mark01
08-22-2023, 05:26 AM
I'm working on a data analysis project using Python and Pandas, and I'm facing an issue with manipulating a DataFrame that contains daily data. I have a DataFrame with two columns: date and value. I want to calculate the monthly average of the value column based on the daily data.
Here's a simplified version of my code:
import pandas as pd
# Sample data
data = {'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-02-01', '2023-02-02'],
'value': [10, 15, 20, 5, 8]}
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
# Calculate monthly average
monthly_avg = df.resample('M', on='date').mean()
When I run this code, the monthly_avg DataFrame seems to have NaN values for all the rows. I suspect this is because I don't have data for every day in a month. Is there a way to calculate the monthly average even if I have missing days within a month? Or do I need to preprocess the data differently before calculating the monthly average? I have looked on other websites, but I was unable to find the answer. I would appreciate any advice on how to correctly compute the monthly average using Pandas from this daily data. I appreciate your help in advance.
Here's a simplified version of my code:
import pandas as pd
# Sample data
data = {'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-02-01', '2023-02-02'],
'value': [10, 15, 20, 5, 8]}
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
# Calculate monthly average
monthly_avg = df.resample('M', on='date').mean()
When I run this code, the monthly_avg DataFrame seems to have NaN values for all the rows. I suspect this is because I don't have data for every day in a month. Is there a way to calculate the monthly average even if I have missing days within a month? Or do I need to preprocess the data differently before calculating the monthly average? I have looked on other websites, but I was unable to find the answer. I would appreciate any advice on how to correctly compute the monthly average using Pandas from this daily data. I appreciate your help in advance.