Jika anda ingin mengelola data yang sangat besar (Big Data), pasti kalian malas dan mengatakan bahwa "Sebanyak ini kah data?, Gimana cara hitungnya?" kini zaman udah canggih kalian tinggal menggunakan toolls saja. yaitu:
1. Visual Studio Code
2. Google Collab
- Jika tidak mencoba berarti tidak tau, maka dari itu cobalah, JEJAK MAHASISWA -
Lihat Disini Datanya Yaaa...
1. Code Untuk Mengetahui Dataset.
Silahkan diketik yaa jangan copy paste muluu... KATANYA MAU BELAJAR
# Coding 1
# Menampilkan Isi Semua Data
import pandas as pd
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
df.head()
# Coding 2
# Menampilkan Nilai Rata-rata Pada Data Tersebut
import pandas as pd
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
df.describe()
# Coding 3
# Menampilkan tanpa menggunakan df.describe()
import pandas as pd
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
rata_rata_total = df.select_dtypes(include='number').mean()
print(f'Nilai rata-rata dari kolom data adalah:\n{rata_rata_total}')
# Coding 4
# Menampilkan Nilai Maksimum dan Minimum, Apakah sama dengan menggunakan df.describe()
import pandas as pd
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
# Proses Menginput data yang berada di dalam link Github
rata_rata_data = df.select_dtypes(include='number').mean()
max_data = df.select_dtypes(include='number').max()
min_data = df.select_dtypes(include='number').min()
# Cara Menampilkan Nilai Rata-rata, Maksimum dan Minimum
print(f'Nilai rata-rata dari kolom data adalah:\n{rata_rata_data}')
print(f'\nNilai maksimum dari kolom data adalah:\n{max_data}')
print(f'\nNilai minimum dari kolom data adalah:\n{min_data}')
# Coding 5
# Menampilkan Seluruh Isi Data nya
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv")
df
2. Data Penjualan Menggunakan Grafik Seaborn
# Coding 1
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Muat data dari URL
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
# Tampilkan beberapa baris pertama data
print(df.head())
# Buat scatter plot dengan regresi menggunakan seaborn.lmplot
sns.lmplot(x="Promo(X1)", y="Jual(Y)", data=df)
# Tampilkan plot
plt.show()
# Coding 2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn.objects as so
# URL data
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
# Baca data
df = pd.read_csv(data)
print(df.head())
x_column = 'Karyawan(X2)'
y_column = 'Jual(Y)'
p = so.Plot(data=df, x=x_column, y=y_column)
p.show()
# Coding 3
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn.objects as so
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
print(df.head())
(
so.Plot(df, x="Promo(X1)", y="Jual(Y)")
.add(so.Bar(), so.Hist())
)
# Coding 4
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn.objects as so
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
print(df.head())
(
so.Plot(df, x="Promo(X1)", y="Karyawan(X2)", color="Jual(Y)")
.add(so.Dot(alpha=.5), so.Dodge(), so.Jitter(.4), orient="y")
)
# Coding 5
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# URL dataset
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
# Baca file CSV ke dalam DataFrame
df = pd.read_csv(data)
print(df.head())
# Ubah DataFrame dari format lebar ke format panjang
df_long = pd.melt(df, id_vars=['Promo(X1)'], value_vars=['Karyawan(X2)', 'Jual(Y)'], var_name='Type', value_name='Value')
# Buat grafik garis
sns.lineplot(data=df_long, x='Promo(X1)', y='Value', hue='Type')
# Tambahkan label sumbu
plt.ylabel('Value')
plt.xlabel('Promo(X1)')
plt.title('Grafik Hubungan antara Promo, Karyawan, dan Penjualan')
# Tampilkan plot
plt.show()
# Coding 6
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn.objects as so
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
print(df.head())
p = (
so.Plot(df, x="Karyawan(X2)", y="Jual(Y)")
.add(so.Dot(), color="Jual(Y)")
)
p.label(x="Karyawan", y="Jual", color="")
# Coding 7
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
print(df.head())
# Buat grafik garis
sns.lineplot(data=df, x='TIME', y='Jual(Y)')
# Tambahkan label dan judul
plt.title('Tren Penjualan (Jual) dari Waktu ke Waktu')
plt.xlabel('Waktu (TIME)')
plt.ylabel('Jual (Y)')
# Tampilkan plot
plt.show()
# Coding 8
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
print(df.head())
sns.scatterplot(data=df, x="Promo(X1)", y="Jual(Y)")
# Tampilkan plot
plt.show()
# Coding 9
# Menampilkan Grafik Seaborn dan Matplotlib
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
print(df.head())
sns.pairplot(df)
# Tampilkan plot
plt.show()
# Coding 10
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
df = pd.read_csv(data)
print(df.head())
sns.pairplot(df, kind="kde")
plt.show()
3. Data Penjualan Menggunakan Grafik Matplotlib
# Coding 1
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# URL dataset
data = 'https://raw.githubusercontent.com/Alam-A99/Case-Study/refs/heads/main/Data_Regresi.csv'
# Baca file CSV ke dalam DataFrame
df = pd.read_csv(data)
print(df.head())
# Ubah dataframe jadi array numerik
arr = df.values
# Setup subplot
fig, axs = plt.subplots(2, 2, figsize=(6, 6), layout="constrained")
for ax in axs.flat:
im = ax.pcolormesh(arr, cmap="viridis", shading="auto")
# Tambahkan colorbar
fig.colorbar(im, ax=axs, shrink=0.6)
plt.show()
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