How to pandas-groupby a column and get a specific result per group depending on the values of another column? - python-3.x

Question

I have a DataFrame with two columns: one column is date and the other column contains values True or False.
Assume this code to get the Dataframe:
d_range=pd.date_range(start='01-01-2018', end='01-06-2018', freq='0.2D', )
d_range=d_range.date
my_list=[]
for i in range(0,d_range.size):
if 0<i<18:
my_list.append(False)
else:
my_list.append(True)
df=pd.DataFrame({'date':d_range, 'met criteria':my_list})
df.set_index(['date'])
This will give us this DataFrame:
print(df)
date criteria
0 2018-01-01 True
1 2018-01-01 False
2 2018-01-01 False
3 2018-01-01 False
4 2018-01-01 False
5 2018-01-02 False
6 2018-01-02 False
7 2018-01-02 False
8 2018-01-02 False
9 2018-01-02 False
10 2018-01-03 False
11 2018-01-03 False
12 2018-01-03 False
13 2018-01-03 False
14 2018-01-03 False
15 2018-01-04 False
16 2018-01-04 False
17 2018-01-04 False
18 2018-01-04 True
19 2018-01-04 True
20 2018-01-05 True
21 2018-01-05 True
22 2018-01-05 True
23 2018-01-05 True
24 2018-01-05 True
25 2018-01-06 True
I need an outcome that will group by 'date' and if there is at least one True value then the result will be True, otherwise it will be False.
The outcome should look like:
date criteria
2018-01-01 True
2018-01-02 False
2018-01-03 False
2018-01-04 True
2018-01-05 True
2018-01-06 True
Can you suggest some code that will do that, please?

Answer 1

Here is a way to do it:
In [1]:
import pandas as pd
d_range=pd.date_range(start='01-01-2018', end='01-06-2018', freq='0.2D', )
d_range=d_range.date
my_list=[]
for i in range(0,d_range.size):
if 0<i<18:
my_list.append(False)
else:
my_list.append(True)
df=pd.DataFrame({'date':d_range, 'met criteria':my_list})
def True_or_Not(x):
return x>0
df.groupby('date').sum().apply(True_or_Not)
df
Out [1]:
met criteria
date
2018-01-01 True
2018-01-02 False
2018-01-03 False
2018-01-04 True
2018-01-05 True
2018-01-06 True

Answer 2

You can use isin method on this. Basically, filter the dataframe using the unique values in your date column, then check each resulting dataframe if True exists in the criteria column.
Populate a dictionary based on the result, create a new dataframe with column 1 values equal to your dates and column 2 values equal to the mapped bool values from your_dict
date_unique = list(set(df['date'].values.tolist()))
your_dict = {}
for d in date_unique:
test_df = df[df['date'].isin([d])]
if 'True' in test_df['criteria']:
your_dict[d] = True
else:
your_dict[d] = False
output_df = pd.DataFrame()
output_df['date'] = date_unique
output_df['criteria'] = output_df['date'].map(your_dict)
Please note that 'True' is different from True, one is a string and the other is a bool data type in python. Whatever/however you read your criteria column in the original dataframe, you should apply the correct type to the condition inside the loop.