# Import the pandas library as "pd"
import pandas as pd

# Create a dataframe with a column named "price" and values of 5, 7, 9, 2, 4
df = pd.DataFrame({'price': [5, 7, 9, 2, 4]})

# Calculate the rolling mean of the "price" column using a window size of 3
rolling_window = df['price'].rolling(window=3).mean()

# Print the results of the rolling mean calculation
print(rolling_window)

# Output:
# 0         NaN
# 1         NaN
# 2    7.000000
# 3    6.000000
# 4    5.000000
# Name: price, dtype: float64

# The rolling mean function calculates the mean of a rolling window, which is a subset of data points within a specified window size. 
# In this case, the window size is 3, so the first two values are NaN (not a number) because there are not enough data points to calculate the mean. 
# The third value is the mean of the first three values (5, 7, 9), and so on. 
# This can be useful for identifying trends or smoothing out noisy data.

# The following code creates a dataframe with a column named "price" and 5 rows of data
df = pd.DataFrame({'price': [np.nan, 6.5, 7.5, 4.5, 4.0]}) # Creates a dataframe with a column named "price" and 5 rows of data

# The following code prints the dataframe
print(df) # Prints the dataframe to the console, displaying the column "price" and its corresponding values in each row

# Import pandas library
import pandas as pd

# Create a dataframe
df = pd.DataFrame({'price': [10, 20, 30, 40, 50]})

# Use the shift function to shift the values in the 'price' column by 1 period
df['lagged_price'] = df['price'].shift(periods=1)

# Print the 'price' and 'lagged_price' columns of the dataframe
print(df[['price', 'lagged_price']])

# Output:
# price  lagged_price
# 0     10           NaN
# 1     20          10.0
# 2     30          20.0
# 3     40          30.0
# 4     50          40.0

# The shift function shifts the values in a series by a specified number of periods (i.e., lagging)
# In this case, the 'price' column is shifted by 1 period, so the first value becomes NaN (not a number)
# The 'lagged_price' column now contains the previous value of 'price' for each row

# The following code creates a dataframe with two columns: price and lagged_price
# The lagged_price column will contain the previous value of the price column, shifted by one row

import pandas as pd # Importing the pandas library to work with dataframes

# Creating the dataframe with the given values
df = pd.DataFrame({'price': [5.0, 7.0, 9.0, 2.0, 4.0]})

# Creating a new column called lagged_price and assigning it the value of the price column, shifted by one row
df['lagged_price'] = df['price'].shift(1)

# Printing the dataframe to see the output
print(df)

# Output:
#    price  lagged_price
# 0    5.0           NaN
# 1    7.0           5.0
# 2    9.0           7.0
# 3    2.0           9.0
# 4    4.0           2.0

# Calculates the standard deviation of a rolling window with a window size of 5
df['volatility'] = df['price'].rolling(window=5).std()

# Prints the columns 'price' and 'volatility' from the dataframe
print(df[['price', 'volatility']])

# Importing necessary libraries
import pandas as pd

# Creating a dataframe with two columns - price and volatility
df = pd.DataFrame({'price': [5.0, 7.0, 9.0, 2.0, 4.0], 'volatility': [None, 2.368498, 2.581989, 2.581989, 1.870798]})

# Printing the dataframe
print(df)

# Output:
#    price  volatility
# 0    5.0         NaN
# 1    7.0    2.368498
# 2    9.0    2.581989
# 3    2.0    2.581989
# 4    4.0    1.870798