I've been trying to create this machine learning tool to make predictions on the amount of orders in the next year per month but I have been getting this error:
ValueError: to assemble mappings requires at least that [year, month, day] be specified: [month] is missing
here is my code. I am passing in the month and it should be getting assigned a number that is supposed to represent the respective month, but form some reason this does not appear to be happening. I am also aware that the months are not all capitalized but this should not be an issue as they are all getting passed to lowercase.
import pandas as pd
# Example DataFrame creation from CSV (replace this with your actual CSV upload logic)
data = {
'Year': [2021, 2021, 2021, 2022, 2022, 2023, 2023],
'Month': ['january', 'february', 'march', 'january', 'february', 'march', 'april'],
'OrderCount': [60, 55, 70, 64, 56, 76, 70]
}
df = pd.DataFrame(data)
# Convert 'Month' to numerical values (January = 1, February = 2, etc.)
month_map = {
'january': 1, 'february': 2, 'march': 3, 'april': 4, 'may': 5, 'june': 6,
'july': 7, 'august': 8, 'september': 9, 'october': 10, 'november': 11, 'december': 12
}
# Map month names to numbers
df['Month'] = df['Month'].str.lower()
df['MonthNum'] = df['Month'].map(month_map)
# Convert Year and MonthNum to integers
df['Year'] = df['Year'].astype(int)
df['MonthNum'] = df['MonthNum'].astype(int)
# Combine Year and Month into a DateTimeIndex
# The next line is where the issue is likely occurring
df['Date'] = pd.to_datetime(df[['Year', 'MonthNum']].assign(DAY=1))
# Print the resulting DataFrame to see if 'Date' was successfully created
print(df)
If you check to_datetime documentation, you will find that it requires the column called month. Your month column contains the month names.
You should rename the columns before using to_datetime like this:
df=df.rename(columns={"Month": "MonthName", "MonthNum": "Month"}). This way, pandas will look for the month numeric column and find it.