I've been posting a lot lately as I'm new to Python/Pandas. I have a pandas DF called NOTES_TAT_v1. It looks like this:
PT_FIN Date Interpreter Needed Interpreter Used
1 27 January, 2020 1 1
2 27 January, 2020 1 0
3 27 January, 2020 0 0
4 28 January, 2020 0 0
5 28 January, 2020 1 1
6 29 January, 2020 1 0
I'm trying to aggregate the Interpreter columns by Date so the output would look like this:
Date Interpreter Needed Interpreter Used
27 January, 2020 2 1
28 January, 2020 1 1
29 January, 2020 2 1
I tried using this code just for the Interpreter Needed column (I ultimately want both Interpreter columns): NOTES_TAT_v2=NOTES_TAT_v1.groupby('Day').Interpreter_Needed.value_counts(). But I'm getting errors Traceback (most recent call last) & DataFrameGroupBy' object has no attribute 'Interpreter_Needed'. Do I need to do a crosstab?
import pandas as pd
df = pd.read_csv('df.txt', sep=r"[ ]{2,}")
print(df)
PT_FIN Date Interpreter Needed Interpreter Used
0 1 27 January, 2020 1 1
1 2 27 January, 2020 1 0
2 3 27 January, 2020 0 0
3 4 28 January, 2020 0 0
4 5 28 January, 2020 1 1
5 6 29 January, 2020 1 0
df_gb = df[['Interpreter Needed', 'Interpreter Used']].groupby([df['Date']])
print(df_gb.sum())
Interpreter Needed Interpreter Used
Date
27 January, 2020 2 1
28 January, 2020 1 1
29 January, 2020 1 0
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