Python|Pandasdataframe.resample()

# importing pandas as pd
import pandas as pd
# By default the "date" column was in string format,
# we need to convert it into date-time format
# parse_dates =["date"], converts the "date" 
# column to date-time format. We know that 
# resampling works with time-series data only
# so convert "date" column to index
# index_col ="date", makes "date" column, the index of the data frame
df = pd.read_csv("apple.csv", parse_dates =["date"], index_col ="date")
# Printing the first 10 rows of dataframe
df[:10]


# Resampling the time series data based on months
# we apply it on stock close price
# 'M' indicates month
monthly_resampled_data = df.close.resample('M').mean()
# the above command will find the mean closing price
# of each month for a duration of 12 months.
monthly_resampled_data


# importing pandas as pd
import pandas as pd
# We know that resampling works with time-series data
# only so convert "date" column to index
# index_col ="date", makes "date" column.
df = pd.read_csv("apple.csv", parse_dates =["date"], index_col ="date")
# Resampling the time series data based on weekly frequency
# we apply it on stock open price 'W' indicates week
weekly_resampled_data = df.open.resample('W').mean()
# find the mean opening price of each week 
# for each week over a period of 1 year.
weekly_resampled_data


# importing pandas as pd
import pandas as pd
# We know that resampling works with time-series
#  data only so convert our "date" column to index
# index_col ="date", makes "date" column
df = pd.read_csv("apple.csv", parse_dates =["date"], index_col ="date")
# Resampling the time series data
#  based on Quarterly frequency
# 'Q' indicates quarter
Quarterly_resampled_data = df.open.resample('Q').mean()
# mean opening price of each quarter
# over a period of 1 year.
Quarterly_resampled_data