import numpy as np
arr = np.array([0, 1, 2, 3, 4])
np.arange(0, 10)          # [0 1 2 ... 9]
np.arange(0, 10, 3)       # [0 3 6 9]

np.zeros((5, 5))
np.ones((2, 4))
np.random.randint(0, 100, (3, 3))
np.linspace(0, 10, 6)      # 6 evenly spaced points from 0 to 10

arr2 = np.random.randint(0, 100, 10)
arr2.max(), arr2.min(), arr2.mean()
arr2.argmin(), arr2.argmax()
arr3.reshape(3, 3)

mat = np.arange(0, 100).reshape(10, 10)
mat[0, 1]            # single element
mat[:, 0]             # first column
mat[0:3, 0:3]          # top-left 3x3 block
mat[0:3, -3:-1]         # slicing with negative indices

step_counts = pd.Series([3620, 7891, 9761], name='steps')
step_counts.index = pd.date_range('20240329', periods=3)
step_counts['2024-03-30']   # access like a dictionary
step_counts.dtypes

joined_data = list(zip(step_data, cycling_data))
activity_df = pd.DataFrame(joined_data,
    index=pd.date_range('20240329', periods=6),
    columns=['Walking', 'Cycling'])

activity_df.loc['2024-04-01':'2024-04-03', ['Walking']]
activity_df.iloc[0, :]      # first row, all columns
activity_df.iloc[:2, 0]     # first 2 rows, first column

data = pd.read_csv('Iris_Data.csv')
data.head()
data.shape                 # (150, 5)
data['sepal_width']

group_sizes = data.groupby('species').size()

data.petal_length.mean(), data.petal_length.median()
data.petal_length.std(), data.petal_length.var()

plt.hist(data.petal_length, bins=25)
plt.plot(data.sepal_length, data.sepal_width, marker='o')
plt.xlabel('Petal Length (cm)')
plt.title('Distribution of Petal Lengths')