Using Python Pandas df.loc to locate a partially variable value

Question

I'm trying to use the df.loc from pandas to locate a partially variable value in my dataframe.

in my dataframe there are times and dates combined for example

Time            Col1    Col2
1-1-2019 01:00     5    7
1-1-2019 02:00     6    9
1-2-2019 01:00     8    3

if I use df.loc[df['Time'] == ['1-1-2019']] I want to locate the first 2 columns of this dataframe that being 1-1-2019 01:00 and 1-1-2019 02:00.

it is giving my an error: Lengths must match . To be fair that is a logical error for pandas to give because I only am inputting the day, not the time.

Is there a way to make the search value partialy variable? So pandas look for the 1-1-2019 01:00 and 1-1-2019 02:00?

Answer 1

First idea is remove times, better say set to 0 by Series.dt.floor or Series.dt.normalize :

df['Time'] = pd.to_datetime(df['Time'])
df1 = df.loc[df['Time'].dt.floor('d') == '2019-01-01']
#alternative
#df1 = df.loc[df['Time'].dt.normalize() == '2019-01-01']
print (df1)
                 Time  Col1  Col2
0 2019-01-01 01:00:00     5     7
1 2019-01-01 02:00:00     6     9

Or compare dates by Series.dt.date :

from datetime import  date
df['Time'] = pd.to_datetime(df['Time'])
df1 = df.loc[df['Time'].dt.date == date(2019,1,1)]
#in some version should working
#df1 = df.loc[df['Time'].dt.date == '2019-01-01']

Or convert to strings YYYY-MM-DD by Series.dt.strftime and compare:

df['Time'] = pd.to_datetime(df['Time'])
df1 = df.loc[df['Time'].dt.strftime('%Y-%m-%d') == '2019-01-01']

Answer 2

The following will subset the df on your condition

df[df.Time.str.contains('1-1-2019')]

                  Time Col1    Col2
0  2019-01-01 01:00:00       5    7
1  2019-01-01 02:00:00       6    9