import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df=pd.read_csv('energy.csv')
df.head()

	Date	commercial	industrial
0	1/1/01	629	13128
1	2/1/01	548	11421
2	3/1/01	553	12454
3	4/1/01	550	11674
4	5/1/01	575	11751

df.dtypes

	0
Date	object
commercial	int64
industrial	int64

dtype: object

df['Date']=pd.to_datetime(df['Date'])
df.head()

/tmp/ipython-input-3951332809.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  df['Date']=pd.to_datetime(df['Date'])

	Date	commercial	industrial
0	2001-01-01	629	13128
1	2001-02-01	548	11421
2	2001-03-01	553	12454
3	2001-04-01	550	11674
4	2001-05-01	575	11751

#1. Load the energy dataset into colab and create a line
# chart showing the commercial energy generation over time
plt.figure(figsize=(12,5))
plt.plot_date(x=df['Date'], y=df['commercial'], linestyle='--', linewidth=2,
              c='blue', marker= 'x', markersize=10, mfc='black', mec='red',
              fmt='x--')
plt.title('Commercial Energy Over Time')
plt.xlabel('Date')
plt.show()

/tmp/ipython-input-2017423839.py:4: MatplotlibDeprecationWarning: The plot_date function was deprecated in Matplotlib 3.9 and will be removed in 3.11. Use plot instead.
  plt.plot_date(x=df['Date'], y=df['commercial'], linestyle='--', linewidth=2,
/tmp/ipython-input-2017423839.py:4: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string "x--" (-> linestyle='--'). The keyword argument will take precedence.
  plt.plot_date(x=df['Date'], y=df['commercial'], linestyle='--', linewidth=2,
/tmp/ipython-input-2017423839.py:4: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string "x--" (-> marker='x'). The keyword argument will take precedence.
  plt.plot_date(x=df['Date'], y=df['commercial'], linestyle='--', linewidth=2,

#2. Create one figure that display both commercial
# and industrial energy generations over time together
plt.figure(figsize=(12,5))
sns.lineplot(data= df, x='Date',y='commercial', linestyle='--', markersize='8',
             marker='o', color='peru', mfc='lime', mec='yellow', label='commercial')
sns.lineplot(data=df, x='Date',y='industrial', linestyle='--', markersize='12',
             marker='.', color='teal', mfc='magenta', mec='crimson', label='industrial')
plt.show()

#3. Create a function func_(x) using python,
# which is the x cubed plus two times x squared plus twelve
def func_(x):
  outcome= x**3 + 2 * x**2 + 12
  return outcome

#4. Define input x along with its the output of
# function y and visualize their relation
x = np.linspace(1,10,100)
y = func_(x)

plt.figure(figsize=(4, 4))
plt.plot(x, y, color='indigo', linestyle='--', linewidth=3)
#plt.title('Cubic Function')
plt.xlabel('x')
plt.ylabel('y')
plt.grid()
plt.show()

#5. transform your ipynb file to the html file
!jupyter nbconvert --to html bana310_HW2and3.ipynb

[NbConvertApp] Converting notebook bana310_HW2and3.ipynb to html
[NbConvertApp] WARNING | Alternative text is missing on 3 image(s).
[NbConvertApp] Writing 442364 bytes to bana310_HW2and3.html