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How to use area method in ATX
Best Python code snippet using ATX
format_datasharing_csvs.py
Source:format_datasharing_csvs.py 
1import pandas as pd2import os3# script to format final files for data sharing4# PER GLACIER5# per glacier cumulative series6list_fn_cumul_pergla = ['/home/atom/ongoing/work_worldwide/vol/dh_06_rgi60_int_base.csv']7for fn_cumul_pergla in list_fn_cumul_pergla:8 df = pd.read_csv(fn_cumul_pergla,index_col=0)9 df = df.round({'dh':3,'err_dh':3,'dt':1,'std_dt':1,'perc_area_meas':3,'perc_area_res':3,'err_corr_150':3,'err_corr_2000':3,10 'err_corr_5000':3,'err_corr_20000':3,'err_corr_50000':3,'err_corr_200000':3,'valid_obs':2,'valid_obs_py':2,11 'area':0,'lon':4,'lat':4,'perc_err_cont':3})12 df = df[['rgiid','time','area','dh','err_dh','perc_area_meas','perc_area_res','valid_obs','valid_obs_py','dt','std_dt'13 ,'err_corr_150','err_corr_2000','err_corr_5000','err_corr_20000','err_corr_50000','err_corr_200000','lat','lon']]14 df.to_csv(os.path.join(os.path.dirname(fn_cumul_pergla),os.path.splitext(os.path.basename(fn_cumul_pergla))[0]+'_fmt.csv'))15# RGI O1 REGIONS WITH TAREA16# RGI O1 regional cumulative series17list_fn_cumul_reg = ['/home/atom/ongoing/work_worldwide/vol/final/dh_01_rgi60_int_base_reg.csv']18for fn_cumul_reg in list_fn_cumul_reg:19 df = pd.read_csv(fn_cumul_reg)20 df = df.drop(columns=['area_valid_obs_py','perc_err_cont'])21 df = df.round({'dh':3,'err_dh':3,'dvol':0,'err_dvol':0,'dm':4,'err_dm':4,'dt':1,'std_dt':1,'perc_area_meas':3,'perc_area_res':322 ,'valid_obs':2,'valid_obs_py':2, 'area':0,'area_nodata':0})23 df = df[['reg','time','area','dh','err_dh','dvol','err_dvol','dm','err_dm','perc_area_meas','perc_area_res','valid_obs','valid_obs_py','area_nodata']]24 df.to_csv(os.path.join(os.path.dirname(fn_cumul_reg),os.path.splitext(os.path.basename(fn_cumul_reg))[0]+'_fmt.csv'),index=None)25# RGI O1 regional rates26list_fn_rates_reg = ['/home/atom/ongoing/work_worldwide/vol/final/dh_01_rgi60_int_base_reg_subperiods.csv']27for fn_rates_reg in list_fn_rates_reg:28 df = pd.read_csv(fn_rates_reg,index_col=0)29 df = df.round({'dhdt':3,'err_dhdt':3,'dvoldt':0,'err_dvoldt':0,'dmdt':4,'err_dmdt':4,'dmdtda':3,'err_dmdtda':3,'perc_area_meas':3,'perc_area_res':3,30 'valid_obs':2,'valid_obs_py':2,'area':0,'area_nodata':0, 'tarea':0})31 df = df[['reg','period','area','tarea','dhdt','err_dhdt','dvoldt','err_dvoldt','dmdt','err_dmdt','perc_area_meas','perc_area_res','valid_obs','valid_obs_py','area_nodata']]32 df.to_csv(os.path.join(os.path.dirname(fn_rates_reg),os.path.splitext(os.path.basename(fn_rates_reg))[0]+'_fmt.csv'),index=None)33# TILES34# tile cumulative series35list_fn_cumul_tile = ['/home/atom/ongoing/work_worldwide/vol/final/dh_world_tiles_2deg.csv']36for fn_cumul_tile in list_fn_cumul_tile:37 df = pd.read_csv(fn_cumul_tile)38 df = df.drop(columns=['area_valid_obs_py','perc_err_cont'])39 df = df.round({'dh':3,'err_dh':3,'dvol':0,'err_dvol':0,'dm':4,'err_dm':4,'dt':1,'std_dt':1,'perc_area_meas':3,'perc_area_res':340 ,'valid_obs':2,'valid_obs_py':2, 'area':0,'area_nodata':0,'tile_lonmin':1,'tile_latmin':1,'tile_size':1})41 df = df[['tile_lonmin','tile_latmin','tile_size','time','area','dh','err_dh','dvol','err_dvol','dm','err_dm','perc_area_meas','perc_area_res','valid_obs','valid_obs_py','area_nodata']]42 df.to_csv(os.path.join(os.path.dirname(fn_cumul_tile),os.path.splitext(os.path.basename(fn_cumul_tile))[0]+'_fmt.csv'),index=None)43# tile rates44list_fn_rates_tile = ['/home/atom/ongoing/work_worldwide/vol/final/dh_world_tiles_2deg_subperiods.csv']45for fn_rates_tile in list_fn_rates_tile:46 df = pd.read_csv(fn_rates_tile,index_col=0)47 df = df.drop(columns=['tarea'])48 df = df.round({'dhdt':3,'err_dhdt':3,'dvoldt':0,'err_dvoldt':0,'dmdt':4,'err_dmdt':4,'dmdtda':3,'err_dmdtda':3,'perc_area_meas':3,'perc_area_res':3,49 'valid_obs':2,'valid_obs_py':2,'area':0,'area_nodata':0,'tile_lonmin':1,'tile_latmin':1,'tile_size':1})50 df = df[['tile_lonmin','tile_latmin','tile_size','period','area','dhdt','err_dhdt','dvoldt','err_dvoldt','dmdt','err_dmdt','perc_area_meas','perc_area_res','valid_obs','valid_obs_py','area_nodata']]51 df.to_csv(os.path.join(os.path.dirname(fn_rates_tile),os.path.splitext(os.path.basename(fn_rates_tile))[0]+'_fmt.csv'),index=None)52#SHP with TW/NTW sorting53# shp cumulative series54list_fn_cumul_shp = ['/home/atom/ongoing/work_worldwide/vol/final/subreg_HIMAP_cumul.csv']55for fn_cumul_shp in list_fn_cumul_shp:56 df = pd.read_csv(fn_cumul_shp)57 df = df.round({'dh':3,'err_dh':3,'dvol':0,'err_dvol':0,'dm':4,'err_dm':4,'dt':1,'std_dt':1,'perc_area_meas':3,'perc_area_res':358 ,'valid_obs':2,'valid_obs_py':2, 'area':0,'area_nodata':0})59 df = df[['subreg','time','area','dh','err_dh','dvol','err_dvol','dm','err_dm','perc_area_meas','perc_area_res','valid_obs','valid_obs_py','area_nodata']]60 df.to_csv(os.path.join(os.path.dirname(fn_cumul_shp),os.path.splitext(os.path.basename(fn_cumul_shp))[0]+'_fmt.csv'),index=None)61# shp rates62list_fn_rates_shp = ['/home/atom/ongoing/work_worldwide/vol/final/subreg_HIMAP_rates.csv']63for fn_rates_shp in list_fn_rates_shp:64 df = pd.read_csv(fn_rates_shp,index_col=0)65 df = df.drop(columns=['tarea'])66 df = df.round({'dhdt':3,'err_dhdt':3,'dvoldt':0,'err_dvoldt':0,'dmdt':4,'err_dmdt':4,'dmdtda':3,'err_dmdtda':3,'perc_area_meas':3,'perc_area_res':3,67 'valid_obs':2,'valid_obs_py':2,'area':0,'area_nodata':0})68 df = df[['subreg','period','area','dhdt','err_dhdt','dvoldt','err_dvoldt','dmdt','err_dmdt','perc_area_meas','perc_area_res','valid_obs','valid_obs_py','area_nodata']]...
solution.py
Source:solution.py
1# -*- coding: utf-8 -*-2import sys3import json4import pandas as pd5from pandas.io.json import json_normalize6if len(sys.argv) < 2:7 print('Usage: python solution.py [filename] - Executa o desafio 5 usando o nome de arquivo passado')8 sys.exit()9filename = sys.argv[1]10with open(filename, encoding='utf-8') as json_data:11 data = json.load(json_data)12funcionarios = json_normalize(data['funcionarios'])13areas = json_normalize(data['areas'])14# Questao 115global_max = funcionarios.loc[funcionarios.salario == funcionarios.salario.max()]16for index, row in global_max.iterrows():17 print('global_max|{0}|{1:.2f}'.format(18 ' '.join([row.nome, row.sobrenome]),19 row.salario 20 ))21global_min = funcionarios.loc[funcionarios.salario == funcionarios.salario.min()]22for index, row in global_min.iterrows():23 print('global_min|{0}|{1:.2f}'.format(24 ' '.join([row.nome, row.sobrenome]),25 row.salario 26 ))27print('global_avg|{0:.2f}'.format(28 round(funcionarios.salario.mean(), 2)29))30# Questao 231area_group = funcionarios.groupby(by='area')32for area, data in area_group:33 area_max = data.loc[data.salario == data.salario.max()]34 for index, row in area_max.iterrows():35 print('area_max|{0}|{1}|{2:.2f}'.format(36 areas.loc[areas.codigo == area].iloc[0].nome,37 ' '.join([row.nome, row.sobrenome]),38 row.salario 39 ))40 area_min = data.loc[data.salario == data.salario.min()]41 for index, row in area_min.iterrows():42 print('area_min|{0}|{1}|{2:.2f}'.format(43 areas.loc[areas.codigo == area].iloc[0].nome,44 ' '.join([row.nome, row.sobrenome]),45 row.salario 46 ))47 48 area_avg = data.loc[data.salario == data.salario.mean()]49 print('area_avg|{0}|{1:.2f}'.format(50 areas.loc[areas.codigo == area].iloc[0].nome,51 data.salario.mean()52 ))53# Questao 354area_employees = funcionarios['area'].value_counts()55most_employees = area_employees.loc[area_employees == area_employees.max()]56for area in most_employees.index:57 print('most_employees|{0}|{1}'.format(58 areas.loc[areas.codigo == area].iloc[0].nome,59 most_employees[area]60 ))61least_employees = area_employees.loc[area_employees == area_employees.min()]62for area in least_employees.index:63 print('least_employees|{0}|{1}'.format(64 areas.loc[areas.codigo == area].iloc[0].nome,65 least_employees[area]66 ))67# Questao 468last_names = funcionarios.groupby(by='sobrenome')69for sobrenome, data in last_names:70 if data.shape[0] > 1:71 last_name_max = data.loc[data.salario == data.salario.max()]72 for index, row in last_name_max.iterrows():73 print('last_name_max|{0}|{1}|{2:.2f}'.format(74 sobrenome,75 ' '.join([row.nome, row.sobrenome]),76 row.salario ...
aRectangle.py
Source:aRectangle.py
...7 if l == w:8 self.is_square = True9 else:10 self.is_square = False11# def area(self):12# self.area = self.l * self.w13# return self.area14# def perimeter(self):15# self.perimeter = (2 * self.l) + (2 * self.w)16# return self.perimeter17 def __lt__(self, other):18 return self.area < other.area19 def ___le__(self, other):20 return self.area <= other.area21 def __eq__(self, other):22 return self.area == other.area23 def __ne__(self, other):24 return self.area != other.area25 def __gt__(self, other):26 return self.area > other.area27 def __ge__(self, other):28 return self.area >= other.area29# def is_square(self):30# if self.l == self.w:31# return True32# else:33# return False34 def __str__(self):35 return ("Width: " + str(self.w) + ", Length: " + str(self.l) + ", Area: " + str(self.area) + ", Perimeter: " + str(self.perimeter) + ", is Square: " + str(self.is_square) + "\n")36r = aRectangle(2,5)37r2 = aRectangle(2,2)38#r.area()39#r.perimeter()40#r.is_square()41#print(r, r2)42print(r > r2)43print(r < r2)...
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