I have some data I want to count by month. The column I want count has three different possible values, each representing a different car sold. Here is an example of my dataframe:
Date Type_Car_Sold
2015-01-01 00:00:00 2
2015-01-01 00:00:00 1
2015-01-01 00:00:00 1
2015-01-01 00:00:00 3
... ...
I want to make it so I have a dataframe that counts each specific car type sold by month separately, so looking like this:
Month Car_Type_1 Car_Type_2 Car_Type_3 Total_Cars_Sold
1 15 12 17 44
2 9 18 20 47
... ... ... ... ...
How exactly would I go about doing this? I've tried doing:
cars_sold = car_data['Type_Car_Sold'].groupby(car_data.Date.dt.month).agg('count')
but that just sums up all the cars sold in the month, rather than breaking it down by the total amount of each type sold. Any thoughts?
Maybe not the cleanest solution, but this should get you pretty close
import pandas as pd
from datetime import datetime
df = pd.DataFrame({
"Date": [datetime(2022,1,1), datetime(2022,1,1), datetime(2022,2,1), datetime(2022,2,1)],
"Type": [1, 2, 1, 1],
})
df['Date'] = df["Date"].dt.to_period('M')
df['Value'] = 1
print(pd.pivot_table(df, values='Value', index=['Date'], columns=['Type'], aggfunc='count'))
Type 1 2
Date
2022-01 1.0 1.0
2022-02 2.0 NaN