[Help Me] Plot tidak masuk ke Flask Dashboard ketika di render #833
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saskia-dwi-ulfah
ammaralwandi97
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Ini kode app.py saya ini kode HTML nya ini bagian yang terpenting : Tetapi hasilnya Zonk Plot Tidak Muncul |
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Answered by
saskia-dwi-ulfah
Dec 19, 2023
Replies: 1 comment 3 replies
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Pagi Pak @ammaralwandi97, Setelah mencoba code Bapak, kemungkinan tidak munculnya plot tersebut disebabkan oleh typo pada salah satu line code berikut: dimana parameter Semoga membantu! |
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3 replies
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sudah dicoba, imagenya ga mau keluar mas |
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Dear Pak @ammaralwandi97,
from flask import Flask, render_template
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from io import BytesIO
import base64
from bs4 import BeautifulSoup
import requests
from datetime import datetime
import plotly.express as px
#don't change this
matplotlib.use('Agg')
app = Flask(__name__) #do not change this
#insert the scraping process here
weblist = ['home', 'home/all-jobs', 'home/work_from_home/y', 'home?sort=Salary', 'home?sort=Freshness', 'home/i/it-and-software',
'home/i/sales-and-marketing', 'home/i/accounting-and-finance',
'home/w/500-director-or-executive', 'home/w/400-mid-senior-level-or-manager',
'home/w/300-associate-or-supervisor', 'home/w/200-entry-level-or-junior-and-apprentice', 'home/w/100-internship-or-ojt',
'home/t/contractual', 'home/t/freelance', 'home/t/part-time', 'home/t/full-time']
temp = [] # initiating a tuple
url_get = requests.get(f"https://kalibrr.com/id-ID/home")
for sites in weblist:
url_get = requests.get(f"https://kalibrr.com/id-ID/{sites}")
soup = BeautifulSoup(url_get.content, "html.parser")
table = soup.find('div', attrs={'class': 'k-px-4 md:k-px-10 k-flex k-flex-col'})
spans = table.find_all('span', attrs={'class': 'k-text-gray-500'})
row = table.find_all('span', attrs={'class': 'k-inline-flex k-items-center k-mb-1'})
row_length = len(row)
# spans2 = table.find_all('p', attrs={'class': 'k-text-gray-500'})
post_list = []
for span in spans:
if "Rekruter" in span.text:
post_list.append(span)
for i in range(1, row_length):
# scraping process
# title pekerjaan
title = table.find_all('a', attrs={'class': 'k-text-black'})[i].text
# perusahaan
company = table.find_all('a', attrs={'class': 'k-text-subdued k-font-bold'})[i].text
# lokasi
location = table.find_all('span', attrs={'class': 'k-text-gray-500 k-block k-pointer-events-none'})[i].text
# tanggal pekerjaan di post
post_date = post_list[i].text
# deadline submit permohonan
deadline = table.find_all('span', attrs={'class': 'k-text-xs k-font-bold k-text-gray-600'})[i].text
# pengalaman
entry_level = table.find_all('a', attrs={'class': 'k-font-dm-sans k-text-xs k-font-bold k-text-gray-600'})[i].text
# employ
employment = table.find_all('span', attrs={'class': 'k-hidden'})[i].text
temp.append((title, company, location, post_date,
deadline, entry_level, employment))
#change into dataframe
df = pd.DataFrame(temp, columns=['title','company','location','post_date','deadline','entry_level','employment'])
#insert data wrangling here
df_clean = df.drop_duplicates()
df_clean['company'] = df_clean['company'].astype('category')
df_clean['location'] = df_clean['location'].astype('category')
df_clean['entry_level'] = df_clean['entry_level'].astype('category')
df_clean['employment'] = df_clean['employment'].astype('category')
df_clean['deadline'] = df_clean['deadline'].str.replace('Apply before ','')
month_map = {'Jan': '01', 'Feb': '02', 'Mar': '03', 'Apr': '04', 'May': '05', 'Jun': '06',
'Jul': '07', 'Aug': '08', 'Sep': '09', 'Oct': '10', 'Nov': '11', 'Dec': '12'}
df_clean['day'] = df_clean['deadline'].str.extract('(\d+)').astype(int)
df_clean['month'] = df_clean['deadline'].str.extract('([a-zA-Z]+)')
df_clean['month'] = df_clean['month'].map(month_map)
df_clean_dec = df_clean[df_clean['deadline'].str.contains('Dec') & (df_clean['day'] > int(datetime.now().strftime('%d')))]
df_clean_not_dec = df_clean[df_clean['deadline'].str.contains('Dec')==False]
df_clean_dec['year'] = 2023
df_clean_not_dec['year'] = 2024
df_clean_dec['formatted_date'] = pd.to_datetime(df_clean_dec[['day', 'month', 'year']], format='%d%m%Y')
df_clean_not_dec['formatted_date'] = pd.to_datetime(df_clean_not_dec[['day', 'month', 'year']], format='%d%m%Y')
df_clean = pd.concat([df_clean_dec,df_clean_not_dec])
df_clean['deadline_day'] = df_clean['formatted_date'] - datetime.now()
#Sesuaikan Nilai Jakarta
df_jakarta = df_clean[df_clean['location'].str.contains('Jakarta')==True]
df_nonjkt = df_clean[df_clean['location'].str.contains('Jakarta')==False]
df_jakarta.loc[:,['location']] = 'Jakarta, Indonesia'
df_clean = pd.concat([df_jakarta,df_nonjkt])
#Sesuaikan Nilai Filipina
df_filipina = df_clean[df_clean['location'].str.contains('Philippines')==True]
df_nonfilipina = df_clean[df_clean['location'].str.contains('Philippines')==False]
df_filipina.loc[:,['location']] = 'Phillippines'
df_clean = pd.concat([df_filipina,df_nonfilipina])
df_tangerang = df_clean[df_clean['location'].str.contains('Tangerang')==True]
#Sesuaikan Nilai Tangerang
df_nontangerang = df_clean[df_clean['location'].str.contains('Tangerang')==False]
df_tangerang.loc[:,['location']] = 'Tangerang, Indonesia'
df_clean = pd.concat([df_tangerang,df_nontangerang])
df_clean = df_clean.sort_values(by='deadline_day').loc[:, ['title', 'company', 'location', 'post_date', 'entry_level', 'employment', 'deadline_day', 'formatted_date']]
df_clean = df_clean.reset_index()
# move from def index2(): filter and pivot the dataframe
df_pivot = df_clean[df_clean['location'].isin(['Tangerang, Indonesia', 'Phillippines', 'Jakarta, Indonesia'])].pivot_table(columns='location', index='entry_level', values='title', aggfunc='count').fillna(0)
df_clean['location'] = df_clean['location'].str.replace(', Indonesia','')
#end of data wranggling
@app.route("/")
def index():
card_data = f'{pd.crosstab(index=df_clean["location"], columns="Total")["Total"].sum()}'
# generate first plot
plt.subplots(figsize=(18,6))
pd.crosstab(index=df_clean['location'], columns='Total').sort_values(by='Total').plot(kind='barh', ylabel='Lokasi', xlabel='Jumlah Lowongan', legend=False)
figfile = BytesIO()
plt.tight_layout()
plt.savefig(figfile, format='png', transparent=True, dpi=100)
plt.close()
#set max width
max_width = 800
figfile.seek(0)
figdata_png = base64.b64encode(figfile.getvalue()).decode('utf-8')
plot_result = figdata_png
#plt.close(plot_result) # Close the figure to free memory
# generate second plot
plt.subplots(figsize=(18,6))
df_pivot.plot(kind='barh', ylabel='Posisi Masuk', xlabel='Jumlah Lowongan')
figfile2 = BytesIO()
plt.tight_layout()
plt.savefig(figfile2, format='png', transparent=True,dpi=100)
plt.close()
#set max width
max_width = 800
figfile2.seek(0)
figdata_png2 = base64.b64encode(figfile2.getvalue()).decode('utf-8')
plot_result2 = figdata_png2
#plt.close(plot_result2) # Close the figure to free memory
# render to template
return render_template('index.html',
card_data=card_data,
plot_result=plot_result,
plot_result2=plot_result2)
'''
@app.route("/entry_level")
def index2():
# filter and pivot the dataframe
df_pivot = df_clean[df_clean['location'].isin(['Tangerang, Indonesia', 'Phillippines', 'Jakarta, Indonesia'])].pivot_table(columns='location', index='entry_level', values='title', aggfunc='count').fillna(0)
# plot the data
bx = df_pivot.plot(kind='barh', ylabel='Posisi Masuk', xlabel='Jumlah Lowongan',figsize=(14,9))
# save the plot to a BytesIO object
figfile2 = BytesIO()
plt.savefig(figfile2, format='png', transparent=True)
figfile2.seek(0)
# encode the image to base64
figdata_png2 = base64.b64encode(figfile2.getvalue())
plot_result2 = str(figdata_png2)[2:-1]
return render_template('index.html', plot_result2=plot_result2)
'''
if __name__ == "__main__":
app.run(debug=True)Semoga jawaban ini dapat membantu, Best Regards, |
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Answer selected by
ammaralwandi97
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terima kasih , sangat membantu |
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Dear Pak @ammaralwandi97,
plot_result2yang di-definepadaendpointyang berbeda. Anda bisa memindahkan semua kode untuk membuatplot_result2ke fungsiindexdi bawahplot_result1.plt.tight_layoutsetiap sebelum menyimpan gambar denganplt.savefig. Berikut adalah penggunaanplt.savefig()dari kode repositori capstone Anda: