf-8")
df_holidays = pd.read_csv("./OriginalData/holidays_events.csv", encoding="utf-8")
df_trans = pd.read_csv("./OriginalData/transactions.csv", encoding="utf-8")
# Rename some columns
df_holidays = df_holidays.rename(columns = {"type":"holiday_type"})
df_oil = df_oil.rename(columns = {"dcoilwtico":"oil"})
df_stores = df_stores.rename(columns = {
"type":"store_type", "cluster":"store_cluster"})
# Combine df_train and df_test for data handling operations
df = pd.concat([df_train, df_test])
# Add columns from oil, stores and transactions datasets into main data
df = df.merge(df_trans, on = ["date", "store_nbr"], how = "left")
df = df.merge(df_oil, on = "date", how = "left")
df = df.merge(df_stores, on = "store_nbr", how = "left")
# Split holidays data into local, regional, national and events
events = df_holidays[df_holidays["holiday_type"] == "Event"]
df_holidays = df_holidays.drop(labels=(events.index), axis=0)
local = df_holidays.loc[df_holidays["locale"] == "Local"]
regional = df_holidays.loc[df_holidays["locale"] == "Regional"]
national = df_holidays.loc[df_holidays["locale"] == "National"]
# Inspect local holidays sharing same date & locale. Drop the transfer row
local[local.duplicated(["date", "locale_name"], keep = False)]
local = local.drop(265, axis = 0)
# Inspect regional holidays sharing same date & locale. None exist
regional[regional.duplicated(["date", "locale_name"], keep = False)]
# Inspect national holidays sharing same date & locale. Drop bridge days
national[national.duplicated(["date"], keep = False)]
national = national.drop([35, 39, 156], axis = 0)
# Inspect events sharing same date. Drop the earthquake row
events[events.duplicated(["date"], keep = False)]
events = events.drop(244, axis = 0)
# Add local_holiday binary column to local holidays data, to be merged into main
# data.
local["local_holiday"] = (
local.holiday_type.isin(["Transfer", "Additional", "Bridge"]) |
((local.holiday_type == "Holiday") & (local.transferred == False))
).astype(int)
# Add regional_holiday binary column to regional holidays data
regional["regional_holiday"] = (
regional.holiday_type.isin(["Transfer", "Additional", "Bridge"]) |
((regional.holiday_type == "Holiday") & (regional.transferred == False))
).astype(int)
# Add national_holiday binary column to national holidays data
national["national_holiday"] = (
national.holiday_type.isin(["Transfer", "Additional", "Bridge"]) |
((national.holiday_type == "Holiday") & (national.transferred == False))
).astype(int)
# Add event column to events
events["event"] = 1
# Merge local holidays binary column to main data, on date and city
local_merge = local.drop(
labels = [
"holiday_type", "locale", "description", "transferred"], axis = 1).rename(
columns = {"locale_name":"city"})
df = df.merge(local_merge, how="left", on=["date", "city"])
df["local_holiday"] = df["local_holiday"].fillna(0).astype(int)
# Merge regional holidays binary column to main data
regional_merge = regional.drop(
labels = [
"holiday_type", "locale", "description", "transferred"], axis = 1).rename(
columns = {"locale_name":"state"})
df = df.merge(regional_merge, how="left", on=["date", "state"])
df["regional_holiday"] = df["regional_holiday"].fillna(0).astype(int)
# Merge national holidays binary column to main data, on date
national_merge = national.drop(
labels = [
"holiday_type", "locale", "locale_name", "description",
"transferred"], axis = 1)
df = df.merge(national_merge, how="left", on="date")
df["national_holiday"] = df["national_holiday"].fillna(0).astype(int)
# Merge events binary column to main data
events_merge = events.drop(
labels = [
"holiday_type", "locale", "locale_name", "description",
"transferred"], axis = 1)
df = df.merge(events_merge, how="left", on="date")
df["event"] = df["event"].fillna(0).astype(int)
# Set datetime index
df = df.set_index(pd.to_datetime(df.date))
df = df.drop("date", axis=1)
# CPI adjust sales and oil, with CPI 2010 = 100, and CPI 2017 = CPI 2016
cpis = {
"2010":100, "2013":112.8, "2014":116.8, "2015":121.5, "2016":123.6,
"2017":123.6
}
for year in [2013, 2014, 2015, 2016, 2017]:
df["sales"].loc[df.index.year==year] = df["sales"].loc[
df.index.year==year] / cpis[str(year)] * cpis["2010"]
df["oil"].loc[df.index.year==year] = df["oil"].loc[
df.index.year==year] / cpis[str(year)] * cpis["2010"]
# Split train and test, drop sales and transactions from test
df_train = df.iloc[range(0, len(df_train)), :]
df_test = df.iloc[range(len(df_train), len(df)), :]
df_test = df_test.drop(["sales", "transactions"], axis = 1)
# Check missing values in train and test. For train, NAs in oil and transactions.
# For test, NAs in oil.
pd.isnull(df_train).sum()
pd.isnull(df_test).sum()
# Time interpolate missing values in oil (train-test separately). Some are left
# in train, these are all from the first day in the data. Backfill them with the
# next day's oil price.
df_train["oil"] = df_train["oil"].interpolate("time")
df_test["oil"] = df_test["oil"].interpolate("time")
df_train["oil"] = df_train["oil"].fillna(method="bfill")
# Time interpolate missing values in transactions (train only). Some are left,
# all from the first day in the data, 01-01-2013. Fill them in with transactions
# from 01-01-2014.
df_train["transactions"] = df_train["transactions"].interpolate("time")
df_train["transactions"] = df_train["transactions"].fillna(
df_train["transactions"].loc[
(df_train.index.day == 1) & (df_train.index.month == 1) &
(df_train.index.year == 2014)].median()
)
# Export modified train and test data
df_train.to_csv(
"./ModifiedData/Final/train_modified.csv", index=True, encoding="utf-8")
df_test.to_csv(
"./ModifiedData/Final/test_modified.csv", index=True, encoding="utf-8")