hoc python co ban nang cao kha ngang

Cloud Type: dentification of cloudy and cloud free areas (xác định các khu vực có mây và khơng có
mây).
Cloud top temperature and height: tính chiều cao của đỉnh mây
COT: cloud optical thinkness (độ dày quang học của đám mây)

Add value in list: list.insert
list.extend
old value a[::2]
even value a[1::2]
Write a program to separate date and time from the datetime column in Python Pandas
(tutorialspoint.com)
Processing time with Pandas DataFrame - GeeksforGeeks
Python: Pandas DataFrame: Làm việc với dữ liệu một cách thú vị | V1Study

import pandas as pd
import datetime
import numpy as np
data = pd.read_csv('all_202007.csv',header= 0)
"""
f = pd.DataFrame(data, columns=['value'])
print(df)
filter = df['value'] >= -998
df1 = df[filter]
print(df1)
"""
#print(data)
df = pd.DataFrame(data,columns=['utc_time'])
#print(df)


df['date'] = pd.to_datetime(df['utc_time']).dt.date

df['hour'] = pd.to_datetime(df['utc_time']).dt.hour
df['minute'] = pd.to_datetime(df['utc_time']).dt.minute
df['second'] = pd.to_datetime(df['utc_time']).dt.second
df_hour = df['hour']
df_minute = df['minute']
df_date = df['date']
df['time'] = pd.to_datetime(df['utc_time']).dt.time
df_time = df['time']
print(df_hour)
print(df_minute)
idx = (df_hour >0) * (df_hour <12)
df[idx]
data = pd.read_csv('all_202007.csv',header= 0)
print(df_hour[idx])
data1 = data[idx]
print(data1)
data1.to_csv('dulieu.csv')
from netCDF4 import Dataset
import pandas as pd
import numpy as np
data =
Dataset('NC_H08_20190802_0050_L2CLP010_FLDK.02401_02401.nc')
print(data)
lats = data.variables.keys()
print(lats)
Hour = data.variables['Hour'][:]
print(Hour)
"""
latitude = data.variables['latitude'][:]
latitude = np.array(latitude)

longitude = data.variables['longitude'][:]
longitude = np.array(longitude)
Hour = data.variables['Hour'][:]
clot = data.variables['CLOT'][:]
cltt = data.variables['CLTT'][:]
clth = data.variables['CLTH'][:]
cler_23 = data.variables['CLER_23'][:]
cltype = data.variables['CLTYPE'][:]
qa = data.variables['QA'][:]
Hour = np.array(Hour)
clot = np.array(clot)
cltt = np.array(cltt)
clth = np.array(clth)
cler_23 = np.array(cler_23)
cltype = np.array(cltype)
qa = np.array(qa)
print(latitude)
print(longitude)
print(Hour)
print(clot)
print(cltt)
print(clth)
print(cler_23)
print(qa)
#dict = {'latitude': latitude,'longitude': longitude, 'Hour': Hour, 'clot': clot,
'cltt': cltt, 'clth': clth, 'cler_23': cler_23, 'qa': qa }
dict = {'latitude': latitude,'longitude': longitude}
df = pd.DataFrame(dict)

df_new = pd.concat([df, pd.DataFrame(Hour)], axis=1)
df_new1 = pd.concat([df_new, pd.DataFrame(clot)], axis=1)
df_new2 = pd.concat([df_new1, pd.DataFrame(cltt)], axis=1)
df_new3 = pd.concat([df_new2, pd.DataFrame(clth)], axis=1)
df_new4 = pd.concat([df_new3, pd.DataFrame(cler_23)], axis=1)
df_new5 = pd.concat([df_new4, pd.DataFrame(qa)], axis=1)


df_new5.to_csv('my data6.csv')
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from osgeo import gdal
import pandas as pd
import numpy as np
import os
import geopandas
from shapely.geometry import Point
data= pd.read_csv('all_201908.csv',header=0)
lon = data['lon']
lat = data['lat']
value = data['value']
df = pd.DataFrame({"lon":lon, "lat":lat, "value":value})
geometry = [Point(xy) for xy in zip(df.iloc[:, 0], df.iloc[:, 1])]
gdf = geopandas.GeoDataFrame(df, geometry=geometry)
gdf.plot(column='value')
plt.savefig('raster1.tiff')