Pythonを用いた空間統計分析のための衛星データ（LST, NDVI）の処理1

#!/usr/bin/env python
# coding: utf-8

import pandas as pd
import tables
import numpy as np
import pandas as pd
import seaborn as sns
import os


## NDVIの処理

path = "./Data/JapanAttributeData_NDVI"
files = os.listdir(path)
ff = files[0:]

# 都道府県名リスト
prelist = ["Hokkaido","Aomori", "Iwate", "Miyagi", "Akita", "Yamagata", 
           "Fukushima", "Ibaraki","Tochigi","Gunma","Saitama","Chiba",
           "Tokyo","Kanagawa", "Niigata", "Toyama", "Ishikawa","Fukui",
           "Yamanashi","Nagano","Gifu","Shizuoka","Aichi","Mie",
           "Shiga","Kyoto","Osaka","Hyogo","Nara","Wakayama","Tottori",
           "Shimane", "Okayama","Hiroshima","Yamaguchi","Tokushima",
           "Kagawa","Ehime","Kochi", "Fukuoka","Saga","Nagasaki",
           "Kumamoto","Oita","Miyazaki","Kagoshima","Okinawa"]

X = len(prelist)
mylist1 = list(range(0,X))

ff[0][6:13]


os.makedirs("./Data/Prefecture_NDVI/", exist_ok=True)

# 都道府県ごとのフォルダを作る
for j in range(len(prelist)):
    os.makedirs("./Data/Prefecture_NDVI/" + prelist[j], exist_ok=True)


# ばらす
for i in range(len(ff)):    
    City = pd.read_csv(path + "/" + ff[i],header=0,engine="python")
    # いつのデータか
    when = ff[i][6:13]
    
    for g in range(len(prelist)):    
        CITY_1 = City[(City["JCODE"] > mylist1[g]*1000 + 999) & 
        (City["JCODE"] < (mylist1[g]+1)*1000 + 999)]
        ### 都道府県データを取り出す
        CIT = pd.concat([CITY_1],ignore_index=True)
        CIT_prefecture = CIT.loc[:,['X','Y','NDVI']]
        CIT_prefecture.to_csv("./Data/Prefecture_NDVI/"
        + str(prelist[g])+ "/" + str(prelist[g]) 
        + '_ZENTAI_' + str(when) + '_NDVI.csv')
        
        ### 市区町村ごとにデータを取り出す
    
        # 市区町村名の列ラベルを参照
        CIT_name1 = list(CIT["CITY_ENG"])
        # 順番をそろえつつ重複を消す処理
        CIT_name01 = sorted(set(CIT_name1), key=CIT_name1.index)
        CIT_name01 = CIT_name01[0:-1]
        
        for h in range(len(CIT_name01)):
            C001 = CIT[CIT["CITY_ENG"] == CIT_name01[h]]
            CC11 = C001.loc[:,['X','Y','NDVI']]
            CC11.to_csv("./Data/Prefecture_NDVI/" 
            + str(prelist[g]) + "/" + str(CIT_name01[h]) 
            + '_' + str(when) + '_NDVI.csv')


## LSTの処理

path = "./Data/JapanAttributeData_LST"
files = os.listdir(path)
ff = files[0:]

# 都道府県名リスト
prelist = ["Hokkaido","Aomori", "Iwate", "Miyagi", "Akita", "Yamagata", 
           "Fukushima", "Ibaraki","Tochigi","Gunma","Saitama","Chiba",
           "Tokyo","Kanagawa", "Niigata", "Toyama", "Ishikawa","Fukui",
           "Yamanashi","Nagano","Gifu","Shizuoka","Aichi","Mie",
           "Shiga","Kyoto","Osaka","Hyogo","Nara","Wakayama","Tottori",
           "Shimane", "Okayama","Hiroshima","Yamaguchi","Tokushima",
           "Kagawa","Ehime","Kochi", "Fukuoka","Saga","Nagasaki",
           "Kumamoto","Oita","Miyazaki","Kagoshima","Okinawa"]

X = len(prelist)
mylist1 = list(range(0,X))

os.makedirs("./Data/Prefecture_LST/", exist_ok=True)


# 都道府県ごとのフォルダを作る
for j in range(len(prelist)):
    os.makedirs("./Data/Prefecture_LST/" + prelist[j], exist_ok=True)

# ばらす

for i in range(len(ff)):    
    City = pd.read_csv(path + "/" + ff[i],header=0,engine="python")
    # いつのデータか
    when = ff[i][5:12]
    
    for g in range(len(prelist)):    
        CITY_1 = City[(City["JCODE"] > mylist1[g]*1000 + 999) & 
        (City["JCODE"] < (mylist1[g]+1)*1000 + 999)]
        ### 都道府県データを取り出す
        CIT = pd.concat([CITY_1],ignore_index=True)
        CIT_prefecture = CIT.loc[:,['X','Y','LST']]
        CIT_prefecture.to_csv("./Data/Prefecture_LST/" 
        + str(prelist[g])+ "/" + str(prelist[g]) 
        + '_ZENTAI_' + str(when) + '_LST.csv')
        
        ### 市区町村ごとにデータを取り出す
    
        # 市区町村名の列ラベルを参照
        CIT_name1 = list(CIT["CITY_ENG"])
        # 順番をそろえつつ重複を消す処理
        CIT_name01 = sorted(set(CIT_name1), key=CIT_name1.index)
        CIT_name01 = CIT_name01[0:-1]
        
        for h in range(len(CIT_name01)):
            C001 = CIT[CIT["CITY_ENG"] == CIT_name01[h]]
            CC11 = C001.loc[:,['X','Y','LST']]
            CC11.to_csv("./Data/Prefecture_LST/" + str(prelist[g]) 
            + "/" + str(CIT_name01[h]) + '_' + str(when) + '_LST.csv')